Meeting agenda
Minnesota Department of Health
Environmental Health Tracking and Biomonitoring Advisory Panel Meeting
March 10, 2009
1:00 p.m. – 4:00 p.m.
Red River Room at Snelling Office Park
1645 Energy Park Drive, St. Paul, MN
Item type/Anticipated outcome
Time
Agenda item
Presenter(s)
1:00
Introductions
Beth Baker, chair
1:05
Welcome
John Stine, Asst.
Commissioner of
Health
Mr. Stine will present a brief legislative
update. Panel members are invited to give
input on the strategic direction for the next
two years and on potential
partnerships/sources of funding.
1:20
Minneapolis Children’s
Arsenic Study
Adrienne Kari
Betsy Ehdlund
Tannie Eshenaur
Discussion item.
Staff will provide a brief overview of the
arsenic pilot project results and plans for
disseminating the results to the study
community and the general public. Panel
members are invited to provide feedback on
the analyses; to make recommendations for
communicating the findings; and to make
recommendations for further action based on
the pilot study results.
2:15
Biomonitoring Project
updates:
• Vision and Purpose
• East Metro PFC
• Lake Superior
Mercury
• Riverside Prenatal
Various staff
Information sharing.
2:30
Panel members are invited to ask questions or
provide input on any of these items.
Break
i
Time
Agenda item
Presenter(s)
Item type/Anticipated outcome
2:45
Environmental health
tracking data report
Jean Johnson
Jeannette Sample
Discussion item.
EHT staff will present a sample of data that
will form the basis of the environmental
health tracking data report (to be released in
late April or May). Panel members are invited
to ask questions about the data; to make
suggestions for additional analyses and ways
to present the data; and to make
recommendations for disseminating the
information to stakeholders.
3:30
Tracking Project updates: Various staff
• Public Data Portal
• Communications
Planning
3:45
New business
4:00
Adjourn
Information sharing.
Panel members are invited to ask questions or
provide input on any of these items.
Discussion Item
Beth Baker will invite panel members to
suggest topics for future discussion.
Next EHTB advisory panel meeting:
Tuesday, June 2**, 1-4 pm, Red River Room, Snelling Office Park
**This is a change from the original date which was June 9**
Mark your calendars – Upcoming meeting dates
Tuesday, June 2, 2009
Tuesday, September 15, 2009
Tuesday, December 8, 2009
All meetings will be held from 1-4 pm and will take place at
MDH’s Snelling Office Park location at 1645 Energy Park Drive.
ii
Meeting Materials for March 10, 2009
Environmental Health Tracking & Biomonitoring Advisory Panel
Table of Contents
Agenda........................................................................................................................................... i
Table of contents ...................................................................................................................... iii
Materials related to specific agenda items
Minneapolis Children’s Arsenic Study
Section overview: Minneapolis Children’s Arsenic Study........................................................1
Minneapolis Children’s Arsenic Study: preliminary results......................................................3
Biomonitoring Project Updates
Section overview: Biomonitoring Project Updates..................................................................11
Status update on vision and purposes for biomonitoring.........................................................13
Status update on the East Metro PFC Biomonitoring Study....................................................15
Status update on the Lake Superior Mercury Biomonitoring Study........................................16
Status update on the Riverside Prenatal Biomonitoring Study................................................17
Environmental Health Tracking Data Report
Section overview: Environmental health tracking data report.................................................19
Draft environmental health tracking data report: indicator profiles ......................................21
Environmental Health Tracking Project updates
Section overview: Tracking Project updates...........................................................................69
Status update on public data portal ..........................................................................................71
Status update on communications outreach planning..............................................................73
General reference materials
Section overview: General reference materials .............................................................................75
NEW: EHTB advisory panel meeting summary (from December 9, 2008) ..................................77
EHTB advisory panel roster...........................................................................................................83
Biographical sketches of advisory panel members........................................................................85
EHTB steering committee roster ...................................................................................................89
EHTB inter-agency workgroup roster..............................................................................................90
Glossary of terms used in environmental health tracking and biomonitoring ...............................92
Acronyms used in environmental health tracking and biomonitoring...........................................96
EHTB statute (Minn. Statutes 144.995-144.998)....................................................................................... 98
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iv
Section overview: Minneapolis Children’s Arsenic Study
Included in this section is one draft document presenting a summary of the analytical results of
the Minneapolis Children’s Arsenic Study. A total of 65 children participated in this project.
Arsenic speciation of the urine collected for the Minneapolis Children’s Arsenic Study was
completed in January 2009 by the MDH Public Health Laboratory. Individual results were
mailed out to participants in early February.
This is the first of the four pilot projects to be completed. The study design and methods are
described in previous Advisory Panel meeting materials. EHTB Biomonitoring Coordinator,
Adrienne Kari, and Public Health Laboratory Chemist, Betsy Ehdlund, will describe their
analytical methods and results in a brief presentation. Staff will also describe plans for
communicating results to community members. Panel members are invited to provide comments
to address the following questions:
•
•
•
•
•
•
What are the most important findings of this study?
Are the interpretations and conclusions appropriate?
Are there methodological limitations that should be emphasized?
Are there additional analyses of the data that should be pursued?
What specific methods would you recommend for effectively communicating these
results to the community and to the general public?
Are there any follow-up actions that you would recommend to the community or to
public health officials based on these results?
ACTION NEEDED: At this time, no formal action is needed by the advisory panel. Panel
members are invited to ask questions or provide input on any of these questions during the
designated time on the meeting agenda.
1
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2
Minneapolis Children’s Arsenic Study: Preliminary Results*
*These are preliminary results, presented for review purposes only --- Please do not quote
or cite.
Recruitment and participation
The following tables provide a break down for several steps/portions of the study
recruitment and enrollment process.
As was described in previous reports, the initial project area was to include properties
with a soil arsenic measure greater than 20ppm. Table one describes the recruitment
process while limited to those properties.
Table One
Properties with soil arsenic value > 20ppm
Number of households on properties > 20ppm
Number of vacant households – identified by post office and project staff
Number of > 20ppm households requiring a visit to return survey
Number of > 20ppm households requiring at least 3 visits
Number of > 20ppm households that did not have children between 3 and 10
Number of > 20ppm households that did have children between 3 and 10
N
511
883
107
654
334
537
105
Due to lower than expected participation of families in the initial target households, the
study was expanded to include all households with a measured soil arsenic concentration
<20 ppm. Table two describes the number of properties invited into the project with the
expanded recruitment area, as well as the total enrollment and retention numbers for the
entire project. A total of 65 children completed the project and provided 2 first morning
void urine specimens.
Table Two
Number of properties mailed expanded recruitment flyer
Number of fliers returned due to vacancy by post office/neighbor from
expanded recruitment
Total number of children invited to participate
Number of children enrolled (the consent materials were returned)
Number of children that completed the pilot project
3
N
2656
161
101
75
65
12%
74%
38%
61%
12%
Characteristics of Study Participants
Tables 3 through 8 describe the study participants. The information was collected on a short
questionnaire that parents were asked to complete at the time of urine collection. The
questionnaire collects demographic information and information about common sources of
arsenic exposure. Exposure source information was collected to help interpret a child’s result,
particularly for cases with a total arsenic level above 50 ug/g creatinine1 . The children ranged in
age from 3 to 11 at the time of their urine collection. The average age of our group was 6.5.
Table 3
Gender of the child
Male
Female
32
33
Table 4
Race/Ethnicity of the child
African – American; Non-Hispanic Black
Asian – American
Chicano/Latino
Native American
Non-Hispanic White
Other
I prefer not to say
Number of Children
10
4
15
1
30
3
2
Table 5
Sources of arsenic exposure
Presence of Green Treated Wood
Was the Child on a Special Diet
Did the Child consume Fish
Was the Child taking any Medicines
Did anyone smoke in the home
Presence of uncovered Soil on the Property
Was the Child taking supplements
Yes
11
4
5
3
6
45
23
No
47
60
60
62
58
20
42
Table 6
Weather at the time of urine collection
Mix of rainy and dry
Mostly dry
Mostly wet
Missing
Number of Children
30
31
3
1
Table 7
Type of Residence the Child lives in
Townhome/apartment
3 or 4 plex
Duplex
House
Number of Children
2
8
12
39
4
UnSure/Missing
7
1
1
Table 8
Time playing in the yard
Less than 1
hour
Number of hours the child spent in
the yard on average during the past
week
(number of children)
Number of hours the child spent in
the yard on average during the past 2
days
(number of children)
1-2
3-4
5-6
More than 7
hours hours hours hours
23
22
12
4
15
36
10
4
4
Total and Speciated Urine Arsenic Results
Data analysis of the urinary arsenic levels was completed using SAS 9.1. Of the 65 urine
samples, 23 (35.38%) had total arsenic levels greater than 15 ug/g creatinine and speciation
was completed for this group. The geometric means were completed for all of the urinary
arsenic variables as they all had log normal distributions. The following figures, for total
urinary arsenic with creatinine correction, depict the skew found in the un-transformed data
and the approach to normalization that log transformation provided.
Figure One
[Histogram of Total Urinary Arsenic]
Figure Two
[Histogram of Log Transformed Total
Urinary Arsenic]
100
40
80
35
30
60
P
e
r
c
e
n
t
25
P
e
r
c 20
e
n
t
40
15
20
10
5
0
15
45
75
105
135
165
195
0
Tot al Ar seni c wi t h cr cor r ect i on
1. 75
2. 25
2. 75
3. 25
3. 75
4. 25
4. 75
l ogAsTot ccr
Figure Four
[Boxplot of Log Transformed Total
Urinary Arsenic]
Figure Three
[Boxplot of Total Urinary Arsenic]
200
6
T
o
t
a 150
l
5
A
r
s
e
n
i
c
w
i
100
t
h
l 4
o
g
A
s
T
o
t
c
c
r 3
c
r
c
o
r
r
e
c
t
i
o
n
50
2
0
1
Chi l d
1
1
Chi l d
5
5. 25
Table 9 reports the distribution of total urinary arsenic level for the 65 participants. For the 23 specimens with total arsenic >15 that
were speciated, total inorganic level (both creatinine corrected and not), and the dimethylarsinic acid level is reported. Table 9 also
provides the distribution of arsenic levels found in a nationally representative sample from the 2003-2004 CDC NHANES for
reference.
Table 9
Minneapolis Children’s Arsenic Study
Variable
N GeoMean
w/95% CI
Total Urinary
65 13.456
Arsenic
(11.37,15.93)
(creatinine corrected)
Total Inorganic
23 12.910
Arsenic
(10.59, 15.72)
(creatinine corrected)
Total Inorganic
23 13.533
Arsenic
(10.10, 17.65)
(ug/L)
Dimethylarsinic Acid 23 9.311
(ug/L)
(7.08, 12.26)
NHANES2*
N
50th with CI
95th with CI
11.265
(9.77, 13.39)
48.759
(24.62, 191.27)
12.89
(8.74, 17.81)
26.90
(21.85, 28.95)
11.09
(8.22, 19.55)
35.65
(28.28, 66.43)
----
7.78
(6.30, 11.60)
20.82
(17.56, 55.72)
292
290
GeoMean
w/95% CI
8.25
(6.58, 10.3)
3.73
50th with CI
7.14
(5.93, 9.45)
95th with
CI
38.2
(14.7, 188)
6.0
14.7
3.90
(3.00, 4.00)
12.0
(7.3, 18.4)
*Caldwell K, Jones R, Verdon C, Jarrett J, Caudill S, Osterloh J. Levels of urinary total and speciated arsenic in the US population:
National Health and Nutrition Examination Survey 2003 – 2004. Journal of Exposure Science and Environmental Epidemiology
(2008), 1-10.
We observed a difference in the geometric mean for both total urinary arsenic and total inorganic arsenic (not creatinine corrected)
between this study population and the NHANES population. This may be attributed to a number of differences between our study
population and the NHANES population, including recruitment and sampling methods. NHANES recruits over an entire calendar
year; our project recruitment and sample collection was limited to the late summer months. This difference in seasonality may have
an effect on the mean urinary arsenic levels in a population. A second consideration, NHANES collects spot urines at the time of the
participants visit, our protocol called for the collection of 2 first morning voids to best approximate a 24 hour urine collection. With
the collection of 2 first morning voids our urinary arsenic measures may reflect a better method for capturing arsenic exposure.
NHANES reports several age categories, with the youngest category of 6 to 11 years of age; this is slightly different than our
distribution which had an average age of 6 and a youngest age of 3. NHANES also collects a nationally representative sample,
including both rural and urban areas, where as our project population is urban. A final consideration is the inclusion of sibling sets in
our study group, where as NHANES data points are completely independent from one another.
6
Correlations with Soil Arsenic
Each of the children included in the Minneapolis Children’s Arsenic Study were living on
a property tested by either the Environmental Pollution Agency (EPA), Minnesota
Department of Agriculture (MDA), or Minnesota Department of Health (MDH)3. The
number of samples taken for each property varied between two and four. To complete
the correlations between the soil arsenic level and the urinary arsenic levels two analyses
were run. The first analysis to measure correlation was run using used the highest soil
arsenic level for the property. This analysis then assumes that the entire property is at the
high soil arsenic value, even though lower soil arsenic levels had been found. The
second analysis used the average of all of the soil arsenic levels found on the property,
assuming that the entire property would have soil arsenic levels between the lowest and
highest arsenic concentrations found.
Geometric means were calculated for both the high soil arsenic and average soil arsenic
statistics as the distributions for both were log normal. Using only the high soil arsenic
value the geometric mean soil arsenic concentration for the 65 children was 27.2 ppm.
The geometric mean for the average soil arsenic concentration for the 65 children was 20
ppm.
To compare the soil arsenic concentrations with the urinary arsenic concentrations; the
urine and soil variables were log transformed to adjust for the log normal distributions.
Table 9 presents the correlations between the high soil arsenic concentrations and urinary
concentrations. Correlations between the average soil arsenic concentrations and total
urinary concentrations were also non-significant.
Table 9
Correlations between the normalized high soil arsenic concentrations and total urinary
arsenic levels.
R squared P
Correlations
value
High Soil value with Total Arsenic (creatinine corrected)
.007
.521
High Soil value with Total Arsenic (ug/L)
.024
.223
High Soil value with Total Inorganic Arsenic (creatinine corrected) .010
.645
High Soil value with Total Inorganic Arsenic (ug/L)
.029
.4332
High Soil value with Total Organic Arsenic (creatinine corrected)
.002
.862
High Soil value with Total Organic Arsenic (ug/L)
.0001
.962
7
Figure Five
[Scatterplot of Log Transformed High Soil value against log transformed Total urinary
arsenic]
l ogHi ghSoi l
7
6
5
4
3
2
1
1
2
3
4
5
6
l ogAsTot ccr
Figure Six
[Scatterplot of log transformed high soil concentration against log transformed total
inorganic arsenic]
l ogHi ghSoi l
7
6
5
4
3
2
1
1. 7
1. 8
1. 9
2. 0
2. 1
2. 2
2. 3
2. 4
2. 5
2. 6
l ogTot I nAsccr
8
2. 7
2. 8
2. 9
3. 0
3. 1
3. 2
3. 3
3. 4
To reach our recruitment goal of 100 children within the necessary time frame we
allowed for enrollment of sibling sets into the study. By doing this we lose independence
of data points in the analysis. So, to determine if the inclusion of the sibling sets in the
analysis alters the results, basic analyses were completed comparing those in the study
group that were siblings to those in the study group that were not. A difference was
found between the sibling group and the non-sibling group in regards to the gender
percentages; with a greater number of males falling into the sibling category than
females. To further investigate the possible effect of sibling status, one sibling was
randomly selected from each sibling set and the analysis was re-run. The correlations
between soil arsenic levels and urinary arsenic values remained non-significant in the
adjusted analysis as they had been in the total group analysis.
With a significant difference in gender proportions between the sibling and non-sibling
groups an analysis was completed investigating the relationship between gender and
arsenic measurements. None of the total or speciated arsenic variables had significant
differences between males and females.
As is a common problem with biomonitoring data, most of the measurement data was
highly skewed requiring a log transformation to normalize the distributions. The skew in
the distribution is most likely due to a few strong outliers in the study group. There were
three children with Total Urinary Arsenic Values greater than 50 ug/g creatinine, with a
fourth very close to 50 at 48.9 ug/g creatinine. One of these children did consume fish,
which helps to explain the increased level found. Of the four children with elevated
levels, 2 were boys, 2 were girls, 1 was non-hispanic white, and 3 were chicano/latino.
The parents of all four children received recommendations to complete follow up with
their primary care provider to determine possible exposure routes and prevent future
exposure. With the exception of the single child that ate fish there was nothing
identified on the short survey or talking with the parents that would pinpoint the source of
exposure. None of the families identified the presence of CCA treated wood, only one
child was taking supplements (emergen-c), and it was a mix of rainy and dry weather for
all 4 children during the urine collection. None of the children lived on the properties
with the highest soil arsenic concentrations.
Conclusion
Overall 65 children completed the Minneapolis Children’s Arsenic Study.
Concentrations of total and inorganic arsenic in the study group were measured at levels
slightly higher than the national reference population. Differences in population
exposure characteristics and in specimen collection procedures may explain this finding.
There was no relationship found between the soil arsenic values and the urinary arsenic
levels.
9
References
1. Carrizales L, Razo I, Tellez-Hernandez J, Torres-Nerio R, Torres A, Batres L,
Cubillas A, Diaz-Barriga F. Exposure to arsenic and lead of children living near a
copper smelter in San Luis Potosi, Mexico: Importance of soil contamination for
exposure of children. Environmental Research 101 (2006): 1-10.
2. Caldwell K, Jones R, Verdon C, Jarrett J, Caudill S, and Osterloh J. Levels of
urinary total and speicated arsenic in the US population: National Health and
Nutrition Examination Survey 2003 – 2004. Journal of Exposure Science and
Environmental Epdiemiology (2008) 1- 10
3. Minnesota Department of Health, Health Consultation; Off Site Soils: CMC
Heartland Partners Lite Yard Site Minneapolis, Hennepin County, Minnesota.
August 9, 2006. ATSDR.
Arsenic Results Communication Plan
In the week following the Scientific Advisory Panel's review of the preliminary aggregate
data, the report will be revised and finalized. A news release will be issued by the MDH
Communications Office describing the purpose, methods, summary results, and
recommendations from the pilot project. It will also include information about a
community meeting at the YWCA where the previous community meeting was held.
Prior notice will be provided to local government, elected representatives, and clinics and
community groups that have been involved in the study. MDH staff will offer to provide
a presentation on the pilot project's results and recommendations to the various
community groups and clinics that have expressed interest previously as well as new
groups. A selection of resource materials will be available for residents, community
groups and clinics at the presentations and on the MDH Web site: a one page summary of
the pilot project; a checklist for sources of arsenic exposure; and information sheets on
CCA treated wood, soil testing procedures and laboratories, arsenic, reducing contact
with contaminated soil, and a summary of the EPA remediation process.
10
Section overview: Biomonitoring Project updates
Given the limited time available for advisory panel meetings, updates on some items will be
provided to the panel as information items only. This information is intended to keep panel
members apprised of progress being made in program areas that are not a featured part of the
current meeting’s agenda and/or to alert panel members to items that will need to be discussed in
greater depth at a future meeting.
Included in this section of the meeting packet are written status updates on the following items:
•
State Biomonitoring Program Vision and Purpose
•
East Metro PFC Biomonitoring Study
•
Lake Superior Mercury Biomonitoring Study
•
Riverside Prenatal Biomonitoring Study
ACTION NEEDED: At this time, no formal action is needed by the advisory panel. Panel
members are invited to ask questions or provide input on any of these topics during the
designated time on the meeting agenda.
11
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12
Status Update: Biomonitoring vision and purpose
The EHTB statute requires the program to make recommendations to the legislature about the
development of an ongoing biomonitoring program in Minnesota. To guide a strategic planning
process for arriving at these recommendations, MDH has enlisted the assistance of a consultant,
Barb Deming, from Management, Analysis and Development (MAD) at the State Department of
Administration.
The planning process has involved the EHTB workgroup, steering committee and advisory
panel. The aims of the process are to articulate a vision and purpose of a state biomonitoring
program. The purpose statement is intended to describe why a state biomonitoring program
exists, whereas the vision will seek to describe more broadly what will be different in Minnesota
as a result of biomonitoring.
The first step of the planning process was a series of interviews conducted with the staff of other
biomonitoring programs in the United States and with members of the EHTB workgroup,
steering committee, and advisory panel. The draft summary document of biomonitoring
interviews and focus groups was distributed in December 2008 so that advisory panel members
would have an understanding of the many viewpoints about biomonitoring that are held by panel
members and state agency staff. MAD consultant, Barb Deming is getting final reviews
completed and will be finalizing this document.
A draft vision statement for a biomonitoring program was developed based on discussions at a
November 12 retreat and a subsequent sub-group meeting. At the December 9, 2008 meeting,
Advisory Panel members were asked to comment on the vision statement and to prioritize a set
of statements on the purposes of biomonitoring. Based on the comments received by the panel
the draft vision statement was revised and is being sent to Panel members for further review. As
of 2/19/09, a draft statement of the purposes of a state biomonitoring program is also being
prepared for Advisory Panel members to review.
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14
Status update on the East Metro PFC Biomonitoring Study
Recruitment and sample collection
As of February 19, 2009, 202 people from the Lake Elmo/Cottage Grove and Oakdale
communities had consented to take part in the PFC Biomonitoring Project. Of those, 98
individuals from each community completed the necessary blood draw and questionnaire by the
end date of January 5, 2009, for a total of 196 participants in the study.
Laboratory analysis and quality assessment
The MDH Public Health Laboratory participated in an external quality assurance assessment for
quantitating PFOA and PFOS in serum. Our results were in the acceptable tolerance range,
indicating good accuracy for these analytes in this matrix.
The MDH Public Health Laboratory has analyzed all 196 serum samples for the East Metro PFC
biomonitoring project. We measured seven PFCs: PFBA, PFPeA, PFHxA, PFOA, PFBS,
PFHxS, and PFOS. We performed a total of 422 analyses, which included the participants’
specimens, dilutions when needed, and the quality control checks. Analytical results have been
validated internally and submitted to our epidemiology partners for further analysis
Results Communication and Summary Data analysis
To date EHTB epidemiology staff have received laboratory results for 193 samples and those
individuals have been mailed their individual results. Once results for all samples have been
received summary data analysis will begin.
15
Status update on the Lake Superior Mercury Biomonitoring
Study
Participant Recruitment
The first letters requesting informed consent were sent on November 24th. As of February 12th,
2009 written informed consent has been received for 74 participants. Local public health
departments are assisting with obtaining consent for the study. In four of the counties, public
health staff are attempting to obtain informed consent through WIC and new baby visits. Of the
74 consents received, thirteen were enrolled by local public health. Due to the change in the
consent process, a project revision was submitted and approved by the IRB. Because local public
health staff do not have access to newborn screening data, fewer exclusions will apply, babies
who were transfused and repeat samples will still be excluded.
Wisconsin began enrollment on February 1st, 2009. Due to changes in the storage of specimens
in Michigan, requiring informed consent, Michigan blood spots will not be available for the
project. Staff are considering a variety of options for altering the study.
MDH PHL staff have completed testing of materials and process to ensure there is no mercury in
products used or carry-over between specimens.
16
Status update on the Riverside Prenatal Biomonitoring Study
Approvals and study protocol development and start up
Development of study materials was completed. Approval of the Prenatal Riverside
Biomonitoring Project from the University of Minnesota IRB has been received. The University
of Minnesota IRB stipulated that for approval the Prenatal Riverside Biomonioring Project could
not return individual results due to concerns that results would cause anxiety (harm) to
participants and counseling would not be available for the participants. Counseling is not
available because individual participant’s identities are not provided to MDH under our
agreement with the researcher. Under guidance from the MDH steering committee it was
decided to meet this stipulation and continue with the project, providing aggregate community
results at the end of the project.
The protocol has since been submitted to the MDH IRB for review and possible exemption.
Once approval has been obtained study recruitment of women into the project will begin.
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18
Section overview: Environmental Public Health Tracking Data
Report
In this section, MDH staff present examples of the ways that data will be presented and
described in Minnesota’s first Environmental Public Health Tracking data report.
In order for EPHT data to be useful, it must be disseminated in ways that make the
information accessible and understandable by a wide audience of stakeholders. The
publication of a MN Environmental Public Health Tracking Report, to be released later in
the spring of 2009, is planned as one way of disseminating data to the public this year.
Potential audiences for the data report will include local and state public health officials,
environmental agency officials, policy makers, and non-governmental organizations.
Tracking staff have been working with data stewards and communications staff on
determining the best methods for how data should be displayed. This includes making
decisions about the use of graphical displays and tables, and the implementation of data
suppression rules to protect data privacy. Using methods that are consistent with the
national EPHT program, MDH has developed a template, known as an indicator profile,
for presenting all data content areas so the data are presented in consistent format and
information is easy to find with standard messaging topics to accompany the data tables.
Advisory Panel members are asked to review drafts of tracking data reported in the form
of several indicator profiles. Indicator profiles are reported here for two of the nine
EPHT content areas that will eventually make up the complete report. They are:
1) Cancers: lung and mesothelioma
2) Birth Outcomes / Vital Statistics: prematurity, growth retardation, mortality,
fertility, sex ratio
Action Item:
For discussion at the Advisory Panel meeting, members are invited to ask
questions, and to make recommendations with respect to the following questions.
Is the content understandable and appropriate for the intended audience?
What parts of the indicator profile will be most useful and informative?
Are there other headings or sub-headings that are needed?
Are there other methods for displaying and/or interpreting the data that are
recommended?
19
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20
Cancer Content Area
Indicators:
1. Lung
2. Mesothelioma
21
Complete Indicator Profile of Lung Cancer
Definition
Measure 1: Annual counts of lung and bronchus cancer among Minnesota residents.
- Counts of unique invasive primary incident cases of lung and bronchus cancer
diagnosed during a specified calendar year to residents of a specified geographic
region among all residents of that geographic region.
Measure 2: Age-adjusted incidence rates for lung cancer per 100,000 Minnesota residents
in a geographic area per year.
- A weighted average of the age-specific lung and bronchus cancer rates (the number
of cancer counts in an age group per 100,000 people in that age group). Ageadjustment is a statistical method that minimizes differences in rates that would
occur solely because the populations being compared do not have the same age
distributions. Rates are directly age-adjusted to the 2000 U.S. standard population
(19 age groups).
Numerator
Measure 1 and 2: Counts of unique invasive primary incident cases of lung and bronchus
cancer diagnosed during a specified calendar year within residents of a specified
geographic region. Incidence data are collected by the Minnesota Cancer Surveillance
System (MCSS). Lung cancer diagnosis includes International Classification of Diseases
for Oncology 3rd edition (ICD-O-3) site codes C340-C349, between the years 2000-2004.
In situ cancers are excluded. All cancers are confirmed either microscopically or by death
certificate only.
Denominator
Measure 1: N/A
Measure 2: Estimated Minnesota population of a geographic area from the National
Cancer Institute’s (NCI) Surveillance, Epidemiology, and End Results (SEER) Program.i
A description of the methodology used to develop the bridged single-race estimates is
available on the National Center for Health Statistics web site.ii
Data Sources
•
•
Minnesota Cancer Surveillance System (MCSS): 2000-2004
National Cancer Institute, Surveillance, Epidemiology, and End Results (SEER)
Program: 2000-2004
Why Is This Important?
Definition of Lung Cancer
Cancer of the lung and bronchus (hereafter, lung cancer), is cancer that forms in tissues of the
lung, usually in the cells lining air passages, and the large air passages that lead from the
trachea (windpipe) to the lungs.
Burden of lung cancer
22
In both the United States and Minnesota, lung and bronchus cancer is the second most
commonly diagnosed cancer among men and women and the leading cause of cancer
death among men and women.iii iv
The proportion of new cancer cases and cancer deaths due to lung and bronchus cancer in
Minnesota are similar to U.S. trends. Lung cancer accounts for 12% of cancer of 25% of
cancer deaths in Minnesota.v Each year in Minnesota, an average of 2,830 cases of lunch
and bronchus cancer are diagnosed.vi Nationally, lung cancer accounts for about 15% of
all new cancers and 29% of cancer deaths. During 2008, there will be about 215,020 new
cases of lung cancer in the U.S.vii
Nationally, the incidence rate among men is declining significantly, while the rate in
women is stabilizing after a long period of increase.viii In Minnesota, American Indian
males and females and black males have the highest incidence and mortality rates of lung
and bronchus cancer, while Asian/Pacific Islander men and women have the lowest.
Nationally, incidence rates are lowest among Hispanics and Asian/Pacific Islanders and
highest in blacks.ix
The connection to environmental health
Smoking is the leading cause of lung and bronchus cancer worldwide, accounting for
approximately 90% of lung cancer in men and 80% of lung cancer in women.x Radon, an
invisible, odorless gas is the second leading cause of lung cancer in the U.S.xi Geological
formation in the upper Midwest yield higher than average radon levels; MDH estimates
that about one in three Minnesota homes have enough radon to pose a significant risk to
the occupants' health over many years of exposure.xii Passive smoking also contributes
to development of the disease among nonsmokers. Occupational exposure to asbestos,
arsenic, chromium, and metal dust, and environmental exposures to air pollution also
increase risk of lung and bronchus cancer. xiii
Known risk factors
Studies have found the following risk factors for lung cancer:xiv
• Tobacco smoke: Tobacco smoke causes most cases of lung cancer. It's by far the
most important risk factor for lung cancer. Secondhand smoke can cause lung cancer
in nonsmokers. The more a person is exposed to smoke, the greater the risk of lung
cancer.
• Radon: Radon is a radioactive gas emitted naturally from rocks and soil. The risk
of lung cancer from radon is even higher for smokers.
• Asbestos and other substances: People who have certain jobs (such as those who
work in the construction and chemical industries) have an increased risk of lung
cancer. Exposure to asbestos, arsenic, chromium, nickel, soot, tar, and other
substances can cause lung cancer. The risk is highest for those with years of
exposure. The risk of lung cancer from these substances is even higher for smokers.
• Air pollution: Air pollution may slightly increase the risk of lung cancer. The risk
from air pollution is higher for smokers.
• Family history of lung cancer: People with a father, mother, brother, or sister who
had lung cancer may be at slightly increased risk of the disease, even if they don't
smoke.
23
Known control or prevention measures
Smoking cessation is the best way to prevent lung and bronchus cancer. Homeowners are
encouraged to test their homes for radon. If it is present, a qualified contractor can
usually mitigate the problem. For more information, contact the MDH Indoor Air Unit at
(651) 201-4601. Screening for lung and bronchus cancer has not yet been proven to
improve survival, even among smokers.
What are the National Objectives?
There are no Healthy People 2010 Objectives for lung cancer incidence.
Healthy People 2010 Objective 3-2. Reduce the lung cancer death rate to 43.3 deaths per
100,000 population.
How Are We Doing?
Minnesota has not met the Healthy People 2010 lung cancer death rate target of 43.3
deaths per 100,000 population. From 2001-2005, the annual death rate for both sexes in
Minnesota was 46.6 deaths per 100,000.xv Nationally during 2001-2005, the annual
death rate was 54.1 deaths per 100,000.xvi Historically, Minnesota lung cancer mortality
has been below the national rates by a considerable margin. Minnesota has been below
the national target goal for women; in 2004 the annual death rate for women was 38.0
versus 59.4 for men.xvii It has been suggested that the incidence of lung cancer among
women lags behind that of men due to differences in smoking patterns historically.The
annual incidence rate for lung and bronchus cancer for 2001-2004 for both sexes was
58.7 for Minnesota and 68.9 for the United States.xviii
What Is Being Done?
“Cancer Plan Minnesota 2005-2010” (www.cancerplanmn.org) is the state’s first
comprehensive cancer control plan. Developed through a broad-based collaboration of
public, private and non-profit organizations, the plan was released in April 2005 and
serves as a common framework for action to reduce the burden of cancer for all
Minnesotans. The plan includes 24 objectives and numerous strategies covering all facets
of cancer control: prevention, early detection, treatment, quality of life, cancer disparities,
and data and research needs.
The Minnesota Cancer Alliance, a coalition of health organizations, community groups
and volunteers, was formed to implement Cancer Plan Minnesota. It provides a forum
through which cancer control activities can be better coordinated to make optimal use of
limited resources and to more fully realize opportunities for innovation. One of the goals
of the Alliance is to reduce tobacco usage which will reduce the incidence of lung cancer.
Another goal of both the Alliance and of the Department of Health is to reduce health
disparities which are particularly high with lung cancer.
The Minnesota Health Department Tobacco Prevention and Control Program connects
health professionals, and the public to valuable resources including smoking cessation,
reports detailing the effects of tobacco use, educational materials, and guidance on
24
community-based tobacco prevention programs with the goal of reducing tobacco
consumption.
Program Information
Minnesota Cancer Surveillance System (MCSS)
Minnesota Department of Health
85 E. 7th Place
P.O. Box 64882
St. Paul, MN 55164-0882
Website: www.health.state.mn.us/divs/hpcd/cdee/mcss/index.html
Email: [email protected]
Phone: 651-201-5900
Related Indicators
•
•
•
Mesothelioma indicator (a Minnnesota-specific indicator)
Future cancer profiles in adults include: breast, bladder, brain and central nervous
system, thyroid, Non-Hodgkin Lymphoma, and leukemias.
Future cancer profiles in children include: brain and central nervous system, and
leukemias.
Limitations and Challenges
Limitations of the measures:
Because such a high proportion of lung cancers are caused by tobacco use, any
geographical analysis of lung cancer rates are meaningless unless the regional tobacco
usage patterns are known and can be taken into account. Since most cancers have a
latency period (time from exposure to a carcinogen to diagnosis with cancer) of decades,
one would ideally want to know the collective tobacco usage patterns of the population
decades ago. This is rarely ever available. Smoking patterns do vary considerably in
Minnesota by region and race.
Limitations of the data source:
MCSS cancer data can fluctuate for a few years as new cases are reported and duplicate
cases are removed (e.g. 2006 case data was considered final in 2009). In addition, current
incidence rates may vary from future reports if population estimates for the current year
of data are revised.
Counts and rates of cancers will be calculated based upon residential address at time of
diagnosis. Since most cancers have a long latency period, address data may not be
meaningful. No information will be available on the latency of cancer cases. No personal
exposure information will be available, including smoking history, diet, lifestyle or
history of cancer.
Graphical Data Views
Table 1: Number of new cases and incidence rates by year, Minnesota, 2000-2004,
Lung and Bronchus Cancer
25
Incidence
Year of
Diagnosis
New Cases
Males
2000
2001
2002
2003
2004
Annual Rate
Females
1,513
1,526
1,528
1,564
1,566
Males
1,170
1,248
1,300
1,355
1,360
Females
73.0
72.3
71.4
71.7
70.9
45.7
48.0
49.3
50.6
49.8
Data Notes: Lung cancer diagnosis includes ICD-O-3 site codes C340-C349. Cases were
microscopically confirmed or Death Certificate Only. In situ cancers were excluded.
Rates are per 100,000 persons and age-adjusted to the U.S. 2000 standard population. All
analyses were conducted by MCSS.
Data Source: Cancer data from MCSS. Population estimates for rates from the SEER
Program.
Interpretation: Lung and bronchus cancer incidence rates are about 47 percent higher
among men than women.
Table 2: Number of new cases and average annual incidence rates by age,
Minnesota, 2000-2004, Lung and Bronchus Cancer
Incidence 2000-2004
Age at
Diagnosis
(years)
Total Cases
Males
0–
20 –
35 –
50 –
65 –
74 –
19
34
49
64
74
85
85 and older
3
21
389
2,055
2,659
2,172
398
Average Rate
Females
Males
2
21
396
1,839
2,069
1,743
363
0.1
0.8
12.9
106.7
385.2
495.5
291.4
Females
0.1
0.8
13.3
93.9
261.0
272.7
111.3
Data Notes: Lung cancer diagnosis includes ICD-O-3 site codes C340-C349. Cases were
microscopically confirmed or Death Certificate Only. In situ cancers were excluded.
Rates are per 100,000 persons and age-adjusted to the U.S. 2000 standard population. All
analyses were conducted by MCSS.
Data Source: Cancer data from MCSS. Population estimates for rates from the SEER
Program.
Interpretation: Incidence rates for lung and bronchus cancer increase with age. About
89 percent of cases are diagnosed between 50 and 85 years of age. Lung and bronchus
cancer incidence rates are about 47 percent higher among men than women.
26
Table 3: Number of new cases and average annual incidence rates by race and
ethnicity, Minnesota, 2000-2004, Lung and Bronchus Cancer
Incidence 2000-2004
Race and
Ethnicity
Total Cases
Males
All Races
Average Rate
Females
Males
Females
7,697
6,433
71.8
48.7
76
91
129
108.8
Am erican
Indian
Asian/Pacific
Isl.
Black
NonHispanic
White
52
173
42
117
37.9
105
24.5
57.8
7,330
6,112
71.3
48.3
Hispanic (All
Races)
32
37
37.6
39.5
Data Notes: Lung cancer diagnosis includes ICD-O-3 site codes C340-C349. Cases were
microscopically confirmed or Death Certificate Only. In situ cancers were excluded.
Rates are per 100,000 persons and age-adjusted to the U.S. 2000 standard population. All
analyses were conducted by MCSS. Non-Hispanic persons reported with unknown or
other race are included in all races combined, but are excluded from race-specific data.
Hispanic includes persons of any race. See text for comments on the accuracy of raceand ethnic-specific cancer rates. A description of the methodology used to develop the
bridged single-race estimates is available on the National Center for Health Statistics web
site (www.cdc.gov/nchs/about/major/dvs/popbridge/popbridge.htm). Race-specific rates based on
fewer than 10 cases or deaths are not presented.
Data Source: Cancer data from MCSS. Population estimates for rates from the SEER
Program.
Interpretation: In Minnesota, American Indian males and females and black males have
the highest incidence and mortality rates of lung and bronchus cancer, while
Asian/Pacific Islander men and women have the lowest. Nationally, incidence rates are
lowest among Hispanics and Asian/Pacific Islanders and highest in blacks. Lung and
bronchus cancer incidence rates are about 47 percent higher among men than women.
27
FOCUS ON MINNESOTA
Table 4: Minnesota lung cancer incidence by sex, 1988-2004.
Minnesota Lung Cancer
Incidence by sex 1988-2004
90
80
70
60
50
40
30
20
10
0
Males
Females
1988 1990 1992 1994 1996 1998 2000 2002 2004
Source: MCSS (October 2007). Cases were microscopically confirmed (1988-2004) In situ
cancer excluded.
Rates are per 100,000 persons and are age-adjusted to the 2000 US population.
Table 5: Minnesota lung cancer incidence by region, 1988-2004.
Minnesota Lung Cancer
Incidence by Region 1988-2004
70
60
50
40
30
20
10
0
M
So
et
ut
ro
he
as
So
te
ut
rn
h
C
en
So
tr
ut
al
hw
es
te
rn
C
e
W
nt
es
r
t C al
en
N
or
tr
al
th
w
es
N
te
or
rn
th
ea
st
er
n
Both
Sexes
Source: MCSS (October 2007). Cases were microscopically confirmed (1988-2004) In situ
cancer excluded.
Rates are per 100,000 persons and are age-adjusted to the 2000 US population.
28
Complete Indicator Profile of Mesothelioma
Definition
Measure 1: Annual counts of mesothelioma among Minnesota residents.
- Counts of unique invasive primary incident cases of mesothelioma diagnosed
during a specified calendar year to residents of a specified geographic region among
all residents of that geographic region.
Measure 2: Age-adjusted incidence rates for mesothelioma per 100,000 Minnesota
residents in a geographic area per year.
- A weighted average of the age-specific mesothelioma rates (the number of cancer
counts in an age group per 100,000 people in that age group). Age-adjustment is a
statistical method that minimizes differences in rates that would occur solely
because the populations being compared do not have the same age distributions.
Rates are directly age-adjusted to the 2000 U.S. standard population (19 age
groups).
Numerator
Measure 1 and 2: Counts of unique invasive primary incident cases of mesothelioma
diagnosed during a specified calendar year within residents of a specified geographic
region. Incidence data are collected by the Minnesota Cancer Surveillance System
(MCSS). Mesothelioma diagnosis includes International Classification of Diseases for
Oncology 3rd edition (ICD-O-3) histology codes 9050-9053, between the years 19882004. In situ cancers are excluded. All cancers are confirmed either microscopically or by
death certificate only.
Denominator
Measure 1: N/A
Measure 2: Estimated Minnesota population of a geographic area from the National
Cancer Institute’s (NCI) Surveillance, Epidemiology, and End Results (SEER)
Program.xix A description of the methodology used to develop the bridged single-race
estimates is available on the National Center for Health Statistics web site.xx
Data Sources
•
•
Minnesota Cancer Surveillance System (MCSS): 2000-2004
National Cancer Institute, Surveillance, Epidemiology, and End Results (SEER)
Program: 2000-2004
Why Is This Important?
Definition of mesothelioma
Mesothelioma is a cancer of the tissues that line the chest and abdominal. Mesothelioma
is thought to be caused almost exclusively by inhalation of asbestos fibers, which can
damage mesothelial tissues.
29
Burden of mesothelioma
Nationally, the age-adjusted incidence rate of mesothelioma was 1.07 per 100,000 population
in 2005; 1.98 among males and 0.42 among females.xxi In the geographic areas covered by
SEER, the male incidence rate increased through the 1970’s and 1980’s and has generally
declined since the early 1990’s.xxii About 70 percent of mesotheliomas were diagnosed
among persons 65 years of age and older. This reflects both the long delay between exposure
and diagnosis and the fact that asbestos use in the U.S. has dropped by 98 percent since the
early 1970s.xxiii National data indicate that mesothelioma incidence is lower among persons
of color than among non-Hispanic whites.xxiv
About 65 Minnesotans are diagnosed with mesothelioma each year.xxv In 2005, the ageadjusted incident rate per 100,000 Minnesota residents was 2.3 among males and 0.5 among
females.xxvi Mesothelioma is four times more common among men than women both in
Minnesota and nationally, reflecting that most exposures to asbestos occur occupationally in
jobs primarily held by men. The incidence of mesothelioma has increased significantly
among men in Minnesota by an average of 1.7 percent per year since statewide cancer
reporting was implemented in 1988.xxvii Because the delay between exposure to asbestos
and development of mesothelioma is 30-50 years, it is likely that increasing rates reflect
exposures that occurred before the hazards of asbestos were well known. Rates among
women in Minnesota were stable.
The connection to environmental health
Mesothelioma is a sentinel event for exposure to asbestos fibers and it is highly and
rapidly fatal. Exposure is often occupationally related. Asbestos was widely used in
manufacturing during and following World War II.
Known risk factors
Occupations which may have involved exposure to asbestos include mining, ship
building, and railroad, factory, and construction work. Family members of people
working with asbestos are also at increased risk because fibers may be brought into the
home on work clothes. Persons exposed to asbestos are also at greater risk of developing
lung cancer. The combination of exposure to asbestos and smoking is associated with a
50-90 fold increase in the risk of lung cancer.xxviii
Although mesothelioma is relatively rare, it is indicative of asbestos exposure which will
also cause lung cancer and asbestosis. The Northeast Region of Minnesota has had high
rates of mesothelioma for at least two decades and this is expected to continue due to
known past occupational exposures, especially among a cohort of taconite miners from
across northern Minnesota, who appear to have an unusually high occurrence of this
disease.
Known control or prevention measures
There are no effective screening tests for mesothelioma in the general population.
Elimination of asbestos exposure in the past few decades is likely to result in a declining
rate of mesothelioma at some point in the future.
30
What are the National Objectives?
There are no Healthy People 2010 Objectives for mesothelioma incidence.
How Are We Doing?
Overall mesothelioma incidence rates in Minnesota are similar to those reported by
SEER.xxix Although the male incidence rate has generally declined since the early
1990’s in the geographic areas covered by SEER, xxx the incidence has increased
significantly among men in Minnesota since statewide cancer reporting was implemented in
1988.xxxi The Northeast Region of Minnesota has had high rates of mesothelioma for at
least two decades and this is expected to continue due to known past occupational
exposures, especially among a cohort of taconite miners from across northern Minnesota,
who appear to have an unusually high occurrence of this disease.
What Is Being Done?
“Cancer Plan Minnesota 2005-2010” (www.cancerplanmn.org) is the state’s first
comprehensive cancer control plan. Developed through a broad-based collaboration of
public, private and non-profit organizations, the plan was released in April 2005 and
serves as a common framework for action to reduce the burden of cancer for all
Minnesotans. The plan includes 24 objectives and numerous strategies covering all facets
of cancer control: prevention, early detection, treatment, quality of life, cancer disparities,
and data and research needs.
The Minnesota Cancer Alliance, a coalition of health organizations, community groups
and volunteers, was formed to implement Cancer Plan Minnesota. It provides a forum
through which cancer control activities can be better coordinated to make optimal use of
limited resources and to more fully realize opportunities for innovation.
Program Information
Minnesota Cancer Surveillance System (MCSS)
Minnesota Department of Health (MDH)
85 E. 7th Place
P.O. Box 64882
St. Paul, MN 55164-0882
Website: www.health.state.mn.us/divs/hpcd/cdee/mcss/index.html
Email: [email protected]
Phone: 651-201-5900
Related Indicators
•
•
•
Lung and bronchus cancer indicator
Future cancer profiles in adults include: breast, bladder, brain and central nervous
system, thyroid, Non-Hodgkin Lymphoma, and leukemias.
Future cancer profiles in children include: brain and central nervous system, and
leukemias.
Limitations and Challenges
31
Limitations of the measures:
Because it is a sentinel event for asbestos exposure, mesothelioma is a useful indicator of
an occupational exposure. However, because people do move with some frequency and
mesotheliomas have an unusually long latency period (often 40 or more years),
geographical interpretations will be diluted.
Limitations of the data source:
MCSS cancer data can fluctuate for a few years as new cases are reported and duplicate
cases are removed (e.g. 2006 case data was considered final in 2009). In addition, current
incidence rates may vary from future reports if population estimates for the current year
of data are revised.
Counts and rates of cancers will be calculated based upon residential address at time of
diagnosis. Since most cancers have a long latency period, address data may not be
meaningful. No information will be available on the latency of cancer cases. No personal
exposure information will be available, including smoking history, diet, lifestyle or
history of cancer.
Graphical Data Views
Table 1: Number of new cases and incidence rates by year, Minnesota, 1988-2004,
Mesothelioma
Incidence
Year of
Diagnosis
or Death
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
New Cases
Males
Annual Rate
Females
Males
Females
26
34
33
39
33
41
39
47
48
39
8
8
11
13
15
14
9
9
5
17
1.5
1.9
1.9
2.1
1.8
2.2
2.1
2.5
2.4
2
0.4
0.3
0.5
0.6
0.6
0.6
0.4
0.4
0.2
0.7
57
57
58
40
52
52
49
12
9
14
7
14
17
18
2.9
2.8
2.9
1.9
2.5
2.5
2.4
0.4
0.4
0.5
0.3
0.5
0.6
0.6
32
Data Notes: Mesothelioma diagnosis includes ICD-O-3 histology codes 9050 through
9053. Cases were microscopically confirmed or Death Certificate Only. In situ cancers
were excluded. Rates are per 100,000 persons and age-adjusted to the U.S. 2000 standard
population. All analyses were conducted by MCSS.
Data Source: Cancer data from MCSS. Population estimates for rates from the SEER
Program.
Interpretation: The incidence of mesothelioma has increased significantly among men
in Minnesota by an average of 1.7 percent per year since statewide cancer reporting was
implemented in 1988. Because the delay between exposure to asbestos and development
of mesothelioma is 30-50 years, it is likely that increasing rates reflect exposures that
occurred before the hazards of asbestos were well known. Rates among women in
Minnesota were stable.
Table 2: Number of new cases and average annual incidence rates by age,
Minnesota, 2000-2004, Mesothelioma
Incidence 2000-2004
Age at
Diagnosis or
Death (years)
Total Cases
Males
0 – 19
20 – 34
35 – 49
50 – 64
65 – 74
74 – 85
85 and older
Average Rate
Females
Males
Females
0
0
0
1
0
0
0
0
10
51
63
94
5
13
19
20
0.3
2.6
9.1
21.4
0.2
0.7
2.4
3.1
33
12
24.2
3.7
Data Notes: Mesothelioma diagnosis includes ICD-O-3 histology codes 9050 through
9053. Cases were microscopically confirmed or Death Certificate Only. In situ cancers
were excluded. Rates are per 100,000 persons and age-adjusted to the U.S. 2000 standard
population. All analyses were conducted by MCSS.
Data Source: Cancer data from MCSS. Population estimates for rates from the SEER
Program.
Interpretation: About 75 percent of mesotheliomas diagnosed in Minnesota are among
persons age 65 years and older. This reflects both the long delay between exposure and
diagnosis, and the fact that asbestos use in the U.S. has dropped by 98 percent since the
early 1970s.
Table 3: Number of new cases and average annual incidence rates by race and
ethnicity, Minnesota, 2000-2004, Lung and Bronchus Cancer
33
Incidence 2000-2004
Race and
Ethnicity
Total Cases
Males
All Races
Average Rate
Females
Males
Females
251
70
2.4
0.5
0
0
~
~
American
Indian
Asian/Pacific
Isl.
Black
NonHispanic
White
1
5
0
0
~
~
~
~
245
70
2.5
0.5
Hispanic (All
Races)
0
0
~
~
Data Notes: Mesothelioma diagnosis includes ICD-O-3 histology codes 9050 through
9053. Cases were microscopically confirmed or Death Certificate Only. In situ cancers
were excluded. Rates are per 100,000 persons and age-adjusted to the U.S. 2000 standard
population. All analyses were conducted by MCSS. Non-Hispanic persons reported with
unknown or other race are included in all races combined, but are excluded from racespecific data. Hispanic includes persons of any race. See text for comments on the
accuracy of race- and ethnic-specific cancer rates. A description of the methodology used
to develop the bridged single-race estimates is available on the National Center for
Health Statistics web site
(www.cdc.gov/nchs/about/major/dvs/popbridge/popbridge.htm). Race-specific rates
based on fewer than 10 cases or deaths are not presented.
Data Source: Cancer data from MCSS. Population estimates for rates from the SEER
Program.
Interpretation: National data indicate that mesothelioma incidence is lower among
persons of color than among non-Hispanic whites.
34
FOCUS ON MINNESOTA
Table 4: Minnesota mesothelioma incidence by sex, 1988-2004.
Minnesota Mesothelioma
Incidence by sex 1988-2004
3.5
3
2.5
2
Males
Females
1.5
1
0.5
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
0
Source: MCSS (October 2007). Cases were microscopically confirmed (1988-2004) In situ
cancer excluded.
Rates are per 100,000 persons and are age-adjusted to the 2000 US population.
Table 5: Minnesota mesothelioma incidence by region, 1988-2004.
Minnesota Mesothelioma
Incidence by Region 1988-2004
2.5
2
1.5
Both
Sexes
1
0.5
M
So
et
ut
ro
he
a
So
st
er
ut
n
h
C
So
en
t
ut
hw ral
es
te
rn
C
en
W
tr
es
t C al
en
N
or
tr
al
th
w
e
st
N
or
er
n
th
ea
st
er
n
0
Source: MCSS (October 2007). Cases were microscopically confirmed (1988-2004) In situ
cancer excluded.
Rates are per 100,000 persons and are age-adjusted to the 2000 US population.
35
i www.seer.cancer.gov/popdata
iiwww.cdc.gov/nchs/about/major/dvs/popbridge/popbridge.htm
iii NCI. A snapshot of lung cancer. Updated 2008. Available from:
http://planning.cancer.gov/disease/Lung-Snapshot.pdf
iv Brown M, Perkins C, Soler J, Bushhouse S. Cancer in Minnesota, 1988-2004. St. Paul, Minnesota:
Minnesota Cancer Surveillance System, Minnesota Department of Health, September 2008.
v M, Perkins C, Soler J, Bushhouse S. Cancer in Minnesota, 1988-2004. St. Paul, Minnesota: Minnesota
Cancer Surveillance System, Minnesota Department of Health, September 2008.
vi M, Perkins C, Soler J, Bushhouse S. Cancer in Minnesota, 1988-2004. St. Paul, Minnesota: Minnesota
Cancer Surveillance System, Minnesota Department of Health, September 2008.
vii American Cancer Society. Cancer Facts & Figures 2008. Atlanta: American Cancer Society; 2008.
viii American Cancer Society. Cancer Facts & Figures 2008. Atlanta: American Cancer Society; 2008.
ix M, Perkins C, Soler J, Bushhouse S. Cancer in Minnesota, 1988-2004. St. Paul, Minnesota: Minnesota
Cancer Surveillance System, Minnesota Department of Health, September 2008.
x 2004 Surgeon General’s Report—The Health Consequence of Smoking.
xi National Research Council. Biological Effects of Ionizing Radiation (BEIR) VI Report: "The Health
Effects of Exposure to Indoor Radon" National Academies Press, Washington, DC. 1999.
xii MDH Indoor Air Quality Program. Radon in Minnesota Homes. Available from:
http://www.health.state.mn.us/divs/eh/indoorair/radon/index.html
xiii M, Perkins C, Soler J, Bushhouse S. Cancer in Minnesota, 1988-2004. St. Paul, Minnesota: Minnesota
Cancer Surveillance System, Minnesota Department of Health, September 2008.
xiv National Cancer Institute. “What you need to know about lung cancer” NIH Publication No. 07-1553.
Available from: http://www.cancer.gov/cancertopics/wyntk/lung/page4
xv NCI State Cancer Profiles. http://Statecancerprofiles.cancer.gov
xvi NCI State Cancer Profiles. http://Statecancerprofiles.cancer.gov
xvii M, Perkins C, Soler J, Bushhouse S. Cancer in Minnesota, 1988-2004. St. Paul, Minnesota: Minnesota
Cancer Surveillance System, Minnesota Department of Health, September 2008.
xviii NCI State Cancer Profiles. http://Statecancerprofiles.cancer.gov
xix www.seer.cancer.gov/popdata
xxwww.cdc.gov/nchs/about/major/dvs/popbridge/popbridge.htm
xxi SEER Fast Stats, 2000-2005. Available from: http://seer.cancer.gov/faststats/index.php
xxii Weill H, Hughes JM, Churg AM. Changing trends in US mesothelioma incidence. Occup Environ Med
2004;61:438–441.
xxiii American Cancer Society. Minnesota cancer facts and figures 2009. Available from:
http://www.cancerplanmn.org/sites/528d17b0-2c73-45c9-894d872fc0beac4e/uploads/2009_MN_Cancer_Facts___Figures.pdf
xxiv Brown M, Perkins C, Soler J, Bushhouse S. Cancer in Minnesota, 1988-2004. St. Paul, Minnesota:
Minnesota Cancer Surveillance System, Minnesota Department of Health, September 2008.
xxv Perkins C, Bushhouse S. Cancer in Minnesota, 2006: Preliminary Report. Minnesota Cancer
Surveillance System, St Paul, MN, January 2009. Available online at
http://www.health.state.mn.us/divs/hpcd/cdee/mcss
xxvi American Cancer Society. Minnesota cancer facts and figures 2009. Available from:
http://www.cancerplanmn.org/sites/528d17b0-2c73-45c9-894d872fc0beac4e/uploads/2009_MN_Cancer_Facts___Figures.pdf
xxvii Perkins C, Bushhouse S. Cancer in Minnesota, 2006: Preliminary Report. Minnesota Cancer
Surveillance System, St Paul, MN, January 2009. Available online at
http://www.health.state.mn.us/divs/hpcd/cdee/mcss
xxviii Brown M, Perkins C, Soler J, Bushhouse S. Cancer in Minnesota, 1988-2004. St. Paul, Minnesota:
Minnesota Cancer Surveillance System, Minnesota Department of Health, September 2008.
xxix Brown M, Perkins C, Soler J, Bushhouse S. Cancer in Minnesota, 1988-2004. St. Paul, Minnesota:
Minnesota Cancer Surveillance System, Minnesota Department of Health, September 2008.
xxx Weill H, Hughes JM, Churg AM. Changing trends in US mesothelioma incidence. Occup Environ Med
2004;61:438–441.
36
xxxi Perkins C, Bushhouse S. Cancer in Minnesota, 2006: Preliminary Report. Minnesota Cancer
Surveillance System, St Paul, MN, January 2009. Available online at
http://www.health.state.mn.us/divs/hpcd/cdee/mcss
37
Birth Outcomes/Vital Statistics Content Area
Indicators:
1. Prematurity
2. Growth Retardation
3. Mortality
4. Fertility
5. Sex Ratio
38
Complete Indicator Profile of Prematurity
Definition
Measure 1: Percent of Preterm Births among Singleton Live Born Infants
- Number of live born singleton infants born before 37 weeks gestation per 100 live
singleton births.
Measure 2: Percent of Very Preterm Births among Singleton Live Born Infants
- Number of live born singleton infants born before 32 weeks gestation per 100 live
singleton births.
Measure 3: Percent of Very Low Birth Weight Births among Singleton Live Born Infants
- Number of live born singleton infants born with a very low birth weight (VLBW; i.e., less
than 1,500 grams or 3 pounds, 5 ounces) per 100 live singleton births.
Numerator
Measure 1: Percent of Preterm Births among Singleton Live Born Infants
- Number of live singleton infants born before 37 weeks of gestation to resident mothers.
Measure 2: Percent of Very Preterm Births among Singleton Live Born Infants
- Number of live singleton infants born before 32 weeks of gestation to resident mothers.
Measure 3: Percent of Very Low Birth Weight Births among Singleton Live Born Infants
- Number of live born singleton infants with a birth weight of less than 1,500 grams.
Denominator
Measure 1: Percent of Preterm Births among Singleton Live Born Infants
- Total number of live singleton infants born to resident mothers.
Measure 2: Percent of Very Preterm Births among Singleton Live Born Infants
- Total number of live singleton infants born to resident mothers.
Measure 3: Percent of Very Low Birth Weight Births among Singleton Live Born Infants
- Total number of live singleton infants born to resident mothers.
Data Sources
Birth certificate data are collected from various sources including the mother, clinic, and
hospital. Cause of death for death records is reported by the attending physician or
coroner/medical examiners. These data are entered directly into Vital Records Vision 2000
System, which electronically records and maintains vital records (Birth, Death, and Fetal Death)
for the State of Minnesota. Currently 100% of birth and death records are filed electronically.
Birth and death certificates and fetal death reports filed with the Office of the Registrar,
Minnesota Department of Health for calendar year 2001-2006 are the source documents for data
on vital events of Minnesota residents.
Why Is This Important?
Definition of prematurity
A preterm baby is a live born singleton infant born before 37 weeks gestation. A very preterm
baby is a live born singleton infant born before 32 weeks gestation. The interval between the first
39
day of the mother’s last normal menstrual period and the day of birth is one method used to
determine the gestational age of the newborn. The National Center for Health Statistics (NCHS)
report gestational age based on an algorithm that utilizes both the mother’s reported last normal
menses and clinician’s estimate of gestational age. The Minnesota Department of Health uses a
different method to determine gestational age.
A very low birth weight (VLBW) baby is a live born singleton infant with a birth weight of less
than 1,500 grams (or 3 pounds, 5 ounces). Birth weight is the first weight of the newborn obtained
after birth. VLBW is primarily associated with preterm birth.1
Burden of prematurity
Preterm birth is the leading cause of death in the first month of life and a contributing cause in more
than a third of all infant deaths. Babies who survive an early birth face the risk of serious lifelong
health problems and even late preterm infants have a greater risk of breathing problems, feeding
difficulties, temperature instability (hypothermia), jaundice, delayed brain development and an
increased risk of cerebral palsy and mental retardation.2
Preterm infants are at greater risk of serious health problems for several reasons: the earlier is a baby
is born, the less it will weigh, the less developed its organs will be, and the more medical
complications it will likely face later in life. Very preterm infants have the greatest risk of death and
lasting disabilities. Preterm births account for health care expenditure of over $3 billion per year that
has resulted in improved survival rates among preterm babies.3
A newborn’s weight at birth is closely related to its risk of early death and long-term morbidity.4 5
Infants born at the lowest weights are the most likely not to survive the first year: the risk of dying in
the first year of life is estimated to be about 100 times higher for VLBW infants than for normal
weight infants.6
Nationally, the preterm birth rate in 2006 was 11.09% of all singleton births. The percentage of all
infants delivered at less than 37 completed weeks of gestation has climbed 20 percent since 1990.
Most of this rise is attributable to the increases in all late preterm births (34–36 weeks), up 25 percent
since 1990. The very preterm birth rate in 2006 was 1.65% of all singleton births. The VLBW rate in
2006 was 1.14% of all singleton births, up from 1.05% in 1990.5
Among all 2007 births in Minnesota, 10% were born premature.7 Among singletons, 5,283 were born
preterm in 2006, or 8.5% of all singleton births in 2006. This number was up from 7.8% in 2002.8 In
2006, there were 810 very low birth weight infants born in Minnesota, or 1.1% of all resident births.9
The connection to environmental health
The fetus is developing along with critical organ systems during pregnancy; there are critical
windows of development where environmental exposures could damage growth and function.
Outdoor air pollution is associated with reduced term birth weight and preterm delivery.10 Some
studies have reported that environmental factors, including exposure to air pollution, drinking water
contaminated with chemical disinfection by-products, and exposure to pesticides affect the birth
weight of newborns. However, the strength of the association of each of these risk factors with
VLBW rate remains relatively uncertain.
Environmental tobacco smoke is a risk factor for reduced birth weight and preterm delivery.
40
Tobacco use during pregnancy causes the passage of chemicals from the placenta into the fetal blood
supply. These substances restrict the growing infant’s access to oxygen and can lead to adverse
pregnancy and birth outcomes such as low birth weight, preterm delivery, intrauterine growth
retardation, and infant mortality.11
Known risk factors
Studies have shown that major risk factors associated with preterm birth include
Sources: 12 13 14 2 15 16
- Plural births
- Previous preterm birth
- Certain uterine or cervical abnormalities of the mother
- Mother’s age, race, poverty (for example, African-American women, women younger than 17
and older than 35, and poor women are at greater risk than other women)
- Male fetal gender (associated with singleton preterm birth)
- Certain lifestyles and environmental factors, including:
o Late or no prenatal care,
o Maternal smoking, alcohol consumption (especially, in early pregnancy), using
illegal drugs, exposure to the medication diethylstilbestrol (DES), domestic violence,
lack of social support, stress, long working hours with long periods of standing, being
underweight before pregnancy, obesity, marital status, and spacing (less than 6-9
months between birth and the beginning of the next pregnancy),
o Neighborhood-level characteristics,
o Environmental contaminants (e.g., exposure to air pollution and drinking water
contaminated with chemical disinfection by-products or lead).
Demographic risk factors associated with VLBW include mother’s age, (17 years and younger or 35
years and older) and marital status of the mother (single).
What are the National Objectives?
Measure 1: Percent of Preterm Births among Singleton Live Born Infants
- Healthy People 2010 Objective 16-11a. Reduce total preterm births to 7.6%
- Healthy People 2010 Objective 16-11b. Reduce births at 32 to 36 weeks of gestation to
6.4%.
Measure 2: Percent of Very Preterm Births among Singleton Live Born Infants
- Healthy People 2010 Objective 16-11c. Reduce births at less than 32 weeks of gestation to
1.1%.
Measure 3: Percent of Very Low Birth Weight Births among Singleton Live Born Infants
- Healthy People 2010 Objective 16-10c. Reduce very low birth weight to 0.9%.
How Are We Doing?
Although Minnesota is below the national preterm birth rate, the state has not met the Healthy People
2010 goal of 7.6% for prematurity. Minnesota’s rate has been increasing and moving further from the
national target. Both Minnesota and the nation are also above the Healthy People 2010 goal for very
preterm birth rates.
What Is Being Done?
The Maternal and Child Health Section of MDH includes the following programs:
41
•
•
•
Family Home Visiting (FHV): goals include improving family health status and
achieving maternal goals like child spacing
Women, Infants & Children (WIC) Program: a nutrition program targeted for pregnant
women, new mothers, babies, and young children
Minnesota Pregnancy Risk Assessment Monitoring System (PRAMS) is a CDC initiative
to reduce infant mortality and low birth weight, gathering state-specific information using
a survey of mothers who have recently had a baby, used to address public health issues
and develop effective programs to improve the health of mothers and babies in
Minnesota.
Program Information
Minnesota Center for Health Statistics
Minnesota Department of Health
Golden Rule Building, 3rd floor
85 E. 7th Place
PO Box 64882
St. Paul, MN 55164-0882
Email: [email protected]
Website: http://www.health.state.mn.us/divs/chs/
Related Indicators
•
•
•
•
Growth retardation indicator
Infant mortality indicator.
Fertility indicator.
Sex ratio indicator.
Limitations and Challenges
Limitations of the measures:
Preterm measures are subject to uncertainties associated with gestational age estimates. The
interval between the first day of the mother’s last normal menstrual period (LMP) and the day of
birth is one method used to determine the gestational age of the newborn. However, this
measurement is subject to error for many reasons. The National Center for Health Statistics
(NCHS) report gestational age based on an algorithm that utilizes both the mother’s reported last
normal menses and clinician’s estimate of gestational age. The Minnesota Department of Health uses
a different method to determine gestational age.
Very preterm birth rates are difficult to interpret: a low very preterm birth rate might indicate a
low-risk population, high fetal mortality, poor reproductive health of a population or a high
abortion rate; a high very preterm birth rate might be a sign of maternal characteristics that
predispose to very preterm birth or the result of advanced technology and life-saving techniques.
Although the percent of VLBW births has increased during the past 20 years, in large part this
could be due to improvements in fetal health. Conditions that may have resulted in a fetal death
decades ago today might result in fetal survival and a live VLBW birth.
Limitations of the data source:
42
Due to the continuing nature of the Vital Records collection process, it is not unusual for a birth
record to be corrected or amended weeks or months after it was originally processed by Vital
Records. Adoptions, which can take months to process, are subject to amendments to the original
birth record. It is possible where a birth record arrives at Vital Records with the demographic
characteristics of the birth mother (including mother’s race/ethnicity, education level, etc.) only
to be amended months later and replaced with the demographic characteristics of the adoptive
mother replacing those of the birth mother. Because of the time it takes to correct and amend
birth records, the final birth file for a particular calendar year can take many months after the end
of the calendar year to close and be made available for epidemiological use.
Another limitation of the data source is that the place of residence during pregnancy (and, with
infant death, residence during first year of life) may not be represented by maternal residence at
time of birth (or death).
The quality of vital statistics data is directly related to the completeness and accuracy of the
information contained in the source documents. The Minnesota Department of Health maintains
two programs to improving the quality of information received on birth and death certificates in
order to ensure that the information is as complete and accurate as possible: a query program to
contact hospital personnel, funeral directors, and/or physicians concerning incomplete or
conflicting information; and a field program focused on educating participants in the vital
registration system.7
Graphical Data Views
Table 1: Percent of preterm births by year, Minnesota, 2001-2006
9.0%
8.8%
8.6%
8.4%
8.2%
8.0%
7.8%
7.6%
7.4%
7.2%
7.0%
2001
2002
2003
2004
2005
2006
Data Notes: Percent prematurity is defined as the number of live born singleton infants born
before 37 weeks gestation per 100 live singleton births with a non-missing gestational age.
Data Source: Minnesota Center for Health Statistics, Minnesota Department of Health.
Interpretation: In general, percent preterm is increasing in Minnesota.
43
Table 2: Percent of preterm births by maternal age, Minnesota, 2001-2006
25%
20%
15%
10%
5%
0%
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-50
Data Notes: Percent prematurity is defined as the number of live born singleton infants born
before 37 weeks gestation per 100 live singleton births with a non-missing gestational age.
Data Source: Minnesota Center for Health Statistics, Minnesota Department of Health.
Interpretation: Women younger than 17 and older than 35 are at risk for preterm birth.
Table 3: Percent of preterm births by maternal race, Minnesota, 2001-2006
12%
WHITE
10%
BLACK
8%
ASIAN/PACIFIC
ISLANDER
6%
NATIVE
4%
OTHER/UNKNOWN
2%
0%
2001-2006
Data Notes: Percent prematurity is defined as the number of live born singleton infants born
before 37 weeks gestation per 100 live singleton births with a non-missing gestational age.
Data Source: Minnesota Center for Health Statistics, Minnesota Department of Health.
Interpretation: White women have the lowest risk for preterm birth in Minnesota.
Table 4: Percent of very low birth weight by maternal race, Minnesota, 2001-2006
44
2.5%
WHITE
2.0%
BLACK
1.5%
ASIAN/PACIFIC
ISLANDER
1.0%
NATIVE
0.5%
OTHER/UNKNOWN
0.0%
2001-2006
Data Notes: Percent prematurity is defined as the number of live born singleton infants born
before 37 weeks gestation per 100 live singleton births with a non-missing gestational age.
Data Source: Minnesota Center for Health Statistics, Minnesota Department of Health.
Interpretation: White women have the lowest risk for delivering very low birth weight babies in
Minnesota.
45
Complete Indicator Profile of Growth Retardation
Definition
Measure 1: Percent of Low Birth Weight Births among Singleton Term Live Born Infants
- Percent of low birth weight term singleton births (number of live born singleton infants
born at term with a birth weight of less than 2,500 grams per 100 live term singleton
births).
Numerator
Measure 1: Percent of Low Birth Weight Births among Singleton Term Live Born Infants
- Number of live born singleton infants born at term (at or above 37 completed weeks of
gestation) with a birth weight of less than 2,500 grams.
Denominator
Measure 1: Percent of Low Birth Weight Births among Singleton Term Live Born Infants
- Total number of live born singleton infants born at term.
Data Sources
Birth certificate data are collected from various sources including the mother, clinic, and
hospital. Cause of death for death records is reported by the attending physician or
coroner/medical examiners. These data are entered directly into Vital Records Vision 2000
System, which electronically records and maintains vital records (Birth, Death, and Fetal Death)
for the State of Minnesota. Currently 100% of birth and death records are filed electronically.
Birth and death certificates and fetal death reports filed with the Office of the Registrar,
Minnesota Department of Health for calendar year 2001-2006 are the source documents for data
on vital events of Minnesota residents.
Why Is This Important?
Definition of growth retardation
A low birth weight (LBW) baby is a live born singleton infant with a birth weight of less than 2,500
grams or 5 pounds, 8 ounces at birth. A low birth weight infant can be born too soon (premature) or
too small (growth retarded) or both. Thus, low birth weight is a heterogenous category which
contains both premature and growth retarded infants. There are different factors which influence
preterm delivery and growth retardation so it is more helpful to look at the components of low birth
weight than just the larger category.
Intrauterine growth retarded infants are considered those who are gestationally full-term (at or above
37 completed weeks of gestation) but of a low birth weight (<2,500 g).17 Because LBW is associated
with multiple births and preterm birth, the focus of the measure is restricted to singleton infants
born at term.
Burden of growth retardation
46
A newborn’s weight at birth is closely related to its risk of early death and long-term morbidity.5
Compared to infants of normal weight (2,500 through 3,999 grams or 5.9 to 8.7 pounds), low birth
weight infants may be at increased risk of perinatal morbidity, infections, and the longer-term
consequences of impaired development, such as delayed motor and social development or learning
disabilities.
In the United States, the low birth weight (LBW) rate among all births was 8.3% in 2006, the highest
level in four decades. The percentage of all infants born at less than 2,500 grams has risen 19 percent
since 1990.5 Among term births, the low birth weight rate in the United States was 3.2%.5
In Minnesota, the low birth weight rate among all infants in 2006 was 6.5%.5 The percent of low
birth weight babies has increased from 5.1% in 1990.18 Among singletons the rate is 4.9%, up from
4.6% in 2002.8 Between the time periods 2001-2005 African Americans were the only racial group in
Minnesota to experience a noticeable decline in low birth weight, but at 8.2 percent still remain two
times greater than for whites.9
The connection to environmental health
The fetus is developing along with critical organ systems during pregnancy; there are critical
windows of development where environmental exposures could damage growth and function.
Exposure to air pollution (both indoor and outdoor) and drinking water contaminated with chemical
disinfection by-products or lead may be environmental risk factors linked to an increased risk of low
birth weight.
Environmental tobacco smoke is a risk factor for growth retardation. Tobacco use during pregnancy
causes the passage of chemicals from the placenta into the fetal blood supply. These substances
restrict the growing infant’s access to oxygen and can lead to adverse pregnancy and birth outcomes
such as low birth weight, preterm delivery, intrauterine growth retardation, and infant mortality.11
Known risk factors
Smoking accounts for 20 to 30 percent of all LBW births in the United States.1 In addition to
maternal smoking, maternal alcohol use; poor nutrition and inadequate maternal weight gain; stress;
and domestic violence or other abuse have been related to an increased risk of low birth weight. Also
at increased risk of having LBW babies are mothers under 15 or over 35, unmarried mothers and
women who have had previous preterm birth. Socioeconomic factors such as low income and lack of
education are reported as risk factors of having a LBW baby.19
What are the National Objectives?
-
Healthy People 2010 Objective 16-10a. Reduce low birth weight to 5.0%.
How Are We Doing?
Minnesota has not met the Healthy People 2010 goal for low birth weight among all infants.
Although Minnesota is well below the national rate, the percent of low birth weight babies among
singletons has increased slightly.
What Is Being Done?
The Maternal and Child Health Section of MDH includes the following programs:
47
•
•
•
Family Home Visiting (FHV): goals include improving family health status and
achieving maternal goals like child spacing
Women, Infants & Children (WIC) Program: a nutrition program targeted for pregnant
women, new mothers, babies, and young children
Minnesota Pregnancy Risk Assessment Monitoring System (PRAMS) is a CDC initiative
to reduce infant mortality and low birth weight, gathering state-specific information using
a survey of mothers who have recently had a baby, used to address public health issues
and develop effective programs to improve the health of mothers and babies in
Minnesota.
Program Information
Minnesota Center for Health Statistics
Minnesota Department of Health
Golden Rule Building, 3rd floor
85 E. 7th Place
PO Box 64882
St. Paul, MN 55164-0882
Email: [email protected]
Website: http://www.health.state.mn.us/divs/chs/
Related Indicators
•
•
•
•
Prematurity indicator
Infant mortality indicator.
Fertility indicator.
Sex ratio indicator.
Limitations and Challenges
Limitations of the measures:
The LBW birth rate might be an indicator of pregnancy outcome that does not necessarily inform
about the true health risk associated with a LBW birth. Between 1990 and 2006, the full birth
weight distribution in the United States changed with a rise in the percentage of singleton births
weighing less than 3,500 grams and a decline in the percentage of heavier infants.20 The reasons
behind the shift towards lower birth weights may be due to obstetric intervention earlier in
pregnancy, older maternal age at childbearing, and increased use of infertility therapies.5 The
LBW birth rates should be interpreted with caution.
Limitations of the data source:
Due to the continuing nature of the Vital Records collection process, it is not unusual for a birth
record to be corrected or amended weeks or months after it was originally processed by Vital
Records. Adoptions, which can take months to process, are subject to amendments to the original
birth record. It is possible where a birth record arrives at Vital Records with the demographic
characteristics of the birth mother (including mother’s race/ethnicity, education level, etc.) only
to be amended months later and replaced with the demographic characteristics of the adoptive
mother replacing those of the birth mother. Because of the time it takes to correct and amend
48
birth records, the final birth file for a particular calendar year can take many months after the end
of the calendar year to close and be made available for epidemiological use.
Another limitation of the data source is that the place of residence during pregnancy (and, with
infant death, residence during first year of life) may not be represented by maternal residence at
time of birth (or death).
The quality of vital statistics data is directly related to the completeness and accuracy of the
information contained in the source documents. The Minnesota Department of Health maintains
two programs to improving the quality of information received on birth and death certificates in
order to ensure that the information is as complete and accurate as possible: a query program to
contact hospital personnel, funeral directors, and/or physicians concerning incomplete or
conflicting information; and a field program focused on educating participants in the vital
registration system.7
Graphical Data Views
Table 1: Percent of low birth weight by year, Minnesota, 2001-2006
2.0%
1.9%
1.8%
1.7%
1.6%
1.5%
1.4%
1.3%
1.2%
1.1%
1.0%
2001
2002
2003
2004
2005
2006
Data Notes: Percent of low birth weight term singleton births (number of live born singleton
infants born at term with a birth weight of less than 2,500 grams per 100 live term singleton
births) among live births with a non-missing birth weight.
Data Source: Minnesota Center for Health Statistics, Minnesota Department of Health.
Interpretation: Percent low birth weight has been increasing in Minnesota.
Table 2: Percent of low birth weight by maternal age, Minnesota, 2001-2006
49
4.0%
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-50
Data Notes: Percent of low birth weight term singleton births (number of live born singleton
infants born at term with a birth weight of less than 2,500 grams per 100 live term singleton
births) among live births with a non-missing birth weight.
Data Source: Minnesota Center for Health Statistics, Minnesota Department of Health.
Interpretation: Mothers under 15 or over 35 are at increased risk of having a low birth weight
baby.
Table 3: Percent of low birth weight by maternal race, Minnesota, 2001-2006
3.5%
3.0%
WHITE
2.5%
BLACK
2.0%
ASIAN/PACIFIC
ISLANDER
1.5%
NATIVE
1.0%
OTHER/UNKNOWN
0.5%
0.0%
2001-2006
Data Notes: Percent of low birth weight term singleton births (number of live born singleton
infants born at term with a birth weight of less than 2,500 grams per 100 live term singleton
births) among live births with a non-missing birth weight.
Data Source: Minnesota Center for Health Statistics, Minnesota Department of Health.
Interpretation: African Americans have a two fold greater risk than for whites for having a low
birth weight baby.
50
Complete Indicator Profile of Infant Mortality
Definition
Measure 1: Infant Mortality Rate per 1000 Live Born Infants
- Number of deaths occurring between the ages of 0-365 days in any given year per 1000
live births in the same year.
Measure 2: Neonatal Mortality Rate per 1000 Live Born Infants
- Number of infant deaths less than 28 days old in any given year per 1000 live births in the
same year.
Measure 3: Perinatal Mortality Rate per 1000 Live Born Infants
- Number of infant deaths less than 7 days old plus fetal deaths at 28+ weeks gestation in any
given year divided by the number of live births plus fetal deaths in the same year.
Measure 4: Postnatal Mortality Rate per 1000 Live Born Infants
- Number of infant deaths aged 28-365 days in a given year divided by the number of live
births in the same year.
Numerator
Measure 1: Infant Mortality Rate per 1000 Live Born Infants
- Number of infant deaths occurring between the ages of 0-365 days.
Measure 2: Neonatal Mortality Rate per 1000 Live Born Infants
- Number of infant deaths occurring before 28 days.
Measure 3: Perinatal Mortality Rate per 1000 Live Born Infants
- Number of deaths occurring before 7 days plus fetal deaths 28+ weeks gestation.
Measure 4: Postnatal Mortality Rate per 1000 Live Born Infants
- Number of infant deaths occurring between the ages of 28-365 days.
Denominator
Measure 1: Infant Mortality Rate per 1000 Live Born Infants
- Total number of live infants born to resident mothers.
Measure 2: Neonatal Mortality Rate per 1000 Live Born Infants
- Total number of live infants born to resident mothers.
Measure 3: Perinatal Mortality Rate per 1000 Live Born Infants
- Total number of live infants born to resident mothers plus the total number of fetal deaths.
Measure 4: Postnatal Mortality Rate per 1000 Live Born Infants
- Total number of live infants born to resident mothers.
Data Sources
Birth certificate data are collected from various sources including the mother, clinic, and
hospital. Cause of death for death records is reported by the attending physician or
coroner/medical examiners. These data are entered directly into Vital Records Vision 2000
System, which electronically records and maintains vital records (Birth, Death, and Fetal Death)
for the State of Minnesota. Currently 100% of birth and death records are filed electronically.
51
Birth and death certificates and fetal death reports filed with the Office of the Registrar,
Minnesota Department of Health for calendar year 2001-2006 are the source documents for data
on vital events of Minnesota residents.
Why Is This Important?
Definition of infant mortality
Infant death is a critical indicator of the health of a population. It reflects the overall state of maternal
health as well as the quality and accessibility of primary health care available to pregnant women and
infants. Neonatal mortality is an important indicator to measure newborn and maternal health status
and medical care pre and post delivery. Postneonatal death reflects events experienced in infancy.
Fetal death often is associated with maternal complications of pregnancy. The perinatal mortality rate
is a useful overall measure of perinatal health and the quality of health care provided to pregnant
women and newborns.1
Burden of infant mortality
The U.S. infant mortality rate generally declined throughout the 20th century. In 1900, the U.S.
infant mortality rate was approximately 100 infant deaths per 1,000 live births, while in 2000, the
rate was 6.89 infant deaths per 1,000 live births. However, the U.S. infant mortality rate did not
decline significantly from 2000 to 2005. In 2004, the United States ranked 29th in the world for infant
mortality.21
Infant mortality rates have declined for most racial and ethnic groups, but large disparities among
groups remain. Non-Hispanic black, Puerto Rican , and American Indian or Alaska Native women
have the highest infant mortality rates; rates are lowest for Asian or Pacific Islander, Central and
South American, and Cuban women.21 In 2005, the infant mortality rate was highest for infants of
non-Hispanic black women at 13.6 and lowest for infants of mothers of Cuban origin at 4.4.21
Infant mortality is made up of two components: neonatal mortality (death in the first 28 days of life)
and postneonatal mortality (death from the infants’ 29th day but within the first year). The rate of
neonatal mortality declined from 8.5 in 1980 to 4.5 in 2004.22 The neonatal mortality rate among
African Americans more than twice that of whites.22 The overall postneonatal mortality rate has
remained steady from 2000-2004 at 2.3.22 From 1990-2003 in the United States, the fetal mortality
rate declined by 29% to 3.04, and the perinatal mortality rate declined by 26% to 6.74.23
In Minnesota, there was a downward trend in the number and rate of infant deaths from 1990 to
2004. The infant mortality rate decreased from 7.3 in 1990 to 4.7 in 2004.18 In 2007, Minnesota’s
infant mortality rate increased to 5.5, up from the rate of 5.2 in 2006.7 In Minnesota from 2000-2004,
infant mortality rates for African Americans (9.5/1,000) and American Indians (10.2/1,000)
decreased but continue at more than two times that for whites (4.5/1,000).9 Neonatal morality was 3.3
deaths per 1,000 births and postneonatal mortality was 1.8 deaths per 1,000 births. The rates of both
neonatal and postneonatal deaths have exhibited a downward trend in Minnesota.
The connection to environmental health
There are critical windows of development during pregnancy where environmental exposures could
damage growth and function of a fetus. Adverse birth outcomes have been associated with some
environmental exposures and pollution measures. Several studies have found a relationship between
postneonatal mortality from respiratory causes and exposure to fine particulate matter (PM2.5) air
52
pollution24,25 but there is uncertainty regarding the association between PM or ozone and
postneonatal mortality due to sudden infant death syndrome (SIDS).25,24,26
Environmental tobacco smoke is a risk factor for infant mortality. Tobacco use during pregnancy
causes the passage of chemicals from the placenta into the fetal blood supply. These substances
restrict the growing infant’s access to oxygen and can lead to adverse pregnancy and birth outcomes
such as low birth weight, preterm delivery, intrauterine growth retardation, and infant mortality.11
Known risk factors
Overall, the leading cause of infant death in the United States in 2005 was deformations and
congenital malformations, which accounted for 20% of all infant deaths. Disorders relating to short
gestation and low birth-weight were second, accounting for 17 percent of all infant deaths, followed
by SIDS, which accounted for 8 percent of infant deaths.11 However, cause of death varies over the
first year of life. Categorizing infant mortality into deaths occurring during specific periods of time
may limit etiologic heterogeneity in a measure such as overall infant mortality.
Neonatal mortality is typically associated with events surrounding the prenatal period and the
delivery, whereas postneonatal deaths are more likely to be associated with conditions or events that
arise after the delivery and may reflect environmental factors.27 The leading causes of neonatal death
include birth defects, disorders related to short gestation and low birth weight, and pregnancy
complications. Postneonatal death reflects events experienced in infancy, including SIDS, birth
defects, injuries, and homicide.28 Unexplained fetal death and death related to growth restriction are
the leading causes of fetal loss.29 Markers of increased risk for fetal loss include prepregnancy
obesity, lower SES, non-Hispanic black race, and advanced maternal age.29,23
Known control or prevention measures
The causes of neonatal mortality most likely to be preventable are those related to preterm birth and
low birth weight, which represent approximately 20 percent of neonatal deaths.1 Many birth defects
are unlikely to be preventable given current scientific knowledge. Birth defects account for
approximately 17 percent of postneonatal deaths, but the remainder of postneonatal deaths are likely
to stem from preventable causes.1
What are the National Objectives?
Measure 1: Infant Mortality Rate per 1000 Live Born Infants
• Healthy People 2010 Objective 16-1c. Reduce all infant deaths to 4.5 per 1,000 live
births.
Measure 2: Neonatal Mortality Rate per 1000 Live Born Infants
• Healthy People 2010 Objective 16-1d. Reduce neonatal deaths to 2.9 per 1,000 live
births.
Measure 3: Perinatal Mortality Rate per 1000 Live Born Infants
• Healthy People 2010 Objective 16-1b. Reduce fetal and infant deaths during perinatal
period to 4.5 per 1,000 live births plus fetal deaths.
Measure 4: Postnatal Mortality Rate per 1000 Live Born Infants
• Healthy People 2010 Objective 16-1e. Reduce postneonatal deaths to 1.2 per 1,000 live
births.
53
How Are We Doing?
Minnesota has not met the Healthy People 2010 goal for overall infant mortality; although rates
have been generally decreasing and the state was close to the goal in 2004, rates have risen in
recent years. Minnesota is also above the goals for neonatal mortality and postneonatal mortality,
although these rates are decreasing.
What Is Being Done?
The Infant Mortality Reduction Initiative identifies medical, social, and environmental factors
associated with fetal, infant, and maternal death through the analysis of vital records and other
data. This initiative is part of the Maternal and Child Health Section within MDH. It includes
Minnesota’s Safe and Asleep Campaign, which targets preventable sleep-related unintentional
injury to Minnesota babies, and the American Indian Infant Mortality Review Project, which
addresses the infant mortality rate disparities among Minnesota’s American Indian infants as
compared to white infants.
The Maternal and Child Health Section of MDH includes the following programs:
•
Family Home Visiting (FHV): goals include improving family health status and
achieving maternal goals like child spacing
•
Women, Infants and Children (WIC) Program: a nutrition program targeted for pregnant
women, new mothers, babies, and young children
•
Minnesota Pregnancy Risk Assessment Monitoring System (PRAMS) is a CDC initiative
to reduce infant mortality and low birth weight, gathering state-specific information using
a survey of mothers who have recently had a baby, used to address public health issues
and develop effective programs to improve the health of mothers and babies in
Minnesota.
Program Information
Minnesota Center for Health Statistics
Minnesota Department of Health
Golden Rule Building, 3rd floor
85 E. 7th Place
PO Box 64882
St. Paul, MN 55164-0882
Email: [email protected]
Website: http://www.health.state.mn.us/divs/chs/
Related Indicators
•
•
•
•
Prematurity indicator.
Growth retardation indicator
Fertility indicator.
Sex ratio indicator.
Limitations and Challenges
Limitations of the measures:
54
An important limitation of this health outcome measure is the heterogeneity in its etiology.
Environmental exposure-related causes of infant death are only one piece of a puzzle that
includes many other factors such as access to and quality of health care, competency in childcare
and understanding of injury prevention.
Some births or deaths may be excluded from the data because of the difficulty in distinguishing a
death shortly after birth as a live birth; a death soon after birth might be reported as a fetal death
rather than live birth and infant death.
Limitations of the data source:
Due to the continuing nature of the Vital Records collection process, it is not unusual for a birth
record to be corrected or amended weeks or months after it was originally processed by Vital
Records. Adoptions, which can take months to process, are subject to amendments to the original
birth record. It is possible where a birth record arrives at Vital Records with the demographic
characteristics of the birth mother (including mother’s race/ethnicity, education level, etc.) only
to be amended months later and replaced with the demographic characteristics of the adoptive
mother replacing those of the birth mother. Because of the time it takes to correct and amend
birth records, the final birth file for a particular calendar year can take many months after the end
of the calendar year to close and be made available for epidemiological use.
Another limitation of the data source is that the place of residence during pregnancy (and, with
infant death, residence during first year of life) may not be represented by maternal residence at
time of birth (or death).
The quality of vital statistics data is directly related to the completeness and accuracy of the
information contained in the source documents. The Minnesota Department of Health maintains
two programs to improving the quality of information received on birth and death certificates in
order to ensure that the information is as complete and accurate as possible: a query program to
contact hospital personnel, funeral directors, and/or physicians concerning incomplete or
conflicting information; and a field program focused on educating participants in the vital
registration system.7
Graphical Data Views
Table 1: Percent infant mortality by year, Minnesota, 2001-2005
55
1.0%
0.9%
0.8%
0.7%
0.6%
0.5%
0.4%
0.3%
0.2%
0.1%
0.0%
2001
2002
2003
2004
2005
Data Notes: Percent of infant mortality is the number of deaths occurring between the ages of 0365 days in any given year per 1000 live births in the same year.
Data Source: Minnesota Center for Health Statistics, Minnesota Department of Health.
Interpretation: Infant mortality rate did not decline significantly from 2001 to 2005.
Table 2: Percent perinatal mortality by maternal age, Minnesota, 2001-2005
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-50
Data Notes: Percent of perinatal mortality is the number of infant deaths less than 7 days old
plus fetal deaths at 28+ weeks gestation in any given year divided by the number of live births
plus fetal deaths in the same year.
Data Source: Minnesota Center for Health Statistics, Minnesota Department of Health.
Interpretation: Perinatal morality is highest for the youngest and oldest mothers in Minnesota.
Table 3: Percent post-neonatal mortality by maternal race, Minnesota, 2001-2005
56
0.7%
0.6%
WHITE
0.5%
BLACK
0.4%
ASIAN/PACIFIC
ISLANDER
0.3%
NATIVE
0.2%
OTHER/UNKNOWN
0.1%
0.0%
2001-2005
Data Notes: Percent of post-neonatal mortality is the number of infant deaths aged 28-365 days
in a given year divided by the number of live births in the same year.
Data Source: Minnesota Center for Health Statistics, Minnesota Department of Health.
Interpretation: Post-neonatal morality is highest for Native and Black mothers in Minnesota.
57
Complete Indicator Profile of Fertility
Definition
Measure 1: Total Fertility Rate
- The number of live births per 1,000 women in the same age group (reproductive age, 15-44
years).
Numerator
Measure 1: Total Fertility Rate
- Number of live births per time period by age group (reproductive age, 15-44 years) in time
period.
Denominator
Measure 1: Total Fertility Rate
- Number of women by age group (reproductive age, 15-44 years) in time period.
Data Sources
Birth certificate data are collected from various sources including the mother, clinic, and
hospital. Cause of death for death records is reported by the attending physician or
coroner/medical examiners. These data are entered directly into Vital Records Vision 2000
System, which electronically records and maintains vital records (Birth, Death, and Fetal Death)
for the State of Minnesota. Currently 100% of birth and death records are filed electronically.
Birth and death certificates and fetal death reports filed with the Office of the Registrar,
Minnesota Department of Health for calendar year 2001-2006 are the source documents for data
on vital events of Minnesota residents.
Why Is This Important?
Definition of fertility
Several indicators have been used to track fertility on a global, national, state and local level. The
total fertility rate (TFR) indicates the average number of births to a hypothetical cohort of 1,000
women if they experienced the age-specific birth rates observed in a given year. The TRF adjusts for
age-specific differences in fertility and shows the potential impact of current fertility patterns on
reproduction allowing for more valid comparisons of rates across time and space.
The TFR can be used as an estimate of whether the childbearing population is replacing itself.
Replacement is the level at which a given generation can exactly replace itself, generally considered
to be 2,100 births per 1,000 women.5
Burden of fertility
The U.S TFR was 2,100.5 (or 2.1 births per woman) in 2006, the highest reported since 1971
(2,266.5).5 This is the first year the U.S. TFR has been above replacement since 1971. In Minnesota,
the fertility rate increased by 1.5 percent from 68.6 in 2006 to 69.6 in 2007. This increase was driven
58
both by the small increase in number of births and by a 1.2% decrease in the estimated population of
females age 15-44. The Minnesota fertility rate has been rising in recent years and the 2007 rate is
the highest since 1980.7
The connection to environmental health
Approximately 10% of problems with fertility are unknown and environmental contaminants
including endocrine disruptors have been hypothesized as major contributors. The case of DES
revealed environmental contamination can have multi-generational impacts on reproduction that need
to be studied and tracked long term. Understanding the geographic distribution and trends in fertility
can provide basic descriptive clues into changes that may be influenced by environmental risk
factors.
Environmental causes of infertility include: DBCP (dibromochloropropane) pesticide for males and
workplace organic solvents. Air pollution has been suggested to cause infertility through DNA
damage on sperm. Endocrine disruptors have been hypothesized as a contributor to fertility problems.
Known risk factors
According to the CDC’s National Survey of Family Growth Survey, 2002, 7% of married couples in
which the woman was of reproductive age reported that they had not used contraception for 12
months and the woman had not become pregnant. Chlamydia and gonorrhea are two preventable
causes of infertility.30
Among couples who use assisted reproductive technology (ART), causes of infertility include:31
• Tubal factor: means that the woman’s fallopian tubes are blocked or damaged, making it
difficult for the egg to be fertilized or for an embryo to travel to the uterus.
• Ovulatory dysfunction: means that the ovaries are not producing eggs normally. Such
dysfunctions include polycystic ovary syndrome and multiple ovarian cysts.
• Diminished ovarian reserve: means that the ability of the ovary to produce eggs is reduced.
Reasons include congenital, medical, or surgical causes or advanced age.
• Endometriosis: involves the presence of tissue similar to the uterine lining in abnormal
locations. This condition can affect both fertilization of the egg and embryo implantation.
• Uterine factor: means a structural or functional disorder of the uterus that results in reduced
fertility.
• Male factor: refers to a low sperm count or problems with sperm function that make it
difficult for a sperm to fertilize an egg under normal conditions.
• Other causes of infertility include: immunological problems, chromosomal abnormalities,
cancer chemotherapy, and serious illnesses.
• Unexplained cause: means that no cause of infertility was found in either the woman or the
man.
What are the National Objectives?
There are no national objectives for this indicator.
How Are We Doing?
Since 1990, Minnesota typically had very slightly lower fertility rates than the U.S. population.
However, in recent years Minnesota fertility rates have been the same as U.S. fertility rates. In
2007, Minnesota fertility rate was 69.6, while the U.S. fertility rate was 69.2.7
59
What Is Being Done?
The Maternal and Child Health Section of MDH includes the following programs:
•
Family Home Visiting (FHV): goals include improving family health status and
achieving maternal goals like child spacing
•
Women, Infants and Children (WIC) Program: a nutrition program targeted for pregnant
women, new mothers, babies, and young children
•
Minnesota Pregnancy Risk Assessment Monitoring System (PRAMS) is a CDC initiative
to reduce infant mortality and low birth weight, gathering state-specific information using
a survey of mothers who have recently had a baby, used to address public health issues
and develop effective programs to improve the health of mothers and babies in
Minnesota.
Program Information
Minnesota Center for Health Statistics
Minnesota Department of Health
Golden Rule Building, 3rd floor
85 E. 7th Place
PO Box 64882
St. Paul, MN 55164-0882
Email: [email protected]
Website: http://www.health.state.mn.us/divs/chs/
Related Indicators
•
•
•
•
Prematurity indicator
Growth retardation
Infant mortality indicator.
Sex ratio indicator.
Limitations and Challenges
The fertility measure is influenced by social/demographic choices for reproduction, maternal
age, parity and social class measures as well as the use of contraception and infertility treatments
leading to multiple births. These factors all may lead to variations in overall fertility across
populations and geographic locations and need to be considered along with the existing
measures. The fertility rate estimate may increase from year to year due to a decrease in number
of women aged 15-44 years, rather than solely resulting from number of births/pregnancy that
year.
Graphical Data Views
Table 1: Total fertility rate by year, Minnesota, 2001-2006
60
2.5
2.4
2.3
2.2
2.1
2
1.9
1.8
1.7
1.6
1.5
2001
2002
2003
2004
2005
2006
Data Notes: Total fertility rate is the number of live births per 1,000 women in the same age
group (reproductive age, 15-44 years).
Data Source: Minnesota Center for Health Statistics, Minnesota Department of Health.
Interpretation: The Minnesota fertility rate has been rising in recent years. This increase is being
driven both by the small increase in number of births and by a decrease in the estimated population
of females age 15-44.
61
Complete Indicator Profile of Sex Ratio
Definition
Measure 1: Sex Ratio
- The ratio of male to female births at birth, among singletons only.
Numerator
Measure 1: Sex Ratio
- Number of male births.
Denominator
Measure 1: Sex Ratio
- Number of female births.
Data Sources
Birth certificate data are collected from various sources including the mother, clinic, and
hospital. Cause of death for death records is reported by the attending physician or
coroner/medical examiners. These data are entered directly into Vital Records Vision 2000
System, which electronically records and maintains vital records (Birth, Death, and Fetal Death)
for the State of Minnesota. Currently 100% of birth and death records are filed electronically.
Birth and death certificates and fetal death reports filed with the Office of the Registrar,
Minnesota Department of Health for calendar year 2001-2006 are the source documents for data
on vital events of Minnesota residents.
Why Is This Important?
Definition of sex ratio
Sex ratio is the number of male to female births, measured at birth. The chance that a birth will be
male is generally considered random with a slightly higher chance of being male; the expected sex
ratio at birth is 1.05 males to females.32
Burden of sex ratio
The sex composition of a population is partially determined by the number of male births relative to
the number of female births. The sex ratio at birth also affects critical demographic measures; the
number of years required for the population to double its size given a rate of population growth rises
as the ratio of males to females at birth increases.32 Since male infants are more susceptible to illness
and have higher infant mortality rates than females, data about the sex ratio at birth is helpful in
understanding trends in infant morbidity.32
In the United States, the sex ratio declined between 1942 and 1959, increased between 1959 and
1971, and declined from 1971 to 2002.32 The highest sex ratio occurred in 1946 (1.059) and the
lowest in 1991 and 2001 (1.046).32 White women were the only race group to have any significant
changes in the sex ratio between 1970 and 2002.32 The decrease in sex ratio at birth in the U.S. was
found only among Whites and not among African-Americans.33 A reduced sex ratio at birth has been
62
linked to older age at childbearing. For combined years 1940 to 2002, the two oldest age groups, 40–
44 years and 45 years and over, have the lowest total sex ratios .32.In 2007, the Minnesota overall sex
ratio was 1.035.7
The connection to environmental health
Although the mechanism which determines the sex of the infant is not completely understood,
decreases in male births have also been associated with pesticides 34 and cigarette smoking 35.
Known risk factors
Sex ratio is associated with age, race and Hispanic origin of mother, and birth order of the
child.32
What are the National Objectives?
There are no national objectives for this indicator.
How Are We Doing?
In 2006, the sex ratio in Minnesota was similar to the national sex ratio. Both Minnesota and the
U.S. had declining sex ratios in recent years.
What Is Being Done?
The Maternal and Child Health Section of MDH includes the following programs:
•
Family Home Visiting (FHV): goals include improving family health status and
achieving maternal goals like child spacing
•
Women, Infants and Children (WIC) Program: a nutrition program targeted for pregnant
women, new mothers, babies, and young children
•
Minnesota Pregnancy Risk Assessment Monitoring System (PRAMS) is a CDC initiative
to reduce infant mortality and low birth weight, gathering state-specific information using
a survey of mothers who have recently had a baby, used to address public health issues
and develop effective programs to improve the health of mothers and babies in
Minnesota.
Program Information
Minnesota Center for Health Statistics
Minnesota Department of Health
Golden Rule Building, 3rd floor
85 E. 7th Place
PO Box 64882
St. Paul, MN 55164-0882
Email: [email protected]
Website: http://www.health.state.mn.us/divs/chs/
Related Indicators
•
•
•
Prematurity indicator
Growth retardation
Infant mortality indicator.
63
•
Fertility indicator.
Limitations and Challenges
The sex ratio at birth is affected by factors from conception to birth including fetal loss. Changes
in the sex ratio at birth in the United States have been attributed to many different factors. The
effect of all factors should be considered in understanding the annual variation and overall
decline in the sex ratio at birth.
Graphical Data Views
Table 1: Sex ratio by year, Minnesota, 2001-2006
1.08
1.07
1.06
1.05
1.04
1.03
1.02
2001
2002
2003
2004
2005
2006
Data Notes: Sex ratio is the ratio of male to female births at birth among singletons only.
Data Source: Minnesota Center for Health Statistics, Minnesota Department of Health.
Interpretation: The sex ratio in Minnesota has been generally declining in recent years.
Table 1: Sex ratio by maternal age, Minnesota, 2001-2006
64
1.25
1.20
1.15
1.10
1.05
1.00
0.95
0.90
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
Data Notes: Sex ratio is the ratio of male to female births at birth among singletons only.
Data Source: Minnesota Center for Health Statistics, Minnesota Department of Health.
Interpretation: The sex ratio is highest among the youngest mothers in Minnesota.
65
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Martin JA, Hamilton BE, Sutton PD. Births: Final Data for 2005. National Vital Statistics
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Section overview: Tracking Project updates
Given the limited time available for advisory panel meetings, updates on some items will be
provided to the panel as information items only. This information is intended to keep panel
members apprised of progress being made in program areas that are not a featured part of the
current meeting’s agenda and/or to alert panel members to items that will need to be discussed in
greater depth at a future meeting.
Included in this section of the meeting packet are written status updates on the following items:
•
Public Data Portal
•
Communications Planning
ACTION NEEDED: At this time, no formal action is needed by the advisory panel. Panel
members are invited to ask questions or provide input on any of these topics during the
designated time on the meeting agenda.
69
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70
Public Data Portal Update
Dissemination of tracking data is a primary objective of the Tracking program. Tracking staff
recognize that developing a public data portal would not only be consistent with the work being
done in other tracking states and the national program, but would be an effective way of making
data widely available to the public. To this end, staff are developing such a portal specific to
Minnesota EPHT by adapting (with appropriate authorizations) an existing data portal system.
The chosen candidate is an open source system developed by the Utah Department of Health, the
Indicator-Based Information System for Public Health (IBIS-PH, or just IBIS). The IBIS site can
be viewed at http://ibis.health.utah.gov. Not only is this a well-established and powerful data
portal, new CDC funded states continue to adopt IBIS (Minnesota, Alaska and Washington State,
have recently been added to the list of Missouri, New Jersey, New Mexico, Arizona, and Utah.)
Tracking program staff are working with the MDH Information Systems & Technology
Management (IS&TM) division staff to evaluate the Utah IBIS system and begin designing a
Minnesota web portal based on this system. IS&TM is providing project management, systems
architecture, development, and other IT consultation to EHTB as needed.
To date, evaluation of the IBIS system has shown that the development environment and
software IBIS uses are compatible with MDH systems, thus minimizing needed costs and
development resources. The software license agreement between MDH and Utah was signed and
accepted January 5, 2009. MDH has downloaded the software programs from the IBIS site and is
currently readying and configuring the hardware infrastructure and will soon install the programs
and begin a detailed evaluation to determine the level of resources and time required to adapt the
programs to meet our needs. Estimated completion of the evaluation is March 6, 2009.
Concurrent with development of the data portal architecture and system evaluation, staff have
moved forward and prioritized the indicators and views to be included in the initial data portal
demonstration. Two initial content areas have been selected: drinking water quality and
hospitalization data. EHTB staff have been working with ISTM program staff designing the
specific indicators and report views, how the data will be best represented (maps, tables, charts),
needs of data users, etc. Seven indicator profiles containing a total of forty views are planned,
with demonstration release slated for early May 2009 timeframe.
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Tracking Communications Outreach
Our Communications Coordinator for Minnesota’s Environmental Public Health Tracking program (MN
EPHT, formerly known as MEHTS), Mary Jeanne Levitt, is developing and implementing a
communications plan. Our communications goal is to create public awareness about what MN EPHT is
and what MN EPHT can do to improve our capacity to understand, respond to and prevent chronic
disease in Minnesota. Our long term goal is to establish public demand/support for MN EPHT.
Our public awareness campaign includes creating a name that the public can easily identify, MN EPHT,
along with a logo specific to MN EPHT; establishing relationships with community organizations, such as
Healthy Legacy, to identify events, conferences, and workshops where MN EPHT staff can educate the
public about MN EPHT; updating information on the current MN EPHT website, while creating a new
format for the MN EPHT web, compliant with MDH rules; updating the MN EPHT fact sheet, and
exploring conference opportunities, such as the ISES 2009 Conference in November.
The Minnesota Physician (MP) newsletter will include an article in the April issue about the tracking and
biomonitoring programs. The editor of MP, Donna Ahrens, has suggested modifying the article for their
consumer oriented publication, Minnesota Health Care News, which is distributed statewide. We are
moving forward on this second publication opportunity.
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Section overview: General reference materials
One new document is included in this meeting packet as items that may be of interest to panel
members:
•
EHTB advisory panel meeting summary (from December 9, 2008)
•
More reference materials will be available at the meeting.
In addition, the following items are included in each meeting packet as reference materials:
•
EHTB advisory panel roster (revised)
•
Biographical sketches of advisory panel members (revised)
•
EHTB steering committee roster
•
EHTB interagency workgroup roster (revised)
•
Glossary of terms used in environmental health tracking and biomonitoring
•
Acronyms used in environmental health tracking and biomonitoring
•
EHTB statute (Minn. Statutes 144.995-144.998)
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Summary of the
Minnesota Department of Health (MDH)
Environmental Health Tracking and Biomonitoring Advisory Panel Meeting
December 9, 2008
1:00 p.m.- 4:00 p.m.
Advisory Panel Members - Present
Bruce Alexander (acting chair)
Alan Bender
Debra McGovern
Geary Olson
Susan Palchick
Gregory Pratt
Daniel Stoddard
Samuel Yamin
Lisa Yost
Advisory Panel Members – Regrets
John Adgate
Beth Baker (chair)
Cecilia Martinez
Guest
Barb Deming
Welcome and introductions
Bruce Alexander served as the acting chair for this meeting. He welcomed all participants, and he invited
the panel members and other participants to introduce themselves. Bruce announced that David Wallinga
had submitted his resignation from the EHTB advisory panel, effective November 26, 2008. David
explained that he had been awarded a fellowship that would add to the demands on his time.
Biomonitoring vision
Michonne Bertrand, staff liaison to the EHTB Advisory Panel, introduced Barb Deming, staff member in
the Management Analysis and Development Division of the Minnesota Management and Budget Agency.
Michonne had invited Barb to this meeting to facilitate a discussion towards finalizing a vision statement
for the biomonitoring program. She introduced the topic by pointing out that the Minnesota statutes that
created the Environmental Health Tracking and Biomonitoring (EHTB) Program specifies that the
advisory panel is to make recommendations to the commissioner of health and the legislature on priorities
for an ongoing biomonitoring program. Toward this end, Barb Deming reported that she had conducted a
series of interviews with staff of other biomonitoring programs in the U.S. and with members of the
EHTB advisory panel, EHTB steering committee, and EHTB workgroup. She had also facilitated a oneday retreat on November 12, 2008 for members of the advisory panel, steering committee, and workgroup
to discuss a vision and purpose for an ongoing biomonitoring program.
The background book contained a report of the interviews and a summary of the retreat. It was agreed that
the final report will have useful information and should be made available for widespread distribution.
Barb asked the panel members to help finalize the vision statement for a state biomonitoring program.
She reminded the group that raw materials for a vision statement were developed during the retreat, and
that a subgroup had convened afterward to distill the materials into a draft vision, comprised of four
statements. These were presented in the background book.
In response to a question about ‘breakthrough’ ideas emerging at the retreat, Alan Bender noted a lack of
consensus regarding biomonitoring among the various programs across the U.S., as well as among the
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EHTB program participants Minnesota. While consensus was evident at a high level, participants at the
retreat expressed diverse ideas in how to implement a state biomonitoring program. This report of the
wide span of ideas arising from the interviews and the retreat is a valuable product.
Dan Stoddard suggested the vision should better convey the unique potential benefits of biomonitoring
for a lay audience and legislators. He offered the following for discussion:
“The biomonitoring program will protect the health of Minnesota’s citizens by evaluating
the concentrations of chemicals in the body and linking them to patterns of disease and
the concentrations of these chemicals in the world around us.”
Susan Palchick noted that the discussions at the retreat were both at the level of a lofty vision and at the
level of the specific utility of biomonitoring in the continuum of environmental hazards, risks, exposures,
dose, physiological effects, and clinical disease.
Bruce Alexander and Lisa Yost advised that, in the draft vision containing four statements, the term
“research” should be changed to “public health assessment” or “biomonitoring assessment.” It is a
broader and perhaps more palatable concept for a non-scientific audience.
Greg Pratt and Alan Bender wondered if the vision statement wording proposed by Dan would
inadvertently imply a causal relationship between environmental chemicals and disease, or that all
exposures lead to disease. Lisa suggested that biomonitoring supports other data to understand levels of
known, adverse effects. Geary Olsen noted that the term “linking” is often interpreted as causation. He
suggested that the vision statement capture the purpose of biomonitoring in characterizing exposure.
Derivatives of biomonitoring and exposure would be a better understanding of risk and disease. Dan
advised that a vision statement for a lay audience should convey the potential of biomonitoring for finding
causality for disease. Bruce suggested that the vision could state that biomonitoring helps us to better
understand chemical exposures, and that this is necessary for learning how chemicals in the environment
affect health. The vision could state that biomonitoring will help lay a foundation for exploring the
relationships between exposures and disease.
In bringing the discussion to conclusion, Bruce noted that the advisory panel would not be crafting a final
version of a vision statement. Rather, he recommended that the comments made by the advisory panel
members should be brought back to the MDH staff to finalize the draft vision statement. In closure, Barb
Deming asked the panel members to summarize the main discussion points. Dan offered that the vision
should capture, in plain language, the potential benefits of biomonitoring without conveying false
expectations. Bruce offered that biomonitoring is a tool for measuring exposure, and the numerous
purposes include understanding chemicals in the environment, risk, and disease.
Biomonitoring purpose
Jean Johnson, EHTB program director, distributed a handout that listed 16 possible purposes of
biomonitoring (see attached). She explained that the origins of her list came from several publications,
including the 2006 report on Human Biomonitoring for Environmental Chemicals produced by the
National Research Council, as well as other states’ biomonitoring programs. She had distributed an earlier
version of the handout at the November retreat, during which she invited EHTB program participants to
identify their top priorities. Jean noted that the purposes could be categorized as: screening and scoping;
status and trends; exposure and health research; and risk assessment.
Alan Bender asked for clarification regarding the public’s interest in man-made chemicals vs. biotoxins.
Panel members recognized that the public is concerned about toxic chemicals that include metals and
other naturally occurring chemicals, synthetic chemicals, and chemicals of biological origin. Lisa Yost
recommended that an important public health purpose would be to triage or identify avoidable exposures
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to chemicals causing adverse effects (referring to numbers 5 and 6 on the list of potential purposes). In
response to a question, Jean Johnson clarified that the data collected by the CDC’s national biomonitoring
program are available only as national data and are not broken down by state. In fact, given the sampling
strategy used by CDC, in any year, the national sample may or may not include participants from
Minnesota. Thus, NHANES data provide a national baseline or reference, but not a state baseline. Dan
Stoddard noted that the draft interview report from Barb Deming included recommendations by leaders in
other biomonitoring programs to focus on studies that are feasible and actionable, and he suggested
heeding the advice not to “bite off more than you can chew.” Greg Pratt recommended against prioritizing
activities that are better suited to universities or other entities (referring to numbers 8, 12 and 14). Samuel
Yamin recommended that the purpose should be connected to the distinct needs of Minnesota’s
communities and that biomonitoring data should be actionable and demonstrate clear benefits to
communities while also being based in science; he suggested that purposes 3, 4 & 5 would support these
goals. Bruce Alexander also recommended that biomonitoring should be science-driven and not solely
driven by community concerns. Lisa Yost noted that expectations are that biomonitoring will be based on
community concerns and that decisions about biomonitoring should be based on science.
Barb Deming distributed stickers and asked each panel member to review the handout of 16 purposes and
then identify 4 first-tier priorities and 4 second-tier priorities. Dan recommended that the panel first
discuss the relative rankings of three categories, viz. actionability; emerging/unknown risks; and
surveillance. Bruce suggested that all 16 purposes on the handout are valid but that some purposes are
fundamental or primary while other purposes are advanced benefits that can be pursued after the primary
purposes are met. Lisa agreed that some purposes would form the basis of an ongoing state program with
other purposes being add-ons that could be achieved, potentially without a lot of additional investment
and/or through collaborations with others.
In response to questions about the intent or audience for this exercise in prioritizing the purposes of
biomonitoring, Michonne Bertrand noted that the articulation of purpose is an ongoing process for the
EHTB program. One possible use would be to guide plans to seek a second round of biomonitoring funds
from the state legislature. Another use is to provide the EHTB program staff with a strong base to guide
its activities. Dan remarked that the purpose and the chemical selection process could be intertwined.
Greg noted that how MDH explains priorities depends on the audience; we would convey different
messages to the state legislature and to the scientific community. Deb McGovern noted that an exercise to
identify priorities is valuable in light of limited funds.
After the panel members placed stickers next to specific items on the list of 16 possible purposes of
biomonitoring, it was recognized that the highest scoring items were those that addressed the
fundamentals of a state biomonitoring program. One example is “monitor the distribution of exposure
among specific communities and subgroups of the communities that are identified as likely to be
exposed.” (The items receiving the highest number of votes were 2, 3, 4, 5 &7.) Purposes that had been
identified as advanced or value-added (e.g. supporting ancillary research projects) received lower scores.
Bruce advocated for developing a fundamental program and for the state to seek other funding to address
advanced purposes, such as ancillary research.
Advisory panel roles
Mary Manning, a member of the EHTB Steering Committee and the director of the MDH Health
Promotion and Chronic Disease Division, thanked the panel members, on behalf of the Health
Commissioner, for their contributions to the EHTB program. She referred to the background book, which
contained an evaluation survey of EHTB panel members that had been conducted in September and
October 2008. The survey results identified particular topics as opportunities for improvement. Towards
that end, she invited the panel members to discuss the roles and decision-making processes for the EHTB
advisory panel.
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Geary Olsen asked if, at the conclusion of the four biomonitoring pilot projects, the MDH staff would be
seeking feedback from the voluntary participants regarding their experiences. He suggested that the
advisory panel could have a role in reviewing the participants’ perspectives. Jean Johnson replied that she
plans to survey participants at the conclusion of the PFCs pilot project regarding the communications
aspect, and she would welcome the panel’s advice.
Examples of influential recommendations by the advisory panel were highlighted. These included the
selection of the chemicals – both cotinine and environmental phenols – for the fourth biomonitoring pilot;
the incorporation of a second morning’s urine collection in the arsenic pilot study, and the approach for
the chemical selection process. Greg Pratt remarked that the chief role for the advisory panel is to prevent
MDH staff from “going off the deep end.”
Samuel Yamin remarked that he would have re-ordered agenda items in some panel meetings so that
critical topics would have been discussed earlier in the meetings. He remarked that today’s exercise in
prioritizing biomonitoring purposes was a useful mechanism for the panel to provide open-ended advice
to the program staff. Greg Pratt suggested that agendas could be developed with guidance from the panel
chair. Mary Manning invited panel members to submit additional comments after the meeting.
Legislative report
Michonne Bertrand described the statutory requirement for the EHTB program to submit a report to the
legislature by January 15, 2009 to describe the status of environmental health tracking and biomonitoring
activities. She referred to the outline that was contained in the background book, and she invited panel
members to provide suggestions to strengthen the legislative report. In December, the EHTB program
staff will draft the report for review by the EHTB workgroup, EHTB steering committee, MDH
communications office, and MDH Commissioner’s office.
Michonne noted that the January 2009 report will give an overview of the environmental health tracking
and biomonitoring activities. It will describe the efforts needed for high-quality studies and meaningful
community connections. It will explain how the environmental health tracking endeavors are targeted to
public health action, and how the EHTB program will wrap up its biomonitoring efforts when the
biomonitoring funds expire in June 2009. This report will not contain specific data for the environmental
health tracking projects and the biomonitoring pilot projects. It is expected that those data will be released
in separate reports during 2009. She also anticipates that statements of the biomonitoring vision and
purpose will be in future reports.
Michonne observed that the timeline for internal review and release would not allow for panel members
to review a complete draft of the report before it is submitted to the legislature. Michonne encouraged
panel members to submit their own recommendations to the legislature, particularly if they were to find
that their perspectives are not adequately represented in the MDH report. Samuel Yamin recommended
that the MDH report to the legislative should include a vision for intertwining the environmental health
tracking activities and the biomonitoring activities. Michonne will send the final version of the report to
panel members along with contact information for the legislators who will receive the report.
Update: Biomonitoring pilot projects
The background book contained updates about communicating results to participants in the biomonitoring
pilot projects. Samuel Yamin asked if the EHTB staff is planning to incorporate the approach of
biomonitoring equivalents into the PFCs data interpretation. He thanked MDH staff for hosting a seminar
on biomonitoring equivalents in fall 2008.
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Pam Shubat, EHTB workgroup member and supervisor of the MDH Health Risk Assessment Unit,
responded that the biomonitoring equivalents model is one of several important considerations in health
risk assessments. In fact, MDH is already considering this strategy in its data interpretation for low level,
internal doses of PFCs. She reported that MDH staff are re-visiting its health risk assessment in light of
research advances. However, no decision has yet been made about applying biomonitoring equivalents in
the biomonitoring pilot projects specifically.
Update: Environmental health tracking data portal
Al Williams, supervisor in the MDH Chronic Disease and Environmental Epidemiology Section, reported
on the status of data portals for the CDC-funded, National Environmental Public Health Tracking
Network. These data portals will serve as a web-based interface for stakeholders, the public, and other
users to interact with the tracking data posted by national and state programs.
The scheduled debut of the CDC-hosted portal has been delayed until February 2009. After assessing
portals under development in other states, EHTB staff members have identified the system developed by
the Utah Department of Health as a national leader. Utah’s data portal, known as the Indicator-Based
Information System for Public Health (IBIS-PH or IBIS), will be the prototype for Minnesota’s own data
portal. In fact, five other states are already building on the IBIS template.
Jerry Alholm, staff member in the MDH Information Technology and Systems Management Office,
announced that architectural components of Utah’s IBIS are compatible with the MDH guidelines for
hardware and software. Currently, MDH and the Utah Department of Health are drafting a software
license agreement to allow MDH staff to evaluate the costs and development resources for adapting IBIS
to Minnesota’s needs. This fits with Utah’s vision for a multi-state consortium to continually upgrade and
enhance the data portals.
In response to questions, Al reported that the EHTB program hopes to post the Minnesota data on
selected toxins in public drinking water first, to be followed by the hospitalization data and the other
indicators thereafter. Although IBIS has the capability for both secure and non-secure (public) portals, the
EHTB program anticipates that the Minnesota data will be posted only on a public site for now.
Closure
Bruce Alexander thanked participants for their contributions to the EHTB panel meeting. He announced
that the next panel meeting is scheduled for Tuesday, March 10, 2009, 1-4 p.m. at Snelling Office Park.
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Voting results
(first-tier priority/
second-tier priority)
Possible purposes of biomonitoring
1.
Conduct exploratory investigations of chemicals found in human
tissues to collect qualitative information as a first indication of a
potential problem
1/2
2.
Monitor the distribution of exposure among the general population and
sub-groups of the population (racial/ethnic, age, gender, and
geographic distributions)
6/0
3.
Monitor the distribution of exposure among specific communities and
subgroups of the communities that are identified as likely to be
exposed
8/2
4.
Monitor trends or changes in population or specific communities’
exposures to chemicals over time
6/2
5.
Identify highly exposed communities for targeting community-wide
public health interventions
5/5
6.
Identify highly exposed individuals for targeting follow-up exposure
investigations and interventions
0/3
7.
Evaluate the effectiveness of interventions and policies designed to
reduce exposure (such as product bans/replacements, drinking water
treatment, behavioral education, contaminant abatement)
4/4
8.
Support research by establishing a repository of biospecimens
(biobank) for ancillary research projects and laboratory methods
development research.
0/1
9.
Support research by establishing a cohort(s) of individuals with
measured exposure levels who consent to be contacted for follow-up
health studies
0/0
10.
Enhance response capacity to investigate community and occupational
exposure incidents.
0/2
11.
Respond to community concerns about chemicals in the environment
2/3
12.
Provide data to support pharmacokinetic and pharmacodynamic
research
0/1
13.
Provide data to support risk assessments for purposes of establishing
health-based criteria for regulating chemicals in the environment
0/5
14.
Advance generalizable knowledge of laboratory methods for emerging
contaminants
0/1
15.
Provide data to support identification of adverse health outcomes
associated with exposure (epidemiological studies)
4/1
16.
Provide data to support identification of important pathways and
sources of exposure (source investigations)
0/4
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EHTB advisory panel roster
John L. Adgate, PhD
University of Minnesota School of Public Health
Environmental Health Sciences Division
MMC 807 Mayo
420 Delaware Street SE
Minneapolis, Minnesota 55455
612-624-2601
[email protected]
University of Minnesota representative
Cecilia Martinez, PhD
Center for Energy and Environmental Policy
University of Delaware
Newark, Delaware 19716
302-831-8405
Local office:
Inver Grove Heights, Minnesota
651-470-5945
[email protected]
[email protected]
Nongovernmental organization representative
Bruce H. Alexander, PhD
University of Minnesota School of Public Health
Environmental Health Sciences Division
MMC 807 Mayo
420 Delaware Street SE
Minneapolis, Minnesota 55455
612-625-7934
[email protected]
Minnesota House of Representatives appointee
Debra McGovern
Minnesota Steel Industries, LLC
Environmental & Regulatory Affairs
555 West 27th Street
Hibbing, MN 55746
218-263-3331
[email protected]
Statewide business organization representative
Beth Baker, MD, MPH
Specialists in Occupational and Environmental
Medicine
Fort Road Medical Building
360 Sherman Street, Suite 470
St. Paul, MN 55102
952-270-5335
[email protected]
At-large representative
Geary Olsen, DVM, PhD
3M Medical Department
Corporate Occupational Medicine
MS 220-6W-08
St. Paul, Minnesota 55144-1000
651-737-8569
[email protected]
Statewide business organization representative
Alan Bender, DVM, PhD
Minnesota Department of Health
Health Promotion and Chronic Disease Division
85 East 7th Place
PO Box 64882
Saint Paul, MN 55164-0882
651-201-5882
[email protected]
MDH appointee
Susan Palchick, PhD, MPH
Hennepin County Human Services and Public
Health Department
Public Health Protection
1011 South 1st Street, Suite 215
Hopkins, Minnesota 55343
612-543-5205
[email protected]
At-large representative
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Gregory Pratt, PhD
Minnesota Pollution Control Agency
Environmental Analysis and Outcomes Division
520 Lafayette Road
St. Paul, MN 55155-4194
651-296-7664
[email protected]
MPCA appointee
Samuel Yamin, MPH
Minnesota Center for Environmental
Advocacy
26 E. Exchange St., Ste. 206
St. Paul, MN 55101
(651) 223-5969
[email protected]
Minnesota Senate appointee
Daniel Stoddard, MS, PG
Minnesota Department of Agriculture
Pesticide and Fertilizer Management Division
625 Robert Street North
St. Paul, Minnesota 55155-2538
651-201-6291
[email protected]
MDA appointee
Lisa Yost, MPH, DABT
Exponent, Inc.
15375 SE 30th Pl, Ste 250
Bellevue, Washington 98007
Local office
St. Paul, Minnesota
651-225-1592
[email protected]
At-large representative
Note: As of November 26, 2008, there is a vacancy on the EHTB advisory panel for a
nongovernmental organization representative. This vacancy will be posted with the Secretary of
State’s Office.
Rev. November 26, 2008
Please submit corrections to [email protected]
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Biographical sketches of advisory panel members
John L. Adgate is an Associate Professor in the Division of Environmental Health Sciences at
the University of Minnesota School of Public Health. His research focuses on improving exposure
assessment in epidemiologic studies by documenting the magnitude and variability of human exposure to
air pollutants, pesticides, metals, and allergens using various measurement and modeling techniques,
including biomonitoring. He has written numerous articles and book chapters on exposure assessment,
risk analysis, and children’s environmental health. He has also served on multiple U.S. EPA Science
Advisory Panels exploring technical and policy issues related to residential exposure to pesticides, metals,
and implementation of the Food Quality Protection Act of 1996, and was a member of the Institute of
Medicine’s Committee on Research Ethics in Housing Related Health Hazard Research in Children.
Bruce H. Alexander is an Associate Professor in the Division of Environmental Health Sciences
at the University of Minnesota School of Public Health. Dr. Alexander is an environmental and
occupational epidemiologist with expertise in cancer, reproductive health, respiratory disease,
injury, exposure assessment, and use of biological markers in public health applications.
Beth Baker is Medical Director of Employee Health at Regions Hospital and a staff physician at the
HealthPartners. She is President of Medical and Toxicology Consulting Services, Ltd. Dr. Baker is an
Assistant Professor in the Medical School and Adjunct Assistant Professor in the School of Public Health at
the University of Minnesota. She is board certified in internal medicine, occupational medicine and medical
toxicology. Dr. Baker is a member of the Board of Trustees for the Minnesota Medical Association and is
on the Board of Directors of the American College of Occupational and Environmental Medicine.
Alan Bender is the Section Chief of Chronic Disease and Environmental Epidemiology at the
Minnesota Department of Health. He holds a Doctor of Veterinary Medicine degree from the
University of Minnesota and a PhD in Epidemiology from Ohio State University. His work has focused
on developing statewide surveillance systems, including cancer and occupational health, and exploring
the links between occupational and environmental exposures and chronic disease and mortality.
Cecilia Martinez has a B.S. degree from Stanford University and a Ph.D from the University of Delaware.
She is an Adjunct Faculty at the Center for Energy and Environmental Policy where she leads projects on
environmental mapping and community health. Her research interests include environmental policy,
indigenous rights and the environment, and sustainable development. Dr. Martinez has numerous publications
including Environmental Justice: Discourses in International Political Economy with John Byrne and Leigh
Glover. Her interests include policy research on sustainable energy and environmental policy.
Debra McGovern has more than 28 years of environmental experience. She has 15 years of
experience in Minnesota governmental regulation and 13 years of experience in heavy process
industry, and is well versed in Minnesota’s regulatory requirements. Ms. McGovern has created and
implemented numerous environmental programs and is active in many organizations. Ms. McGovern is
the former Environmental Policy Committee Chairperson for the Minnesota Chamber of Commerce,
and currently serves on the Board of Directors for the Minnesota Environmental Initiative (MEI).
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Geary Olsen is a staff scientist in the Medical Department of the 3M Company. He obtained a
Doctor of Veterinary Medicine (DVM) degree from the University of Illinois and a Master of
Public Health (MPH) in veterinary public health and PhD in epidemiology from the University
of Minnesota. For 22 years he has been engaged in a variety of occupational and environmental
epidemiology research studies while employed at Dow Chemical and, since 1995, at 3M. His
primary research activities at 3M have involved the epidemiology, biomonitoring (occupational
and general population), and pharmacokinetics of perfluorochemicals. Recently, he completed a
3-year appointment on the Board of Scientific Counselors for the U.S. Centers for Disease
Control and Prevention (CDC) ATSDR/NCEH.
Susan Palchick is the Administrative Manager for Epidemiology, Environmental Health,
Assessment and Public Health Emergency Preparedness at Hennepin County Human Services
and Public Health Department. She has been with Hennepin County for 11 years and also serves
as the Environmental Health Director for Hennepin County. Prior to coming to Hennepin
County, Susan was the program manager for the Metropolitan Mosquito Control District
(MMCD) for 10 years. Susan is on the National Association of County and City Health Officials
(NACCHO) environmental health essential services committee. She is the principal investigator for an
Advanced Practice Center (APC) grant from NACCHO which focuses on environmental health
emergency preparedness. Susan received her Ph.D. in Medical Entomology from the University of
California-Davis; Master of Public Health in Epidemiology from the University of California-Berkeley;
M.S. in Entomology from University of Wisconsin-Madison; and B.S. (with honors) in Agricultural
Journalism-Natural Science from the University of Wisconsin-Madison.
Greg Pratt is a research scientist at the Minnesota Pollution Control Agency. He holds a Ph.D.
from the University of Minnesota in Plant Physiology where he worked on the effects of air
pollution on vegetation. Since 1984 he has worked for the MPCA on a wide variety of issues
including acid deposition, stratospheric ozone depletion, climate change, atmospheric fate and
dispersion of air pollution, monitoring and occurrence of air pollution, statewide modeling of air
pollution risks, and personal exposure to air pollution. He is presently cooperating with the
Minnesota Department of Health on a research project on the Development of Environmental
Health Outcome Indicators: Air Quality Improvements and Community Health Impacts.
Daniel Stoddard is the Assistant Director for Environmental Programs for the Pesticide and
Fertilizer Management Division at the Minnesota Department of Agriculture (MDA). He holds a master’s
degree in Management of Technology which focuses on the management of multi-disciplinary technical
organizations and projects, and he is a licensed Professional Geologist. He currently administers the
MDA’s non-point source programs for pesticides and inorganic fertilizer. These include: monitoring
surface water and groundwater for pesticides; monitoring pesticide use; registering pesticide products;
developing and promoting voluntary best management practices; developing regulatory options; and,
responding to local contamination problems. He previously worked in or managed a variety of other
environmental and regulatory programs at the MDA and the Minnesota Pollution Control Agency, and as
an environmental consultant.
Samuel Yamin is the Public Health Scientist for the Minnesota Center for Environmental
Advocacy. Before joining MCEA, Samuel worked as a toxicologist for the New Hampshire
Bureau of Environmental and Occupational Health, and prior to that as an environmental
epidemiologist for the Delaware Division of Public Health. While working for those agencies, his
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responsibilities included exposure assessment, risk analysis and hazard communication for pollutants in
water, air, soils and indoor environments. Samuel has also worked extensively on the subject of
environmental carcinogens and the potential impacts on public health. Samuel’s experience in
hazardous materials management and environmental regulatory programs also includes two years of
work with the Environmental Health and Safety Department at Ionics, Inc., a Massachusetts-based
manufacturer of drinking water purification technology. Samuel holds a Master of Public Health in
Environmental Health Sciences from Tufts University School of Medicine and a Bachelor of Science
in Environmental Health and Safety from Oregon State University.
Lisa Yost is a Managing Scientist at Exponent Inc., a national consulting firm, in their Health
Sciences Group and she is based in Saint Paul, Minnesota. Ms. Yost completed her training at the
University of Michigan School of Public Health and is a board-certified toxicologist with
expertise in evaluating human health risks associated with substances in soil, water, and the food
chain. She has conducted or supervised risk assessments under CERCLA, RCRA, or state-led
regulatory contexts involving a wide range of chemicals and exposure situations. Her particular
areas of specialization include exposure and risk assessment, risk communication, and the
toxicology of chemicals such as PCDDs and PCDFs, PCBs, pentachlorophenol (PCP),
trichloroethylene (TCE), mercury, and arsenic. Ms. Yost is a recognized expert in risk assessment
and has collaborated in original research on exposure issues including background
dietary intake of inorganic arsenic. She is currently assisting in a number of projects including a
complex multi-pathway risk assessment for PDDD/Fs that will integrate extensive biomonitoring
data collected by the University of Michigan. Ms. Yost is also an Adjunct Instructor at the
University of Minnesota, School of Public Health.
Rev. November 24, 2008
Please submit additions and corrections to [email protected]
87
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88
EHTB steering committee roster
Mary Manning, RD, MBA
Division Director
Health Promotion and Chronic Disease
Division
Minnesota Department of Health
PO Box 64882
St. Paul, Minnesota 55164-0882
651-201-3601
[email protected]
Norman Crouch, PhD (chair)
Assistant Commissioner
Minnesota Department of Health
PO Box 64975
St Paul, Minnesota 55164-0975
651-201-5063
[email protected]
Joanne Bartkus, PhD
Division Director
Public Health Laboratory Division
Minnesota Department of Health
PO Box 64899
St Paul, Minnesota 55164-0899
651-201-5256
[email protected]
John Linc Stine
Division Director
Environmental Health Division
Minnesota Department of Health
PO Box 64975
St Paul, Minnesota 55164-0975
651-201-4675
[email protected]
Rev. February 19, 2008
89
EHTB inter-agency workgroup roster
Frank Kohlasch, JD
Environmental Data Management Unit
Environmental Analysis & Outcomes
Division
Minnesota Pollution Control Agency
520 Lafayette Road N
St. Paul, Minnesota 55155-4194
651-205-4581
[email protected]
Jerry Alholm
Information Systems & Technology Management
Minnesota Department of Health
PO Box 64975
St. Paul, Minnesota 55164-0975
651-201-4973
[email protected]
Michonne Bertrand, MPH
Chronic Disease & Environmental
Epidemiology
Health Promotion and Chronic Disease
Division
Minnesota Department of Health
PO Box 64882
St. Paul, Minnesota 55164-0882
651-201-3661
[email protected]
Louise Liao, PhD
Environmental Laboratory
Public Health Laboratory Division
Minnesota Department of Health
PO Box 64899
St Paul, Minnesota 55164-0899
651-201-5303
[email protected]
Carin Huset, PhD
Environmental Laboratory
Public Health Laboratory Division
Minnesota Department of Health
PO Box 64899
St Paul, Minnesota 55164-0899
651-201-5329
[email protected]
Rita Messing, PhD
Site Assessment & Consultation
Environmental Health Division
Minnesota Department of Health
PO Box 64975
St Paul, Minnesota 55164-0899
651-201-4916
[email protected]
Jean Johnson, PhD
Chronic Disease & Environmental
Epidemiology
Health Promotion and Chronic Disease
Division
Minnesota Department of Health
PO Box 64882
St. Paul, Minnesota 55164-0882
651-201-5902
[email protected]
Pam Shubat, PhD
Health Risk Assessment
Environmental Health Division
Minnesota Department of Health
PO Box 64975
St Paul, Minnesota 55164-0899
651-201-4925
[email protected]
90
John Soler, MPH
Chronic Disease & Environmental
Epidemiology
Health Promotion and Chronic Disease
Division
Minnesota Department of Health
PO Box 64882
St. Paul, Minnesota 55164-0882
651-201-5481
[email protected]
Allan Williams, MPH, PhD
Chronic Disease & Environmental
Epidemiology
Health Promotion and Chronic Disease
Division
Minnesota Department of Health
PO Box 64882
St. Paul, Minnesota 55164-0882
651-201-5905
[email protected]
Erik Zabel, PhD
Environmental Impact Analysis
Environmental Health Division
Minnesota Department of Health
PO Box 64975
St Paul, Minnesota 55164-0899
651-201-4931
[email protected]
Joe Zachmann, PhD
Pesticide & Fertilizer Management Division
Minnesota Department of Agriculture
625 Robert Street North
St. Paul, Minnesota 55155-2538
651-201-6588
[email protected]
Rev. November 14, 2008
91
Glossary of terms used in environmental health tracking and
biomonitoring
Biomarker: According to the National Research Council (NRC), a biomarker is an indicator of a
change or an event in a human biological system. The NRC defines three types of biomarkers in
environmental health, those that indicate exposure, effect, and susceptibility.
Biomarker of exposure: An exogenous substance, its metabolites, or the product of an
interaction between the substance and some target molecule or cell that can be measured
in an organism.
Biomarker of effect: A measurable change (biological, physiological, etc.) within the
body that may indicate an actual or potential health impairment or disease.
Biomarker of susceptibility: An indicator that an organism is especially sensitive to
exposure to a specific external substance.
Biomonitoring: As defined by Minnesota Statute 144.995, biomonitoring is the process by which
chemicals and their metabolites are identified and measured within a biospecimen. Biomonitoring data
are collected by analyzing blood, urine, milk or other tissue samples in the laboratory. These samples
can provide physical evidence of current or past exposure to a particular chemical.
Biospecimen: As defined by Minnesota Statute 144.995, biospecimen means a sample of human
fluid, serum, or tissue that is reasonably available as a medium to measure the presence and
concentration of chemicals or their metabolites in a human body.
Community: As defined by Minnesota Statute 144.995, community means geographically or
nongeographically based populations that may participate in the biomonitoring program. A nongeographical
community includes, but is not limited to, populations that may share a common chemical exposure
through similar occupations; populations experiencing a common health outcome that may be linked to
chemical exposures; populations that may experience similar chemical exposures because of comparable
consumption, lifestyle, product use; and subpopulations that share ethnicity, age, or gender.
Designated chemicals: As defined by Minnesota Statute 144.995, designated chemicals are those
chemicals that are known to, or strongly suspected of, adversely impacting human health or
development, based upon scientific, peer-reviewed animal, human, or in vitro studies, and baseline
human exposure data. They consist of chemical families or metabolites that are included in the federal
Centers for Disease Control and Prevention studies that are known collectively as the National Reports
on Human Exposure to Environmental Chemicals Program and any substances specified by the
commissioner after receiving recommendations from the advisory panel in accordance with the criteria
specified in statute for the selection of specific chemicals to study.
Environmental data: Concentrations of chemicals or other substances in the land, water, or air. Also,
information about events or facilities that release chemicals or other substances into the land, water, or air.
92
Environmental epidemiology: According to the National Research Council, environmental
epidemiology is the study of the effect on human health of physical, biologic, and chemical factors in
the external environment. By examining specific populations or communities exposed to different
ambient environments, environmental epidemiology seeks to clarify the relation between physical,
biologic, and chemical factors and human health.
Environmental hazard: As defined by Minnesota Statute 144.995, an environmental hazard is a
chemical or other substance for which scientific, peer-reviewed studies of humans, animals, or cells
have demonstrated that the chemical is known or reasonably anticipated to adversely impact human
health. People can be exposed to physical, chemical, or biological agents from various environmental
sources through air, water, soil, and food. For EPHT, environmental hazards include biological toxins,
but do not include infectious agents (e.g. E. coli in drinking water is not included).
Environmental health indicators: Environmental health indicators or environmental public health
indicators are descriptive summary measures that identify and communicate information about a
population’s health status with respect to environmental factors. Within the environmental public health
indicators framework, indicators are categorized as hazard indicators, exposure indicators, health effect
indicators, and intervention indicators. See www.cste.org/OH/SEHIC.asp and
www.cdc.gov/nceh/indicators/introduction.htm for more information.
Environmental justice: The fair treatment and meaningful involvement of all people regardless of
race, national origin, color or income when developing, implementing and enforcing environmental
laws, regulations and policies. Fair treatment means that no group of people, including a racial, ethnic,
or socioeconomic group, should bear more than its share of negative environmental impacts.
Environmental health tracking: As defined in Minnesota Statute 144.995, environmental health
tracking is the collection, integration, integration, analysis, and dissemination of data on human
exposures to chemicals in the environment and on diseases potentially caused or aggravated by those
chemicals. Environmental health tracking is synonymous with environmental public health tracking.
Environmental public health surveillance: Environmental public health surveillance is public
health surveillance of health effects integrated with surveillance of environmental exposures and hazards.
Environmental Public Health Tracking Network: The National Environmental Public Health
Tracking Network is a Web-based, secure network of standardized health and environmental data. The
Tracking Network draws data and information from state and local tracking networks as well as
national-level and other data systems. It will provide the means to identify, access, and organize hazard,
exposure, and health data from these various sources and to examine and analyze those data on the
basis of their spatial and temporal characteristics. The network is being developed by the Centers for
Disease Control and Prevention (CDC) in collaboration with a wide range of stakeholders. See
www.cdc.gov/nceh/tracking/network.htm for more information.
Environmental Public Health Tracking Program: The Congressionally-mandated national
initiative that will establish a network that will enable the ongoing collection, integration, analysis, and
interpretation of data about the following factors: (1) environmental hazards, (2) exposure to
environmental hazards, and (3) health effects potentially related to exposure to environmental hazards.
Visit www.cdc.gov/nceh/tracking/ for more information.
93
Epidemiology: The study of the distribution and determinants of health-related states or events in
specified populations, and the application of this study to the control of health problems.
Exposure: Contact with a contaminant (by breathing, ingestion, or touching) in such a way that the
contaminant may get in or on the body and harmful effects may occur.
Exposure indicator: According to the Council of State and Territorial Epidemiologists (CSTE), an
exposure indicator is a biological marker in tissue or fluid that identifies the presence of a substance or
combination of substances that may potentially harm the individual.
Geographic Information Systems (GIS): Software technology that enables the integration of
multiple sources of data and displaying data in time and space.
Hazard: A factor that may adversely affect health.
Hazard indicator: A condition or activity that identifies the potential for exposure to a contaminant or
hazardous condition.
Health effects: Chronic or acute health conditions that affect the well-being of an individual or
community.
Health effect indicator: The disease or health problem itself, such as asthma attacks or birth defects,
that affect the well-being of an individual or community. Health effects are measured in terms of illness
and death and may be chronic or acute health conditions.
Incidence: The number of new events (e.g., new cases of a disease in a defined population) within a
specified period of time.
Institutional Review Board: An Institutional Review Board (IRB) is a specially constituted review
body established or designated by an entity to protect the welfare of human subjects recruited to
participate in biomedical or behavioral research. IRBs check to see that research projects are well
designed, legal, ethical, do not involve unnecessary risks, and include safeguards for participants.
Intervention: Taking actions in public health so as to reduce adverse health effects, regulatory, and
prevention strategies.
Intervention indicator: Programs or official policies that minimize or prevent an environmental
hazard, exposure or health effect.
National Health and Nutrition Examination Survey (NHANES): A continuous survey,
conducted by CDC, of the health and nutritional status of adults and children in the United States. The
survey is unique in that it combines interviews and physical examinations. Since 1970, children in the
survey were biomonitored for lead poisoning, and since 1999, an increasing number of environmental
contaminants has been included in the survey. Visit www.cdc.gov/exposurereport/report.htm for more
information.
94
National Human Exposure Assessment Survey (NHEXAS): An EPA survey designed to
evaluate comprehensive human exposure to multiple chemicals on a community and regional scale.
The study was carried out in EPA Region V, of which Minnesota is a part. Individual households from
four Minnesota Counties were included in the survey. Visit www.epa.gov/heasd/edrb/nhexas.htm for
more information.
Persistent chemicals: Chemical substances that persist in the environment, bioaccumulate through
the food web, and pose a risk of causing adverse effects to human health and the environment.
Population-based approach: A population-based approach uses a defined population or
community as the organizing principle for targeting the broad distribution of diseases and health
determinants. A population-based approach attempts to measure or shape a community’s overall health
status profile, seeking to affect the determinants of disease within an entire community rather than
simply those of single individuals.
Prevalence: The number of events (e.g., instances of a given health effect or other condition) in
a given population at a designated time.
Public health surveillance: The ongoing, systematic collection, analysis, and interpretation of
outcome-specific data used to plan, implement, and evaluate public health practice.
Standard: Something that serves as a basis for comparison. A technical specification or written
report drawn up by experts based on the consolidated results of scientific study, technology, and
experience; aimed at optimum benefits; and approved by a recognized and representative body.
Revised October 10, 2007
Please submit additions and changes to [email protected]
95
Acronyms used in environmental health tracking and
biomonitoring
ACGIH
American Conference of Governmental Industrial Hygienists
ATSDR
Agency for Toxic Substances and Disease Registry, DHHS
CDC
Centers for Disease Control and Prevention, DHHS
CERCLA
Comprehensive Environmental Response; Compensation and Liability Act
(Superfund)
CSTE
Council of State and Territorial Epidemiologists
DHHS
US Department of Health and Human Services, including the US Public Health
Service, which includes the CDC, ATSDR, NIH and other agencies
EPA
US Environmental Protection Agency
EHTB
Environmental Health Tracking and Biomonitoring (the name of Minnesota
Statutes 144.995-144.998 and the program established therein)
EPHI
Environmental Public Health Indicators
ICD
International Classification of Diseases
IRB
Institutional Review Board
MARS
Minnesota Arsenic Study, conducted by MDH in 1998-1999
MDA
Minnesota Department of Agriculture
MDH
Minnesota Department of Health
MEHTS
Minnesota Environmental Health Tracking System
MNPHIN
Minnesota Public Health Information Network, MDH
MPCA
Minnesota Pollution Control Agency
NCEH
National Center for Environmental Health, CDC
NCHS
National Center for Health Statistics
96
NGO
Non-governmental organization
NHANES
National Health and Nutrition Examination Survey, National Center for Health
Statistics (NCHS) in the CDC
NHEXAS
National Human Exposure Assessment Survey, EPA
NIOSH
National Institute for Occupational Safety and Health, CDC
NIEHS
National Institute of Environmental Health Sciences, NIH
NIH
National Institutes of Health, DHHS
NLM
National Library of Medicine, NIH
NPL
National Priorities List (Superfund)
NTP
National Toxicology Program, NIEHS, NIH
PFBA
Perfluorobutanoic acid
PFC
Perfluorochemicals, including PFBA, PFOA and PFOS
PFOA
Perfluorooctanoic acid
PFOS
Perfluorooctane sulfonate
PHL
Public Health Laboratory, MDH
PHIN
Public Health Information Network, CDC
POP
Persistent organic pollutant
SEHIC
State Environmental Health Indicators Collaborative
Revised October 10, 2007
Please submit additions and changes to [email protected]
97
EHTB statute: Minn. Statutes 144.995-144.998
Minnesota: Environmental Health Tracking and Biomonitoring
$1,000,000 each year is for environmental health tracking and biomonitoring. Of this amount, $900,000 each year is
for transfer to the Minnesota Department of Health. The base appropriation for this program for fiscal year 2010 and
later is $500,000.
(i) "Environmental hazard" means a chemical or
other substance for which scientific, peer-reviewed
studies of humans, animals, or cells have
demonstrated that the chemical is known or
reasonably anticipated to adversely impact human
health.
(j) "Environmental health tracking" means
collection, integration, analysis, and dissemination of
data on human exposures to chemicals in the
environment and on diseases potentially caused or
aggravated by those chemicals.
144.995 DEFINITIONS; ENVIRONMENTAL
HEALTH TRACKING AND
BIOMONITORING.
(a) For purposes of sections 144.995 to 144.998,
the terms in this section have the meanings given.
(b) "Advisory panel" means the Environmental
Health Tracking and Biomonitoring Advisory Panel
established under section 144.998.
(c) "Biomonitoring" means the process by which
chemicals and their metabolites are identified and
measured within a biospecimen.
(d) "Biospecimen" means a sample of human fluid,
serum, or tissue that is reasonably available as a
medium to measure the presence and concentration of
chemicals or their metabolites in a human body.
(e) "Commissioner" means the commissioner of the
Department of Health.
(f) "Community" means geographically or
nongeographically based populations that may
participate in the biomonitoring program. A
"nongeographical community" includes, but is not
limited to, populations that may share a common
chemical exposure through similar occupations,
populations experiencing a common health outcome
that may be linked to chemical exposures,
populations that may experience similar chemical
exposures because of comparable consumption,
lifestyle, product use, and subpopulations that share
ethnicity, age, or gender.
(g) "Department" means the Department of Health.
(h) "Designated chemicals" means those chemicals
that are known to, or strongly suspected of, adversely
impacting human health or development, based upon
scientific, peer-reviewed animal, human, or in vitro
studies, and baseline human exposure data, and
consists of chemical families or metabolites that are
included in the federal Centers for Disease Control
and Prevention studies that are known collectively as
the National Reports on Human Exposure to
Environmental Chemicals Program and any
substances specified by the commissioner after
receiving recommendations under section 144.998,
subdivision 3, clause (6).
144.996 ENVIRONMENTAL HEALTH
TRACKING; BIOMONITORING.
Subdivision 1. Environmental health tracking. In
cooperation with the commissioner of the Pollution
Control Agency, the commissioner shall establish an
environmental health tracking program to:
(1) coordinate data collection with the Pollution
Control Agency, Department of Agriculture,
University of Minnesota, and any other relevant state
agency and work to promote the sharing of and
access to health and environmental databases to
develop an environmental health tracking system for
Minnesota, consistent with applicable data practices
laws;
(2) facilitate the dissemination of aggregate public
health tracking data to the public and researchers in
accessible format;
(3) develop a strategic plan that includes a mission
statement, the identification of core priorities for
research and epidemiologic surveillance, and the
identification of internal and external stakeholders,
and a work plan describing future program
development and addressing issues having to do with
compatibility with the Centers for Disease Control
and Prevention's National Environmental Public
Health Tracking Program;
(4) develop written data sharing agreements as
needed with the Pollution Control Agency,
Department of Agriculture, and other relevant state
agencies and organizations, and develop additional
procedures as needed to protect individual privacy;
98
(5) organize, analyze, and interpret available data,
in order to:
(i) characterize statewide and localized trends and
geographic patterns of population-based measures of
chronic diseases including, but not limited to, cancer,
respiratory diseases, reproductive problems, birth
defects, neurologic diseases, and developmental
disorders;
(ii) characterize statewide and localized trends and
geographic patterns in the occurrence of
environmental hazards and exposures;
(iii) assess the feasibility of integrating disease rate
data with indicators of exposure to the selected
environmental hazards such as biomonitoring data,
and other health and environmental data;
(iv) incorporate newly collected and existing
health tracking and biomonitoring data into efforts to
identify communities with elevated rates of chronic
disease, higher likelihood of exposure to
environmental hazards, or both;
(v) analyze occurrence of environmental hazards,
exposures, and diseases with relation to
socioeconomic status, race, and ethnicity;
(vi) develop and implement targeted plans to
conduct more intensive health tracking and
biomonitoring among communities; and
(vii) work with the Pollution Control Agency, the
Department of Agriculture, and other relevant state
agency personnel and organizations to develop,
implement, and evaluate preventive measures to
reduce elevated rates of diseases and exposures
identified through activities performed under sections
144.995 to 144.998; and
(6) submit a biennial report to the chairs and
ranking members of the committees with jurisdiction
over environment and health by January 15,
beginning January 15, 2009, on the status of
environmental health tracking activities and related
research programs, with recommendations for a
comprehensive environmental public health tracking
program.
Subd. 2. Biomonitoring. The commissioner shall:
(1) conduct biomonitoring of communities on a
voluntary basis by collecting and analyzing
biospecimens, as appropriate, to assess environmental
exposures to designated chemicals;
(2) conduct biomonitoring of pregnant women and
minors on a voluntary basis, when scientifically
appropriate;
(3) communicate findings to the public, and plan
ensuing stages of biomonitoring and disease tracking
work to further develop and refine the integrated
analysis;
(4) share analytical results with the advisory panel
and work with the panel to interpret results,
communicate findings to the public, and plan ensuing
stages of biomonitoring work; and
(5) submit a biennial report to the chairs and
ranking members of the committees with jurisdiction
over environment and health by January 15,
beginning January 15, 2009, on the status of the
biomonitoring program and any recommendations for
improvement.
Subd. 3. Health data. Data collected under the
biomonitoring program are health data under section
13.3805.
144.997 BIOMONITORING PILOT
PROGRAM.
Subdivision 1. Pilot program. With advice from
the advisory panel, and after the program guidelines
in subdivision 4 are developed, the commissioner
shall implement a biomonitoring pilot program. The
program shall collect one biospecimen from each of
the voluntary participants. The biospecimen selected
must be the biospecimen that most accurately
represents body concentration of the chemical of
interest. Each biospecimen from the voluntary
participants must be analyzed for one type or class of
related chemicals. The commissioner shall determine
the chemical or class of chemicals to which
community members were most likely exposed. The
program shall collect and assess biospecimens in
accordance with the following:
(1) 30 voluntary participants from each of three
communities that the commissioner identifies as
likely to have been exposed to a designated chemical;
(2) 100 voluntary participants from each of two
communities:
(i) that the commissioner identifies as likely to
have been exposed to arsenic; and
(ii) that the commissioner identifies as likely to
have been exposed to mercury; and
(3) 100 voluntary participants from each of two
communities that the commissioner identifies as
likely to have been exposed to perfluorinated
chemicals, including perfluorobutanoic acid.
Subd. 2. Base program. (a) By January 15, 2008,
the commissioner shall submit a report on the results
of the biomonitoring pilot program to the chairs and
ranking members of the committees with jurisdiction
over health and environment.
(b) Following the conclusion of the pilot program,
the commissioner shall:
(1) work with the advisory panel to assess the
usefulness of continuing biomonitoring among
members of communities assessed during the pilot
program and to identify other communities and other
designated chemicals to be assessed via
biomonitoring;
(2) work with the advisory panel to assess the pilot
program, including but not limited to the validity and
99
accuracy of the analytical measurements and
adequacy of the guidelines and protocols;
(3) communicate the results of the pilot program to
the public; and
(4) after consideration of the findings and
recommendations in clauses (1) and (2), and within
the appropriations available, develop and implement
a base program.
Subd. 3. Participation. (a) Participation in the
biomonitoring program by providing biospecimens is
voluntary and requires written, informed consent.
Minors may participate in the program if a written
consent is signed by the minor's parent or legal
guardian. The written consent must include the
information required to be provided under this
subdivision to all voluntary participants.
(b) All participants shall be evaluated for the
presence of the designated chemical of interest as a
component of the biomonitoring process. Participants
shall be provided with information and fact sheets
about the program's activities and its findings.
Individual participants shall, if requested, receive
their complete results. Any results provided to
participants shall be subject to the Department of
Health Institutional Review Board protocols and
guidelines. When either physiological or chemical
data obtained from a participant indicate a significant
known health risk, program staff experienced in
communicating biomonitoring results shall consult
with the individual and recommend follow-up steps,
as appropriate. Program administrators shall receive
training in administering the program in an ethical,
culturally sensitive, participatory, and communitybased manner.
Subd. 4. Program guidelines. (a) The
commissioner, in consultation with the advisory
panel, shall develop:
(1) protocols or program guidelines that address
the science and practice of biomonitoring to be
utilized and procedures for changing those protocols
to incorporate new and more accurate or efficient
technologies as they become available. The
commissioner and the advisory panel shall be guided
by protocols and guidelines developed by the Centers
for Disease Control and Prevention and the National
Biomonitoring Program;
(2) guidelines for ensuring the privacy of
information; informed consent; follow-up counseling
and support; and communicating findings to
participants, communities, and the general public.
The informed consent used for the program must
meet the informed consent protocols developed by
the National Institutes of Health;
(3) educational and outreach materials that are
culturally appropriate for dissemination to program
participants and communities. Priority shall be given
to the development of materials specifically designed
to ensure that parents are informed about all of the
benefits of breastfeeding so that the program does not
result in an unjustified fear of toxins in breast milk,
which might inadvertently lead parents to avoid
breastfeeding. The materials shall communicate
relevant scientific findings; data on the accumulation
of pollutants to community health; and the required
responses by local, state, and other governmental
entities in regulating toxicant exposures;
(4) a training program that is culturally sensitive
specifically for health care providers, health
educators, and other program administrators;
(5) a designation process for state and private
laboratories that are qualified to analyze
biospecimens and report the findings; and
(6) a method for informing affected communities
and local governments representing those
communities concerning biomonitoring activities and
for receiving comments from citizens concerning
those activities.
(b) The commissioner may enter into contractual
agreements with health clinics, community-based
organizations, or experts in a particular field to
perform any of the activities described under this
section.
144.998 ENVIRONMENTAL HEALTH
TRACKING AND BIOMONITORING
ADVISORY PANEL.
Subdivision 1. Creation. The commissioner shall
establish the Environmental Health Tracking and
Biomonitoring Advisory Panel. The commissioner
shall appoint, from the panel's membership, a chair.
The panel shall meet as often as it deems necessary
but, at a minimum, on a quarterly basis. Members of
the panel shall serve without compensation but shall
be reimbursed for travel and other necessary
expenses incurred through performance of their
duties. Members appointed by the commissioner are
appointed for a three-year term and may be
reappointed. Legislative appointees serve at the
pleasure of the appointing authority.
Subd. 2. Members. (a) The commissioner shall
appoint eight members, none of whom may be
lobbyists registered under chapter 10A, who have
backgrounds or training in designing, implementing,
and interpreting health tracking and biomonitoring
studies or in related fields of science, including
epidemiology, biostatistics, environmental health,
laboratory sciences, occupational health, industrial
hygiene, toxicology, and public health, including:
(1) at least two scientists representative of each of
the following:
(i) nongovernmental organizations with a focus on
environmental health, environmental justice,
99
children's health, or on specific chronic diseases; and
(ii) statewide business organizations; and
(2) at least one scientist who is a representative of
the University of Minnesota.
(b) Two citizen panel members meeting the
scientific qualifications in paragraph (a) shall be
appointed, one by the speaker of the house and one
by the senate majority leader.
(c) In addition, one representative each shall be
appointed by the commissioners of the Pollution
Control Agency and the Department of Agriculture,
and by the commissioner of health to represent the
department's Health Promotion and Chronic Disease
Division.
Subd. 3. Duties. The advisory panel shall make
recommendations to the commissioner and the
legislature on:
(1) priorities for health tracking;
(2) priorities for biomonitoring that are based on
sound science and practice, and that will advance the
state of public health in Minnesota;
(3) specific chronic diseases to study under the
environmental health tracking system;
(4) specific environmental hazard exposures to
study under the environmental health tracking
system, with the agreement of at least nine of the
advisory panel members;
(5) specific communities and geographic areas on
which to focus environmental health tracking and
biomonitoring efforts;
(6) specific chemicals to study under the
biomonitoring program, with the agreement of at
least nine of the advisory panel members; in making
these recommendations, the panel may consider the
following criteria:
(i) the degree of potential exposure to the public or
specific subgroups, including, but not limited to,
occupational;
(ii) the likelihood of a chemical being a carcinogen
or toxicant based on peer-reviewed health data, the
chemical structure, or the toxicology of chemically
related compounds;
(iii) the limits of laboratory detection for the
chemical, including the ability to detect the chemical
at low enough levels that could be expected in the
general population;
(iv) exposure or potential exposure to the public or
specific subgroups;
(v) the known or suspected health effects resulting
from the same level of exposure based on peerreviewed scientific studies;
(vi) the need to assess the efficacy of public health
actions to reduce exposure to a chemical;
(vii) the availability of a biomonitoring analytical
method with adequate accuracy, precision,
sensitivity, specificity, and speed;
(viii) the availability of adequate biospecimen
samples; or
(ix) other criteria that the panel may agree to; and
(7) other aspects of the design, implementation,
and evaluation of the environmental health tracking
and biomonitoring system, including, but not limited
to:
(i) identifying possible community partners and
sources of additional public or private funding;
(ii) developing outreach and educational methods
and materials; and
(iii) disseminating environmental health tracking
and biomonitoring findings to the public.
Subd. 4. Liability. No member of the panel shall
be held civilly or criminally liable for an act or
omission by that person if the act or omission was in
good faith and within the scope of the member's
responsibilities under sections 144.995 to 144.998.
INFORMATION SHARING.
On or before August 1, 2007, the commissioner of
health, the Pollution Control Agency, and the
University of Minnesota are requested to jointly
develop and sign a memorandum of understanding
declaring their intent to share new and existing
environmental hazard, exposure, and health outcome
data, within applicable data privacy laws, and to
cooperate and communicate effectively to ensure
sufficient clarity and understanding of the data by
divisions and offices within both departments. The
signed memorandum of understanding shall be
reported to the chairs and ranking members of the
senate and house of representatives committees
having jurisdiction over judiciary, environment, and
health and human services.
Effective date: July 1, 2007
This document contains Minnesota Statutes, sections
144.995 to 144.998, as these sections were adopted in
Minnesota Session Laws 2007, chapter 57, article 1,
sections 143 to 146. The appropriation related to
these statutes is in chapter 57, article 1, section 3,
subdivision 4. The paragraph about information
sharing is in chapter 57, article 1, section 169. The
following is a link to chapter 57:
http://ros.leg.mn/bin/getpub.php?type=law&year=20
07&sn=0&num=57
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