A multilevel non-hierarchical study of birth weight and socioeconomic status

The associations of objective and perceived neighborhood disadvantage with stress among pregnant black women

BACKGROUND

Neighborhood disadvantage may impact risk of preterm birth through stress. Few studies have examined how neighborhood disadvantage relates to stress during pregnancy, especially for Black women.

METHODS

Secondary data analysis of 572 women in a prospective cohort in Detroit, MI and Columbus, OH. Participants completed questionnaires including the ROSS Neighborhood Disorder Scale, the crime subscale of the Perceived Neighborhood Scale (PNS), and the Perceived Stress Scale. An objective neighborhood disadvantage index (NDI) was created using principal components analysis after geocoding residential addresses and linking to Census data.

RESULTS

All models used logistic regression. Adjusted for maternal age and annual household income, perceived stress was positively associated with perceived neighborhood disorder (p < .01). In a separate model, perceived neighborhood crime was positively associated with perceived neighborhood disorder (p = .005). In a joint model adjusted for age and income, the association of disorder with stress was similar in magnitude (p < .01) but the association between crime and stress weakened. The NDI was not associated with perceived stress before or after adjustment for confounders.

CONCLUSIONS

Perceived neighborhood disadvantage may capture a different dimension than objective neighborhood disadvantage. Future studies should test stress as a pathway by which neighborhood environment increases risk of preterm birth.

A Comprehensive Review on Social Inequalities and Pregnancy Outcome-Identification of Relevant Pathways and Mechanisms

Scientific literature tends to support the idea that the pregnancy and health status of fetuses and newborns can be affected by maternal, parental, and contextual characteristics. In addition, a growing body of evidence reports that social determinants, measured at individual and/or aggregated level(s), play a crucial role in fetal and newborn health. Numerous studies have found social factors (including maternal age and education, marital status, pregnancy intention, and socioeconomic status) to be linked to poor birth outcomes. Several have also suggested that beyond individual and contextual social characteristics, living environment and conditions (or "neighborhood") emerge as important determinants in health inequalities, particularly for pregnant women. Using a comprehensive review, we present a conceptual framework based on the work of both the Commission on Social Determinants of Health and the World Health Organization (WHO), aimed at describing the various pathways through which social characteristics can affect both pregnancy and fetal health, with a focus on the structural social determinants (such as socioeconomic and political context) that influence social position, as well as on intermediary determinants. We also suggest that social position may influence more specific intermediary health determinants; individuals may, on the basis of their social position, experience differences in environmental exposure and vulnerability to health-compromising living conditions. Our model highlights the fact that adverse birth outcomes, which inevitably lead to health inequity, may, in turn, affect the individual social position. In order to address both the inequalities that begin in utero and the disparities observed at birth, it is important for interventions to target various unhealthy behaviors and psychosocial conditions in early pregnancy. Health policy must, then, support: (i) midwifery availability and accessibility and (ii) enhanced multidisciplinary support for deprived pregnant women.

Urban social environment and low birth weight in 360 Latin American cities

OBJECTIVE

Using data compiled by the SALURBAL project (Urban Health in Latin America; 'Salud Urbana en América Latina') we quantified variability in low birth weight (LBW) across cities in Latin America, and evaluated the associations of socio-economic characteristics at various levels (maternal, sub-city and city) with the prevalence of LBW.

METHODS

The sample included 8 countries, 360 cities, 1321 administrative areas within cities (sub-city units) and birth registers of more than 4.5 million births for the year 2014. We linked maternal education from birth registers to data on socioeconomic characteristics of sub-cities and cities using the closest available national population census in each country. We applied linear and Poisson random-intercept multilevel models for aggregated data.

RESULTS

The median prevalence of city LBW by country ranged from a high of 13% in Guatemala to a low of 5% in Peru (median across all cities was 7.8%). Most of the LBW variability across sub-cities was between countries, but there were also significant proportions between cities within a country, and within cities. Low maternal education was associated with higher prevalence of LBW (Prevalence rate ratios (PRR) for less than primary vs. completed secondary or more 1.12 95% CI 1.10, 1.13) in the fully adjusted model. In contrast, higher sub-city education and a better city social environment index were independently associated with higher LBW prevalence after adjustment for maternal education and age, city population size and city gross domestic product (PRR 1.04 95% CI 1.03, 1.04 per SD higher sub-city education and PRR 1.02 95% CI 1.00, 1.04 per SD higher SEI). Larger city size was associated with a higher prevalence of LBW (PRR 1.06; 95% CI 1.01, 1.12).

CONCLUSION

Our findings highlight the presence of heterogeneity in the distribution of LBW and the importance of maternal education, local and broader social environments in shaping LBW in urban settings of Latin America. Implementing context-sensitive interventions guided to improve women's education is recommended to tackle LBW in the region.

Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices

Recognizing that health outcomes are influenced by and occur within multiple social and physical contexts, researchers have used multilevel modeling techniques for decades to analyze hierarchical or nested data. Cross-Classified Multilevel Models (CCMM) are a statistical technique proposed in the 1990s that extend standard multilevel modeling and enable the simultaneous analysis of non-nested multilevel data. Though use of CCMM in empirical health studies has become increasingly popular, there has not yet been a review summarizing how CCMM are used in the health literature. To address this gap, we performed a scoping review of empirical health studies using CCMM to: (a) evaluate the extent to which this statistical approach has been adopted; (b) assess the rationale and procedures for using CCMM; and (c) provide concrete recommendations for the future use of CCMM. We identified 118 CCMM papers published in English-language literature between 1994 and 2018. Our results reveal a steady growth in empirical health studies using CCMM to address a wide variety of health outcomes in clustered non-hierarchical data. Health researchers use CCMM primarily for five reasons: (1) to statistically account for non-independence in clustered data structures; out of substantive interest in the variance explained by (2) concurrent contexts, (3) contexts over time, and (4) age-period-cohort effects; and (5) to apply CCMM alongside other techniques within a joint model. We conclude by proposing a set of recommendations for use of CCMM with the aim of improved clarity and standardization of reporting in future research using this statistical approach.

Inequalities in birth weight and maternal education: a time-series study from 1996 to 2013 in Brazil

Maternal education represents one of the most important social determinants of inequality in birth weight (BW) in developing countries. The present study sought to investigate secular trends in health inequality considering the difference in mean BW between extremes of maternal educational attainment in Brazil. Using a time-series design, data from 6,452,551 live births which occurred in all Brazilian state capitals from 1996 to 2013 were obtained from the Information System on Live Births. Secular trends of the difference in mean birth weight between low (<8 years of schooling) and high (≥12 years of schooling) educational attainment were analyzed. The main finding was that differences in mean birth weight between the two extremes of maternal educational attainment decreased over time. There was a significant decrease in mean BW in neonates born to mothers with higher educational attainment, and a slight increase in those born to mothers with lower educational attainment. One of the key factors involved in decreasing inequality was an increase in the number of antenatal visits. In view of these results, we conclude, that despite a slight increase of mean birth weight among mothers with low education, the reduction of inequality in pregnancy outcomes over time in Brazil is attributable to a worsening scenario for mothers who are better off rather than to improvements for the most vulnerable group of mothers.

Relationship of Socioeconomic Status Indicators to Obesity in Hispanic and Non-Hispanic White Middle-Aged Women: A Population Study

BACKGROUND

Obesity rates differ between Hispanic and White (non-Hispanic) women in the United States, with higher rates among Hispanic women. Socioeconomic processes contribute to this disparity both at the individual and the environmental level. Understanding these complex relationships requires multilevel analyses within cohorts of women that have a shared environment. In population-based samples of Hispanic and White (non-Hispanic) women from the same neighborhoods, we evaluated within each ethnic group a) The association of individual-level socioeconomic status (SES) with body mass index (BMI); and b) The additional contribution of neighborhood-level measures of SES.

METHODS

Using population-based multi-stage sampling methods, we oversampled low SES and Hispanic block groups. During household screening, we identified women aged 30 to 50 years. Among White women, we specifically oversampled women with low educational levels. 515 Hispanic and 503 White women completed baseline. Height and weight were measured. Baseline surveys, in Spanish and English, included four measures of SES. Three measures of area-level SES were examined. Analysis of loge BMI on each SES measure used linear mixed models, incorporating design effects.

RESULTS

Among White women, low education, social status, and neighborhood SES were associated with higher BMI (p < 0.001, p < 0.0001, and p < 0.05, respectively), independent of other SES measures. Although the highest grouped category of education, income and subjective social status within the Hispanic cohort had the lowest mean estimated BMI, the point estimates across categories were not monotonic, and had wide confidence intervals. As a result, in contrast to the findings among White women, no statistically significant associations were found between BMI and measures of SES among Hispanic women.

DISCUSSION

Neighborhood and individual measures of SES operate differently in Hispanic compared with White women. We had assumed the measures we included to be most salient and operate similarly for both groups of women. Rather the salient factors for Hispanic women have yet to be identified. Improved understanding may ultimately inform the design of culturally-relevant multilevel obesity prevention strategies.

Perinatal Risk Factors and ADHD in Children and Adolescents: A Hierarchical Structure of Disorder Predictors

OBJECTIVE

The aim of the study was to hierarchically assess the predictive power of low and high birth weight, pre-term and post-term birth, and low Apgar score as the risk factors for ADHD.

METHOD

The data of 132 boys diagnosed with ADHD and 146 boys from control group, aged 6 to 18 years, have been analyzed. The boys were categorized according to term of birth, birth weight, and Apgar score. CART method (Classification and Regression Trees) was used for assessment of the relationship between perinatal factors and the risk of ADHD.

RESULTS

Low Apgar score (21.97% vs. 13.01%) and post-term birth (12.12% vs. 0.68%) were more frequent in the sample than in the control group. CART method additionally indicated low birth weight as associated with the risk of ADHD. Among analyzed risk factors, Apgar score had the highest predictive value.

CONCLUSION

The decreased Apgar score is the most important perinatal risk factor of ADHD. Research results also indicated a high significance of post-term birth in predicting the disorder.

Cross-classified occupational exposure data

We demonstrate the regression analysis of exposure determinants using cross-classified random effects in the context of lead exposures resulting from blasting surfaces in advance of painting. We had three specific objectives for analysis of the lead data, and observed: (1) high within-worker variability in personal lead exposures, explaining 79% of variability; (2) that the lead concentration outside of half-mask respirators was 2.4-fold higher than inside supplied-air blasting helmets, suggesting that the exposure reduction by blasting helmets may be lower than expected by the Assigned Protection Factor; and (3) that lead concentrations at fixed area locations in containment were not associated with personal lead exposures. In addition, we found that, on average, lead exposures among workers performing blasting and other activities was 40% lower than among workers performing only blasting. In the process of obtaining these analyses objectives, we determined that the data were non-hierarchical: repeated exposure measurements were collected for a worker while the worker was a member of several groups, or cross-classified among groups. Since the worker is a member of multiple groups, the exposure data do not adhere to the traditionally assumed hierarchical structure. Forcing a hierarchical structure on these data led to similar within-group and between-group variability, but decreased precision in the estimate of effect of work activity on lead exposure. We hope hygienists and exposure assessors will consider non-hierarchical models in the design and analysis of exposure assessments.

City-Specific Spatiotemporal Infant and Neonatal Mortality Clusters: Links with Socioeconomic and Air Pollution Spatial Patterns in France

Infant and neonatal mortality indicators are known to vary geographically, possibly as a result of socioeconomic and environmental inequalities. To better understand how these factors contribute to spatial and temporal patterns, we conducted a French ecological study comparing two time periods between 2002 and 2009 for three (purposefully distinct) Metropolitan Areas (MAs) and the city of Paris, using the French census block of parental residence as the geographic unit of analysis. We identified areas of excess risk and assessed the role of neighborhood deprivation and average nitrogen dioxide concentrations using generalized additive models to generate maps smoothed on longitude and latitude. Comparison of the two time periods indicated that statistically significant areas of elevated infant and neonatal mortality shifted northwards for the city of Paris, are present only in the earlier time period for Lille MA, only in the later time period for Lyon MA, and decrease over time for Marseille MA. These city-specific geographic patterns in neonatal and infant mortality are largely explained by socioeconomic and environmental inequalities. Spatial analysis can be a useful tool for understanding how risk factors contribute to disparities in health outcomes ranging from infant mortality to infectious disease-a leading cause of infant mortality.

Changes in Socioeconomic Inequality of Low Birth Weight and Macrosomia in Shaanxi Province of Northwest China, 2010-2013: A Cross-sectional Study

Socioeconomic disparities in birth weights (BWs) are associated with lifelong differences in health and productivity. Understanding socioeconomic disparities in BW is presently of concern to develop public health interventions that promote a good start in life in Northwest China. In the study, our objective is to investigate the socioeconomic disparities in low and high BW from 2010 to 2013 in this region.Those single live births were recruited using a stratified multistage sampling method in Shaanxi province from August to December 2013. Data were collected with a structured questionnaire and a review of birth certificates. Socioeconomic status (SES) was stratified based on the calculated household wealth index. Prevalence differences (PDs) and concentration indices (CIs) were used to depict the SES inequality of low BW (LBW) and macrosomia.Information for 28722 single live births born were obtained in Shaanxi province. From 2010 to 2013, the overall rates of LBW decreased, and the difference in LBW across differing SES groups decreased by 0.7% (boys, 0.4%; girls, 0.8%). From 2010 to 2013, the overall rates of macrosomia increased by 14.3% (boys, 17.5%; girls, 7.8%), whereas the PDs in macrosomia across various SES groups remained unchanged. From 2010 to 2013, concentration indices for SES inequalities in LBW and macrosomia confirmed the results shown by differences in prevalence. Compared with mothers of high SES, those in low SES group were significantly older, less educated, engaged in farming with less availabile healthcare, and engaged in unhealthy lifestyles (eg, exposure to secondhand smoke) during pregnancy, regardless of the baby's sex.From 2010 to 2013, in Shaanxi province, the negative association between socioeconomic status and LBW weakened. Rates of macrosomia were higher in those of high SES, but the SES disparities varied insignificantly over the same time. Our findings may provide valuable insights to direct healthcare policies for pregnant women to reduce inequalities in health, quality of life, and productivity for their children as they age into adulthood.

Associations between residence at birth and mental health disorders: a spatial analysis of retrospective cohort data

BACKGROUND

Mental health disorders impact approximately one in four US adults. While their causes are likely multifactorial, prior research has linked the risk of certain mental health disorders to prenatal and early childhood environmental exposures, motivating a spatial analysis to determine whether risk varies by birth location.

METHODS

We investigated the spatial associations between residence at birth and odds of depression, bipolar disorder, and post-traumatic stress disorder (PTSD) in a retrospective cohort (Cape Cod, Massachusetts, 1969-1983) using generalized additive models to simultaneously smooth location and adjust for confounders. Birth location served as a surrogate for prenatal exposure to the combination of social and environmental factors related to the development of mental illness. We predicted crude and adjusted odds ratios (aOR) for each outcome across the study area. The results were mapped to identify areas of increased risk.

RESULTS

We observed spatial variation in the crude odds ratios of depression that was still present even after accounting for spatial confounding due to geographic differences in the distribution of known risk factors (aOR range: 0.61-3.07, P = 0.03). Similar geographic patterns were seen for the crude odds of PTSD; however, these patterns were no longer present in the adjusted analysis (aOR range: 0.49-1.36, P = 0.79), with family history of mental illness most notably influencing the geographic patterns. Analyses of the odds of bipolar disorder did not show any meaningful spatial variation (aOR range: 0.58-1.17, P = 0.82).

CONCLUSION

Spatial associations exist between residence at birth and odds of PTSD and depression, but much of this variation can be explained by the geographic distributions of available risk factors. However, these risk factors did not account for all the variation observed with depression, suggesting that other social and environmental factors within our study area need further investigation.

The social determinants of infant mortality and birth outcomes in Western developed nations: a cross-country systematic review

Infant mortality (IM) and birth outcomes, key population health indicators, have lifelong implications for individuals, and are unequally distributed globally. Even among western industrialized nations, striking cross-country and within-country patterns are evident. We sought to better understand these variations across and within the United States of America (USA) and Western Europe (WE), by conceptualizing a social determinants of IM/birth outcomes framework, and systematically reviewing the empirical literature on hypothesized social determinants (e.g., social policies, neighbourhood deprivation, individual socioeconomic status (SES)) and intermediary determinants (e.g., health behaviours). To date, the evidence suggests that income inequality and social policies (e.g., maternal leave policies) may help to explain cross-country variations in IM/birth outcomes. Within countries, the evidence also supports neighbourhood SES (USA, WE) and income inequality (USA) as social determinants. By contrast, within-country social cohesion/social capital has been underexplored. At the individual level, mixed associations have been found between individual SES, race/ethnicity, and selected intermediary factors (e.g., psychosocial factors) with IM/birth outcomes. Meanwhile, this review identifies several methodological gaps, including the underuse of prospective designs and the presence of residual confounding in a number of studies. Ultimately, addressing such gaps including through novel approaches to strengthen causal inference and implementing both health and non-health policies may reduce inequities in IM/birth outcomes across the western developed world.

Spatio-temporal patterning of small area low birth weight incidence and its correlates: a latent spatial structure approach

Low birth weight (LBW) defined as infant weight at birth of less than 2500 g is a useful health outcome for exploring spatio-temporal variation and the role of covariates. LBW is a key measure of population health used by local, national and international health organizations. Yet its spatio-temporal patterns and their dependence structures are poorly understood. In this study we examine the use of flexible latent structure models for the analysis of spatio-temporal variation in LBW. Beyond the explanatory capabilities of well-known predictors, we observe spatio-temporal effects, which are not directly observable using conventional modeling approaches. Our analysis shows that for county-level counts of LBW in Georgia and South Carolina the proportion of black population is a positive risk factor while high-income is a negative risk factor. Two dominant residual temporal components are also estimated. Finally our proposed method provides a better goodness-of-fit to these data than the conventional space–time models.