Neighbourhood Social Determinants of Health and Geographical Inequalities in Premature Mortality in Taiwan: A Spatiotemporal Approach

A Spatio-Temporal Analysis of OECD Member Countries' Health Care Systems: Effects of Data Missingness and Geographically and Temporally Weighted Regression on Inference

Determinants of health care quality and efficiency are of importance to researchers, policy-makers, and public health officials as they allow for improved human capital and resource allocation as well as long-term fiscal planning. Statistical analyses used to understand determinants have neglected to explicitly discuss how missing data are handled, and consequently, previous research has been limited in inferential capability. We study OECD health care data and highlight the importance of transparency in the assumptions grounding the treatment of data missingness. Attention is drawn to the variation in ordinary least squares coefficient estimates and performance resulting from different imputation methods, and how this variation can undermine statistical inference. We also suggest that parametric regression models used previously are limited and potentially ill-suited for analysis of OECD data due to the inability to deal with both spatial and temporal autocorrelation. We propose the use of an alternative method in geographically and temporally weighted regression. A spatio-temporal analysis of health care system efficiency and quality of care across OECD member countries is performed using four proxy variables. Through a forward selection procedure, medical imaging equipment in a country is identified as a key determinant of quality of care and health outcomes, while government and compulsory health insurance expenditure per capita is identified as a key determinant of health care system efficiency.

Lifetime exposure to PM2.5 air pollution and disability-adjusted life years due to cardiopulmonary disease: A modeling study based on nationwide longitudinal data

BACKGROUND

As knowledge of the health risks of PM_2.5 has grown, the focus of PM_2.5-related health impact assessments has evolved from simple risk models to burden-of-disease estimates. We proposed an ecological approach to directly estimate the impacts of lifetime exposure to PM_2.5 on expected health losses due to cardiopulmonary diseases for older adult populations in Taiwan.

METHODS

We created study cohorts of 3.5 million older people living in 350 townships in Taiwan. We used a weighted regression model, with adjustments of area characteristic variables, to directly estimate the impacts of lifetime mean PM_2.5 exposure on health losses among the 350 cohorts. Potential avoidable disability-adjusted life years (DALYs) by assuming that ambient PM_2.5 met the air quality standard was estimated.

RESULTS

Each 10 μg/m³ increase in lifetime mean exposure to PM_2.5 for an individual corresponded to an increment of 0.25 (95 % confidence interval (CI): 0.18-0.32) DALYs due to cardiopulmonary diseases, after adjusting for a wide range of ecological covariates. We estimated that 611.8 (95 % CI: 440.4-783.2) DALYs per 1000 older adult population could potentially be avoided by achieving air quality standards of WHO in Taiwan.

CONCLUSIONS

Reducing PM_2.5 pollution in Taiwan associated with significant health co-benefits, providing important implications for public health and environmental management.

Intraurban Geographic and Socioeconomic Inequalities of Mortality in Four Cities in Colombia

Mortality inequalities have been described across Latin American countries, but less is known about inequalities within cities, where most populations live. We aimed to identify geographic and socioeconomic inequalities in mortality within the urban areas of four main cities in Colombia. We analyzed mortality due to non-violent causes of diseases in adults between 2015 and 2019 using census sectors as unit of analysis in Barranquilla, Bogotá, Cali, and Medellín. We calculated smoothed Bayesian mortality rates as main health outcomes and used concentration indexes (CInd) for assessing inequalities using the multidimensional poverty index (MPI) as the socioeconomic measure. Moran eigenvector spatial filters were calculated to capture the spatial patterns of mortality and then used in multivariable models of the association between mortality rates and quintiles of MPI. Social inequalities were evident but not consistent across cities. The most disadvantaged groups showed the highest mortality rates in Cali. Geographic inequalities in mortality rates, regardless of the adults and poverty distribution, were identified in each city, suggesting that other social, environmental, or individual conditions are impacting the spatial distribution of mortality rates within the four cities.

Transforming Social Determinants to Educational Outcomes: Geospatial Considerations

In recovering from one of the worst educational crises in recorded history due to the pandemic, in a mission to rebuild and become more resilient, there has been a heightened urgency to provide resources to communities most in need. However, precisely identifying those needs have become all the more important due to the increase in popularity of e-learning as a suitable option and the improvement of technologies. Most notably, socially disadvantaged and historically marginalized communities were disproportionately and severely impacted by several aspects of the pandemic, in terms of health, economics, access to education, and sustainable well-being. This differential effect was modeled spatially with the combination of aerial photogrammetry, traditional geospatial mapping, and other robust AI-driven techniques to synthesize and analyze the various types of data. In this original research study, we apply various spatial health variables, relate them to educational variables in an initial empirical process of understanding how to address equity-related considerations from the context of the learner’s experience, providing the empirical evidence for the development of locally tailored learner support and assistance, meeting students where they are by specifically identifying and targetting geographically underserved areas. We found that there were clear statistically significant relationships between educational attainment and several physical (p < 0.001), mental (p = 0.003), access to healthy food/food security (p < 0.001), and uptake of preventative health measures (p < 0.001), which also varied geographically. Geographic variations in learning experiences demonstrates the unquestionable need to understand a variety of physical, mental, and dietary factors surrounding the student’s success. Understanding a combination of these factors in a geospatial context will allow educational institutions to best serve the needs of learners.