A data set which included Missouri birth records from 1978–1998 was provided in a de-identified fashion for this analysis by the Missouri Department of Health and Senior Services. The study was considered exempt from review by the Missouri Department of Health and Senior Services IRB and the Human Subjects Committee of Washington University in St. Louis. This data set has been a rich source for the analysis of factors associated with birth timing [5–10].
A total of 1,577,082 births occurred in Missouri between 1978 and 1998. To optimize our capability to examine the influence of important individual factors, we limited our study to those births which occurred between 1989 and 1998 due to an unacceptable amount of missing demographic data for births that occurred before 1989 due to revisions in the birth certificate format in 1989 and improvements in edit and query systems after that date. Because deliveries which occur prior to 20 completed weeks of pregnancy are considered miscarriages rather than births, we limited our analysis to births recorded as having occurred at 20 weeks or greater. We excluded births resulting from a multifetal gestation, intrauterine fetal demise, or involving a major congenital malformation due to their know propensity to deliver preterm, potentially for mechanisms unrelated to the exposure we wished to evaluate. After these exclusions, the study population consisted of 675,044 births. We further limited our analysis to births occuring to mothers whose reported residence at the time of delivery was in the state of Missouri. There were 40,050 births in Missouri to mothers who resided in other states during the study period, yeilding a final population of 634,994 births available for analysis.
Preterm birth, as defined by the World Health Organization, is a delivery which occurs at less than 37 completed weeks of gestation. We performed our primary analysis with preterm birth defined as less than 35 completed weeks in order to enrich for a population of deliveries which were truly preterm by avoiding births occuring at borderline gestational ages between 35 and 37 weeks, in an effort to minimize misclassification bias. We defined early preterm birth as birth occuring prior to 32 completed weeks of gestation, because the risk of neonatal morbidity is higher for births of shorter gestations.
We included the following individual-level measures: maternal age, maternal race (self-reported), maternal and paternal highest educational attainment, residence within city limits, birth sequence, marital status, presence of medical risk factors, marital status, indicators of low income (recipient of foodstamps, Medicaid, or WIC state-funded assistance), adequacy of prenatal care received, health-related behaviors (maternal tobacco or alcohol use), and presence of medical risk factors. Individual-level risk factors were selected from the data set based on clinical relevance and association with preterm birth.
Maternal and paternal education levels are recorded in the database in years of education completed, which we dichotomized as educational levels of < 12 years versus 12 years or more in order to identify those with less than a high school education. The variable of maternal education had minimal missing data, and paternal education was less complete with 22.6% missing data. A composite dichotomous variable of low socioeconomic status was created from the individual dichotomous variables of recipient of any of three state funded support programs (Medicaid, foodstamps, and Special Supplemental Nutrition Program for Women, Infants and Children [WIC]). A dichotomous variable of inadequate prenatal care was created from a continuous variable which indicated the month of pregnancy when prenatal care was initiated. Inadequate prenatal care was defined as having initiated prenatal care after 20 weeks of pregnancy, which is the latter half of pregnancy. A composite variable of heterogeneous medical risk factors indicated pregnancies complicated by anemia (hematocrit < 30% or hemoglobin < 10 gm/dL), maternal cardiac disease, acute or chronic lung disease, diabetes (insulin dependent), diabetes (other), genital herpes, hydramnios/oligohydramnios, hemoglobinopathy, chronic hypertension, pre-eclampsia, eclampsia, incompetent cervix, previous infant weighing > 4000 gm, previous preterm or small-for-gestational-age infant, maternal renal disease, Rh sensitization, or uterine bleeding.
Poverty rate was obtained from US census data (1990) and was defined as the percentage of the population falling below the US federal poverty line at the county level of the mother's reported residence as a measure of area socioeconomic position. The poverty rate is a measure that is robust across various diseases and levels of geography; it has a link to possible policy implications, and is comparable over time[13, 14]. County-level poverty rates were divided into quartiles representing low poverty (first quartile, 0 – 6.86%), second quartile (6.89% – 11.91%), third quartile (11.92% – 14.49%) and high poverty (fourth quartile, ≥ 14.50%) to allow for nonlinear effects. The first quartile served as the reference group for comparison.
We used multilevel logistic regression analysis to estimate the effect of county-level poverty on preterm birth risk. This analytic method can estimate not only fixed effects of individual-level and area-level covariates, but also the random effect of geographic variation of preterm birth across counties. When significant geographic variation exists, this indicates that preterm birth was not randomly distributed. The 634,994 births were nested within 115 counties in Missouri (the City of St. Louis acts politically as a county).
Data were analyzed with SAS GLIMMIX macro (version 9.1, SAS Institute Inc., Cary, NC). Findings with a p-value of < 0.05 were considered statistically significant.
We calculated median odds ratios (MOR) and interval odds ratios (IOR)[15, 16] to estimate the effect of cluster-level (level 2) factors on preterm birth via several successive regression models (Models I through V). The methods for calculating MOR and IOR have been previously described and are directly comparable with the fixed-effects odds ratios[15, 16]. MORs were calculated using the following equation:
0.75 is the 75th percentile of standard normal distribution, V
is the variance of PTB between counties, and exp(·) is the exponential function. If the MOR is equal to 1, there is no variation between counties (no level 2 variation), but it is large if considerable intra-county variation exists.
80%-IORs were computed using the following equation:
0.10 and Z
0.90 are the values of the standard normal distribution at the 10th and 90th percentiles, respectively. β is the regression coefficient of each category of county-level poverty rate. Larger geographic variation results in a broader range of the IOR. If the IOR does not cross the value of one, it suggests that county-level poverty substantially contributed to the geographic heterogeneity of PTB.