•
•
,
PERINATAL MORTALITY: A REVIEW OF
THE LITERATURE AND A LOGISTIC
REGRESSION ANALYSIS
by
Jennifer Jane Bourbina
Department of Biostatistics
University of North Carolina
Institute of Statistics
Mimeo Series No. 2144
May 1995
PERINATAL MORTALITY: A REVIEW OF THE
LITERATURE AND A LOGISTIC REGRESSION ANALYSIS
by
Jennifer Jane Bourbina
BSPH Honors Research
Department of Biostatistics
University of North Carolina at Chapel Hill
1995
...
Approved:
Craig D. Turnbull, Research Advisor
and Honors Director
L.T.J.. .J
,
L ~
Dana E. Quade, Biostatistics Faculty
J?~&;2k
Berton H. Kaplan, Epidemiology Faculty
•
UH~
,
TABLE OF CONTENTS
I.
II.
INTRODUCTION TO PROPOSED RESEARCH
01
LITERATURE REVIEW . . . . . . . • • • . . . . . . . . . . . . . . . . . . . . . . 03
I I I. METHODS......................................... .. 16
IV.
ANALYSES AND CONCLUSIONS . . . . . . . • . . . . . . . . . . . . . . . . . 27
V.
SUGGESTIONS FOR FUTURE RESEARCH
APPENDIX
37
39
REFERENCES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
oW
PERINATAL MORTALITY: A REVIEW OF THE LITERATURE
AND A LOGISTIC REGRESSION ANALYSIS
•
by
Jennifer Jane Bourbina
Chapter 1
INTRODUCTION TO PROPOSED RESEARCH
Perinatal mortality continues to be a public health
problem despite years of research designed to identify its
risk factors.
Statistics
The State Center for Health and Environmental
(SCHES)
produces vital statistics data files on
births and fetal/infant deaths for North Carolina.
files
have
factors.
These data
been used to analyze perinatal mortality risk
In 1994, the SCHES published a report for its 1988-
1991 data entitled" Infant Death: Sociodemographic and Medical
Risk Factor Analyses for North Carolina"
Meyer 1994).
(Surles, Beuscher,
Also, Turnbull, advisor to this project, wrote
his dissertation in 1971 about perinatal mortality (Turnbull
1971) .
These works inspired my interest in the topic of
perinatal mortality and generated the topic for this research .
...
The objectives of this project are to review the literature
and to analyze selected risk factors for perinatal mortality.
2
We contacted the SCHES to determine the feasibility of
using their birth and infant mortality data from 1988-1991 for
this research.
The SCHES indicated that it would be able to
provide the data.
The majority of the fall semester was spent reviewing
the
literature
in order
to
increase my knowledge
factors associated with perinatal mortality.
of
the
The remainder of
the fall semester and the first two months of the current
spring
semester
dissertation
were
(1971)
devoted
and to
to
a
study
of
selected readings
Turnbull's
on logistic
regression.
•
.
3
Chapter 2
•
SUMMARY OF LITERATURE REVIEW
This project began with a literature review of many of
the
variables
common
to
Turnbull's dissertation.
both
the
SCHES
data
files
and
The particular variables chosen were
selected so comparisons could be made between the SCHES' birth
and infant mortality data for 1988-1991 and the data used for
Turnbull's dissertation.
Many types of studies and statistical methods have
been used to study perinatal mortality.
an
important
factor
and
it
The type of study is
often determines
hypotheses that may be tested.
the
type
of
Lilienfeld suggested that
hypotheses are generally derived from four types of studies:
1. clinical observations
2. analysis of routinely collected data such as
vital statistics
3. retrospective studies, and
4. laboratory studies
and he noted that clinical trials were the "ultimate approach"
(Turnbull 1971, p. 41).
Clearly, conducting clinical trials on
pregnant women requires special circumstances, hence, most of
.
the studies on perinatal and infant mortality have been based
on birth certificates, hospital records I and vital statistics.
4
Sampling bias is probably the greatest source of error
found in studies of perinatal data
(Turnbull 1971,
p.41).
Problems of inference are encountered due to the inability to
define the nature of selectivity which may be associated with
one's sample data (Turnbull 1971, p.42).
If selectivity is
evident, then interpretation must be made with caution.
studies
of
perinatal
outcome
utilize
data
from
hospitals and such data is highly selective;
surrounding
geographic
areas
pregnancies to these hospitals.
often
Many
teaching
for instance,
refer
complicated
Hence, teaching hospitals are
expected to have high perinatal and infant mortality rates.
The
literature
review was
conducted to determine
which variables were associated with perinatal outcome.
One
definition of the perinatal period is:
pertaining to or occurring in the period shortly before
and after birth; defined as beginning with completion of
the twentieth week of gestation and ending 7 days after
birth (Dorland 1994) .
A summary of the literature review follows:
Demographic variables
Age of mother:
Older and younger women are at an increased risk of
perinatal mortality
1971).
(Fox,
Koepsell,
Daling 1994,
Turnbull
While babies born to mothers between the ages of 25
and 34 are at the lowest risk (Kotagal 1993).
Teenage mothers
.
5
tend to have greater proportions of low birth weight infants
(Kotagal 1993,
Sweeney 1989).
The highest risk of infant
mortality occurred among infants of second or higher birth
orders born to uneducated, black women between the ages of 15
and 19; furthermore, adolescents tend to receive less prenatal
care which increases
1993).
the risk of a
poor outcome
(Kotagal
Those adolescents that do receive proper prenatal care
have a better chance of a positive outcome
(Hickey, Cliver,
Goldenberg, Blankson 1993, Morris, Berenson, Lawson, Wiemann
1993).
Teenagers are also more likely to have complications
such as pregnancy induced hypertension,
(Smith 1990).
toxemia,
or anemia
A pregnant adolescent's body is still growing
and hence the mother's body is competing with the developing
fetus for nutrients.
This may explain why teenagers are at an
increased risk for low birth weight, or poor perinatal outcome
(Kotagal 1993).
Older
women
are
also
at
an
increased
delivery which will involve a perinatal death.
classifications of older women:
risk
for
a
There are two
those with low parity,
and
those with high parity who tend to have lower social class.
This
second
type
is
at
higher risk
than
the
first
type
(Milner, Barry-Kinsella, Unwin, Harrison 1992, Turnbull 1971) .
.
Older women of higher parity tend to have more complications .
Advancing
age
and
parity
are
associated
with
increased
incidence of placenta previa. (Spellacy et al 1986, Milner et
al.
1992).
In contrast,
studies conducted on mostly white
6
women,
aged 35 or more,
of higher socio-economic
status,
generally found that older women were not at an increased risk
for an adverse pregnancy outcome (Ales, Druzin, Santini 1990,
•
Spellacy, Miller, Winegar 1986, Roberts, Algert, March 1994) .
These
older
women
may
experience
more
pre-existing
hypertension but this did not increase their risk (Roberts et
al.
also
1994, Newcomb,
found
that
Rodriguez,
the
effect
Johnson 1991).
of
smoking was
greater as maternal age increased;
hence,
Many studies
significantly
older women may
benefit more than younger women from not smoking '(Wen et al.
1990, Fox et al. 1994).
Parity:
I,
Dorland defines parity as:
"the condition of a women
with respect to her having borne viable offspring"
Parity
is
highly
correlated
with
Eriksson, Kaller, Zetterstrom 1989).
maternal
age
(1994).
(Ericson,
Studies have shown that
the greatest risk of mortality is in women with low age and
high
parity
(Ericson
et
al.
1989).
In
addition,
,white
multiparous women aged 30 or more experience a higher risk
than white primiparous women (Turnbull 1971).
Although,
in
general, a woman usually delivers a slightly heavier infant if
she has had at least one previous pregnancy
1994, Turnbull 1971) .
(Amini et al.
~
7
Sex of the Infant:
Female infants are generally reported to have better
perinatal outcomes, despite having lower birth weights than
their male counterparts (Copper et al. 1993, McGregor, Leff,
Orleans,
Baron 1992).
Many studies have
difference between birth weights
significant
1971) .
(Amini,
Most
Catalano,
studies
report
in males
Hirsh,
about
Mann
a
100
found
that
and females
1994,
the
is
Turnbull
gram difference
between male and female birth weights at all gestational ages
(Copper
et
Furthermore,
al.
1993,
Thompson,
Mitchell,
a
higher
incidence
of
preterm
Borman
1994).
delivery
and
premature rupture of membranes has been reported among women
carrying males.
at 33-36 weeks.
Males are more likely than females to deliver
This increase in preterm births was not
accompanied by an increased number of males with low birth
weight
(McGregor et al.
1992).
This could be because of
relatively greater weight at lower gestational age for males,
or because women carrying males seem to have an increased
vulnerability to infection (McGregor et al. 1992).
Males are
also reported to have a higher incidence of complications such
as abruptio placenta, placenta previa, premature rupture of
membranes,
and
cord
prolapse
(Morgan,
Berkowitz,
Thomas,
Reimbold, Quilligan 1994, Critchlow, Leet, Benedetti, Daling
..
1994).
There is much speculation as to why males weigh more,
but have a greater risk of poor outcome or complications since
8
this is counterintui tive i usually heal thier babies weigh more.
One explanation could be that since males have increased size
and
weight
this
results
(McGregor et al. 1992).
in
a
shorter
gestation
period
Another explanation is that despite
being smaller, females have more subcutaneous fat then males,
which may protect and insulate them from heat loss and serve
as a source of energy (Copper et al. 1993).
Race of the Mother:
Race is a complicated issue. Black babies are often
reported as weighing 100-200 grams less than whites (Hulsey,
Levkoff, Alexander 1991, Roberts et al. 1994, Hickey, Cliver,
Goldenberg, Kohatsu, Hoffman 1993).
for multiple risk factors,
Race is often a marker
and controlling for demographic
factors does not eliminate this difference
1994,
Turnbull
1971).
(Roberts et al.
Anemia is usually the most
complication among blacks (Roberts et al. 1994).
common
Studies also
indicate that black women should strive for the upper end of
the suggested weight gain ranges and would tend to benefit
more than white women from weight gain (Hickey et al. 1993).
Research also indicates that black smokers have higher levels
of
serum
smokers.
cotinine
(a
metabolite
of
nicotine)
than
white
This suggests that cigarette smoking may have a
greater effect on birth weight among blacks than among whites
(English, Eskenazi, Christianson 1994) .
9
It is unclear why race itself is a risk factor.
plausible explanation is
economic
status
which
A
that race often indicates sociomay
influence
nutrition (Collins 1992, Smith 1990).
prenatal
care
and
Black women are likely
to have one or more of the sociodemographic characteristics
associated with low birth weight: being unmarried, young, of
high parity,
or having fewer years of education
(Emanuel,
Hale, Berg 1989).
Education of Mother:
Higher levels of education are also associated with a
.
greater chance for perinatal survival. Education is related to
socio-economic status.
The higher a women's education, the
higher her socio-economic level and the more likely she is to
postpone pregnancy until after her adolescent years and have
fewer children
(Emanuel et al. 1989).
Due to her relative
affluence, she is also more likely to receive proper prenatal
care and adequate nutrition.
Hence,
the higher a
women's
education, the better chance she has for a positive perinatal
outcome.
Women who become pregnant during high school may not
graduate,
and may again become pregnant during adolescence
(Sweeney 1989).
.
After a teenager's first birth, she may be
labeled by society as being dependent on welfare and having
little chance
to succeed,
hence a
second pregnancy for a
10
teenager can be educationally and economically devastating
(Sweeney 1989). Thus women with low education are at higher
risk for a perinatal death.
•
Medical History Factors
Hypertension:
Hypertension during pregnancy has been cited as a major
cause of fetal growth retardation,
preterm delivery
Mancuso
1990,
(Ferrazzani,
Lenox,
Uguru,
perinatal mortality and
Caruso,
De-Carolis,
Cibilis
1990,
Martino,
Wilson
Himmelmann, Svenson, Hansson 1994, Turnbull 1971).
1993,
Full term
gestational age is the primary positive factor for survival in
hypertensive pregnancies
(Derham,
Hawkins,
De-Vries,
Elder
1989) .
However,
the results are inconclusive when chronic
hypertension is considered.
Some studies have shown that
chronic hypertension is not associated with an increased risk
for
poor
fetal
superimposed
outcome,
proteinuric
urinary protein)
unless
it
pre-eclampsia
(Ferrazzani et al.
is
accompanied
(the
1990).
presence
However,
by
of
other
studies have shown that women with chronic hypertension with
or without superimposed pre-eclampsia, have a higher incidence
of perinatal death and intrauterine growth retardation (Rey,
11
Couturier 1994).
Hence,
the
level of proteinuria may be
important when considering chronic hypertension (Lao, Chin,
Panesar, Lam 1989) .
Anemia:
Severe anemia is associated with an increased risk of
premature delivery and low birth weight (Bhargava et al. 1991,
Agarwal, Agarwal, Mishra 1991, Turnbull 1971).
Women with
mild to moderate anemia may experience no change in birth
weight, while mothers with severe anemia may deliver an infant
weighing up to 400 grams less than a mother without anemia.
Some studies have reported anemia as being the most common
complication in black women (Roberts et al. 1994).
Anemia is
also important when considering teenage pregnancies because
75% of pregnant teenagers are anemic,
mostly due to poor
nutrition (Smith 1990) .
Diabetes:
Many studies have reported an increased birth weight
(macrosomia)
Gamble,
Ballard,
1993).
in
Manuel,
Rosenn,
women
with
Towmend,
Khowery,
gestational
Roberts 1993,
diabetes
Amini et al.
Miodovnik 1993,
(Cundy,
1994,
Catalano et al.
Macrosomia is typically defined as an infant weighing
in excess of 4000 grams
(Avery,
Rossi 1994). Diabetics are
12
also more
likely to
deliver prematurely
(Turnbull
1971).
Gestational diabetes is estimated to complicate between 0.15%
and 12.3% of all pregnancies (Avery, Rossi 1994).
Black women
have 3.1 times the relative risk for gestational diabetes than
white women
(Avery,
Rossi
1994).
Women with gestational
diabetes usually have a heavier pregravid weight than women
with a normal glucose tolerance (Catalano et al. 1994, Cundy
et al. 1993).
Gestational diabetes is typically more common
in women with high maternal age or parity (Cundy et al. 1993).
Renal Disease:
"
Parturients with end stage renal disease do not have
a good chance of a positive pregnancy outcome
Cox, Harstad, Mason, Pritchard 1990).
typically experience a
1994) .
They also
have
Renal disease patients
shorter gestation
a
(Cunningham,
(Perry 1994,
tendency to have
Hou
hypertension,
abruptio placenta, and premature rupture of membranes (Perry
1994,
Turnbull
1971).
Chronic
renal
insufficiency
is
associated with impaired fertility and a risk of miscarriage.
The
perinatal
outcome
is
strongly
associated
with
the
development of pre-eclampsia (Vogt, Keusch, Baumann, Bucher,
Binswanger 1989) .
13
Events of Labor
Abruptio Placenta:
Dorland defines abruptio placenta as the premature
detachment
of the placenta
(1994).
Abruptio placenta is
associated with increased perinatal mortality and reduced
birth weight
Turnbull
(Roberts
1971).
The
include higher age,
et
ale
risk
1994,
factors
higher parity,
infants (Morgan et ale 1994).
Morgan
for
et
ale
abruptio
hypertension,
1994,
placenta
and male
Abruptio placenta is reported
to occur in approximately 1% of pregnancies (von-Dadelszen,
Peddie 1990).
Black women with abruptio placenta are more
likely to be hypertensive
(Morgan et ale
1994).
However,
hypertensive pregnancies with abruptio placenta are not more
likely
to
experience
a
perinatal
death
than
those
with
abruptio placenta but not hypertension (Morgan et ale 1994).
Placenta previa:
Dorland defines placenta previa as a placenta which
develops in the lower uterine segment (1994).
Placenta previa
has been reported to be associated with intrauterine growth
retardation and low birth weight
(Jakobovitz,
Zubek 1989).
However, some studies have found that placenta previa is not
an independent
risk factor
for
small
for gestational
age
14
infants when maternal age, parity, pre-pregnancy weight, race,
and fetal gender are considered (Wolf et al. 1991).
Studies
have also shown that male deliveries have a higher incidence
of placenta previa (Jakobivitz, Zubek 1989) .
Cord prolapse:
Cord Prolapse is defined by Dorland as the premature
expulsion of the umbilical cord in labor prior to delivery
(1994) .
Pregnancies complicated by cord prolapse are more
likely to result in a birth weight less than 2500 grams or a
premature delivery (Critchlow et al.
1994).
Cord prolapse
occurs when the umbilical cord descends in advance of the
presenting fetus during labor.
A higher percentage of mothers
who experience cord prolapse had borne three or more children
and reported smoking during pregnancy.
Also,
infants with
cord prolapse were likely to be male (Critchlow et al.1994) .
Premature Rupture of Membranes:
Premature rupture of membranes
with
preterm
delivery
(Morales,
Sanchez-Ramos, Benrubi 1989).
(PROM)
Talley
is associated
1993,
Johnston,
PROM at less than 25 weeks was
associated with a relatively high risk of perinatal mortality
and neonatal long term morbidity.
The risk of infection after
PROM is increased (Morales, Talley 1993).
However, PROM after
.
15
34 weeks usually results in survival of the infant (Johnston
et al. 1989).
PROM is more common in males (Johnston et al.
1989) .
The
above
literature
search
was
used
to
further
understanding of the factors affecting perinatal outcome and
to select the variables that would be important to include in
the
research.
Furthermore
it
provided
comparison for the intended analyses.
a
standard
for
16
Chapter Three
•
METHODS
As
seen
by
the
literature
review,
many
of
the
variables which perinatal researchers have available for study
are discrete and many are not normally distributed.
variables
are
not
normally
distributed,
there
are
If the
three
alternatives (Turnbull 1971, p.43):
1. use statistical methods for normal variables, but
consider the estimates obtained as approximations;
the strength of the approximation varying with the
divergence from normality;
2. transform the variables to obtain normality so
normal theory can be used; or
3. utilize methods for which normality is not assumed.
The above factors are important to consider when one selects
a method for statistical analysis.
Based on the review of the literature and the results
of Turnbull's dissertation (1971), a list of variables were
selected for inclusion in the analyses.
It was decided to
select a relatively small number of variables for study so
that the dataset requested from the SCHES for 1988-1991 would
be of a size reasonable to work with, given the time available
to conduct this research.
The variables chosen were: sex of
17
the offspring, age of the mother,
total pregnancies
•
anemia,
(including present),
diabetes,
hypertension,
education of the mother,
hypertensive
abruptio placenta,
separate
analyses
were
analysis
would concern the
race of the mother,
pregnancy,
chronic
and cord prolapse.
tentatively
effects
planned.
of
five
The
Three
first
"demographic"
factors on perinatal mortality (viz., sex (male or female),
maternal age (less than 18, 18-24, 25-34, or 35 and older),
maternal
education
pregnancies
«9
years,
9-12,
>12
years),
total
(primiparous or multiparous), and maternal race
(white or nonwhite).
five
The second analysis would explore the
effects
of
medical
factors
chronic
hypertension,
disease)
on perinatal mortality.
(viz.,
hypertensive
anemia,
pregnancy,
diabetes,
and
renal
The third analysis would
consider the effects of two events of labor (viz., abruptio
placenta, and cord prolapse).
We requested counts for 1988-
1991 for each of the above variables for live births, fetal
deaths, neonatal deaths, fetal plus neonatal deaths (perinatal
deaths),
birth weight,
and gestational age from the SCHES.
Birth weight was to be subdivided into less than or equal to
1500 grams,
1501 through 2500 grams,
equal to 2501 grams.
and
greater than or
Gestational age was to be subdivided
into less than 37 weeks and greater than or equal to 37 weeks.
Hence, counts for 1988-1991 for 96 demographic groups,
32 medical history groups, and 4 labor groups were desired in
one dataset from SCHES (See Appendix I and Attachment A, B, C,
18
and D).
These data were requested in the form of a SAS
dataset on computer diskette.
would
prepare
such
a
The SCHES indicated that it
data
set.
However,
due
to
the
unanticipated illness of a key SCHES staff member, the SCHES
was not able to prepare the requested dataset in enough time
to employ it for this research.
It is anticipated that this
dataset may be used during a 1995 summer internship with the
SCHES.
It was then necessary to locate an alternative dataset
that would fulfill the objectives of my Honors Research; viz,
learning about
indicators of perinatal mortality and also
analyzing
type
this
of
data.
The
dataset
that
Turnbull
employed for his dissertation was used to accomplish these
goals since it had served as the impetus to gain more current
information on variables
that
had been researched in the
literature review.
Turnbull's dataset contained information about single
live
births
and
perinatal
deaths
of
20
or
more
weeks
gestation, as determined by means of a gestation calculator,
which occurred at the MacDonald House, the teaching hospital
of Case Western Reserve,
(Turnbull 171, p. 46).
direction
of
Charles
Cleveland, Ohio from 1962 to 1969
Data collection began in 1962 under the
H.
Hendricks,
former
Professor
and
Chairman of Obstetrics and Gynecology at the University of
North Carolina at Chapel Hill, North Carolina.
of Hendricks' investigation were:
The objectives
..
19
1. to define factors which influence maternal, fetal,
and neonatal morbidity and mortality; and
2. to determine the effects of courses of obstetrical
management on perinatal outcome.
The obstetrician in charge of each delivery completed the
obstetrical records; a coder (usually a physician), abstracted
the data onto precoded forms.
directly onto IBM cards,
The precoded data were punched
edited for
internal consistency,
processed and stored on computer facilities at the University
of
North Carolina at
Chapel Hill,
North Carolina.
This
dataset contains demographic variables, as well as variables
•
relevant to the medical and obstetric history of the mother
(Turnbull 1971, p.46).
The
dataset
contained
27,421
perinatal period and 932 perinatal deaths.
survivors
of
the
The variables that
Turnbull chose for his analyses were sex, age, maternal race,
parity,
and hospital service.
These demographic variables
were analyzed to determine which deliveries were at relatively
high risk of perinatal mortality.
These data would facilitate
learning how to use the method of logistic regression and
interpreting odds ratios.
The dataset was divided into 64 subgroups based on the
levels of
the five demographic variables.
subgroups of males,
women,
primiparous
patients.
There were 32
females, whites, nonwhites, multiparous
women,
staff
patients,
and
private
There were 16 subgroups for each of four levels of
20
maternal age.
female.
white.
The variable sex was coded 1 for male and 0 for
Maternal race was coded 1 for nonwhite and 0 for
Maternal age was categorized into four groups: women
less than 20 years of age, those 20 to 24 years of age, those
25 to 29 years of age,
and those 30 years of age or more.
Each category was coded separately as a 1 if the woman was in
the age group of interest and 0 if she was in any other age
group.
Parity was coded 1 for multiparous women and 0 for
primiparous women.
Hospital service was coded 1 for staff
service, and 0 for private service.
Notice that each of the
"1" codes were assigned to groups that Turnbull's dissertation
identified as having higher risk.
One of the goals of this
research was to quantify how much higher the risk was for a
particular group, given that a dichotomous outcome variable
(perinatal death or survival) was of interest.
After completing the review of the literature and
becoming familiar with the dataset, the specific purpose of
the study was formulated.
The primary obj ectives of this
research were:
1. To research selected factors associated with
perinatal mortality.
2. To employ the technique of logistic regression as a
means for analyzing a public health problem.
The
MacDonald
House
dataset
allowed
the
opportunity
for
practical experience in reasoning through an analysis of a
public health problem with a dichotomous response variable as
~
21
well as using a current state-of-the-art computer software
package,
viz,
dissertation
SAS/Logistic
provided
a
(SAS
1995).
parsimonious
Turnbull's
description
MacDonald House data by identifying risk factors.
of
the
This study
quantifies these differences between the levels of selected
variables by answering the question how much higher is the
risk for one group compared to the risk of another group.
In order to accomplish the desired objectives,
technique of logistic regression was employed.
The procedure
of
the
logistic
regression
is
appropriate
since
response
variable (Y) is dichotomous and normality is not assumed.
this
study,
survival.
.
the
the
In
response variable was perinatal death or
The mean of this dichotomous response variable is
p, the proportion of the times that a perinatal outcome takes
the value 1,
(ie., the proportion of perinatal deaths).
The
probability that a perinatal death occurred is expressed as
p = P(Y = 1).
A simple, linear model such as p = a +
~iXi
is
not appropriate because p should be such that 0 s p s I and
the above simple linear model does not restrict p to the
required range.
Therefore, the logistic model:
p = exp (a + ~iXi) / [1 + exp (a + ~iXi)]
is used because it restricts p to the appropriate range.
formula: p/l-p = exp(a +
Furthermore, In[p/l-p]
~iXi)
= a
The
provides the odds of success.
+ ~iXi is equivalent to fitting a
linear model where a continuous outcome Y has been replaced by
the log odds of success.
The relationship between In[p/l-p]
22
and Xi is linear, but the relationship between p and Xi is not
In order to obtain estimates of the log odds,
linear.
fits
the
likelihood
equation
In [p/1-p]
estimation
via
one
= a + biX i by using maximum
SAS/Logistic
(SAS
1994).
The
estimate of the intercept is a and b is the estimate of the
slope.
This technique can be extended to multiple explanatory
variables by using the formula: In [p/1-p]
=
where
variables
the
Xi'S
are
continuous
random
a + b1x1 + b 2 x 2
•••
(Pagano,
Gauvreau 1993, Ch. 20).
Logistic regression can also be used when not only the
response variable is dichotomous but the explanatory variables
are dichotomous as well.
The same formulas are used,
the
difference is that the estimated coefficient b i has a special
interpretation
(Pagano,
Gauvreau 1993,
Ch.
20).
By taking
exp(b i ) the estimated odds ratio of the response for the two
levels of Xi is obtained.
The odds ratio can also be obtained from a two by two
contingency table.
For example, from the table:
Independent Variable:
Exposed I Not exposed
-------' ---------1-------------
I
Perinatal Death:
-==~---I----~----I------~------
-~~----I----~---------~-----I A+C
B+D
Total
Total
A+B
C+D
A+B+C+D
where A,B,C,D are counts, the odds ratio can be calculated as
AD/BC.
...
23
A
•
confidence
interval
for
the
estimate
of
the
parameter b i can be obtained by using the formula:
(b i ± z(se(b i ».
Note that for this study we used z-values
(standard normal deviates) because the sample sizes for the
effects of interest were large.
interval for the odds ratio,
To determine the confidence
one calculates e C where c
is
either the lower or upper confidence limit for the parameter
estimate.
If the interval for the odds ratio contains the
value 1, one would fail to reject the tested hypothesis that
the
levels
of
the
variable
are
not
different,. given
the
appropriate critical value.
Since the logistic model used was for the perinatal
•
rates of the 64 subgroups and not each individual observation,
•
confidence intervals had to be calculated by hand, employing
the estimates of
(Note:
unless
~
and
the
se(~)
model
from the SAS/Logistic output.
is
run
for
each
individual
observation, the standard SAS/Logistic output does not include
confidence intervals for the parameter estimates and there is
no option available to request them).
The
methods
of
forward
selection
and
backward
elimination for determining the best regression equation can
be applied to logistic regression.
has options
d~signed
The software package SAS
to conduct these tests (SAS 1995).
The first step in forward selection is to select as
the
first
highly
variable to enter the model
correlated
with
the
dependent
that variable most
variable,
that
is
24
calculate
r yx1 ,
r yx2 ,
,rYXN •
Then
fit
the
regression equation by using the variable
associated
selected.
The
second step is to calculate the partial F statistic associated
with each remaining variable.
This F statistic is based on a
regression equation containing each remaining variable and the
variable selected in step one,
F(X2
I
I
Xl), F(X3
(ie. calculate partial
Xl), ... , F(XN
I
Xl)).
Step three is to
choose that variable with the largest partial F statistic.
Step
four
is
to
test
for
significance
of
the
partial
F
statistic of the variable chosen in step three. "If the test
is significant then the variable is added to the regression
equation; if it is not significant, use the model selected in
step one.
Each time the partial F statistic is significant,
determine the partial F statistic for the remaining variables
not in the model and add to the model the variable with the
largest partial F value,
if it is significant.
When the
largest partial F value is not significant, no more variables
are included in the model and the process is terminated.
The technique of backward elimination is similar; but,
rather than deciding whether a variable should be added to the
model, this method determines if a variable should be removed
from the model.
First,
the regression equation with all
independent variables is fit.
calculated
variable
to
determines
for
Then the partial F statistic is
each variable
enter
whether
the
the
model.
as
though it
This
addition
of
were
partial
the
the
last
F
statistic
last
variable
•
25
significantly helps in predicting the dependent variable given
that
..
the
other variables
are
already in the model.
The
variable with the smallest partial F value is selected and
compared to
the
critical value.
smallest partial
F is
If
the
p-value
for
less than the critical value it
the
is
removed from the model and the regression equation is refitted
for
the
remaining
variables.
The
process
continues
by
determining partial F statistics for the variables that remain
in the model.
When all partial F values are greater than the
critical value,
the process is complete and the' regression
equation used to calculate the partial F statistics is kept.
Backward elimination and forward selection may lead to
the
"
same
model.
However,
this
Forward selection may underfit
selection can overfit the model.
use
backward
elimination
does
the
not
model,
always
happen.
while
backward
Hence many authors prefer to
since
it
is
more
(Kleinbaum, Kupper, Muller 1988, p.43, 327).
conservative
The purpose of
using backward elimination in this research was to produce a
parsimonious model in an attempt to replicate the previous
research done by Turnbull.
When conducting several tests,
each at the same a
level, the probability of incorrectly rejecting at least one
hypothesis is larger than a.
the
number
of
tests
This probability increases as
increases.
When
using
the
forward
selection or backward elimination technique, multiple tests of
hypotheses are made.
In fact,
there is a potential test of
26
hypothesis for each independent variable in the model.
One
solution to this problem of multiple comparisons for a given
datset
is
to
correction.
rej ecting
employ
a
procedure
such
as
the
Bonferroni
If there are k tests to be made, then instead of
the
hypotheses
at
rejected at the a/k level.
probability
of
incorrectly
the
a
level,
hypotheses
are
This procedure insures that the
rejecting
at
least
one
true
hypothesis is at most a.
•
27
CHAPTER 4
ANALYSES AND RESULTS
The first stage of analyses utilized SAS/Univariate to
generate
perinatal
mortality
deliveries (SAS 1995).
1).
rates
(PMR)
per
thousand
The overall PMR was 33.99 (see Table
Nonwhite mothers had the highest
whites had the lowest PMR of 23.16.
PMR of 46.96,
while
Hospital Service also had
a large difference in the PMR between the two levels; staff
•
patients had a PMR of 46.80, while private patients had a PMR
of 23.78.
Mothers aged less than 20 had a PMR of 41.69, the
highest of the four age categories.
Maternal age 30 or more
had a PMR of 32.26; whereas mothers between the ages of 20 to
24 had a PMR of 31.52,
and mothers aged 25 to 29 had the
lowest PMR of the four age groups of 27.87.
a
PMR of 37.44,
Male infants had
while female infants had a
PMR of 30.35.
Multiparous women showed a PMR of 35.83, but primiparous women
had a PMR of 30.34.
These PMR's indicate which groups are at
higher risk, as seen in Turnbull's dissertation .
•
•
28
TABLE 1
Perinatal Mortality Rates per Thousand Deliveries
PMR
Group
Variable
====================================
Race
Nonwhite
White
46.96
23.16
Hospital
Service
Staff
Private
46.80
23.78
Maternal
Age
< 20
20-24
25-29
> 30
41. 69
31. 52
27.87
32.26
Sex
Males
Females
37.44
30.55
Parity
Multiparous
Primiparous
35.83
30.34
====================================
Total
33.99
====================================
•
In order to quantify the risks identified by Turnbull,
odds ratios were calculated (1971). A separate model for each
variable (ie., sex, race, service, parity, maternal age less
than 20, maternal age 20 to 24, maternal age 25 to 29, and
maternal age 30 or more) was run to determine the unadjusted
odds ratios.
An odds ratio greater than 1 signifies increased
risk, while an odds ratio less than 1 indicates decreased
risk.
If
a
95%
confidence
interval
for
the odds
ratio
contains the value of 1, then one would fail to reject the
hypothesis that risk was the same for the different levels of
a variable (at the a=0.05 level).
If the interval does not
contain the value one, then the hypothesis that risk is the
same in the different levels can be rejected (at a=0.05) .
It
29
Race had the highest odds ratio (2.03), which means
that the risk for whites and nonwhites showed the greatest
discrepancy.
The risk for nonwhites was more than 2 times
•
greater than that of whites.
The 95% confidence interval for
maternal race was 1.77 to 2.32, so the hypothesis that whites
and nonwhites have the same risk was rejected (a=0.05).
Hospital service also had a
ratio (1.97).
relatively large odds
Thus, women seeking private care had a better
chance for a positive outcome than those women who received
staff care.
For hospital service, the 95% confidence interval
was 1.72 to 2.25, and hence the hypothesis that private and
•
staff patients have the same risk is rejected (a=0.05) .
The odds ratio for sex (1.23) indicates that males are
23% more likely than females to experience a perinatal death.
The 95% confidence interval for this estimate was 1.08 to
1.41.
a
Since this interval does not include the value of 1.0,
hypothesis
that
males
and
females
have
equal
risk
is
rejected (a=0.05) and one can claim that the males do have a
higher risk than females for a perinatal mortality.
Multiparous
women
were
at
a
primiparous women for a perinatal death.
higher
risk
than
The odds ratio was
1.18 with 95% confidence interval of 1.02 to 1.36.
This
interval does not include the value 1 so a hypothesis of equal
risk is rejected (a=0.05).
•
Maternal age is also an important factor relating to
perinatal outcome.
The youngest mothers, those less than 20,
30
had an odds ratio of 1.28 when compared to the other three age
groups.
The 95% confidence interval for mothers aged less
than 20 was 1.08 to 1.51, and so the hypothesis that mothers
aged less than 20 had the same risk when compared to the other
three age groups is rejected
(~=0.05).
Similarly, those mothers in the oldest age group (ie.
those
aged
30
and over)
had an odds
compared to the other age groups.
ratio of
1.22
when
The hypothesis that these
mothers had the same risk as compared to the other three age
groups is rejected
(at
~=o.
because the 95%· confidence
05)
interval (1.06, 1.42) does not contain the value 1.
Mothers
aged 20 to 24 had an odds ratio of 0.90 when compared to the
other
three
age
groups.
This
suggests
that
risk
of
a
perinatal death for mothers in this age group is less than in
the other three groups.
However, the 95% confidence interval
for this age group was 0.78 to 1.04.
contains the value 1,
Since the interval
the odds ratio is not deemed to be
statistically different from 1 and so one would fail to reject
the hypothesis (at
~=0.05)
that mothers aged 20 to 24 have any
more or any less risk than mothers in the other three age
groups.
Mothers in the age group 25 to 29, had an odds ratio
of O. 76 when compared to the other three age groups.
the 95% confidence interval was 0.66 to 0.90.
does
not
contain
the
value
1,
so
the
However,
This interval
odds
ratio
is
significantly different from 1; and these mothers experience
•.
31
a decreased risk for perinatal mortality (ie., the appropriate
hypothesis is rejected at a=O.OS) .
Turnbull's
•
nonwhites,
staff
dissertation
patients,
identified
and multiparous
increased risk of a perinatal death.
linear
and
quadratic
effects
of
that
males,
women were
at
(Note: Turnbull examined
maternal
age
instead
of
studying the effects of the four maternal age variables.)
These odds ratios confirmed his findings as well as quantified
the amount of increased risk.
Unadjusted Odds Ratios for Independent Variables
TABLE 2
•
(See Table 2) .
95% Confidence Interval
I
Odds Ratio
Lower Limit Upper Limit
==================== =======================================
Independent Variable
Race
(nonwhite to white)
Hospital Service
(staff to private)
Sex
(males to females)
Parity
(multip. to primip.)
Maternal age < 20
(to all other ages)
Maternal age 20-24
(to all other ages)
Maternal age 25-29
(to all other ages)
Maternal age > 30
(to all other ages)
I
I
I
2.03
1.77
2.32
1.97
1.72
2.25
1.23
1.08
1.41
1.18
1.02
1.36
1.28
1.08
1.51
0.90
0.78
1.04
0.76
0.66
0.89
1.22
1.06
1.42
I
============================================================
The next phase of analysis was to describe the data
via a
•
logistic regression model.
elimination was used.
with
sex,
race,
The method of backward
The first impulse was to fit a model
service,
parity
and
maternal
age
as
32
independent variables, and an intercept.
But, if maternal age
was treated as four different dichotomous variables,
this
would have resulted in a redundant or singular model.
This
means that some of the estimates of the
combinations of the others.
~'s
would be linear
To avoid singularity,
decided to use reference cell coding.
it was
Hence, maternal age
less than 20, 20 to 24, and 25 to 29 were each coded as a "1"
if
the
woman
otherwise.
was
in
the
specified
group
and
as
a
"0"
Maternal age greater than 30 was the reference
cell for the model.
First,
regression
SAS/Logistic
model,
given
was
used
that
perinatal death or survival.
the
to
produce
response
a
logistic
variable
was
The criteria for exclusion from
the model was set at a=O for the backward elimination option.
This allowed all variables to be excluded from the model.
Parity had the largest p-value (0.1862), and no other variable
had a p-value higher than 0.0257.
It was then decided to
repeat the backward selection with a=0.05, since this is the
default selection in SAS.
from the model.
As expected, parity was removed
Table 3 shows the parameter estimates and p-
values from this procedure, given that parity was removed from
the model.
.,
33
Parameter Estimates from Backward Elimination
With Parity Excluded
TABLE 3
Independent
Variable
Adjusted
Odds
Ratio
I Estimate
Parameter I
p-value
============1========== ==========
95% Confidence
Interval
Lower
Upper
Limit
Limit
========= ===============
Race
I
0.45
0.0001
1. 56
1. 25
1. 95
Service
I
0.39
0.0006
1.48
1.18
1. 86
0.21
0.0015
1.24
1. 08
1.41
Age < 20
-0.36
0.0008
0.70
0.57
0.86
Age 20-24
-0.39
0.0001
0.68
0.57
0.81
Age 25-29
-0.34
0.0002
0.71
0.59
0.85
I
Sex
,
============================================================
*Note the odds ratios are defined as: males to females,
nonwhite to white, private to staff, and maternal age to
maternal age 30 or more.
SAS/Logistic produced adjusted odds ratios for the
variables remaining in the model.
race
is
interpreted as:
The adjusted odds ratio for
how much higher
is
the
risk
for
nonwhites versus the risk for whites given that parity has
been removed from the model and all of the other variables are
held constant.
Table 3 shows these adjusted odds ratios, for
the model without parity,
and their respective confidence
intervals.
The
adjusted odds
ratio for
race was
the largest
(1.56) and the 95% confidence interval was 1.25 to 1.95, so
•
the hypothesis that whites and nonwhites have equal risk was
rejected at the a=0.05 level (Table 3).
The unadjusted odds
34
ratio for race was 2.03 (See Table 2).
This implies that the
relationship between race and perinatal mortality is somewhat
distorted by the effects of parity.
The adjusted odds ratio
for hospital service remains the second highest
95% confidence interval is 1.18 to 1.86,
(1.48); the
so the hypothesis
that private and staff patients have the same risk is rejected
The adjusted odds ratios for each of the three
(a=0.05) .
maternal age groups is less than 1,
and each of their 95%
confidence intervals do not contain 1.
Hence each of the
three hypotheses that mothers in the specified age group have
the same risk as the mothers aged 30 or more are rejected
(each at a=0.05).
The
possible
final
"
stage
first-order
variables in the model.
the model
of
analyses
interactions
was
to
between
include
all
independent
Backward elimination was used to fit
(again, maternal age 30 or more was the reference
cell for these comparisons).
Table 4 shows the parameter
estimates, odds ratios, and confidence intervals for the model
selected, given that the SAS/Logistic default critical value
of a=0.05 was employed.
-,
35
TABLE 4 Estimates from Backward Elimination with Interactions
Independent
Variable
I
I
Adjusted
Odds
Ratio
parameter,'
Estimate
p-value
95% Confidence
Interval
Lower
Upper
Limit
Limit
I
--------- --------------============1'==========1========== ---------,---------------
Race
0.31
Sex
0.21
0.0441
I
0.0014
:::: I :::: ::::
Age 20-24
-0.37
0.0001
0.69
0.58
0.82
Age 25-29
-0.34
0.0002
0.71
0.60
0.85
Race*Age<20
-0.38
0.0007
0.69
0.55
0.85
0.52
0.0006
1. 69
1.25
2.29
Race*servicel
I
============================================================
*Note: the odds ratios are defined as: males to females,
maternal age to maternal age 30 or more, nonwhite staff to
white private, and nonwhite age less than 20 to white age more
than 20.
*There were 18 possible interactions: Sex and race, sex and
parity, sex and service, sex and age < 20, sex and age 2024, sex and age 25-29, race and parity, race and service,
race and age <20, race and age 20-24, race and age 25-29,
parity and service, parity and age < 20, parity and age 2024, parity and age 25-29, service and age < 20, service and
age 20-24, and service and age 25-29.
Sex, race, maternal age 20 to 24, maternal age 25 to
29,
the
interaction
between
race
and
service,
and
the
interaction between race and maternal age less than 20 were
deemed to be statistically important factors with respect to
perinatal mortality.
•
As expected, the main effect of parity
was deleted by the backward elimination process
from the
model .
possible
Also
of
interest
was
that
of
the
18
interactions, only the interactions between race and service,
and race and maternal age less than 20 remained in the model.
36
The
highest
interaction between
adjusted
odds
ratio
(1.
race
69) .
and
service
The
95%
had
the
confidence
interval was 1.25 to 2.28, so the interaction is significant
(at
the a=O. 05
level).
The
interaction between race and
maternal age less than 20 had an adjusted odds ratio of 0.69
and
a
95%
confidence
interval
interaction is also significant.
of
0 . 55
to
0 . 85
so
this
37
CHAPTER 5
SUGGESTIONS FOR FUTURE RESEARCH
Further possibilities exist for analyses of Turnbull's
dataset.
The dataset contained information on birth weight
and gestational age.
These variables could be analyzed as
discrete response variables in the same manner as employed for
perinatal mortality.
Gestational age could be analyzed using
logistic regression because it could be categorized into 2
•
levels: those babies born prematurely, (ie., before 37 weeks),
and those born at or after term, (ie., those born at 37 weeks
•
gestation or more).
Birth weight could also be analyzed as a
categorical variable (ie., with 1000 gram categories), or as
a dichotomous variable (s 2500 grams or
~
2501 grams) .
The data that was originally planned for this study
from the SCHES for 1988-1991 could be analyzed using the same
techniques as were used on this MacDonald House dataset, in
fact,
this project may be undertaken this summer during an
internship with SCHES.
The data files from SCHES contain many
more variables than were examined here.
In addition, it would
be interesting to conduct analyses on some of the medical and
obstetric factors.
In order to more fully describe the factors related to
perinatal mortality, one could study higher order interactions
38
in addition to the first order interactions examined here;
since the seHES dataset for 1988-1991 is almost four times as
large as the MacDonald House dataset, such an analyses would
be feasible.
One could also explore residual analyses in
order to study if the residuals suggest that the assumptions
made are incorrect.
Another possibility would be to use poisson, rather
than logistic, regression.
used for modeling rare
Poisson regression is typically
events.
The response variable
poisson regression is a count of events.
for
Each of the above
mentioned datasets contain counts of deaths and survivors in
each subgroup, and hence poisson regression could be used.
39
APPENDIX I -Fax to SCHES requesting the data file
501 NC Highway 54
Royal Park M-11
Carrborro, NC 27510
(919-932-5478)
December 7,1994
NC DEHNR-Statistics
P.O. Box 29538
Raleigh, NC 27626
Dear Sirs,
As you know, we have been in communication with Mr. Delton
Atkinson about my Honors Research Project which will analyze
North Carolina's fetal, neonatal, and perinatal'deaths, as
well as its live births for 1988 through 1991.
I am writing to request a SAS dataset from the North Carolina
Vital Statistics Data Files. We would like this dataset for
singleton, NC residents only. The following is a listing of
variables which we intend to study. [Note: I have listed them
in the order that they are found in your Code Book.]
Sex
Age of Mother
Education of mother
Total pregnancies (including present)
Race of mother
Medical history for this pregnancy:
Anemia
Diabetes
Hypertension, chronic
Hypertension, preg.
Renal disease
Events of labor:
Abruptio placenta
Cord Prolapse
We plan to conduct three analyses. The first analysis concerns
the effects of five "demographic" factors on fetal, neonatal,
and perinatal mortality (viz., sex, maternal age, maternal
education, total pregnancies -including the current--, and
maternal race). The second analysis deals with the effects of
the five medical factors listed above; and, the third analysis
concerns the effects of the two events of labor listed above.
40
For each of these three groups, we would like the counts of
women in each
variable category for:
live births
fetal deaths
neonatal deaths
fetal plus neonatal deaths
birth weight:
<1500 g
1501 through 2500 g
> 2501 g
gestational age:
< 37 weeks
>= 37 weeks
Your Code Book lists:
1."Sex" as 1 if male, 2 if female; this is fine,
2."Maternal age" as 10 through 55 and 99; please
recode this variable as follows:
o if <18
1 if 18 through 24
2 if 25 through 34
3 if 35 or more
3. "Total pregnancies" (including the current
pregnancy) as: 1 through 25, and 99;
please recode this as:
1 if 01
2 if 02 through 25
4. "Maternal Race" as 0-8, and 9;
please recode this as:
o if 1 (white)
1 if 2 through 8
Note: I observed that your coding for this
variable changed between 1989 and 1990.
However, this will not affect our
interests since white remains a "1" in
both methods.
5. "Anemia", "Hypertension chronic",
"Hypertension, preg.", "Diabetes",
"Abruptio placenta", and "Cord Prolapse" are each
coded as:
o if not present, 1 if present, and 9 if
unknown; just delete the unknown code for each.
(Call them: an, hc, hp, db, ap, and cp)
Hence, we are requesting the above mentioned counts for 96
"demographic" groups (see Attac:hment A), 32 "medical history"
groups
(see Attachment B), and 4 "labor" groups
(see
Attachment C). We have constructed Attachment D as a
synopsis of the type of SAS dataset we'd like you to prepare.
41
If you have any questions please feel free to contact me or
Dr. Craig Turnbull (919-966-7259). After December 16, I can
be reached in Raleigh at (919-787-6159).
I look forward to
working with you on this research as well as to meeting you
soon.
Thank you,
Jennifer Bourbina
cc. Dr. Turnbull
,
•
42
Attachment A
Analysis 1
Maternal Age
18-24 I 25-34
Race
35+1 W I NW
Sex
M I F
I
Education
I
Parity
<9 I 9-11 I 12+
P I M
===========================================================================
Group# 1<18
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
I
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
I
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
I
X
X
..
X
X
X
X
X
X
X
X
X
X
X
X
X
X
,
43
Attachment A Continued
•
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
•
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
44
Attachment
B
Analysis 2
Group
#1
Hypert.
chronic
I
Hypert.
pregnancy
Anemia
Renal
Disease
Diabetes
Y
Y
Y
Y
Y
I N
I N
I N
I N
I N
============================================================================
97
X
X
X
X
X
98
X
X
X
X
X
99
X
X
X
X
X
100
X
X
X
X
X
101
X
X
X
X
X
102
X
X
X
X
X
103
X
X
X
X
X
104
X
X
X
X
X
105
X
X
X
X
X
106
X
X
X
X
X
107
X
X
X
X
X
108
X
X
X
X
X
109
X
X
X
X
X
110
X
X
X
X
X
111
X
X
X
X
X
112
X
X
X
X
X
113
X
X
X
X
X
114
X
X
X
X
X
115
X
X
X
X
X
116
X
X
X
X
X
117
X
X
X
X
X
118
X
X
X
X
X
119
X
X
X
X
X
120
X
X
X
X
X
121
X
X
X
X
X
122
X
X
X
X
X
123
X
X
X
X
X
124
X
X
X
X
X
125
X
X
X
X
X
126
X
X
X
X
X
127
X
X
X
X
X
128
X
X
X
X
X
.
45
Attachment C
Analysis 3
Group#
I
Abruptio
Placenta
Yes
I No
I
Cord
Prolapse
Yes
I No
=============================================
129
x
X
130
X
X
X
X
131
X
X
132
..
'
46
Attachment D
Group #
No. of
Live
Births
No. of
Fetal
Deaths
No. of
Fetal +
Neonatal
Deaths
Birth Weight
<15°°11501-25°°12501+
I
Gestational
Age
<37 1 37+ 1
=========================================================================
1
2
3
4
5
count
count
count
count
count
count
count count count
96
97
128
129
132
.
47
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