Summary: June 12, 2012 Meeting of the EHTB Advisory Panel Panel Attendees: Alan Bender, Bruce Alexander, Geary Olsen, Tom Hawkinson, Cathy Villas‐Horns, Fred Anderson, Greg Pratt, David DeGroote; Jill Heins‐Nesvold & Lisa Yost called in for part of the meeting. Pat McGovern and Cathi Lyman‐Onkka could not attend. Other attendees; Rochelle Spielman, Amy Jacobson (Ramsey County), Paul Moyer, Joanne Bartkus, Joe Zachman, Erik Zabel, Chris Sigstadt, Jim Kelly, Lih‐in Rezania, Heather Kehn, Aggie Leitheiser, David Rindal, Mary Manning, John Soler EHTB Staff: Jean Johnson, Barbara Scott Murdock, Dave Stewart, Chuck Stroebel, Mary Jeanne Levitt, Blair Sevcik, Jeannette Sample, Christina Rosebush, Naomi Shinoda, Paula Lindgren, Debra Lee. Bruce Alexander invited panel members, staff, and members of the audience to introduce themselves, and called the meeting to order. Health Risk Communication in the Age of Social Media John Soler, an epidemiologist for MDH’s Minnesota Cancer Surveillance System (MCSS), presented the case of a citizen’s request for municipal level cancer information that, through social media, burgeoned into a risk communication challenge. The information was soon published in an online newsletter, The Fridley Patch, and the requesters got the attention of Erin Brockovich who planned a meeting with the group. The Facebook page also noted Superfund sites, and pointed out that the city water supply had contained low TCE (trichloroethylene) contamination in the past. [Note: Erin Brockovich visited the community in late June]. The community (Fridley) has an observed cancer rate that is slightly higher than expected (7.6%), John pointed out, but much of the excess is due to lung cancer: 29.7% above the state average, 49% above the state average in females. Anoka appears to have a much higher smoking rate than other metro area counties, according to a recent survey. As a part of Anoka, Fridley probably has high smoking rates, which are the likeliest explanation for the excess of cancers. Communities surrounding Fridley show very similar cancer patterns. Nonetheless, the Facebook page led to the perception that past industrial pollutants are or could be a likely explanation of the excess. In the state as a whole, 48% of the population is expected to have a cancer diagnosis at some point in life. This is higher than for the nation as a whole because of greater longevity in 1
Minnesota and good access to health care, which increases the likelihood of timely diagnosis. Doug Schultz, from MDH’s Communications Office, reviewed some risk communication basics, which involve strategies for dealing with people’s emotional responses to fear, and helping people past their fears with interpretation and understanding. Emphasizing that “we need to see risk as they do,” and then communicate with citizens in ways they can understand. He cited Peter Sandman’s definition (risk = hazard + outrage)1 and argued that if communicators can reduce and manage outrage, people will be attentive and can take part in well informed discussions. He advised asking about people’s concerns, listening, and learning how to express empathy. He quoted Buddy Ferguson’s admonition to “let yourself get beaten up, and listen.” To dealing with social media, he recommended… 1) That MDH establish and maintain credibility and transparency in social media 2) That MDH must use social media to track and gauge public opinion. Social media are immediate, direct, and interactive, he said, so MDH needs to employ them earlier so we can get into the conversation as early as possible. The keys are to…  Gather information fast  Use dialog and engagement with stakeholders  Update information quickly  Correct and address errors quickly In the case of the Fridley cancer map, he asked, what could we have done differently? Then added, I’m not sure, but in future, where we see situations in which the data show an increase in some public concern [such as cancer rates], MDH staff can think ahead about how best to address public concerns if and when they arise. In future, MDH should assume that social media will be a likely element in public health issues. Doug advised communicators to identify their audiences and to check with the communications office before replying to a data request to make sure the message is clear. Ideas for MDH’s use of social media include Facebook daily doses, tips via twitter, creating widgets that provide a portal to good information, and links to outside entities, such as the American Public Health Association (APHL), that provide accurate, readable information about cancer or other issues. 1
Geary Olsen notes that Risk = (hazard x exposure) + outrage. Hazard + outrage = perceived risk. 2
Chuck Stroebel reported that the MN Public Health Data Access portal saw a sharp spike in visits and in launching cancer maps during the height of the media coverage of the Fridley cancer scare. Currently, he noted, the portal shows cancer data at the county and state level, but raised a question about how the portal might use local‐level data in future. Note: The MNPH Data Access portal’s cancer page also links to other MDH programs that deal with cancer and to the American Cancer Society’s home page and to its most current report, Cancer Facts & Figures 2012. It also provides a link to the MN Pollution Control Agency, which monitors air and water for cancer‐causing chemicals and other pollutants. Discussion Alan Bender said that, 20 years ago, MDH changed its approach to cancer cluster inquiries by moving quickly to get information and reassuring messages out to the public before the situation had generated much outrage. But social media have shortened the time it takes for the public to become outraged. “Does public health data tracking increase outrage by reducing the time it takes people to get cancer data?” he asked. The time and resources needed to support responses to cluster fears draw these resources away from other public health needs. Bruce Alexander replied that social media are an issue for both MDH and the University of Minnesota. MDH does have to invest resources in this issue, he said. The reality is that social media must be considered, and could be used to engage the community more to solve the problem, rather than creating the problem. Greg Pratt agreed. Misinformation and outrage have been around for a long time, he said, and social media just speed it up. MDH and other agencies have to engage in social media and do their best to get the right story out to the community. Alan said that social media pose less of a problem for MDH per se than for communities, unless MDH can provide a balanced message. Aggie Leitheiser then asked whether social media might provide opportunities for educating the public about preventing health problems. Alan replied that, if a social media response to a community is comprehensive, it could carry preventive messages. It would be difficult and resource‐intensive, but worth exploring. Doug pointed out that one of the important uses of social media is to drive people to link to websites where they can see or hear more about an issue. So as the portal staff put out more data, they might think about what how to respond to potential outrage and try to craft some 140‐character messages to counter misinformation. Greg Pratt 3
noted, however, that the public can be pretty resistant to new information. The public is convinced that industrial pollution causes cancer, he said, and although the MPCA has been explaining for years that non‐point source pollution sources are now more important, people don’t accept it. Geary Olsen summed up the conversation by saying, “Communication is the hardest part of the science.” Tracking Updates Chuck briefly reviewed the written updates, highlighting the NACCHO facilitated discussion. Jean Johnson reviewed the written update about the National Tracking Grantees Workshop, saying that state grantees listed 75 projects that use or link multiple health and environmental datasets. She noted also that the two water quality projects presented and discussed in this Advisory Panel meeting are among those 75 tracking projects. The panel had no questions. Minnesota Water Quality Update Paula Lindgren, research analyst in Environmental Epidemiology, reviewed the current and future status of drinking water quality data that will be available on the MN Public Health Data Access portal (MNPH Data Access). Almost all – 98% – of Minnesota’s community water systems (CWS) provide groundwater; together, they serve 1/3 of the state’s population. The remaining 2% use surface water, are in the Twin Cities, and serve 2/3 of Minnesota’s population. MNPH Data Access currently tracks four chemicals in community water systems: o Arsenic o Nitrate o Two Disinfection By‐Products  Trihalomethanes (THM)  Haloacetic acid (HAA) These chemicals are tested at intervals based on health priorities and on whether the last test was above or below the EPA’s maximum contaminant level (MCL). Testing is more frequent in systems found to have chemical levels above the MCL. CDC has identified six new analytes to feature on their tracking portal and, although these are optional tracking chemicals for EPHT states, Minnesota is considering them. These analytes are… o Atrazine o Radium o Uranium 4
o DEHP (Bis(2‐ethylhexyl)phthalate) o TCE (Trichloroethylene o PCE (Perchloroethylene) Testing in Minnesota has found atrazine in only 5/83 samples; DEHP and uranium have been found rarely. Radium has been found in 10 community water systems and, Paula said, we recommend that the Minnesota portal should include radium data. Paula also showed graphs of various ways to display the data. These ranged from bar graphs showing the percentage of time that CWSs tested below the MCL, to a Minnesota map with dots sized to reflect city or community size and colored to indicate the level of a chemical found in key CWSs. Greg Pratt remarked that he was interested in seeing the total number of CWSs tested/total of all MN CWSs so he could see the percentage of CWSs tested in any one time period. Paula replied that not all CWSs are tested in any one year, so staff at untested CWSs would not like the implication that they weren’t doing their job. More discussion was postponed until after the next presentation. Adverse Child Health Outcomes & Agrichemical Water Contamination in the Midwest Dr. Rachael Jones, assistant professor of occupational and environmental health at the University of Illinois at Chicago (UIC), and doctoral student Kirsten Almberg reviewed this study’s current progress and challenges. The PI for the UIC study is Dr. Leslie Stayner. The primary objectives of the project are to: 1) Enhance the methodology of drinking water contaminant and health data surveillance and linkage; 2) Explore potential associations between birth and childhood health outcomes and exposure to drinking water contaminants, beginning with atrazine and nitrate; 3) Develop partnerships and collaborations with eight Midwestern states: Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, and Wisconsin. Rachael Jones’s talk focused on Minnesota’s CWS drinking water data for nitrate/nitrite and atrazine from years 2000 to 2008. The researchers are also beginning to collect data on private wells. The EPA specifies the MCLs for water contaminants and regulates the CWS monitoring schedule. But, she said, EPA’s regulatory policies require monitoring less frequently than the UIC researchers would like. Atrazine is monitored quarterly in Minnesota, for instance, but if the measured concentration is low or undetectable, monitoring may be repeated only every three years. The sampling is consistent with the Safe Drinking Water Act (SDWA), but too infrequent to detect short‐
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term events or easily allow data linkages with health outcomes. In a typical month, only five Minnesota counties have atrazine measurements, compared to 21 counties with nitrate/nitrite measurements. When atrazine is sampled more often [as it is in EPA’s Atrazine Monitoring Program (AMP)], the researchers find more high atrazine levels. This, however, is because the AMP program targets CWS known to have relatively high concentrations. For health data, UIC has focused to date on such outcomes as low birth weight and pre‐
term births, and on statistics such as sex ratio from birth certificates. Data for these are available as monthly or annual rates in each county. The ability to link the drinking water data, typically measured quarterly, annually, or triennially, with birth outcome data, available monthly or yearly, requires both measures to be referenced to a common temporal and spatial scale. The temporal mismatch between these data presents a challenge, because adverse birth outcomes are often associated with exposures during vulnerable time periods in fetal development. Characterizing these exposures requires drinking water quality estimates for each calendar month, and most contaminants of interest are measured far less frequently. To address the problem, UIC researchers decided to apply multiple imputation, a statistical model that uses observed data and other variables to estimate missing data, in this case, drinking water contaminant concentrations in every month in which no measurement was collected. Their first steps were to run the imputation model 5‐10 times with real data, then combine the imputed water quality data, and use a mixed effects regression model to fit a water quality prediction model. Because multiple imputation has not been used for water quality data before, they tested the method using data from EPA’s Atrazine Monitoring Program (AMP). AMP measures atrazine every two weeks in 89 participating CWSs in five of the project states. After running the model with AMP data, the researchers deleted data to create artificial patterns of missing data, so they could compare the results from multiple imputation to those obtained using the true values. They deleted data for four months, eight months, and 10 months. The more data deleted, the higher the uncertainty introduced into regression model coefficients fitted with the imputed data. In these preliminary regression models, the true coefficients fall into the 95th confidence intervals estimated with the imputed data in 100% of instances tested. Still, their initial results suggest that the method may be appropriate for estimating monthly concentrations from quarterly 6
measurements, and quarterly concentrations from annual measurements. The next step is to collect and integrate data from private wells, which people use when they lack CWS or other public water system service. Such data are collected very infrequently. Spatial scales present a different challenge. Databases of drinking water quality indicate only the address of the CWS office, not the site of the treatment plant, and the service areas are unclear. A single CWS may serve part of a town, several cities, or more than one county. And a county often contains from 6 to 15 CWSs. The UIC researchers combine data from multiple CWSs to estimate the potential hazard at the county level. They then calculate a population‐weighted average contaminant concentration for each county in the month, quarter, or year of interest and link this concentration to the health outcome data. Kirsten Almberg then reviewed the strengths and challenges of county‐level Minnesota birth outcome data for 2004‐2008. The outcomes of interest are low birth weight (LBW) and preterm birth (PTB). The LBW rate is 16‐18/1000 and for PTB, is 69‐77/1000. Minnesota not only includes data on birth weight and pre‐term birth on the birth certificate, but also detailed race/ethnicity data, plus data on risk factors such as exposure to tobacco smoke. One challenge is that Minnesota’s data on these outcomes differ from national data; the UIC team uses MN data as the gold standard data. The UIC researchers used Poisson regression models to explore associations between agrichemicals, low birth weight, and prematurity. They used the annual population‐
weighted mean atrazine and nitrate concentrations in each county, and percentage of county land used for agriculture as indicators of environmental quality. Kirsten did an analysis of LBW and agricultural contaminants, and found some association with soybean and corn density, defined as the county’s soy and/or corn acreage divided by the total acreage of the county. She reported a slight protective effect of soybean density on LBW, possibly a result of the rural setting, but after the researchers made adjustments for potential confounders, the data for other effects were neutral. When a random effect was added to account for correlations within counties, the association between atrazine and prematurity disappeared, as did the protective effect of nitrate for low birth weight and prematurity. In the coming year, the group will incorporate the spatial structure of the data into these epidemiological models. 7
Discussion Greg Pratt noted that the group had many non‐detection data for atrazine and asked, did you have a method for handling low detection levels? Rachael replied that the team has been using the ½ LOD (level of detection/2), but is working on other ways to address the issue. We believe that in some places, the atrazine concentrations are truly zero, she said, so we’re trying to find ways to use that fact, so we can employ a more sophisticated method than ½ LOD. Greg agreed, suggesting that they might apply Bayesian methods or try to achieve a lower detection limit in the analyses. Some papers, he said, report effects below the detection levels used in the UIC study. Bruce Alexander asked, in multiple imputation, aren’t the data supposed to be missing at random? But the pattern for atrazine data is not missing at random. Rachael agreed that the data are not missing at random, and said, we’re trying to figure out whether the pattern can void the analysis. The group is working on evaluating the ability to detect a true association. Of the 7000 CWSs in the eight Midwestern states, she explained, only about 350 CWSs had ever detected atrazine between 2000 and 2008. We’re trying to separate out the patterns for systems that have detected atrazine and not add in the other systems that have never detected atrazine. The imputation model may illuminate what’s happening in CWSs with more frequent measurements, and that may inform what happens in systems with missing measurements. David DeGroote asked whether the presence of atrazine had any relationship with time of year. Rachael answered that the team is looking to see whether the pattern is seasonal, particularly in the AMP systems. In general, atrazine is higher in summer, but sometimes, atrazine levels increase in January, probably as a result of hydrologic changes. David asked whether the UIC team had seen a relationship with birth month. Rachael answered that will be part of the project. The researchers are back‐calculating from birth month to time of conception, but that information is not in the project’s models yet, and the group is consulting with experts in maternal and child health to make sure they’re doing the calculations correctly. It’s more difficult to discern seasonal patterns in CWSs, Rachael explained. The data show some differences in season, especially in spring, but it’s hard to get the seasonal patterns because the water sample collection is repeated in the same months every year. It’s possible that any seasonal pattern is confounded by the water sample collection schedule. Geary Olsen asked about the half‐life of atrazine. Rachael replied it depends on what you read. We see that the half‐life of the parent compound seems to vary from days to weeks. The laboratory test measures only the parent compound, not the metabolites. 8
Joe Zachman, from the Minnesota Department of Agriculture, commented that EPA and USGS monitoring for atrazine is linked to surface water, but the UIC project mostly looks at groundwater. So, he said, it appears that you are using surface water to estimate groundwater concentrations, and at least in Minnesota, we don’t see seasonality of atrazine concentrations in groundwater. How do you reconcile using surface water data to look at groundwater? Rachael replied that all the data we presented today used CWS finished water results, but most of the water systems used for atrazine analysis are surface water. The project has not yet tried to incorporate USGS information on the treatment of the surface or groundwater to potential exposures in finished water quality. Joe added that, for soybeans and wheat, greater density indicates lower atrazine levels in those two crops, so he doesn’t think density [of soy and wheat] indicates exposure for birth defects. Kirsten replied, because we don’t have consistent data, if we address one crop, the crop serves as a proxy for a range of pesticides used on that crop – not atrazine specifically. Bruce observed, you say that Minnesota data is of high quality, but that the analyses in other states showed associations, but not in Minnesota. Kirsten replied that the other states publish some confounders at state‐level only, whereas [in Minnesota] we can adjust for confounders like maternal smoking. Maybe, she suggested, the associations we’ve seen in other states reflect the fact that they don’t have data on confounders, so we can’t correct for them. But, she noted, Minnesota also has very low levels of the exposure variables. Tracking Air Quality Impacts Naomi Shinoda reviewed the background, methods, and results of a five‐year EPA STAR grant that explored the feasibility of using local air monitoring data to track public health impacts of PM2.5 in Minnesota’s 7‐county Metropolitan Area (MSP Metro) and in Olmsted County. Airborne fine particulate matter (PM2.5) exposures are associated with health outcomes such as cardiovascular and respiratory disease. In recent years, Minnesota communities have implemented several local and regional air pollution reduction strategies. The Minnesota Metro Emissions Reduction Project (MERP) is a voluntary $1 billion energy project that converted two coal‐fired power plants to natural gas and installed new emissions control equipment on a third plant in the MSP metro during 2007‐2009. The Clean Air Minnesota coalition, partnering with the Minnesota Pollution Control Agency (MPCA), industry, and other members, began implementing voluntary diesel vehicle 9
emission reduction pilot projects in 2005. These have since become a statewide effort and include installing EPA‐verified retrofit technology to reduce diesel emissions from heavy duty public vehicles and school buses. These local air pollution reduction initiatives coincided with federal regulations implemented over the same time period. Since 2008, MDH staff has collaborated with the MPCA and Olmsted Medical Center to develop epidemiologic methods for tracking the public health impacts of changes in PM2.5 concentrations. They used the following data sources in the analyses:  Air quality data o Hourly ambient PM2.5 concentration measurements from MPCA’s PM2.5 continuous monitors (6 monitors in MSP; 1 monitor in Olmsted County). Data were summarized into daily 24‐hour average concentrations.  Health data  Hospitalizations for: total respiratory disease, chronic lower respiratory disease (CLRD), asthma, cardiovascular disease  Asthma‐related ED visits  Deaths for: all‐causes, cardiopulmonary disease Staff developed two measures of the exposure‐health association for this project:  Odds ratios (from case‐crossover and time series analyses) o Percent change in hospitalizations/deaths per 10µg/m3 increase in PM2.5  Population attributable fractions (PAFs; only from case‐crossover analyses) o Number or percent of excess hospitalizations/deaths that occurred in the study population that were triggered by observed levels of PM2.5 above a “policy relevant” background concentration (5µg/m3). The analyses for the MSP Metro area found that, over the 7 years, respiratory hospitalization outcomes in the MSP metro were associated with PM2.5, with odds ratios ranging from 1.032 to 1.043 per 10µg/m3 increase in the 3‐day average exposure to PM2.5. These results were consistent with the literature. The PAFs showed change over time in the numbers of respiratory hospitalizations triggered by PM2.5. Total respiratory, chronic lower respiratory disease (CLRD), and asthma hospitalizations attributable to short‐term PM2.5 exposures declined by about 3‐4% after the 2003‐2005 baseline period. The analyses found no consistent associations between ambient PM2.5 levels and the risk of cardiovascular hospitalizations and no consistent associations between ambient PM2.5 levels and the risk of mortality. 10
In Olmsted County, smaller population sizes, plus the fact that only one continuous PM2.5 monitor operated in the county, made it difficult to detect associations of health outcomes with PM2.5. Naomi also pointed out that the project’s results are limited to acute serious health events: hospitalizations. In the entire range of health effects that can be caused by air pollution, hospitalizations are a small portion. Many more people may experience symptoms related to air pollution that may limit their activities and quality of life, and lead to absences at work or school. In this study, the attributable fraction doesn’t capture the long‐term effects of cumulative exposure to PM2.5 or the degree to which PM2.5 contributes to chronic health problems. Lessons learned for expanding this work to other parts of MN  Exposure assignment issues  Consider whether air monitors are available at appropriate geographic locations and how they collect data at temporal scales, such as continuous monitoring vs. 1 data collection/3 days or /6‐days  Consider whether modeled air data are available and usable (hierarchical Bayesian modeled data are available statewide only for years 2001‐2006 thus far)  Population size issues  Consider statistical limitations of conducting analyses in smaller populations  Consider to what extent pooling multiple years of data can help to address those limitations Discussion Questions posed to the panel asked  If MN EPHT were to continue with this work and build on it to develop an air quality health impact indicator, how should it proceed?  Is the population‐attributable fraction (PAF) a useful tracking measure that the public would understand? The discussion focused on the PAF. Geary Olsen commented that, although the PAF would be a useful tracking measure, in light of the Fridley cancer and social media conversation, how do you think John Q. Public, the average person on social media, would interpret a PAR? Would the public [average citizens who use the MN EPHT 11
portal] perceive it as an individual risk statistic? Aggie Leitheiser thought they would not understand it – the PAF is complicated and does not address the question, “What does it mean for me?” Alan Bender asked, what are we trying to communicate? Regulatory agencies can decide what are good, bad, or indifferent based on data and apply a color code for good, bad, and indifferent. Jean Johnson said that the EPA project wanted grantees to come up with a method to measure whether a policy had an impact on health, and showed measurable progress and accountability. That, Geary pointed out, makes total sense, but is different from question 2. I just don’t know what the public can see and understand. Bruce added, translating the PAR to hospitalizations prevented, and dollars saved per hospitalization would be measures that most people would get. But one issue of concern is that many other factors other than air pollution, such as smoking, contribute to respiratory disease. Air pollution is a small piece of the puzzle, and most hospitalizations for respiratory disease have nothing to do with air pollution. In addition, the case‐crossover method uses a target population of people who end up in the hospital, not the people at risk of being in the hospital, but aren't. He noted that it’s unusual to see this population compare so closely to another method (the odds ratio) because usually the results from the two methods are very different. Greg said: the message we want to communicate is one that the public understands: cleaner air is better and healthier. Communicating all the scientific methodology and detail is lost on the public. Alan noted Jean’s point that morbidity and mortality [rates] are related to prevention. But as the cost of reducing emissions rises, we need to translate that cost into a body count to justify the policy. Bruce said, “If you can say that every hospitalization costs $10,000 (and that’s likely too low), then you can get a sense of cost.” He then asked how complete the records on hositalization were in the project – whether the project had included hospitalizations at Mayo or the VA. Naomi said yes for Mayo, but no for the VA hospital. Bruce advised that the communications need to make that clear and explain that the cost estimates are low because you don’t have all hospitalizations. Observing that hospitalizations for asthma are small, as most asthma events are emergency department visits, he added, “This means that this project can’t account for all costs of respiratory diseases.” Naomi noted that the National Tracking program is also working on this issue in terms of Health Impact Assessment, and Jean added that 12
the project looked only at a small piece of the puzzle (acute effects), rather than at the long‐term effects. Mercury Biomonitoring Follow up Projects Update The Pregnancy and Newborn Exposure Study In response to the Advisory Panel’s recommendation in March, MDH is working with Dr. Ruby Nguyen at the University of Minnesota to measure mercury in paired cord blood and newborn blood spots in a small population of newborns. The goal is to test whether mercury concentrations in umbilical cord blood (fetal blood) correlate with mercury levels in newborn blood spots, to speciate cord blood to learn the methyl mercury and inorganic mercury content, and to enable the Public Health Laboratory staff to further refine the laboratory method for measuring mercury in newborn blood spots. Jean Johnson reported the progress made since the March meeting: the MDH staff and the UMN have completed work protocols and work contracts for the study. The UMN IRB approval was granted on May 9 and by the AP meeting date, the MDH IRB had approved the protocol, ruling that the project was exempt. Staff working with Dr. Nguyen will recruit 50 to 100 women who are already participants in The Infant Development and Environment Study (TIDES). The Pregnancy and Newborn Exposure Study will measure mercury (total and speciated), lead, and cadmium in cord blood of newborns and will measure total mercury in the corresponding newborn blood spots. Update: specimen collection began in mid‐June and will end by January 1, 2013. Next Steps Jean told panel members that EHTB staff will be meeting with external and internal stakeholders to gather their advice on long‐term goals for public health tracking and biomonitoring of mercury hazards, exposures, and health effects in Minnesota. Stakeholder advice will be incorporated into recommendations that EHTB staff will present to the Advisory Panel in the fall and into the program’s report to the Minnesota Legislature. These steps follow the Advisory Panel’s recommendation in December, 2011, that MDH should do more work on questions raised by the Lake Superior mercury project “To enable MDH to keep on the table the idea of pursuing more studies as resources appear: 1) To what extent are other populations exposed, and 2) what are the sources of exposure? “ The panel also recommended that MDH should develop specific aims and long term objectives. “The long‐term objectives are to develop other long‐term research agendas 13
to characterize exposure in broader portions of Minnesota—to learn the extent of the source of the problem. The program staff should come back to the panel with specific recommendations.” (Advisory Panel meeting notes, December 13, 2011) Discussion Geary Olsen asked, given the newspaper coverage of the mercury findings, did you get public inquiries from the Arrowhead area? Jim Kelly, from Environmental Health, answered that his division had received some calls and interest, but it was not overwhelming; they had few calls from the general public. Geary said, I find that… interesting, given the magnitude of the association. But Jean suggested that people may see this as a familiar issue and feel that they have control over it. In short, they don’t find it as scary as the Superfund site down the street. She added that a recent EHTB staff presentation to representatives from Healthy Legacy was well received, and that people were receptive and interested in continuing the focus on projects that biomonitored children and pregnant women. Biomonitoring Updates Jean alerted the panel to upcoming reports expected from the C8 (PFOA) study of PFC‐
exposed communities in the Ohio Valley and in Parkersburg, West Virginia [Note: these were all expected in July, but now many are expected in October 2012]. She predicted that MDH would receive many media inquiries. In the September meeting, the EHTB program will ask for the panel’s recommendations about biomonitoring or health follow‐up projects in the East Metro community, and for advice in responding to the C8 “probable link” reports. The two other written updates drew no questions. Legislative Report A new law passed in the 2011‐12 legislative session regulates the collection and testing of newborn blood spots and limits the time for retaining specimens and test results. Aggie Leitheiser explained that, in addition, the legislature provided a one‐year authorization for MDH programs to continue while a review of statutory authority is completed. MDH must submit legislative proposals to assure explicit authority is in place to collect, use, store, and disseminate individually identifiable data that may have a genetic component. Because the statute definitions and the Minnesota Supreme Court interpretation of genetic information are so broad, this includes any form of health data. The statute may affect other state agencies and private providers, including UMN or blood/data collected in connection with a drunken driving incident. 14
New Business Bruce Alexander asked whether panel members had suggestions for new business. Hearing none, he asked for a motion to adjourn the meeting. The meeting was adjourned at 3:52 PM. 15