Ashley H. Schempf

Neighborhood effects on birthweight: an exploration of psychosocial and behavioral pathways in Baltimore, 1995--1996.

Neighborhood characteristics have been proposed to influence birth outcomes through psychosocial and behavioral pathways, yet empirical evidence is lacking. Using data from an urban, low-income sample, this study examined the impact of the neighborhood environment on birthweight and evaluated mediation by psychosocial and behavioral factors. The sample included 726 women who delivered a live birth at Johns Hopkins Hospital in Baltimore, Maryland, USA between 1995 and 1996. Census-tract data were used to create a principal component index of neighborhood risk based on racial and economic stratification (% Black, % poverty), social disorder (violent crime rate), and physical deterioration (% boarded-up housing) (alpha=0.82). Information on sociodemographic, psychosocial, and behavioral factors was gathered from a postpartum interview and medical records. Random intercept multilevel models were used to estimate neighborhood effects and assess potential mediation. Controlling for sociodemographic characteristics, a standard deviation increase in neighborhood risk conferred a 76g birthweight decrement. This represents an approximate 300g difference between the best and worst neighborhoods. Although stress (daily hassles), perceived locus-of-control, and social support were related to birthweight, their adjustment reduced the neighborhood coefficient by only 12%. In contrast, the neighborhood effect was reduced by an additional 30% and was no longer statistically significant after adjustment for the behavioral factors of smoking, drug use, and delayed prenatal care. These findings suggest that neighborhood factors may influence birthweight by shaping maternal behavioral risks. Thus, neighborhood level interventions should be considered to address multiple maternal and infant health risks. Future studies should examine more direct measures of neighborhood stress, such as perceived neighborhood disorder, and evaluate alternative mechanisms by which neighborhood factors influence behavior (e.g., social norms and access to goods and services).

The Contribution of Preterm Birth to the Black–White Infant Mortality Gap, 1990 and 2000

OBJECTIVES We evaluated whether the decline of the racial disparity in preterm birth during the last decade was commensurate with a decline in the contribution of preterm birth to the infant mortality gap. METHODS We used linked files of 1990 and 2000 data on US infant births and deaths to partition the gap between Black and White infant mortality rates into differences in the (1) distribution of gestational age and (2) gestational age-specific mortality rates. RESULTS Between 1990 and 2000, the Black-White infant mortality rate ratio did not change significantly (2.3 vs 2.4). Excess deaths among preterm Black infants accounted for nearly 80% of the Black-White infant mortality gap in both 1990 and 2000. The narrowing racial disparity in the preterm birth rate was counterbalanced by greater mortality reductions in White than in Black preterm infants. Extremely preterm birth (<28 weeks) was 4 times higher in Black infants and accounted for more than half of the infant mortality gap. CONCLUSIONS Substantial reductions in the Black-White infant mortality gap will require improved prevention of extremely preterm birth among Black infants.

Prepregnancy Body Mass Index and Gestational Weight Gain in Relation to Child Body Mass Index Among Siblings

There is increasing evidence that in utero effects of excessive gestational weight gain may result in increased weight in children; however, studies have not controlled for shared genetic or environmental factors between mothers and children. Using 2,758 family groups from the Collaborative Perinatal Project, the authors examined the association of maternal prepregnancy body mass index (BMI) and gestational weight gain on child BMI at age 4 years using both conventional generalized estimating equations and fixed-effects models that account for shared familial factors. With generalized estimating equations, prepregnancy BMI and gestational weight gain had similar associations with the child BMI z score (β = 0.09 units, 95% confidence interval (CI): 0.08, 0.11; and β = 0.07 units, 95% CI: 0.04, 0.11, respectively. However, fixed effects resulted in null associations for both prepregnancy BMI (β = 0.03 units, 95% CI: -0.01, 0.07) and gestational weight gain (β = 0.03 units, 95% CI: -0.02, 0.08) with child BMI z score at age 4 years. The positive association between gestational weight gain and child BMI at age 4 years may be explained by shared family characteristics (e.g., genetic, behavioral, and environmental factors) rather than in utero programming. Future studies should continue to evaluate the relative roles of important familial and environmental factors that may influence BMI and obesity in children.

Drug use and limited prenatal care: an examination of responsible barriers

OBJECTIVE The purpose of this study was to determine sociodemographic, psychosocial, and health belief factors that explain the association between maternal drug use and little or no prenatal care. STUDY DESIGN A cohort of 812 low-income women who delivered at Johns Hopkins Hospital were administered a postpartum survey. Drug use was determined by self-report, medical record, and toxicologic screens. Medical records were abstracted to determine little or no prenatal care, as defined by </= 1 visit. RESULTS Adjustments for sociodemographic characteristics and cocaine and opiate use were predictive of little or no prenatal care. The effect of cocaine was explained by psychosocial and health belief factors: external locus of control, fear of being reported to police, and disbelief in the efficacy of care. Opiate use remained strongly related to little or no care in fully adjusted models (odds ratio, 3.16; P < .001). CONCLUSION Different outreach and education strategies may be necessary to enroll cocaine- vs opiate-using women into prenatal care.

Perinatal Outcomes for Asian, Native Hawaiian, and Other Pacific Islander Mothers of Single and Multiple Race/Ethnicity: California and Hawaii, 2003–2005

OBJECTIVES We examined characteristics and birth outcomes of Asian/Pacific Islander (API) mothers to determine whether differences in outcomes existed between mothers of single race/ethnicity and multiple race/ethnicity. METHODS We used data from California and Hawaii birth certificates from 2003 through 2005 to describe variation in birth outcomes for API subgroups by self-reported maternal race/ethnicity (single versus multiple race or API subgroup), and we also compared these outcomes to those of non-Hispanic White women. RESULTS Low birthweight (LBW) and preterm birth (PTB) varied more among API subgroups than between mothers of single versus multiple race/ethnicity. After adjustment for sociodemographic and behavioral risk factors, API mothers of multiple race/ethnicity had outcomes similar to mothers of single race/ethnicity, with exceptions for multiple-race/ethnicity Chinese (higher PTB), Filipino (lower LBW and PTB), and Thai (higher LBW) subgroups. Compared with single-race non-Hispanic Whites, adverse outcomes were elevated for most API subgroups: only single-race/ethnicity Korean mothers had lower rates of both LBW (3.4%) and PTB (5.6%); single-race/ethnicity Cambodian, Laotian, and Marshallese mothers had the highest rates of both LBW (8.8%, 9.2%, and 8.4%, respectively) and PTB (14.0%, 13.7%, and 18.8%, respectively). CONCLUSIONS Strategies to improve birth outcomes for API mothers should consider variations in risk by API subgroup and multiple race/ethnicity.

ON THE APPLICATION OF DECOMPOSITION METHODS

Yang et al.1 applied an underutilized demographic technique, developed by Kitagawa,2 to decompose temporal trends in low birth-weight (LBW) into changes in the distribution of maternal age and parity versus changes in the age- and parity-specific rates of LBW. The authors concluded that temporal increases in LBW were largely the result of changes in age- and parity-specific rates rather than age–parity distributional shifts. The applied method, which elegantly partitions the difference between 2 aggregate rates into differences in factor-specific rates and differences in factor distribution, requires a population standard to which differences in factor-specific rates and proportions are weighted. Although Yang et al. appropriately noted that the size of the 2 components (rates vs distribution) depends on the choice of the standard population, we were puzzled by the authors’ selection of a standard with the age–parity distribution of 1980 and age-and parity-specific LBW rates of 2000 (equation 1a). The authors could have equally arbitrarily specified the reference as having the 2000 age–parity distribution and the 1980 age- and parity-specific rates (equation 1b): Let L1 and L2 be the crude LBW rates for 1980 and 1990; Nij1/N++1 and Nij2/N++2 the age- and parity-specific proportions of all births in 1980 and 1990, respectively; and Rij1 and Rij2 the age- and parity-specific LBW rates in 1980 and 2000, respectively. To avoid defining the standard population as having the distribution of one time and the factor-specific rates of the other time when comparing the same population at 2 points in time, Kitagawa proposed a symmetric solution using the average age–parity distribution to weight the rate component and the average age- and parity-specific rates to weight the distribution component (equation 2)2: Kitagawa noted that this solution yields estimates that are the average of the components from equations 1a and 1b, which provide the range of variation for each component. By using only equation 1a, Yang et al. calculated only one end of this range. This is analogous to presenting one end of a confidence interval instead of the midpoint. We argue, in agreement with Kitagawa, that equation 2 is the more appropriate solution, because it assumes that both the distribution and the rates changed between the 2 time periods and because it does not favor one time over the other in the weights for the 2 components. Others have developed additional techniques that are useful alternatives under certain conditions.3,4 We venture that Yang et al. may have overestimated the contribution of age–parity distributional changes by applying the now higher LBW rates as weights. Perhaps the authors would consider reporting summary results obtained with our suggested alternative.2 We applaud the use of this underutilized demographic technique and wish only to promote its proper application.

Introduction to the Special Issue of Articles from the 2007 National Survey of Children’s Health

Children’s health and well-being can be measured using a variety of metrics. Very often these measures are drawn from data systems which provide primarily national estimates of domain-specific indicators. The National Survey of Children’s Health (NSCH) is unique among these systems in that it collects information on a comprehensive set of physical, mental and social health indicators among children, their access to and utilization of health services as well as family and community level factors that have been shown to influence health and development. The survey is designed to provide both nationaland state-level estimates which are necessary for the development, implementation, and monitoring of effective health promotion and disease prevention programs for children and their families. As such, the NSCH fills a critical void and is the only data system that provides comparable estimates of children’s health from birth to age 17 across every state in the nation. Reflecting a sustained commitment on the part of the Health Resources and Services Administration’s Maternal and Child Health Bureau (MCHB), in collaboration with the Centers for Disease Control and Prevention’s National Center for Health Statistics (NCHS), the 2007 NSCH is the second such effort, building on the success of the first NSCH in 2003 to collect accurate and timely information on the health and wellbeing of children and their families with a third round of the NSCH (2011) soon to be publicly available. The 2007 NSCH not only provides updated estimates of child health at both national and state levels but, coupled with the 2003 NSCH, also provides the capacity to evaluate national, regional, and state trends as well as the impact of federal and state-level programs and policies. The 2007 NSCH was fielded between April 2007 and July 2008 yielding 91,642 completed child-level interviews across the United States including 1,700–1,900 interviews in each State. Consistent with the original NSCH in 2003, the survey was conducted as a module of the State and Local Area Integrated Telephone Survey Program (SLAITS) which utilizes the sampling frame of the National Immunization Survey (NIS). The NIS is a random-digit dial telephone survey that uses computer-assisted telephone interview (CATI) technology to contact and interview households. In order to identify eligible subjects, households were screened for the presence of a child under the age of 18 years; if more than one child resided in the household, one was randomly selected to be the subject of the interview. Respondents were adults knowledgeable about the child’s health. In nearly three-quarters of interviews this was the child’s mother while 20.5 % of the remaining interviews were conducted with the child’s father, 4.2 % were conducted with a grandparent and 1.8 % with other relatives or guardians. Interviews were conducted in English, Spanish, Mandarin, Cantonese, Vietnamese, and Korean. Overall, 5.3 % of interviews were conducted in Spanish and 0.2 % were conducted in one of the four Asian languages [1]. The overall response rate was 51.2 % and the interview completion rate was 66.0 % for all households with children [2]. Similar to the 2003 survey, the 2007 NSCH contained ‘‘core’’ survey items in several domains, including: health and functional status, health care financing, access and The views in this article are those of the authors and not necessarily those of the Health Resources and Services Administration of the US Department of Health and Human Services.