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Environmental variables as potential modifiable risk factors of preterm birth in Philadelphia, PA
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Environmental variables as potential modifiable risk factors of preterm birth in Philadelphia, PA Jamie A. Bastek, MD, MSCE; Mary D. Sammel, ScD; Tara D. Jackson, PhD; Meghan E. Ryan, BA; Meghan A. McShea, BS; Michal A. Elovitz, MD OBJECTIVE: To examine whether variation in neighborhood context
is associated with preterm birth (PTB) outcomes and gestational age (GA) at delivery in Philadelphia, and to determine whether these associations might persist when considering relevant individual-level variables. STUDY DESIGN: We analyzed individual-level data collected for a
prospective cohort study of singleton pregnancies with preterm labor. We merged block-group level data to each individual’s home address. Unadjusted analyses were performed to determine the association between block-group variables and individual-level outcomes. Block-group variables identified as potential risk factors were
incorporated into multivariable individual-level models to determine significance. RESULTS: We analyzed data for 817 women. The prevalence of PTB
<37 weeks was 41.5%. Although in unadjusted analyses several block-group variables were associated with PTB and GA at delivery, none retained significance in individual-level multivariable models. CONCLUSION: Block-group level data were not associated with PTB outcomes or GA at delivery in Philadelphia.
Key words: census block-group, environment, Philadelphia, preterm birth
Cite this article as: Bastek JA, Sammel MD, Jackson TD, et al. Environmental variables as potential modifiable risk factors of preterm birth in Philadelphia, PA. Am J Obstet Gynecol 2015;212:236.e1-10.
P
reterm birth (PTB) is a leading cause of perinatal morbidity and mortality. Well-recognized risk factors for PTB include obstetric history, maternal age, race, and socioeconomic status. However, the degree to which PTB risk might be affected by modifiable risk factors such as an individual’s environment, compared with nonmodifiable risk factors such as an individual’s genetics, remains unknown. Evidence from single-nucleotide polymorphism
and genome-wide association studies have demonstrated significant but only nominally increased risk for PTB with specific allelic variations,1-6 suggesting a lack of evidence regarding a strong genetic causation in most cases of PTB. Other evidence suggests that an individual’s environment must play some role in PTB risk: PTB rates among African-American (18.4%)7 and European-American (11.7%)7 women are higher than PTB rates among African
(11.9%)8 and European (6.2%)8 women, respectively. The concept of possible “neighborhood effects” on health outcomes was introduced over 20 years ago.9 Since that time, there have been data from the nonobstetric literature to suggest an association between an individual’s geographic environmentespecifically, one’s neighborhoodeand chronic disease,10 mental health outcomes,10 cardiovascular mortality,11 serum cortisol
From the Maternal and Child Health Research Program, Department of Obstetrics and Gynecology (Drs Bastek and Elovitz, Ms Ryan, and Ms McShea), Center for Research on Reproduction and Women’s Health, and Department of Biostatistics and Epidemiology (Dr Sammel) and Cartographic Modeling Lab (Dr Jackson), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA. Received March 3, 2014; revised July 18, 2014; accepted Aug. 25, 2014. The authors report no conflict of interest. The material herein represents the research and conclusions of the authors and does not necessarily reflect the views of the Eunice Kennedy Shriver National Institute of Child Health and Human Development. Financial support for this project was provided by March of Dimes grant number 21-FY08-539 (principal investigator: Elovitz). Drs Bastek and Sammel were supported by grant number K12HD001265 (principal investigator: Driscoll; Scholar: Bastek) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development. Presented in poster format at the 33rd annual meeting of the Society for Maternal-Fetal Medicine, San Francisco, CA, Feb. 11-16, 2013, and in poster format at the Women’s Reproductive Health Research Symposium, Department of Obstetrics and Gynecology, Hospital of the University of Pennsylvania, Philadelphia, PA, Oct. 11-12, 2012. Corresponding author: Jamie A. Bastek, MD, MSCE. [email protected] 0002-9378/$36.00 ª 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ajog.2014.08.025
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levels,12 childhood obesity,13 and gynecologic disease. 14-16 To date, however, there has been relatively little literature exploring the potential association between geographic environmente specifically, maternal neighborhoode and PTB. Although several studies have analyzed whether trends in neighborhood characteristics were associated with adverse perinatal outcomes including PTB, these studies were limited by their sole use of population-level data and provide somewhat conflicting results.17-22 Furthermore, they were unable to determine whether the relationship between geographic neighborhood and PTB might be modified by relevant individual-level variables. To that end, our primary objective was to determine whether select geographic environmental variables related to maternal neighborhood are associated with prematurity. Specifically, we studied block-group level administrative (neighborhood crime, structural decline), census (population demographics, social stress, education, and employment), and survey (social capital) data to examine whether variation in individuals’ neighborhood context is associated with PTB outcomes and accounts for variation in gestational age (GA) at delivery in Philadelphia, PA. Our secondary objective was to determine whether any associations identified between environmental variables and PTB and/or GA at delivery might persist when considering relevant individual-level variables.
M ATERIALS
AND
M ETHODS
This study was a planned secondary analysis of data that were collected for a prospective cohort study at a single, urban tertiary care center between April 2009 and May 2012.23-25 The cohort consisted of women with periviablepreterm singleton pregnancies between 22- 33 6/7 weeks GA who presented to the labor and delivery triage unit with complaints concerning for spontaneous PTB, including contractions, cramping, vaginal bleeding, vaginal pressure, leaking vaginal fluid, and abdominal or back pain. For the current study, we included all women from the parent study. Patients
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TABLE 1
Association between individual level and block-group level variables with PTB and GA at delivery P value (PTB)
Variable
P value (GA delivery)
Individual level variables < .001
.002
Prior LEEP/cold knife cone
.32
.64
African-American race
.42
.12
Maternal age
.003
< .001
Prenatal care
< .001
.002
GA first prenatal visit
.90
.38
Body mass index first prenatal visit
.91
.82
Gestational diabetes
.56
.65
Gestational hypertension
.02
.06
Chronic hypertension
.001
.01
Tobacco use
.009
.002
Cocaine use
.02
.02
Properties pending demolition
.61
.07
Properties clean and sealed
.79
.60
Lien sales for delinquent taxes
.66
.87
Vacant lots as listed by board of revision of taxes
.51
.80
Water service shutoffs
.93
.34
Properties demolished
.72
.83
Lien sales for delinquent taxes
.62
.93
Vacant lots as listed by board of revision of taxes
.57
.95
Water service shutoffs
.62
.06
Board of revision of taxes residential sales >$1000
.52
.19
PHS greened lots
.41
.82
Prior PTB
Block-group level variables Vacancy data, 2005
Vacancy data, 2006
Total vacancy rate
.57
.04
Residential vacancy rate
.54
.07
Commercial vacancy rate
.13
.19
Properties demolished
.13
.23
Properties clean and sealed
.99
.85
Lien sales for delinquent taxes
.76
.75
Water service shutoffs
.78
.39
Vacancy data, 2007
Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
(continued)
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TABLE 1
Association between individual level and block-group level variables with PTB and GA at delivery (continued) Variable
P value (PTB)
P value (GA delivery)
Vacancy data, 2009 Vacant lots as listed by board of revision of taxes, 2009
.45
.83
Board of revision of taxes residential sales >$1000
.49
.36
Total vacancy rate
.28
.01
Residential vacancy rate
.46
.04
Commercial vacancy rate
.41
.47
Willful killing
.91
.86
Aggravated assaults
.56
.87
Crime data, 2006
Burglaries
.49
.60
Burglaries and all thefts
.11
.15
Arsons
.63
.98
Other assaults
.11
.04
Transporting/receiving stolen goods
.41
.66
Vandalism and criminal mischief
.05
.18
Weapons violations of uniform firearms act
.53
.53
Prostitution
.57
.31
Sex offenses
.31
.20
All narcotics arrests
.44
.75
Disorderly conduct
.87
.79
Loitering and prowling
.64
.68
Illegal dumping
.86
.85
Crime data, 2009 Willful killing
.87
.63
Aggravated assaults
.97
.86
Burglaries
.89
.62
Burglaries and all thefts
.40
.12
Arsons
.71
.93
Other assaults
.27
.23
Transporting/receiving stolen goods
.23
.29
Vandalism and criminal mischief
.24
.73
Weapons violations of uniform firearms act
.94
.79
Prostitution
.67
.90
Sex offenses
.33
.80
All narcotics arrests
.99
.85
Disorderly conduct
.37
.32
Loitering and prowling
.29
.17
Illegal dumping
.26
.19
Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
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(continued)
were excluded from the parent study and thus from these analyses for multiplegestation, major fetal anomaly, intrauterine fetal demise, severe preeclampsia before enrollment, chronic steroid or immunosuppressive drug use, active immunologic disease, acute systemic febrile illness, and/or pregestational diabetes. Patients with unknown delivery information and/or whose home address was outside Philadelphia, PA, were also excluded from these analyses. Patients were enrolled in the study by trained clinical research coordinators who obtained informed consent at the time of enrollment. The clinical research coordinators enrolled consecutive patients during daytime hours Sunday through Friday and during evening hours Monday through Thursday. Once a patient was enrolled in the study, all management decisions were made by the treating physician according to the standard of care at our institution.
Data collection After enrollment, each patient was tracked for the remainder of her pregnancy and relevant delivery information was obtained through chart review. Pertinent demographic, medical, surgical, obstetric, gynecologic, and social histories were recorded. Maternal home address and zip code were also collected. To examine whether variation in individuals’ neighborhood context is associated with PTB outcomes or accounts for variation in GA at delivery in Philadelphia, we collaborated with the University of Pennsylvania Cartographic Modeling Laboratory, a geographic information system spatial research center. The Cartographic Modeling Laboratory first used geographic information system industry standard software (ArcGIS 10.1; Redlands, CA) to convert, or geocode, each maternal address location into latitude and longitude coordinates that could be spatially joined with (merged) or into (aggregated) other geographic units. Maternal addresses were aggregated into census blockgroups. Of note, census data are aggregated into block-groups, tracts, or other geographies based on the relative homogeneity of the population and
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TABLE 1
Association between individual level and block-group level variables with PTB and GA at delivery (continued) P value (PTB)
Variable
P value (GA delivery)
Research
address, administrative, census, and survey data were spatially joined, or merged, in ArcGIS using the STFID common field. The specific block-group variables that were analyzed are listed in Table 1.
Churches and block group party data Places of worship within block group (2005)
.41
.51
Total block parties within block group (2003-2008)
.77
.90
Income disparity
.75
.64
Families in poverty
.37
.14
Female headed households with no husband and children 0-17 years old
.56
.14
Median household income, Asian
.99
.46
Median household income, black
.88
.85
Median household income, Hispanic
.91
.39
Median household income, native American
.50
.64
Median household income, Pacific
.90
.98
Median household income, biracial
.81
.99
Median household income, white
.72
.42
Median household income, white non-Hispanic
.84
.56
Median household income
.37
.85
Demographic and financial data, 2006
Biracial household
.10
.05
Percent of population below 150% of poverty level
.59
.67
Population below 150% of poverty level
.25
.05
Average household size
.80
.71
Vacant housing units
.43
.17
Per capita income
.39
.97
Population density
.79
.63
White alone, total
.87
.10
Black alone, total
.98
.96
Asian alone, total
.30
.07
Hispanic alone, total
.03
.02
White alone non-Hispanic, total
.70
.22
Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
economic characteristics. Block-groups are the smallest census geographic unit for which census population characteristics and economic status are tabulated. The average population within a blockgroup is approximately 1000 individuals. Administrative (neighborhood crime, structural decline), census (population demographics, social stress, education and employment) and survey (social
(continued)
capital) data from 2003-2009 were collected from the Cartographic Modeling Laboratory’s databases. Administrative, census, and survey data along with maternal address data were aggregated into census block group boundaries using Summary Tape File Identification (STFID), a 12-digit unique identifier field for each specific census block group. All census block group-level
Data analysis The primary outcome of our study was prematurity, assessed as both a dichotomous (PTB <37 weeks — present/absent) and a continuous (GA in delivery, weeks) variable. Pearson c2 analyses or Fisher exact as appropriate were used to determine associations between categorically measured individual-level risk factors and PTB, and nonparametric comparisons including Wilcoxon-Rank sum tests were performed to assess associations between the individual-level risk factors listed in Table 1 and both PTB and GA at delivery. Factor analysis was also performed to determine whether the block-group variables could be combined to reduce the total number of covariates by describing factors (including neighborhood crime, structural decline, population demographics, social stress, education and employment, and social capital). Multiple variables from each category were considered in a factor analysis model. Principle factor method was applied, with the choice of the number of factors decided using the number of Eigen values >1. Factor results were rotated using an oblique rotation, and individual factors were estimated from each model. Estimates of each factor were then used as additional covariates and were examined for significant associations with PTB and GA at delivery, and are listed in Table 2. Unadjusted logistic regression was performed to determine the association between single block-group variables and PT, and unadjusted linear regression was performed to determine the association between block-group variables and GA at delivery. Individual-level variables identified as potential risk factors in unadjusted analyses (P < .2) were used to create multivariable logistic and linear models.26 After starting with the most comprehensive model that included all
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TABLE 1
Association between individual level and block-group level variables with PTB and GA at delivery (continued) P value (PTB)
Variable
P value (GA delivery)
Demographic and financial data, 2008 Income disparity
.76
.66
Families in poverty
.34
.13
Female headed households with no husband and children 0-17 years old
.54
.13
Median household income, Asian
.99
.46
Median household income, black
.88
.85
Median household income, Hispanic
.91
.39
Median household income, native American
.50
.64
Median household income, Pacific
.90
.98
Median household income, biracial
.81
.99
Median household income, white
.72
.42
Median household income, white non-Hispanic
.84
.56
Median household income
.36
.85
Biracial household
.32
.11
Percent of population below 150% of poverty level
.57
.66
Population below 150% of poverty level
.22
.05
Average household size
.92
.81
Vacant housing units
.43
.17
Per capita income
.37
.99
Population density
.74
.58
White alone, total
.82
.09
Black alone, total
.94
.99
Asian alone, total
.30
.06
Hispanic alone, total
.03
.02
White alone non-Hispanic, total
.73
.21
GA, gestational age; LEEP, loop electrosurgical excision procedure; PTB, preterm birth. Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
potential individual-level risk factors, a backward selection method was performed to determine which combination of risk factors generated the most parsimonious yet predictive individuallevel models.27 Finally, each blockgroup variable identified as a potential risk factor in unadjusted analyses (P < .2) was incorporated into the final multivariable, individual-level model to determine whether the association between the block-group variable and PTB retained its significance. The ability
of the initial and final logistic models to classify each woman by her correct status was assessed using area under the receiver-operator characteristic curve.27 Because we studied over 50 blockgroup variables, a P value < .001 was considered statistically significant for all variables in multivariable analyses to account for multiple comparisons.
Sample size To ensure adequate size to perform meaningful calculations, we performed a
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priori sample size calculations. Given that our block-group variables were all continuous, we assumed these variables would be evaluated in tertiles. We assumed a Type 1 error rate of 5%, outcome prevalence of PTB of 30% in the lowest risk tertile of any continuous block-group variable, and a 1:1 ratio among tertiles. Given these assumptions, we determined we would need 801 women total to have 80% power to detect a 1.4-fold increase in the prevalence of PTB between the lowest and highest-risk tertile of any continuous variable. This study was approved by the institutional review board at the University of Pennsylvania.
R ESULTS We enrolled 1006 women into the parent study. After excluding patients with unknown delivery information and/or whose home address was outside Philadelphia, there were 817 women in the cohort for analysis. Consistent with a high-risk cohort and women presenting with signs and symptoms of preterm labor, the prevalence of PTB <37 weeks was 41.5%. The median GA at delivery of 37 weeks (25-75% interquartile range, 35e39 weeks), with deliveries occurring between 24 and 41 weeks. The percentage of PTBs among study participants by block-group is depicted in Figure. Patients from our cohort represented 25% of the 1816 block-groups in Philadelphia.
Analyses of individual-level variables There was a significant association between PTB and prior PTB, race, maternal age, smoking, cocaine use, and no prenatal care, but no association with prior cervical surgery, GA of first prenatal care visit, or initial body mass index (Tables 3 and 4). The initial individual-level multivariable logistic model to predict preterm birth included all potential risk factors listed in Table 1 with P < .2 obtained through unadjusted analyses. After performing backward selection, the final individual-level multivariable logistic model to predict PTB included no prenatal care, gestational hypertension,
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TABLE 2
Associations between factors and PTB and GA at delivery Variables included in factor analyses
P value (PTB)
P value (GA delivery)
Vacancy data, 2005: properties pending demolition, properties clean and sealed, lien sales for delinquent taxes, vacant lots as listed by board of revision of taxes, water service shutoffs Factor 1
.76
.49
Vacancy data, 2006: properties demolished, lien sales for delinquent taxes, vacant lots as listed by board of revision of taxes, water service shutoffs, board of revision of taxes Residential sales >$1000, PHS greened lots, total vacancy rate, residential vacancy rate, commercial vacancy rate Factor 1
.62
.94
Factor 2
.43
.02
Factor 3
.75
.19
Vacancy data, 2007: properties demolished, properties clean and sealed, lien sales for delinquent taxes, water service shutoffs Factor 1
.86
.86
Factor 2
.94
.16
Vacancy data, 2009: board of revision of taxes residential sales >$1000, total vacancy rate, residential vacancy rate, commercial vacancy rate Factor 1
.90
.59
Factor 2
.50
.04
Crime data, 2006: willful killing, aggravated assaults, burglaries, burglaries and all thefts, arsons, other assaults, transporting/receiving stolen goods, vandalism and criminal mischief, weapons violations of UFA, prostitution, sex offenses, all narcotics arrests, disorderly conduct, loitering and prowling, illegal dumping Factor 1
.40
.41
Factor 2
.21
.51
Factor 3
.18
.38
Factor 4
.44
.60
Factor 5
.71
.94
Crime data, 2009: willful killing, aggravated assaults, burglaries, burglaries and all thefts, arsons, other assaults, transporting/receiving stolen goods, vandalism and criminal mischief, weapons violations of UFA, prostitution, sex offenses, all narcotics arrests, disorderly conduct, loitering and prowling, illegal dumping Factor 1
.81
.57
Factor 2
.55
.51
Factor 3
.99
.97
Factor 4
.82
.99
Factor 5
.79
.95
Churches and block group party data: places of worship within block group (2005), total block parties within block group (2003-2008) Factor 1
.91
Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
and chronic hypertension. The predictive ability of the initial individual-level multivariable model was not better than the predictive ability of the final
.62 (continued)
individual-level multivariable model to predict PTB (area under the receiveroperator characteristic curve. 0.60 vs 0.59; P ¼ .58).
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The initial individual-level multivariable linear model to predict GA at delivery included all potential risk factors listed in Table 1 with P < .2 obtained through unadjusted analyses. After performing backward selection, the final, individual-level multivariable linear model to predict GA at delivery included African-American race, maternal age, smoking, and no prenatal care.
Factor analyses of block-group variables Factor analyses were performed to determine whether the block-group variables could be used statistically to identify the extent to which the variables within each domain (neighborhood crime, structural decline, population demographics, social stress, education and employment, and social capital) were correlated with 1 another, and could be combined. Unadjusted logistic regression was performed to estimate the associations between block-group factors and both PTB and GA at delivery. The blockgroup factors listed in Table 2 that achieved our initial statistical threshold (P < .2) were studied in the final, individual-level multivariable logistic and linear models to determine whether their association with PTB or GA were significant. On inclusion into these final models, however, no block-group factors achieved our significance threshold of P < .001. Individual covariate analyses of block-group variables Finally, unadjusted logistic regression was performed to estimate the associations between each block-group level variable and both PTB and GA at delivery The block-group variables listed in Table 1 that achieved our initial statistical threshold (P < .2) were studied in the final, individual-level multivariable logistic and linear models to determine whether their association with PTB or GA were significant. On inclusion into these final models, however, no block-group level variables achieved our significance threshold of P < .001.
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TABLE 2
Associations between factors and PTB and GA at delivery (continued) Variables included in factor analyses
P value (PTB)
P value (GA delivery)
Demographic and income data, 2006: income disparity, families in poverty, female headed households with no husband and children 0-17 years old, median household income Asian, median household income black, median household income Hispanic, median household income native American, median household income Pacific, median household income biracial, median household income white, median household income white non-Hispanic, median household income, biracial household, percent of population below 150% of poverty level, population below 150% of poverty level, average household size, vacant housing units, per capita income, population density, white alone total, black alone total, Asian alone total, Hispanic alone total, white alone non-Hispanic total Factor 1
.98
.28
Factor 2
.51
.91
Factor 3
.42
.01
Factor 4
.58
.51
Factor 5
.19
.51
Factor 6
.10
.63
Factor 7
.82
.54
Factor 8
.36
.73
Demographic and income data, 2008: income disparity, families in poverty, female headed households with no husband and children 0-17 years old, median household income Asian, median household income black, median household income Hispanic, median household income native American, median household income Pacific, median household income biracial, median household income white, median household income white non-Hispanic, median household income, biracial household, percent of population below 150% of poverty level, population below 150% of poverty level, average household size, vacant housing units, per capita income, population density, white alone total, black alone total, Asian alone total, Hispanic alone total, white alone non-Hispanic total Factor 1
.95
.29
Factor 2
.44
.81
Factor 3
.38
.01
Factor 4
.58
.46
Factor 5
.18
.39
Factor 6
.30
.98
Factor 7
.84
.42
Factor 8
.34
.82
GA, gestational age; PTB, preterm birth. Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
C OMMENT Despite our hypotheses that select geographic environmental variables related to maternal neighborhood would be associated with PTB and GA at delivery, we found that neither block-group level administrative (neighborhood crime, structural decline), census (population demographics, social stress, education, and employment), nor survey (social
capital) data could account for PTB outcomes and variations in GA at delivery in Philadelphia, PA. Although some block-group level variables were identified in unadjusted logistic and linear analyses, the association of these variables with PTB and GA at delivery did not persist after incorporation into the individual-level multivariable model.
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Our study had several strengths. Although previous investigators have studied only population-level data to determine whether trends exist between environmental variables and PTB and have presented somewhat conflicting results,17-20 our study incorporates block-group with individual-level data in an effort to determine not only whether an association between blockgroup level data and PTB exists, but also whether this association might be modified by individual-level risk factors. Furthermore, although we performed secondary analyses of previously collected data and had to exclude almost 20% of the original cohort because of lack of delivery data or a maternal homeaddress outside of Philadelphia, we were nevertheless powered to see a 1.4-fold difference in the PTB rate between the highest- and lowest-risk tertiles. Our study was not without limitations. Although we merged individuallevel with block-group level data from 454 of 1816 Philadelphia census blockgroups, we do not have individual-level data from the remaining 75% of Philadelphia block-groups. Therefore it is possible that our findings would be different, and not negative, if we had obtained individual-level data that could be merged to all block-groups rather than just a percentage. We did not randomly sample subjects from each block-group to populate our cohort. Instead, because subjects were enrolled in the parent study because of their presentation to our hospital with complaints concerning for preterm labor, a selection bias may have occurred and the patients in our cohort may not represent the block-groups in which they live. Furthermore, because of the fact that there are multiple hospitals with obstetric services in Philadelphia, our hospital does not attract patients from every part of the city; instead, a majority of our patients live local to the hospital (Figure; Percentage of PTB among study participants by blockgroup). As a result, the block-groups for which we have individual-level data may be too homogeneous for us to detect meaningful differences in environmental exposures.
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FIGURE
Percentage of preterm birth among study participants by block-group
Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
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TABLE 3
Association between individual-level risk factors and PTB Individual-level risk factor
No PTB (n [ 478)
PTB (n [ 339)
P valuea
Prior PTB
170 (37.5)
99 (57.9)
< .001
9 (1.9)
10 (3.0)
.32
417 (87.2)
302 (89.1)
.42
Prior LEEP/cold knife cone African American race Maternal age Prenatal care
23.4 (20.4, 27.8) 7 (1.5)
24.5 (21.3, 29.6) 29 (8.6)
.003 < .001
GA of first prenatal care
13 (9, 19)
13 (9, 19)
.90
Body mass index at first prenatal care
26.6 (22.8e31.9)
26.8 (22.5e32.2)
.91
Gestational diabetes
14 (3.06)
8 (2.38)
.56
Gestational hypertension
45 (9.85)
18 (5.36)
.02
Chronic hypertension
13 (2.72)
27 (7.96)
.001
Tobacco use
69 (14.5)
73 (21.5)
.009
Cocaine use
4 (0.8)
11 (3.2)
.02
GA, gestational age; LEEP, loop electrosurgical excision procedure; PTB, preterm birth. a
Categorical data analyzed by c2 or Fisher exact test as appropriate and presented as n (%) Continuous data analyzed by Wilcoxon-Rank sum tests and presented as median (25, 75%).
Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
It is possible that our results are not generalizeable to locations outside of Philadelphia, and that geographic environmental variables may account for PTB outcomes and variations in GA at delivery in different regions. Another potential limitation is our restriction of study population to those
patients at highest risk for PTB based on their symptoms consistent with preterm labor. It is possible that this high-risk presentation overwhelms any potential environmental risk factor that might be observed in a lower-risk population. Finally, although our clinical research coordinators were available to enroll
TABLE 4
Association between individual-level risk factors and GA at delivery (wks) Individual-level risk factor
Risk factor absent
Risk factor present
P valuea < .001
Prior PTB
38 (35, 39)
36 (33.5, 38)
Prior LEEP / cold knife cone
37 (37, 39)
36 (34, 39)
.71
African American race
38 (36, 39)
37 (35, 39)
.16
Prenatal care
37 (35, 39)
34 (31, 36)
< .001
Gestational diabetes
37 (35, 39)
37.5 (36, 39)
.80
Gestational hypertension
37 (35, 39)
38 (36, 39)
.21
Chronic hypertension
37 (35, 39)
36 (34, 38)
.003
Tobacco use
37 (35, 39)
36 (34, 39)
< .001
Cocaine use
37 (35, 39)
34 (32, 38)
.02
GA, gestational age; LEEP, loop electrosurgical excision procedure; PTB, preterm birth. a
GA (wks) data analyzed by Wilcoxon-Rank sum tests and presented as median (25, 75%).
Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
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patients 12 hours per day on 4 days a week and 8 hours per day on 2 additional days, our cohort was a convenience sample. However, it is unlikely that there were relevant systematic differences between patients who presented to the triage center with complaints of preterm labor while clinical research coordinators were vs were not available for enrollment. Our unanticipated analytical results may indicate a modifiable areal unit problem. Under this hypothesis, study outcomes may differ depending on the geographic unit studied (ie, block-group, tract-group, zip code, city, county, state, country). We chose to study blockgroups in an effort to focus on the smallest census geographic unit for which census population characteristics and economic status are tabulated. Census geographic units are delineated based on optimal population counts (ie, block groups are generally 600-3000 residents, with 1500 being the optimal size), homogeneity of the population, the compactness of the shape, and other standard geographic classifications. Census blockgroups may simply be too fine of a geographic aggregation to yield robust estimates. The analyses presented above were previously performed for this study population on the zip code level with negative results, which were unpublished beause zip codes are larger and more artificial constructs than block-groups, smaller geographic units with more social construct implications. Our analyses would need to be repeated on several different geographic scales to advance our inquiry into a link between geographic aggregations and PTB. Although our analyses did not reveal any significant associations between these block-group variables and PTB or individual variation in GA at delivery in our Philadelphia cohort, there may be other geographic environmental variables related to maternal neighborhood that are associated with these outcomes. Although it was a goal of Healthy People 2010 to decrease the national PTB rate to 7%,28 this statistic remains greater than 12%. Our success in decreasing the PTB rate on a population level may depend on our ability to identify other
ajog.org potentially modifiable risk factors of prematurity. REFERENCES 1. Khoury MJ, Bertram L, Boffetta P, et al. Genome-wide association studies, field synopses, and the development of the knowledge base on genetic variation and human diseases. Am J Epidemiol 2009;170:269-79. 2. Gómez LM, Sammel MD, Appleby DH, et al. Evidence of a gene-environment interaction that predisposes to spontaneous preterm birth: a role for asymptomatic bacterial vaginosis and dna variants in genes that control the inflammatory response. Am J Obstet Gynecol 2010;202:386.E1-6. 3. Macones GA, Parry S, Elkousy M, Clothier B, Ural SH, Strauss JF. A polymorphism in the promoter region of tnf and bacterial vaginosis: preliminary evidence of gene-environment interaction in the etiology of spontaneous preterm birth. Am J Obstet Gynecol 2004;190:1504-8; Discussion 3a. 4. Wang H, Parry S, Macones G, et al. A functional snp in the promoter of the serpinh1 gene increases risk of preterm premature rupture of membranes in African Americans. Proc Natl Acad Sci U S A 2006;103:13463-7. 5. Anum EA, Springel EH, Shriver MD, Strauss JF. Genetic contributions to disparities in preterm birth. Pediatr Res 2009;65:1-9. 6. Wang H, Parry S, Macones G, et al. Functionally significant snp mmp8 promoter haplotypes and preterm premature rupture of membranes (PPROM). Hum Mol Genet 2004;13:2659-69. 7. Martin JA, Hamilton BE, Sutton PD, et al. Births: final data for 2005. Natl Vital Stat Rep 2007;56:1-103. 8. Beck S, Wojdyla D, Say L, et al. The worldwide incidence of preterm birth: a systematic
Obstetrics review of maternal mortality and morbidity. Bull World Health Organ 2010;88:31-8. 9. Mayer SE, Jencks C. Growing up in poor neighborhoods: how much does it matter? Science 1989;243:1441-5. 10. Diez Roux AV, Mair C. Neighborhoods and health. Ann N Y Acad Sci 2010;1186:125-45. 11. Pope CA, Burnett RT, Krewski D, et al. Cardiovascular mortality and exposure to airborne fine particulate matter and cigarette smoke: shape of the exposure-response relationship. Circulation 2009;120:941-8. 12. Dulin-Keita A, Casazza K, Fernandez JR, Goran MI, Gower B. Do neighbourhoods matter? neighbourhood disorder and long-term trends in serum cortisol levels. J Epidemiol Community Health 2010;24. 13. Zhang X, Onufrak S, Holt JB, Croft JB. A multilevel approach to estimating small area childhood obesity prevalence at the census block-group level. Prev Chronic Dis 2013;10:E68. 14. Cohen DA, Ghosh-Dastidar B, Scribner R, et al. Alcohol outlets, gonorrhea, and the los angeles civil unrest: a longitudinal analysis. Soc Sci Med 2006;62:3062-71. 15. Kirby D, Coyle K, Gould JB. Manifestations of poverty and birthrates among young teenagers in california zip code areas. Fam Plann Perspect 2001;33:63-9. 16. Gould JB, Herrchen B, Pham T, Bera S, Brindis C. Small-area analysis: targeting highrisk areas for adolescent pregnancy prevention programs. Fam Plann Perspect 1998;30:173-6. 17. Janevic T, Stein CR, Savitz DA, Kaufman JS, Mason SM, Herring AH. Neighborhood deprivation and adverse birth outcomes among diverse ethnic groups. Ann Epidemiol 2010;20:445-51. 18. Kramer MR, Hogue CR. Place matters: variation in the black/white very preterm birth rate across U.S. metropolitan areas, 20022004. Public Health Rep 2008;123:576-85.
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19. Hillemeier MM, Weisman CS, Chase GA, Dyer AM. Individual and community predictors of preterm birth and low birthweight along the rural-urban continuum in central Pennsylvania. J Rural Health 2007;23:42-8. 20. Bloch JR. Using geographical information systems to explore disparities in preterm birth rates among foreign-born and U.S.-born black mothers. J Obstet Gynecol Neonatal Nurs 2011;40:544-54. 21. Strutz KL, Dozier AM, Van Wijngaarden E, Glantz JC. Birth outcomes across three ruralurban typologies in the Finger Lakes region of New York. J Rural Health 2012;28:162-73. 22. Kent ST, McClure LA, Zaitchik BF, Gohlke JM. Area-level risk factors for adverse birth outcomes: trends in urban and rural settings. BMC Pregnancy Childbirth 2013;13:129. 23. Bastek JA, Brown AG, Foreman MN, et al. The soluble receptor for advanced glycation end products can prospectively identify patients at greatest risk for preterm birth. J Matern Fetal Neonatal Med 2012;25:1762-8. 24. Bastek JA, Weber AL, Mcshea MA, Ryan ME, Elovitz MA. Prenatal inflammation is associated with adverse neonatal outcomes. Am J Obstet Gynecol 2014;210:450.E1-10. 25. Bastek JA, Sammel MD, Srinivas SK, et al. Clinical prediction rules for preterm birth in patients presenting with preterm labor. Obstet Gynecol 2012;119:1119-28. 26. Hosmer DW, Lemeshow S; Alumni and friends memorial book fund. applied logistic regression, 2nd ed. New York: Wiley; 2000. 27. Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983;148:839-43. 28. Healthy People 2010. Washington, DC: US Department of health and human services; 2000.
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OBSTETRICS
Environmental variables as potential modifiable risk factors of preterm birth in Philadelphia, PA Jamie A. Bastek, MD, MSCE; Mary D. Sammel, ScD; Tara D. Jackson, PhD; Meghan E. Ryan, BA; Meghan A. McShea, BS; Michal A. Elovitz, MD OBJECTIVE: To examine whether variation in neighborhood context
is associated with preterm birth (PTB) outcomes and gestational age (GA) at delivery in Philadelphia, and to determine whether these associations might persist when considering relevant individual-level variables. STUDY DESIGN: We analyzed individual-level data collected for a
prospective cohort study of singleton pregnancies with preterm labor. We merged block-group level data to each individual’s home address. Unadjusted analyses were performed to determine the association between block-group variables and individual-level outcomes. Block-group variables identified as potential risk factors were
incorporated into multivariable individual-level models to determine significance. RESULTS: We analyzed data for 817 women. The prevalence of PTB
<37 weeks was 41.5%. Although in unadjusted analyses several block-group variables were associated with PTB and GA at delivery, none retained significance in individual-level multivariable models. CONCLUSION: Block-group level data were not associated with PTB outcomes or GA at delivery in Philadelphia.
Key words: census block-group, environment, Philadelphia, preterm birth
Cite this article as: Bastek JA, Sammel MD, Jackson TD, et al. Environmental variables as potential modifiable risk factors of preterm birth in Philadelphia, PA. Am J Obstet Gynecol 2015;212:236.e1-10.
P
reterm birth (PTB) is a leading cause of perinatal morbidity and mortality. Well-recognized risk factors for PTB include obstetric history, maternal age, race, and socioeconomic status. However, the degree to which PTB risk might be affected by modifiable risk factors such as an individual’s environment, compared with nonmodifiable risk factors such as an individual’s genetics, remains unknown. Evidence from single-nucleotide polymorphism
and genome-wide association studies have demonstrated significant but only nominally increased risk for PTB with specific allelic variations,1-6 suggesting a lack of evidence regarding a strong genetic causation in most cases of PTB. Other evidence suggests that an individual’s environment must play some role in PTB risk: PTB rates among African-American (18.4%)7 and European-American (11.7%)7 women are higher than PTB rates among African
(11.9%)8 and European (6.2%)8 women, respectively. The concept of possible “neighborhood effects” on health outcomes was introduced over 20 years ago.9 Since that time, there have been data from the nonobstetric literature to suggest an association between an individual’s geographic environmentespecifically, one’s neighborhoodeand chronic disease,10 mental health outcomes,10 cardiovascular mortality,11 serum cortisol
From the Maternal and Child Health Research Program, Department of Obstetrics and Gynecology (Drs Bastek and Elovitz, Ms Ryan, and Ms McShea), Center for Research on Reproduction and Women’s Health, and Department of Biostatistics and Epidemiology (Dr Sammel) and Cartographic Modeling Lab (Dr Jackson), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA. Received March 3, 2014; revised July 18, 2014; accepted Aug. 25, 2014. The authors report no conflict of interest. The material herein represents the research and conclusions of the authors and does not necessarily reflect the views of the Eunice Kennedy Shriver National Institute of Child Health and Human Development. Financial support for this project was provided by March of Dimes grant number 21-FY08-539 (principal investigator: Elovitz). Drs Bastek and Sammel were supported by grant number K12HD001265 (principal investigator: Driscoll; Scholar: Bastek) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development. Presented in poster format at the 33rd annual meeting of the Society for Maternal-Fetal Medicine, San Francisco, CA, Feb. 11-16, 2013, and in poster format at the Women’s Reproductive Health Research Symposium, Department of Obstetrics and Gynecology, Hospital of the University of Pennsylvania, Philadelphia, PA, Oct. 11-12, 2012. Corresponding author: Jamie A. Bastek, MD, MSCE. [email protected] 0002-9378/$36.00 ª 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ajog.2014.08.025
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levels,12 childhood obesity,13 and gynecologic disease. 14-16 To date, however, there has been relatively little literature exploring the potential association between geographic environmente specifically, maternal neighborhoode and PTB. Although several studies have analyzed whether trends in neighborhood characteristics were associated with adverse perinatal outcomes including PTB, these studies were limited by their sole use of population-level data and provide somewhat conflicting results.17-22 Furthermore, they were unable to determine whether the relationship between geographic neighborhood and PTB might be modified by relevant individual-level variables. To that end, our primary objective was to determine whether select geographic environmental variables related to maternal neighborhood are associated with prematurity. Specifically, we studied block-group level administrative (neighborhood crime, structural decline), census (population demographics, social stress, education, and employment), and survey (social capital) data to examine whether variation in individuals’ neighborhood context is associated with PTB outcomes and accounts for variation in gestational age (GA) at delivery in Philadelphia, PA. Our secondary objective was to determine whether any associations identified between environmental variables and PTB and/or GA at delivery might persist when considering relevant individual-level variables.
M ATERIALS
AND
M ETHODS
This study was a planned secondary analysis of data that were collected for a prospective cohort study at a single, urban tertiary care center between April 2009 and May 2012.23-25 The cohort consisted of women with periviablepreterm singleton pregnancies between 22- 33 6/7 weeks GA who presented to the labor and delivery triage unit with complaints concerning for spontaneous PTB, including contractions, cramping, vaginal bleeding, vaginal pressure, leaking vaginal fluid, and abdominal or back pain. For the current study, we included all women from the parent study. Patients
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TABLE 1
Association between individual level and block-group level variables with PTB and GA at delivery P value (PTB)
Variable
P value (GA delivery)
Individual level variables < .001
.002
Prior LEEP/cold knife cone
.32
.64
African-American race
.42
.12
Maternal age
.003
< .001
Prenatal care
< .001
.002
GA first prenatal visit
.90
.38
Body mass index first prenatal visit
.91
.82
Gestational diabetes
.56
.65
Gestational hypertension
.02
.06
Chronic hypertension
.001
.01
Tobacco use
.009
.002
Cocaine use
.02
.02
Properties pending demolition
.61
.07
Properties clean and sealed
.79
.60
Lien sales for delinquent taxes
.66
.87
Vacant lots as listed by board of revision of taxes
.51
.80
Water service shutoffs
.93
.34
Properties demolished
.72
.83
Lien sales for delinquent taxes
.62
.93
Vacant lots as listed by board of revision of taxes
.57
.95
Water service shutoffs
.62
.06
Board of revision of taxes residential sales >$1000
.52
.19
PHS greened lots
.41
.82
Prior PTB
Block-group level variables Vacancy data, 2005
Vacancy data, 2006
Total vacancy rate
.57
.04
Residential vacancy rate
.54
.07
Commercial vacancy rate
.13
.19
Properties demolished
.13
.23
Properties clean and sealed
.99
.85
Lien sales for delinquent taxes
.76
.75
Water service shutoffs
.78
.39
Vacancy data, 2007
Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
(continued)
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TABLE 1
Association between individual level and block-group level variables with PTB and GA at delivery (continued) Variable
P value (PTB)
P value (GA delivery)
Vacancy data, 2009 Vacant lots as listed by board of revision of taxes, 2009
.45
.83
Board of revision of taxes residential sales >$1000
.49
.36
Total vacancy rate
.28
.01
Residential vacancy rate
.46
.04
Commercial vacancy rate
.41
.47
Willful killing
.91
.86
Aggravated assaults
.56
.87
Crime data, 2006
Burglaries
.49
.60
Burglaries and all thefts
.11
.15
Arsons
.63
.98
Other assaults
.11
.04
Transporting/receiving stolen goods
.41
.66
Vandalism and criminal mischief
.05
.18
Weapons violations of uniform firearms act
.53
.53
Prostitution
.57
.31
Sex offenses
.31
.20
All narcotics arrests
.44
.75
Disorderly conduct
.87
.79
Loitering and prowling
.64
.68
Illegal dumping
.86
.85
Crime data, 2009 Willful killing
.87
.63
Aggravated assaults
.97
.86
Burglaries
.89
.62
Burglaries and all thefts
.40
.12
Arsons
.71
.93
Other assaults
.27
.23
Transporting/receiving stolen goods
.23
.29
Vandalism and criminal mischief
.24
.73
Weapons violations of uniform firearms act
.94
.79
Prostitution
.67
.90
Sex offenses
.33
.80
All narcotics arrests
.99
.85
Disorderly conduct
.37
.32
Loitering and prowling
.29
.17
Illegal dumping
.26
.19
Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
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(continued)
were excluded from the parent study and thus from these analyses for multiplegestation, major fetal anomaly, intrauterine fetal demise, severe preeclampsia before enrollment, chronic steroid or immunosuppressive drug use, active immunologic disease, acute systemic febrile illness, and/or pregestational diabetes. Patients with unknown delivery information and/or whose home address was outside Philadelphia, PA, were also excluded from these analyses. Patients were enrolled in the study by trained clinical research coordinators who obtained informed consent at the time of enrollment. The clinical research coordinators enrolled consecutive patients during daytime hours Sunday through Friday and during evening hours Monday through Thursday. Once a patient was enrolled in the study, all management decisions were made by the treating physician according to the standard of care at our institution.
Data collection After enrollment, each patient was tracked for the remainder of her pregnancy and relevant delivery information was obtained through chart review. Pertinent demographic, medical, surgical, obstetric, gynecologic, and social histories were recorded. Maternal home address and zip code were also collected. To examine whether variation in individuals’ neighborhood context is associated with PTB outcomes or accounts for variation in GA at delivery in Philadelphia, we collaborated with the University of Pennsylvania Cartographic Modeling Laboratory, a geographic information system spatial research center. The Cartographic Modeling Laboratory first used geographic information system industry standard software (ArcGIS 10.1; Redlands, CA) to convert, or geocode, each maternal address location into latitude and longitude coordinates that could be spatially joined with (merged) or into (aggregated) other geographic units. Maternal addresses were aggregated into census blockgroups. Of note, census data are aggregated into block-groups, tracts, or other geographies based on the relative homogeneity of the population and
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TABLE 1
Association between individual level and block-group level variables with PTB and GA at delivery (continued) P value (PTB)
Variable
P value (GA delivery)
Research
address, administrative, census, and survey data were spatially joined, or merged, in ArcGIS using the STFID common field. The specific block-group variables that were analyzed are listed in Table 1.
Churches and block group party data Places of worship within block group (2005)
.41
.51
Total block parties within block group (2003-2008)
.77
.90
Income disparity
.75
.64
Families in poverty
.37
.14
Female headed households with no husband and children 0-17 years old
.56
.14
Median household income, Asian
.99
.46
Median household income, black
.88
.85
Median household income, Hispanic
.91
.39
Median household income, native American
.50
.64
Median household income, Pacific
.90
.98
Median household income, biracial
.81
.99
Median household income, white
.72
.42
Median household income, white non-Hispanic
.84
.56
Median household income
.37
.85
Demographic and financial data, 2006
Biracial household
.10
.05
Percent of population below 150% of poverty level
.59
.67
Population below 150% of poverty level
.25
.05
Average household size
.80
.71
Vacant housing units
.43
.17
Per capita income
.39
.97
Population density
.79
.63
White alone, total
.87
.10
Black alone, total
.98
.96
Asian alone, total
.30
.07
Hispanic alone, total
.03
.02
White alone non-Hispanic, total
.70
.22
Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
economic characteristics. Block-groups are the smallest census geographic unit for which census population characteristics and economic status are tabulated. The average population within a blockgroup is approximately 1000 individuals. Administrative (neighborhood crime, structural decline), census (population demographics, social stress, education and employment) and survey (social
(continued)
capital) data from 2003-2009 were collected from the Cartographic Modeling Laboratory’s databases. Administrative, census, and survey data along with maternal address data were aggregated into census block group boundaries using Summary Tape File Identification (STFID), a 12-digit unique identifier field for each specific census block group. All census block group-level
Data analysis The primary outcome of our study was prematurity, assessed as both a dichotomous (PTB <37 weeks — present/absent) and a continuous (GA in delivery, weeks) variable. Pearson c2 analyses or Fisher exact as appropriate were used to determine associations between categorically measured individual-level risk factors and PTB, and nonparametric comparisons including Wilcoxon-Rank sum tests were performed to assess associations between the individual-level risk factors listed in Table 1 and both PTB and GA at delivery. Factor analysis was also performed to determine whether the block-group variables could be combined to reduce the total number of covariates by describing factors (including neighborhood crime, structural decline, population demographics, social stress, education and employment, and social capital). Multiple variables from each category were considered in a factor analysis model. Principle factor method was applied, with the choice of the number of factors decided using the number of Eigen values >1. Factor results were rotated using an oblique rotation, and individual factors were estimated from each model. Estimates of each factor were then used as additional covariates and were examined for significant associations with PTB and GA at delivery, and are listed in Table 2. Unadjusted logistic regression was performed to determine the association between single block-group variables and PT, and unadjusted linear regression was performed to determine the association between block-group variables and GA at delivery. Individual-level variables identified as potential risk factors in unadjusted analyses (P < .2) were used to create multivariable logistic and linear models.26 After starting with the most comprehensive model that included all
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TABLE 1
Association between individual level and block-group level variables with PTB and GA at delivery (continued) P value (PTB)
Variable
P value (GA delivery)
Demographic and financial data, 2008 Income disparity
.76
.66
Families in poverty
.34
.13
Female headed households with no husband and children 0-17 years old
.54
.13
Median household income, Asian
.99
.46
Median household income, black
.88
.85
Median household income, Hispanic
.91
.39
Median household income, native American
.50
.64
Median household income, Pacific
.90
.98
Median household income, biracial
.81
.99
Median household income, white
.72
.42
Median household income, white non-Hispanic
.84
.56
Median household income
.36
.85
Biracial household
.32
.11
Percent of population below 150% of poverty level
.57
.66
Population below 150% of poverty level
.22
.05
Average household size
.92
.81
Vacant housing units
.43
.17
Per capita income
.37
.99
Population density
.74
.58
White alone, total
.82
.09
Black alone, total
.94
.99
Asian alone, total
.30
.06
Hispanic alone, total
.03
.02
White alone non-Hispanic, total
.73
.21
GA, gestational age; LEEP, loop electrosurgical excision procedure; PTB, preterm birth. Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
potential individual-level risk factors, a backward selection method was performed to determine which combination of risk factors generated the most parsimonious yet predictive individuallevel models.27 Finally, each blockgroup variable identified as a potential risk factor in unadjusted analyses (P < .2) was incorporated into the final multivariable, individual-level model to determine whether the association between the block-group variable and PTB retained its significance. The ability
of the initial and final logistic models to classify each woman by her correct status was assessed using area under the receiver-operator characteristic curve.27 Because we studied over 50 blockgroup variables, a P value < .001 was considered statistically significant for all variables in multivariable analyses to account for multiple comparisons.
Sample size To ensure adequate size to perform meaningful calculations, we performed a
236.e5 American Journal of Obstetrics & Gynecology FEBRUARY 2015
priori sample size calculations. Given that our block-group variables were all continuous, we assumed these variables would be evaluated in tertiles. We assumed a Type 1 error rate of 5%, outcome prevalence of PTB of 30% in the lowest risk tertile of any continuous block-group variable, and a 1:1 ratio among tertiles. Given these assumptions, we determined we would need 801 women total to have 80% power to detect a 1.4-fold increase in the prevalence of PTB between the lowest and highest-risk tertile of any continuous variable. This study was approved by the institutional review board at the University of Pennsylvania.
R ESULTS We enrolled 1006 women into the parent study. After excluding patients with unknown delivery information and/or whose home address was outside Philadelphia, there were 817 women in the cohort for analysis. Consistent with a high-risk cohort and women presenting with signs and symptoms of preterm labor, the prevalence of PTB <37 weeks was 41.5%. The median GA at delivery of 37 weeks (25-75% interquartile range, 35e39 weeks), with deliveries occurring between 24 and 41 weeks. The percentage of PTBs among study participants by block-group is depicted in Figure. Patients from our cohort represented 25% of the 1816 block-groups in Philadelphia.
Analyses of individual-level variables There was a significant association between PTB and prior PTB, race, maternal age, smoking, cocaine use, and no prenatal care, but no association with prior cervical surgery, GA of first prenatal care visit, or initial body mass index (Tables 3 and 4). The initial individual-level multivariable logistic model to predict preterm birth included all potential risk factors listed in Table 1 with P < .2 obtained through unadjusted analyses. After performing backward selection, the final individual-level multivariable logistic model to predict PTB included no prenatal care, gestational hypertension,
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TABLE 2
Associations between factors and PTB and GA at delivery Variables included in factor analyses
P value (PTB)
P value (GA delivery)
Vacancy data, 2005: properties pending demolition, properties clean and sealed, lien sales for delinquent taxes, vacant lots as listed by board of revision of taxes, water service shutoffs Factor 1
.76
.49
Vacancy data, 2006: properties demolished, lien sales for delinquent taxes, vacant lots as listed by board of revision of taxes, water service shutoffs, board of revision of taxes Residential sales >$1000, PHS greened lots, total vacancy rate, residential vacancy rate, commercial vacancy rate Factor 1
.62
.94
Factor 2
.43
.02
Factor 3
.75
.19
Vacancy data, 2007: properties demolished, properties clean and sealed, lien sales for delinquent taxes, water service shutoffs Factor 1
.86
.86
Factor 2
.94
.16
Vacancy data, 2009: board of revision of taxes residential sales >$1000, total vacancy rate, residential vacancy rate, commercial vacancy rate Factor 1
.90
.59
Factor 2
.50
.04
Crime data, 2006: willful killing, aggravated assaults, burglaries, burglaries and all thefts, arsons, other assaults, transporting/receiving stolen goods, vandalism and criminal mischief, weapons violations of UFA, prostitution, sex offenses, all narcotics arrests, disorderly conduct, loitering and prowling, illegal dumping Factor 1
.40
.41
Factor 2
.21
.51
Factor 3
.18
.38
Factor 4
.44
.60
Factor 5
.71
.94
Crime data, 2009: willful killing, aggravated assaults, burglaries, burglaries and all thefts, arsons, other assaults, transporting/receiving stolen goods, vandalism and criminal mischief, weapons violations of UFA, prostitution, sex offenses, all narcotics arrests, disorderly conduct, loitering and prowling, illegal dumping Factor 1
.81
.57
Factor 2
.55
.51
Factor 3
.99
.97
Factor 4
.82
.99
Factor 5
.79
.95
Churches and block group party data: places of worship within block group (2005), total block parties within block group (2003-2008) Factor 1
.91
Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
and chronic hypertension. The predictive ability of the initial individual-level multivariable model was not better than the predictive ability of the final
.62 (continued)
individual-level multivariable model to predict PTB (area under the receiveroperator characteristic curve. 0.60 vs 0.59; P ¼ .58).
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The initial individual-level multivariable linear model to predict GA at delivery included all potential risk factors listed in Table 1 with P < .2 obtained through unadjusted analyses. After performing backward selection, the final, individual-level multivariable linear model to predict GA at delivery included African-American race, maternal age, smoking, and no prenatal care.
Factor analyses of block-group variables Factor analyses were performed to determine whether the block-group variables could be used statistically to identify the extent to which the variables within each domain (neighborhood crime, structural decline, population demographics, social stress, education and employment, and social capital) were correlated with 1 another, and could be combined. Unadjusted logistic regression was performed to estimate the associations between block-group factors and both PTB and GA at delivery. The blockgroup factors listed in Table 2 that achieved our initial statistical threshold (P < .2) were studied in the final, individual-level multivariable logistic and linear models to determine whether their association with PTB or GA were significant. On inclusion into these final models, however, no block-group factors achieved our significance threshold of P < .001. Individual covariate analyses of block-group variables Finally, unadjusted logistic regression was performed to estimate the associations between each block-group level variable and both PTB and GA at delivery The block-group variables listed in Table 1 that achieved our initial statistical threshold (P < .2) were studied in the final, individual-level multivariable logistic and linear models to determine whether their association with PTB or GA were significant. On inclusion into these final models, however, no block-group level variables achieved our significance threshold of P < .001.
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TABLE 2
Associations between factors and PTB and GA at delivery (continued) Variables included in factor analyses
P value (PTB)
P value (GA delivery)
Demographic and income data, 2006: income disparity, families in poverty, female headed households with no husband and children 0-17 years old, median household income Asian, median household income black, median household income Hispanic, median household income native American, median household income Pacific, median household income biracial, median household income white, median household income white non-Hispanic, median household income, biracial household, percent of population below 150% of poverty level, population below 150% of poverty level, average household size, vacant housing units, per capita income, population density, white alone total, black alone total, Asian alone total, Hispanic alone total, white alone non-Hispanic total Factor 1
.98
.28
Factor 2
.51
.91
Factor 3
.42
.01
Factor 4
.58
.51
Factor 5
.19
.51
Factor 6
.10
.63
Factor 7
.82
.54
Factor 8
.36
.73
Demographic and income data, 2008: income disparity, families in poverty, female headed households with no husband and children 0-17 years old, median household income Asian, median household income black, median household income Hispanic, median household income native American, median household income Pacific, median household income biracial, median household income white, median household income white non-Hispanic, median household income, biracial household, percent of population below 150% of poverty level, population below 150% of poverty level, average household size, vacant housing units, per capita income, population density, white alone total, black alone total, Asian alone total, Hispanic alone total, white alone non-Hispanic total Factor 1
.95
.29
Factor 2
.44
.81
Factor 3
.38
.01
Factor 4
.58
.46
Factor 5
.18
.39
Factor 6
.30
.98
Factor 7
.84
.42
Factor 8
.34
.82
GA, gestational age; PTB, preterm birth. Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
C OMMENT Despite our hypotheses that select geographic environmental variables related to maternal neighborhood would be associated with PTB and GA at delivery, we found that neither block-group level administrative (neighborhood crime, structural decline), census (population demographics, social stress, education, and employment), nor survey (social
capital) data could account for PTB outcomes and variations in GA at delivery in Philadelphia, PA. Although some block-group level variables were identified in unadjusted logistic and linear analyses, the association of these variables with PTB and GA at delivery did not persist after incorporation into the individual-level multivariable model.
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Our study had several strengths. Although previous investigators have studied only population-level data to determine whether trends exist between environmental variables and PTB and have presented somewhat conflicting results,17-20 our study incorporates block-group with individual-level data in an effort to determine not only whether an association between blockgroup level data and PTB exists, but also whether this association might be modified by individual-level risk factors. Furthermore, although we performed secondary analyses of previously collected data and had to exclude almost 20% of the original cohort because of lack of delivery data or a maternal homeaddress outside of Philadelphia, we were nevertheless powered to see a 1.4-fold difference in the PTB rate between the highest- and lowest-risk tertiles. Our study was not without limitations. Although we merged individuallevel with block-group level data from 454 of 1816 Philadelphia census blockgroups, we do not have individual-level data from the remaining 75% of Philadelphia block-groups. Therefore it is possible that our findings would be different, and not negative, if we had obtained individual-level data that could be merged to all block-groups rather than just a percentage. We did not randomly sample subjects from each block-group to populate our cohort. Instead, because subjects were enrolled in the parent study because of their presentation to our hospital with complaints concerning for preterm labor, a selection bias may have occurred and the patients in our cohort may not represent the block-groups in which they live. Furthermore, because of the fact that there are multiple hospitals with obstetric services in Philadelphia, our hospital does not attract patients from every part of the city; instead, a majority of our patients live local to the hospital (Figure; Percentage of PTB among study participants by blockgroup). As a result, the block-groups for which we have individual-level data may be too homogeneous for us to detect meaningful differences in environmental exposures.
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FIGURE
Percentage of preterm birth among study participants by block-group
Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
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TABLE 3
Association between individual-level risk factors and PTB Individual-level risk factor
No PTB (n [ 478)
PTB (n [ 339)
P valuea
Prior PTB
170 (37.5)
99 (57.9)
< .001
9 (1.9)
10 (3.0)
.32
417 (87.2)
302 (89.1)
.42
Prior LEEP/cold knife cone African American race Maternal age Prenatal care
23.4 (20.4, 27.8) 7 (1.5)
24.5 (21.3, 29.6) 29 (8.6)
.003 < .001
GA of first prenatal care
13 (9, 19)
13 (9, 19)
.90
Body mass index at first prenatal care
26.6 (22.8e31.9)
26.8 (22.5e32.2)
.91
Gestational diabetes
14 (3.06)
8 (2.38)
.56
Gestational hypertension
45 (9.85)
18 (5.36)
.02
Chronic hypertension
13 (2.72)
27 (7.96)
.001
Tobacco use
69 (14.5)
73 (21.5)
.009
Cocaine use
4 (0.8)
11 (3.2)
.02
GA, gestational age; LEEP, loop electrosurgical excision procedure; PTB, preterm birth. a
Categorical data analyzed by c2 or Fisher exact test as appropriate and presented as n (%) Continuous data analyzed by Wilcoxon-Rank sum tests and presented as median (25, 75%).
Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
It is possible that our results are not generalizeable to locations outside of Philadelphia, and that geographic environmental variables may account for PTB outcomes and variations in GA at delivery in different regions. Another potential limitation is our restriction of study population to those
patients at highest risk for PTB based on their symptoms consistent with preterm labor. It is possible that this high-risk presentation overwhelms any potential environmental risk factor that might be observed in a lower-risk population. Finally, although our clinical research coordinators were available to enroll
TABLE 4
Association between individual-level risk factors and GA at delivery (wks) Individual-level risk factor
Risk factor absent
Risk factor present
P valuea < .001
Prior PTB
38 (35, 39)
36 (33.5, 38)
Prior LEEP / cold knife cone
37 (37, 39)
36 (34, 39)
.71
African American race
38 (36, 39)
37 (35, 39)
.16
Prenatal care
37 (35, 39)
34 (31, 36)
< .001
Gestational diabetes
37 (35, 39)
37.5 (36, 39)
.80
Gestational hypertension
37 (35, 39)
38 (36, 39)
.21
Chronic hypertension
37 (35, 39)
36 (34, 38)
.003
Tobacco use
37 (35, 39)
36 (34, 39)
< .001
Cocaine use
37 (35, 39)
34 (32, 38)
.02
GA, gestational age; LEEP, loop electrosurgical excision procedure; PTB, preterm birth. a
GA (wks) data analyzed by Wilcoxon-Rank sum tests and presented as median (25, 75%).
Bastek. Environmental risk factors of PTB. Am J Obstet Gynecol 2015.
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patients 12 hours per day on 4 days a week and 8 hours per day on 2 additional days, our cohort was a convenience sample. However, it is unlikely that there were relevant systematic differences between patients who presented to the triage center with complaints of preterm labor while clinical research coordinators were vs were not available for enrollment. Our unanticipated analytical results may indicate a modifiable areal unit problem. Under this hypothesis, study outcomes may differ depending on the geographic unit studied (ie, block-group, tract-group, zip code, city, county, state, country). We chose to study blockgroups in an effort to focus on the smallest census geographic unit for which census population characteristics and economic status are tabulated. Census geographic units are delineated based on optimal population counts (ie, block groups are generally 600-3000 residents, with 1500 being the optimal size), homogeneity of the population, the compactness of the shape, and other standard geographic classifications. Census blockgroups may simply be too fine of a geographic aggregation to yield robust estimates. The analyses presented above were previously performed for this study population on the zip code level with negative results, which were unpublished beause zip codes are larger and more artificial constructs than block-groups, smaller geographic units with more social construct implications. Our analyses would need to be repeated on several different geographic scales to advance our inquiry into a link between geographic aggregations and PTB. Although our analyses did not reveal any significant associations between these block-group variables and PTB or individual variation in GA at delivery in our Philadelphia cohort, there may be other geographic environmental variables related to maternal neighborhood that are associated with these outcomes. Although it was a goal of Healthy People 2010 to decrease the national PTB rate to 7%,28 this statistic remains greater than 12%. Our success in decreasing the PTB rate on a population level may depend on our ability to identify other
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