A multi-dimensional assessment of financial hardship of cancer patients using existing health system data.

BACKGROUND: Existing financial hardship screening does not capture the multifaceted and dynamic nature of the problem. The use of existing health system data is a promising way to enable scalable and sustainable financial hardship screening. METHODS: We used existing data from 303 adult patients with cancer at the University of Virginia Comprehensive Cancer Center (2016-2018). All received distress screening and had a valid financial assistance screening based solely on household size-adjusted income. We constructed a composite index that integrates multiple existing health system data (Epic, distress screening, and cancer registry) to assess comprehensive financial hardship (e.g., material conditions, psychological responses, and coping behaviors). We examined differences of at-risk patients identified by our composite index and by existing single-dimension criterion. Dynamics of financial hardship over time, by age, and cancer type, were examined by fractional probit models. RESULTS: At-risk patients identified by the composite index were generally younger, better educated, and had a higher annual household income, though they had lower health insurance coverage. Identified periods to intervene for most patients are before formal diagnosis, 2 years, and 6 years after diagnosis. Within 2 years of diagnosis and more than 4 years after diagnosis appear critical for subgroups of patients who may suffer from financial hardship disparities. CONCLUSION: Existing health system data provides opportunities to systematically measure and track financial hardship in a systematic, scalable and sustainable way. We find that the dimensions of financial hardship can exhibit different patterns over time and across patient subgroups, which can guide targeted interventions. The scalability of the algorithm is limited by existing data availability.

Potential impact of 5 years of ivermectin mass drug administration on malaria outcomes in high burden countries.

INTRODUCTION: The global progress against malaria has slowed significantly since 2017. As the current malaria control tools seem insufficient to get the trend back on track, several clinical trials are investigating ivermectin mass drug administration (iMDA) as a potential additional vector control tool; however, the health impacts and cost-effectiveness of this new strategy remain unclear. METHODS: We developed an analytical tool based on a full factorial experimental design to assess the potential impact of iMDA in nine high burden sub-Saharan African countries. The simulated iMDA regimen was assumed to be delivered monthly to the targeted population for 3 months each year from 2023 to 2027. A broad set of parameters of ivermectin efficacy, uptake levels and global intervention scenarios were used to predict averted malaria cases and deaths. We then explored the potential averted treatment costs, expected implementation costs and cost-effectiveness ratios under different scenarios. RESULTS: In the scenario where coverage of malaria interventions was maintained at 2018 levels, we found that iMDA in these nine countries has the potential to reverse the predicted growth of malaria burden by averting 20-50 million cases and 36 000-90 000 deaths with an assumed efficacy of 20%. If iMDA has an efficacy of 40%, we predict between 40-99 million cases and 73 000-179 000 deaths will be averted with an estimated net cost per case averted between US$2 and US$7, and net cost per death averted between US$1460 and US$4374. CONCLUSION: This study measures the potential of iMDA to reverse the increasing number of malaria cases for several sub-Saharan African countries. With additional efficacy information from ongoing clinical trials and country-level modifications, our analytical tool can help determine the appropriate uptake strategies of iMDA by calculating potential marginal gains and costs under different scenarios.

Factors influencing high respiratory mortality in coal-mining counties: a repeated cross-sectional study.

BACKGROUND: Previous studies have associated elevated mortality risk in central Appalachia with coal-mining activities, but few have explored how different non-coal factors influence the association within each county. Consequently, there is a knowledge gap in identifying effective ways to address health disparities in coal-mining counties. To specifically address this knowledge gap, this study estimated the effect of living in a coal-mining county on non-malignant respiratory diseases (NMRD) mortality, and defined this as "coal-county effect." We also investigated what factors may accentuate or attenuate the coal-county effect. METHODS: An ecological epidemiology protocol was designed to observe the characteristics of three populations and to identify the effects of coal-mining on community health. Records for seven coal-mining counties (n = 19,692) were obtained with approvals from the Virginia Department of Health Office of Vital Statistics for the years 2005 to 2012. Also requested were records from three adjacent coal counties (n = 10,425) to provide a geographic comparison. For a baseline comparison, records were requested for eleven tobacco-producing counties (n = 27,800). We analyzed the association of 57,917 individual mortality records in Virginia with coal-mining county residency, county-level socioeconomic status, health access, behavioral risk factors, and coal production. The development of a two-level hierarchical model allowed the coal-county effect to vary by county-level characteristics. Wald tests detected sets of significant factors explaining the variation of impacts across counties. Furthermore, to illustrate how the model estimations help explain health disparities, two coal-mining county case studies were presented. RESULTS: The main result revealed that coal-mining county residency increased the probability of dying from NMRD. The coal-county effect was accentuated by surface coal mining, high smoking rates, decreasing health insurance coverage, and a shortage of doctors. In Virginia coal-mining regions, the average coal-county effect increased by 147% (p-value< 0.01) when one doctor per 1000 left, and the effect increased by 68% (p-value< 0.01) with a 1% reduction of health insurance rates, holding other factors fixed. CONCLUSIONS: This study showed a high mortality risk of NMRD associated with residents living in Virginia coal-mining counties. Our results also revealed the critical role of health access in reducing health disparities related to coal exposure.