Inclusion of non-viable neonates in the birth record and its impact on infant mortality rates in Shelby County, Tennessee, USA

Rates of infant death are one of the most common indicators of a population's overall health status. Infant mortality rates (IMRs) are used to make broad inferences about the quality of health care, effects of health policies and even environmental quality. The purpose of our study was threefold: i) to examine the characteristics of births in the area in relation to gestational age and birthweight; ii) to estimate infant mortality using variable gestational age and/or birthweight criteria for live birth, and iii) to calculate proportional mortality ratios for each cause of death using variable gestational age and/or birthweight criteria for live birth. We conducted a retrospective analysis of all Shelby County resident-linked birth and infant death certificates during the years 1999 to 2004. Descriptive test statistics were used to examine infant mortality rates in relation to specific maternal and infant risk factors. Through careful examination of 1999–2004 resident-linked birth and infant death data sets, we observed a disproportionate number of non-viable live births (≤20 weeks gestation or ≤350 grams) in Shelby County. Issuance of birth certificates to these non-viable neonates is a factor that contributes to an inflated IMR. Our study demonstrates the complexity and the appropriateness of comparing infant mortality rates in smaller geographic units, given the unique characteristics of live births in Shelby County. The disproportionate number of pre-viable infants born in Shelby County greatly obfuscates neonatal mortality and de-emphasizes the importance of post-neonatal mortality

Leveraging Existing Cohorts to Study Health Effects of Air Pollution on Cardiometabolic Disorders:India Global Environmental and Occupational Health Hub

Air pollution is a growing public health concern in developing countries and poses a huge epidemiological burden. Despite the growing awareness of ill effects of air pollution, the evidence linking air pollution and health effects is sparse. This requires environmental exposure scientist and public health researchers to work more cohesively to generate evidence on health impacts of air pollution in developing countries for policy advocacy. In the Global Environmental and Occupational Health (GEOHealth) Program, we aim to build exposure assessment model to estimate ambient air pollution exposure at a very fine resolution which can be linked with health outcomes leveraging well-phenotyped cohorts which have information on geolocation of households of study participants. We aim to address how air pollution interacts with meteorological and weather parameters and other aspects of the urban environment, occupational classification, and socioeconomic status, to affect cardiometabolic risk factors and disease outcomes. This will help us generate evidence for cardiovascular health impacts of ambient air pollution in India needed for necessary policy advocacy. The other exploratory aims are to explore mediatory role of the epigenetic mechanisms (DNA methylation) and vitamin D exposure in determining the association between air pollution exposure and cardiovascular health outcomes. Other components of the GEOHealth program include building capacity and strengthening the skills of public health researchers in India through variety of training programs and international collaborations. This will help generate research capacity to address environmental and occupational health research questions in India. The expertise that we bring together in GEOHealth hub are public health, clinical epidemiology, environmental exposure science, statistical modeling, and policy advocacy

Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters

<p>Abstract</p> <p>Background</p> <p>The purpose of this study is to examine the spatial and population (e.g., socio-economic) characteristics of low birthweight using two different cluster estimation techniques. We compared the results of Kulldorff's Spatial Scan Statistic with the results of Rushton's Spatial filtering technique across increasing sizes of spatial filters (circle). We were able to demonstrate that varying approaches exist to explore spatial variation in patterns of low birth weight.</p> <p>Results</p> <p>Spatial filtering results did not show any particular area that was not statistically significant based on SaTScan. The high rates, which remain as the filter size increases to 0.4, 0.5 to 0.6 miles, respectively, indicate that these differences are less likely due to chance. The maternal characteristics of births within clusters differed considerably between the two methods. Progressively larger Spatial filters removed local spatial variability, which eventually produced an approximate uniform pattern of low birth weight.</p> <p>Conclusion</p> <p>SaTScan and Spatial filtering cluster estimation methods produced noticeably different results from the same individual level birth data. SaTScan clusters are likely to differ from Spatial filtering clusters in terms of population characteristics and geographic area within clusters. Using the two methods in conjunction could provide more detail about the population and spatial features contained with each type of cluster.</p

Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters

Background: The purpose of this study is to examine the spatial and population (e.g., socio-economic) characteristics of low birthweight using two different cluster estimation techniques. We compared the results of Kulldorff\u27s Spatial Scan Statistic with the results of Rushton\u27s Spatial filtering technique across increasing sizes of spatial filters (circle). We were able to demonstrate that varying approaches exist to explore spatial variation in patterns of low birth weight. Results: Spatial filtering results did not show any particular area that was not statistically significant based on SaTScan. The high rates, which remain as the filter size increases to 0.4, 0.5 to 0.6 miles, respectively, indicate that these differences are less likely due to chance. The maternal characteristics of births within clusters differed considerably between the two methods. Progressively larger Spatial filters removed local spatial variability, which eventually produced an approximate uniform pattern of low birth weight. Conclusion: SaTScan and Spatial filtering cluster estimation method produced noticeably different results from the same individual level birth data. SaTScan clusters are likely to differ from Spatial filtering clusters in terms of population characteristics and geographic area within clusters. Using the two methods in conjunction could provide more detail about the population and spatial features contained with each type of cluster. © 2005 Ozdenerol et al; licensee BioMed Central Ltd

Assessing the impact of the local environment on birth outcomes: A case for HLM

Hierarchical linear Models (HLM) is a useful way to analyze the relationships between community level environmental data, individual risk factors, and birth outcomes. With HLM we can determine the effects of potentially remediable environmental conditions (e.g., air pollution) after controlling for individual characteristics such as health factors and socioeconomic factors. Methodological limitations of ecological studies of birth outcomes and a detailed analysis of the varying models that predict birth weight will be discussed. Ambient concentrations of criterion air pollutants (e.g., lead and sulfur dioxide) demonstrated a sizeable negative effect on birth weight; while the economic characteristics of the mother\u27s residential census tract (ex. poverty level) also negatively influenced birth weight. © 2007 Nature Publishing Group All rights reserved