Effects of health intervention programs and arsenic exposure on child mortality from acute lower respiratory infections in rural Bangladesh

BackgroundRespiratory infections continue to be a public health threat, particularly to young children in developing countries. Understanding the geographic patterns of diseases and the role of potential risk factors can help improve future mitigation efforts. Toward this goal, this paper applies a spatial scan statistic combined with a zero-inflated negative-binomial regression to re-examine the impacts of a community-based treatment program on the geographic patterns of acute lower respiratory infection (ALRI) mortality in an area of rural Bangladesh. Exposure to arsenic-contaminated drinking water is also a serious threat to the health of children in this area, and the variation in exposure to arsenic must be considered when evaluating the health interventions.MethodsALRI mortality data were obtained for children under 2 years old from 1989 to 1996 in the Matlab Health and Demographic Surveillance System. This study period covers the years immediately following the implementation of an ALRI control program. A zero-inflated negative binomial (ZINB) regression model was first used to simultaneously estimate mortality rates and the likelihood of no deaths in groups of related households while controlling for socioeconomic status, potential arsenic exposure, and access to care. Next a spatial scan statistic was used to assess the location and magnitude of clusters of ALRI mortality. The ZINB model was used to adjust the scan statistic for multiple social and environmental risk factors.ResultsThe results of the ZINB models and spatial scan statistic suggest that the ALRI control program was successful in reducing child mortality in the study area. Exposure to arsenic-contaminated drinking water was not associated with increased mortality. Higher socioeconomic status also significantly reduced mortality rates, even among households who were in the treatment program area.ConclusionCommunity-based ALRI interventions can be effective at reducing child mortality, though socioeconomic factors may continue to influence mortality patterns. The combination of spatial and non-spatial methods used in this paper has not been applied previously in the literature, and this study demonstrates the importance of such approaches for evaluating and improving public health intervention program

The ecology of birth defects: socio-economic and environmental determinants of gastroschisis in North Carolina

Gastroschisis is a serious birth defect that has increased in prevalence in North Carolina over the past decade. The causes of the defect, and the reasons for this increase, are largely unknown. This study uses the disease ecology framework and spatial methodologies - spatial statistics, Geographic Information Systems, and hydrological modeling - to explore the geographic distribution of gastroschisis in North Carolina and suggest possible socioeconomic and environmental factors that may contribute to the disease. Specific questions addressed in this study include: 1) Do significant geographic clusters of gastroschisis exist in North Carolina? 2) Do clusters suggest the presence of point-source environmental pollutants? 3) What area-level socioeconomic characteristics are related to gastroschisis outcomes? 4) What can this tell us about possible causes of the disease? Using data from a population-based birth defects registry, this study uses Kulldorff's spatial scan statistic to identify the location and extent of clusters of gastroschisis births in North Carolina between 1999 and 2004. Spatial clusters are controlled for four major risk factors (maternal age, race, prior births and Medicaid status) to ensure that the clusters are not an artifact of the population composition of the State. The relationship between neighborhood socioeconomic characteristics (e.g., race, poverty, education and unemployment) and gastroschisis outcomes are examined using logistic regression models, which combine individual-level and neighborhood-level variables. Finally, simple hydrological models are used to determine if exposure to upstream textile mill effluent increases the risk for a gastroschisis affected pregnancy. Results indicate the presence of a localized cluster of gastroschisis in the rural southern Piedmont of North Carolina. In addition, both individual-level (Medicaid status) and neighborhood-level (poverty and unemployment) socioeconomic factors appear to contribute to the risk of a gastroschisis affected pregnancy, suggesting that neighborhood-level socioeconomic factors exert an independent causal effect on gastroschisis. Despite the localized nature of the cluster, which often suggests the presence of an environmental contaminant, there is no evidence to support this hypothesis. These results may help understanding the myriad social, economic and environmental factors that combine and interact to influence gastroschisis outcomes

Spatial analysis of elderly access to primary care services

BACKGROUND: Admissions for Ambulatory Care Sensitive Conditions (ACSCs) are considered preventable admissions, because they are unlikely to occur when good preventive health care is received. Thus, high rates of admissions for ACSCs among the elderly (persons aged 65 or above who qualify for Medicare health insurance) are signals of poor preventive care utilization. The relevant geographic market to use in studying these admission rates is the primary care physician market. Our conceptual model assumes that local market conditions serving as interventions along the pathways to preventive care services utilization can impact ACSC admission rates. RESULTS: We examine the relationships between market-level supply and demand factors on market-level rates of ACSC admissions among the elderly residing in the U.S. in the late 1990s. Using 6,475 natural markets in the mainland U.S. defined by The Health Resources and Services Administration's Primary Care Service Area Project, spatial regression is used to estimate the model, controlling for disease severity using detailed information from Medicare claims files. Our evidence suggests that elderly living in impoverished rural areas or in sprawling suburban places are about equally more likely to be admitted for ACSCs. Greater availability of physicians does not seem to matter, but greater prevalence of non-physician clinicians and international medical graduates, relative to U.S. medical graduates, does seem to reduce ACSC admissions, especially in poor rural areas. CONCLUSION: The relative importance of non-physician clinicians and international medical graduates in providing primary care to the elderly in geographic areas of greatest need can inform the ongoing debate regarding whether there is an impending shortage of physicians in the United States. These findings support other authors who claim that the existing supply of physicians is perhaps adequate, however the distribution of them across the landscape may not be optimal. The finding that elderly who reside in sprawling urban areas have access impediments about equal to residents of poor rural communities is new, and demonstrates the value of conceptualizing and modelling impedance based on place and local context

A Bayesian spatio-temporal nowcasting model for public health decision-making and surveillance

As COVID-19 spread through the United States in 2020, states began to set up alert systems to inform policy decisions and serve as risk communication tools for the general public. Many of these systems, like in Ohio, included indicators based on an assessment of trends in reported cases. However, when cases are indexed by date of disease onset, reporting delays complicate the interpretation of trends. Despite a foundation of statistical literature to address this problem, these methods have not been widely applied in practice. In this paper, we develop a Bayesian spatio-temporal nowcasting model for assessing trends in county-level COVID-19 cases in Ohio. We compare the performance of our model to the current approach used in Ohio and the approach that was recommended by the Centers for Disease Control and Prevention. We demonstrate gains in performance while still retaining interpretability using our model. In addition, we are able to fully account for uncertainty in both the time series of cases and in the reporting process. While we cannot eliminate all of the uncertainty in public health surveillance and subsequent decision-making, we must use approaches that embrace these challenges and deliver more accurate and honest assessments to policymakers

Residential mobility in early childhood: Household and neighborhood characteristics of movers and non-movers

Understanding residential mobility in early childhood is important for contextualizing influences on child health and well-being

Evidence for localised HIV related micro-epidemics associated with the decentralised provision of antiretroviral treatment in rural South Africa: a spatio-temporal analysis of changing mortality patterns (2007-2010).

BACKGROUND: In this study we analysed the spatial and temporal changes in patterns of mortality over a period when antiretroviral therapy (ART) was rolled out in a rural region of north-eastern South Africa. Previous studies have identified localised concentrated HIV related sub-epidemics and recommended that micro-level analyses be carried out in order to direct focused interventions. METHODS: Data from an ongoing health and socio-demographic surveillance study was used in the analysis. The follow-up was divided into two periods, 2007-2008 and 2009-2010, representing the times immediately before and after the effects on mortality of the decentralised ART provision from a newly established local health centre would be expected to be evident. The study population at the start of the analysis was approximately 73 000 individuals. Data were aggregated by village and also using a 2 × 2 km grid. We identified villages, grid squares and regions in the site where mortality rates within each time period or rate ratios between the periods differed significantly from the overall trends. We used clustering techniques to identify cause-specific mortality hotspots. FINDINGS: Comparing the two periods, there was a 30% decrease in age and gender standardised adult HIV-related and TB (HIV/TB) mortality with no change in mortality due to other causes. There was considerable spatial heterogeneity in the mortality patterns. Areas separated by 2 to 4 km with very different epidemic trajectories were identified. There was evidence that the impact of ART in reducing HIV/TB mortality was greatest in communities with higher mortality rates in the earlier period. CONCLUSIONS: This study shows the value of conducting high resolution spatial analyses in order to understand how local micro-epidemics contribute to changes seen over a wider area. Such analyses can support targeted interventions

Spatial and environmental connectivity analysis in a cholera vaccine trial

This paper develops theory and methods for vaccine trials that utilize spatial and environmental information. Satellite imagery is used to identify whether households are connected to one another via water bodies in a study area in rural Bangladesh. Then relationships between neighborhood-level cholera vaccine coverage and placebo incidence and neighborhood-level spatial variables are measured. The study hypothesis is that unvaccinated people who are environmentally connected to people who have been vaccinated will be at lower risk compared to unvaccinated people who are environmentally connected to people who have not been vaccinated. We use four data sets including: a cholera vaccine trial database, a longitudinal demographic database of the rural population from which the vaccine trial participants were selected, a household-level geographic information system (GIS) database of the same study area, and high resolution Quickbird satellite imagery. An environmental connectivity metric was constructed by integrating the satellite imagery with the vaccine and demographic databases linked with GIS. The results show that there is a relationship between neighborhood rates of cholera vaccination and placebo incidence. Thus, people are indirectly protected when more people in their environmentally-connected neighborhood are vaccinated. This result is similar to our previous work that used a simpler Euclidean distance neighborhood to measure neighborhood vaccine coverage (Ali et al., 2005). Our new method of measuring environmental connectivity is more precise since it takes into account the transmission mode of cholera and therefore this study validates our assertion that the oral cholera vaccine provides indirect protection in addition to direct protection

Contextual Factors Associated With County-Level Suicide Rates in the United States, 1999 to 2016

Importance Understanding geographic and community-level factors associated with suicide can inform targeted suicide prevention efforts. Objectives To estimate suicide rates and trajectories, assess associated county-level contextual factors, and explore variation across the rural-urban continuum. Design, Setting, and Participants This cross-sectional study included all individuals aged 25 to 64 years who died by suicide from January 1, 1999, to December 31, 2016, in the United States. Spatial analysis was used to map excess risk of suicide, and longitudinal random-effects models using negative binomial regression tested associations of contextual variables with suicide rates as well as interactions among county-level contextual variables. Data analyses were conducted between January 2019 and July 2019. Exposure County of residence. Main Outcomes and Measures Three-year county suicide rates during an 18-year period stratified by rural-urban location. Results Between 1999 and 2016, 453 577 individuals aged 25 to 64 years died by suicide in the United States. Decedents were primarily male (349 082 [77.0%]) with 101 312 (22.3%) aged 25 to 34 years, 120 157 (26.5%) aged 35 to 44 years, 136 377 (30.1%) aged 45 to 54 years, and 95 771 (21.1%) aged 55 to 64 years. Suicide rates were higher and increased more rapidly in rural than in large metropolitan counties. The highest deprivation quartile was associated with higher suicide rates compared with the lowest deprivation quartile, especially in rural areas, although this association declined during the period studied (rural, 1999-2001: incidence rate ratio [IRR], 1.438; 95% CI, 1.319-1.568; P \u3c .001; large metropolitan, 1999-2001: 1.208; 95% CI, 1.149-1.270; P \u3c .001; rural, 2014-2016: IRR, 1.121; 95% CI, 1.032-1.219; P = .01; large metropolitan, 2014-2016: IRR, 0.942; 95% CI, 0.887-1.001; P = .06). The presence of more gun shops was associated with an increase in county-level suicide rates in all county types except the most rural (rural: IRR, 1.001; 95% CI, 0.999-1.004; P = .40; micropolitan: IRR, 1.005; 95% CI, 1.002-1.007; P \u3c .001; small metropolitan: IRR, 1.010; 95% CI, 1.006-1.014; P \u3c .001; large metropolitan: IRR, 1.012; 95% CI, 1.006-1.018; P \u3c .001). High social capital was associated with lower suicide rates than low social capital (IRR, 0.917; 95% CI, 0.891-0.943; P \u3c .001). High social fragmentation, an increasing percentage of the population without health insurance, and an increasing percentage of veterans in a county were associated with higher suicide rates (high social fragmentation: IRR, 1.077; 95% CI, 1.050-1.103; P \u3c .001; percentage of population without health insurance: IRR, 1.005; 95% CI, 1.004-1.006; P \u3c .001; percentage of veterans: IRR, 1.025; 95% CI, 1.021-1.028; P \u3c .001). Conclusions and Relevance This study found that suicide rates have increased across the nation and most rapidly in rural counties, which may be more sensitive to the impact of social deprivation than more metropolitan counties. Improving social connectedness, civic opportunities, and health insurance coverage as well as limiting access to lethal means have the potential to reduce suicide rates across the rural-urban continuum

Integration of Spatial and Social Network Analysis in Disease Transmission Studies

This study presents a case study of how social network and spatial analytical methods can be used simultaneously for disease transmission modeling. The paper first reviews strategies employed in previous studies and then offers the example of transmission of two bacterial diarrheal diseases in rural Bangladesh. The goal is to understand how diseases vary socially above and beyond the effects of the local neighborhood context. Patterns of cholera and shigellosis incidence are analyzed in space and within kinship-based social networks in Matlab, Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera and shigellosis cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. Combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis always clusters in space and only sometimes within social networks. This suggests that the local environment is most important for understanding transmission of both diseases however kinship-based social networks also influence their transmission. Simultaneous spatial and social network analysis can help us better understand disease transmission and this study has offered several strategies on how