Mapping Health Fragility and Vulnerability in Air Pollution-Monitoring Networks in Dallas-Fort Worth.

Environmental air pollution remains a major contributor to negative health outcomes and mortality, but the relationship between socially vulnerable populations and air pollution is not well understood. Although air pollution potentially affects everyone, the combination of underlying health, socioeconomic, and demographic factors exacerbate the impact for socially vulnerable population groups, and the United States Clean Air Act (CAA) describes an obligation to protect these populations. This paper seeks to understand how air pollution monitor placement strategies and policy may neglect social vulnerabilities and therefore potentially underestimate exposure burdens in vulnerable populations. Multivariate logistic regression models were used to assess the association between being in an ozone-monitored area or not on 15 vulnerability indicators. It was found that the odds of not being in an ozone-monitored area (not covered, outside) increased for the predictor mobile homes (OR = 4.831, 95% CI [2.500-9.338] and OR = 8.066, 95% CI [4.390-14.820] for the 10 and 20 km spatial units, respectively) and decreased for the predictor multiunit structures (OR = 0.281, 95% CI [0.281-0.548] and OR = 0.130, 95% CI [0.037, 0.457] for the 10 and 20 km spatial units, respectively) and the predictor speaks English "less than well" (OR = 0.521, 95% CI [0.292-0.931] for 10 km). These results indicate that existing pollution sensor coverage may neglect areas with concentrations of highly vulnerable populations in mobile homes, and future monitoring placement policy decisions must work to address this imbalance.

A multiscale analysis of social and spatial determinants of cancer and noncancer hazards from on-road air pollution in Texas.

Evidence on environmental justice studies linking adverse health effects and on-roadair pollution showing spatial nonstationarity is limited.This study uses cancer and noncancer risk estimates from on-road sources of hazardous air pollutants modeled by the National-Scale Air Toxics Assessment (NATA) at the census tract (N = 5265) level and sociodemographic variables from U.S. Census Bureau to examine the nonstationarity spatial relationship by comparing aspatial and spatial regression modelsglobal ordinary least squares, spatial error model, geographically weighted regression, and multiscale geographically weighted regression. At first glance, census tracts within the highest quartile of cancer and noncancer risks were clustered in the major urban areas. Spatial regression indicates that cancer and non-cancer risk were associated with census tract level percentages of Black, Indigenous, and People of Color (BIPOC). These findings can serve as geospatial guidance for intervening in the processes that drive socio-spatial disparity in air pollution exposure.

Racial/Ethnic Heterogeneity and Rural-Urban Disparity of COVID-19 Case Fatality Ratio in the USA: a Negative Binomial and GIS-Based Analysis.

The 2019 coronavirus disease (COVID-19) has exacerbated inequality in the United States of America (USA). Black, indigenous, and people of color (BIPOC) are disproportionately affected by the pandemic. This study examines determinants of COVID-19 case fatality ratio (CFR) based on publicly sourced data from January 1 to December 18, 2020, and sociodemographic and rural-urban continuum data from the US Census Bureau. Nonspatial negative binomial Poisson regression and geographically weighted Poisson regression were applied to estimate the global and local relationships between the CFR and predictors-rural-urban continuum, political inclination, and race/ethnicity in 2407 rural counties. The mean COVID-19 CFR among rural counties was 1.79 (standard deviation (SD) = 1.07; 95% CI 1.73-1.84) higher than the total US counties (M = 1.69, SD = 1.18; 95% CI: 1.65-1.73). Based on the global NB model, CFR was positively associated with counties classified as "completely rural" (incidence rate ratio (IRR) = 1.24; 95% CI: 1.12-1.39) and "mostly rural" (IRR = 1.26; 95% CI: 1.15-1.38) relative to "mostly urban" counties. Nonspatial regression indicates that COVID-19 CFR increases by a factor of 8.62, 5.87, 2.61, and 1.36 for one unit increase in county-level percent Blacks, Hispanics, American Indians, and Asian/Pacific Islanders, respectively. Local spatial regression shows CFR was significantly higher in rural counties with a higher share of BIPOC in the Northeast and Midwest regions, and political inclination predicted COVID-19 CFR in rural counties in the Midwest region. In conclusion, spatial and racial/ethnic disparities exist for COVID-19 CFR across the US rural counties, and findings from this study have implications for public health.

Fertility knowledge, contraceptive use and unintentional pregnancy in 29 African countries: a cross-sectional study.

OBJECTIVES: We examined the association between incorrect knowledge of ovulation and unintentional pregnancy and child among young women in sub-Saharan Africa countries. METHODS: Using Pearson's Chi-square, t test, multiple logistic regression, and likelihood ratio test, we analyzed Demographic and Health Survey data (2008-2017) of 169,939 young women (15-24 year). RESULTS: The range of prevalence of incorrect knowledge of ovulation was 51% in Comoros and 89.6% in Sao Tome and Principe, while unintentional pregnancy ranged between 9.4% in the Republic of Benin and 59.6% in Namibia. The multivariate result indicates a strong association between incorrect knowledge of ovulation and unintentional pregnancy (OR = 1.17; p < 0.05) and unintentional child (OR = 1.15; p < 0.05). CONCLUSIONS: Adolescent women (15-19) generally have poor knowledge of ovulation and are more likely to report an unintentional pregnancy/child than women between ages 20-24. To reduce the burden of unintentional child/pregnancy in Africa, fertility knowledge should not only be improved on but must consider the sociocultural context of women in different countries that might affect the adoption of such intervention programs. Pragmatic efforts, such as building community support for young women to discuss and share their experiences with professionals and educate them on fertility and sexuality, are essential.

Using GIS to detect cluster and spatial disparity in maternal health indicators: a need for social health interventions.

Maternal mortality remains a serious global health concern. Although global efforts have produced some encouraging results in some World Health Organization's health regions, disparities persist within many countries. Additionally, in many developing countries, inadequate documentation of various health events including maternal mortality and morbidity, make it difficult to determine the true extent of the problem. Maternal health indicators are therefore proxies used in estimating health status in developing countries. Using geospatial and geovisualization techniques, this study examines district level disparities in two maternal health indicators in Ghana antenatal care (ANC) visits and skilled birth attendance (SBA). The results reveal districts with complete lack of access to higher health care professionals and others with underutilization of antenatal services. The findings provide important input for targeting location-specific public health and maternal health interventions.

Evaluation of threshold selection methods for adaptive kernel density estimation in disease mapping.

BACKGROUND: Maps of disease rates produced without careful consideration of the underlying population distribution may be unreliable due to the well-known small numbers problem. Smoothing methods such as Kernel Density Estimation (KDE) are employed to control the population basis of spatial support used to calculate each disease rate. The degree of smoothing is controlled by a user-defined parameter (bandwidth or threshold) which influences the resolution of the disease map and the reliability of the computed rates. Methods for automatically selecting a smoothing parameter such as normal scale, plug-in, and smoothed cross validation bandwidth selectors have been proposed for use with non-spatial data, but their relative utilities remain unknown. This study assesses the relative performance of these methods in terms of resolution and reliability for disease mapping. RESULTS: Using a simulated dataset of heart disease mortality among males aged 35 years and older in Texas, we assess methods for automatically selecting a smoothing parameter. Our results show that while all parameter choices accurately estimate the overall state rates, they vary in terms of the degree of spatial resolution. Further, parameter choices resulting in desirable characteristics for one sub group of the population (e.g., a specific age-group) may not necessarily be appropriate for other groups. CONCLUSION: We show that the appropriate threshold value depends on the characteristics of the data, and that bandwidth selector algorithms can be used to guide such decisions about mapping parameters. An unguided choice may produce maps that distort the balance of resolution and statistical reliability.

Mapping late testers for HIV in Texas.

Understanding the spatial patterns of late testing for HIV infection is critically important for designing and evaluating intervention strategies to reduce the social and economic burdens of HIV/AIDS. Traditional mapping methods that rely on frequency counts or rates in predefined areal units are known to be problematic due to issues of small numbers and visual biases. Additionally, confidentiality requirements associated with health data further restrict the ability to produce cartographic representations at fine geographic scales. While kernel density estimation methods produce stable and geographically detailed patterns of the late testing burden, the resulting pattern depends critically on the definition of the at-risk population. Using three definitions of at risk groups, we examine the cartographic representation of HIV late testers in Texas and show that the resulting spatial patterns and the interpretation of disease burdens are different based on the choice of the at-risk population. Disease mappers should exercise considerable caution in selecting the denominator population for mapping.

Ghana's National Health Insurance Scheme in the context of the health MDGs: an empirical evaluation using propensity score matching.

In 2003 the Government of Ghana established a National Health Insurance Scheme (NHIS) to improve health-care access for Ghanaians and eventually replace the cash-and-carry system. This study evaluates an important aspect of its promise in the context of the Millennium Development Goals #4 and #5 which deal with the health of women and children. We use Propensity Score Matching techniques to balance the relevant background characteristics in our survey data and compare health indicators of recent mothers who are enrolled in the NHIS with those who are not. Our findings suggest that NHIS women are more likely to receive prenatal care, deliver at a hospital, have their deliveries attended by trained health professionals, and experience less birth complications. We conclude that NHIS is an effective tool for improving health outcomes among those who are covered, which should encourage the Ghanaian government to promote further enrollment, in particular among the poor.

Foreign-Born Status and Geographic Patterns of Tuberculosis Genotypes in Tarrant County, Texas.

Regardless of destination, immigrants arrive with health profiles typical of people in their previous surroundings. Thus, immigrants change the epidemiological profile of destination communities, and immigrant neighborhoods may represent islands of infectious disease. Genotyping has emerged as a useful surveillance tool to track the spread of disease at the molecular level. Yet the spatial distribution of infectious disease at the molecular level associated with migration and immigrant neighborhoods has received little attention. Using molecular genotyping to characterize M. tuberculosis isolated from tuberculosis cases, this article analyzes spatial variations of unique molecular M. tuberculosis strains by zip code in Tarrant County, Texas. The results suggest that immigrant neighborhoods have higher rates of unique isolates of tuberculosis (suggestive of remote transmission) compared to neighborhoods occupied by the native-born. Neighborhoods dominated by the native-born have higher rates of clustered isolates (suggestive of recent transmission). Therefore, in addition to being culturally distinct, immigrant neighborhoods may also be pathogenically distinct from surrounding neighborhoods.