Air pollution exposure disparities across US population and income groups.

Air pollution contributes to the global burden of disease, with ambient exposure to fine particulate matter of diameters smaller than 2.5 µm (PM2.5) being identified as the fifth-ranking risk factor for mortality globally1. Racial/ethnic minorities and lower-income groups in the USA are at a higher risk of death from exposure to PM2.5 than are other population/income groups2-5. Moreover, disparities in exposure to air pollution among population and income groups are known to exist6-17. Here we develop a data platform that links demographic data (from the US Census Bureau and American Community Survey) and PM2.5 data18 across the USA. We analyse the data at the tabulation area level of US zip codes (N is approximately 32,000) between 2000 and 2016. We show that areas with higher-than-average white and Native American populations have been consistently exposed to average PM2.5 levels that are lower than areas with higher-than-average Black, Asian and Hispanic or Latino populations. Moreover, areas with low-income populations have been consistently exposed to higher average PM2.5 levels than areas with high-income groups for the years 2004-2016. Furthermore, disparities in exposure relative to safety standards set by the US Environmental Protection Agency19 and the World Health Organization20 have been increasing over time. Our findings suggest that more-targeted PM2.5 reductions are necessary to provide all people with a similar degree of protection from environmental hazards. Our study is observational and cannot provide insight into the drivers of the identified disparities.

Evolution in key indicators of maternal and child health across the wealth gradient in 41 sub-Saharan African countries, 1986-2019.

BACKGROUND: Aggregate trends can be useful for summarizing large amounts of information, but this can obscure important distributional aspects. Some population subgroups can be worse off even as averages climb, for example. Distributional information can identify health inequalities, which is essential to understanding their drivers and possible remedies. METHODS: Using publicly available Demographic and Health Survey (DHS) data from 41 sub-Saharan African countries from 1986 to 2019, we analyzed changes in coverage for eight key maternal and child health indicators: first dose of measles vaccine (MCV1); Diphtheria-Pertussis-Tetanus (DPT) first dose (DPT1); DPT third dose (DPT3); care-seeking for diarrhea, acute respiratory infections (ARI), or fever; skilled birth attendance (SBA); and having four antenatal care (ANC) visits. To evaluate whether coverage diverged or converged over time across the wealth gradient, we computed several dispersion metrics including the coefficient of variation across wealth quintiles. Slopes and 5-year moving averages were computed to identify overall long-term trends. RESULTS: Average coverage increased for all quintiles and indicators, although the range and the speed at which they increased varied widely. There were small changes in the wealth-related gap for SBA, ANC, and fever. The wealth-related gap of vaccination-related indicators (DPT1, DPT3, MCV1) decreased over time. Compared to 2017, the wealth-gap between richest and poorest quintiles in 1995 was 7 percentage points larger for ANC and 17 percentage points larger for measles vaccination. CONCLUSIONS: Maternal and child health indicators show progress, but the distributional effects show differential evolutions in inequalities. Several reasons may explain why countries had smaller wealth-related gap trends in vaccination-related indicators compared to others. In addition to service delivery differences, we hypothesize that the allocation of development assistance for health, the prioritization of vaccine-preventable diseases on the global agenda, and indirect effects of structural adjustment programs on health system-related indicators might have played a role.

Out-of-pocket expenditures and financial risks associated with treatment of vaccine-preventable diseases in Ethiopia: A cross-sectional costing analysis.

BACKGROUND: Vaccine-preventable diseases (VPDs) remain major causes of morbidity and mortality in low- and middle-income countries (LMICs). Universal access to vaccination, besides improved health outcomes, would substantially reduce VPD-related out-of-pocket (OOP) expenditures and associated financial risks. This paper aims to estimate the extent of OOP expenditures and the magnitude of the associated catastrophic health expenditures (CHEs) for selected VPDs in Ethiopia. METHODS AND FINDINGS: We conducted a cross-sectional costing analysis, from the household (patient) perspective, of care-seeking for VPDs in children aged under 5 years for pneumonia, diarrhea, measles, and pertussis, and in children aged under 15 years for meningitis. Data on OOP direct medical and nonmedical expenditures (2021 USD) and household consumption expenditures were collected from 995 households (1 child per household) in 54 health facilities nationwide between May 1 and July 31, 2021. We used descriptive statistics to measure the main outcomes: magnitude of OOP expenditures, along with the associated CHE within households. Drivers of CHE were assessed using a logistic regression model. The mean OOP expenditures per disease episode for outpatient care for diarrhea, pneumonia, pertussis, and measles were $5·6 (95% confidence interval (CI): $4·3, 6·8), $7·8 ($5·3, 10·3), $9·0 ($6·4, 11·6), and $7·4 ($3·0, 11·9), respectively. The mean OOP expenditures were higher for inpatient care, ranging from $40·6 (95% CI: $12·9, 68·3) for severe measles to $101·7 ($88·5, 114·8) for meningitis. Direct medical expenditures, particularly drug and supply expenses, were the major cost drivers. Among those who sought inpatient care (345 households), about 13·3% suffered CHE, at a 10% threshold of annual consumption expenditures. The type of facility visited, receiving inpatient care, and wealth were significant predictors of CHE (p-value < 0·001) while adjusting for area of residence (urban/rural), diagnosis, age of respondent, and household family size. Limitations include inadequate number of measles and pertussis cases. CONCLUSIONS: The OOP expenditures induced by VPDs are substantial in Ethiopia and disproportionately impact those with low income and those requiring inpatient care. Expanding equitable access to vaccines cannot be overemphasized, for both health and economic reasons. Such realization requires the government's commitment toward increasing and sustaining vaccine financing in Ethiopia.

Financial hardship associated with catastrophic out-of-pocket spending tied to primary care services in low- and lower-middle-income countries: findings from a modeling study.

BACKGROUND: Financial risk protection (FRP) is a key component of universal health coverage (UHC): all individuals must be able to obtain the health services they need without experiencing financial hardship. In many low-income and lower-middle-income countries, however, the health system fails to provide sufficient protection against high out-of-pocket (OOP) spending on health services. In 2018, OOP health spending comprised approximately 40% of current health expenditures in low-income and lower-middle-income countries. METHODS: We model the household risk of catastrophic health expenditures (CHE), conditional on having a given disease or condition-defined as OOP health spending that exceeds a threshold percentage (10, 25, or 40%) of annual income-for 29 health services across 13 disease categories (e.g., diarrheal diseases, cardiovascular diseases) in 34 low-income and lower-middle-income countries. Health services were included in the analysis if delivered at the primary care level and part of the Disease Control Priorities, 3rd edition "highest priority package." Data were compiled from several publicly available sources, including national health accounts, household surveys, and the published literature. A risk of CHE, conditional on having disease, was modeled as depending on usage, captured through utilization indicators; affordability, captured via the level of public financing and OOP health service unit costs; and income. RESULTS: Across all countries, diseases, and health services, the risk of CHE (conditional on having a disease) would be concentrated among poorer quintiles (6.8% risk in quintile 1 vs. 1.3% in quintile 5 using a 10% CHE threshold). The risk of CHE would be higher for a few disease areas, including cardiovascular disease and mental/behavioral disorders (7.8% and 9.8% using a 10% CHE threshold), while lower risks of CHE were observed for lower cost services. CONCLUSIONS: Insufficient FRP stands as a major barrier to achieving UHC, and risk of CHE is a major problem for health systems in low-income and lower-middle-income countries. Beyond its threat to the financial stability of households, CHE may also lead to worse health outcomes, especially among the poorest for whom both ill health and financial risk are most severe. Modeling the risk of CHE associated with specific disease areas and services can help policymakers set progressive health sector priorities. Decision-makers could explicitly include FRP as a criterion for consideration when assessing the health interventions for inclusion in national essential benefit packages.

Equitable Prioritization of Health Interventions by Incorporating Financial Risk Protection Weights Into Economic Evaluations.

OBJECTIVES: Financial risk protection (FRP), or the prevention of medical impoverishment, is a major objective of health systems, particularly in low- and middle-income countries where the extent of out-of-pocket (OOP) health expenditures can be substantial. We sought to develop a method that allows decision makers to explicitly integrate FRP outcomes into their priority-setting activities. METHODS: We used literature review to identify 31 interventions in low- and middle-income countries, each of which provided measures of health outcomes, costs, OOP health expenditures averted, and FRP (proxied by OOP health expenditures averted as a percentage of income), all disaggregated by income quintile. We developed weights drawn from the Z-score of each quintile-intervention pair based on the distribution of FRP of all quintile-intervention pairs. We next ranked the interventions by unweighted and weighted health outcomes for each income quintile. We also evaluated how pro-poor they were by, first, ordering the interventions by cost-effectiveness for each quintile and, next, calculating the proportion of interventions each income quintile would be targeted for a given random budget. A ranking was said to be pro-poor if each quintile received the same or higher proportion of interventions than richer quintiles. RESULTS: Using FRP weights produced a more pro-poor priority setting than unweighted outcomes. Most of the reordering produced by the inclusion of FRP weights occurred in interventions of moderate cost-effectiveness, suggesting that these weights would be most useful as a way of distinguishing moderately cost-effective interventions with relatively high potential FRP. CONCLUSIONS: This preliminary method of integrating FRP into priority-setting would likely be most suitable to deciding between health interventions with intermediate cost-effectiveness.

Top and bottom longevity of nations: a retrospective analysis of the age-at-death distribution across 18 OECD countries.

BACKGROUND: Similar to the study of the distribution of income within countries, population-level health disparities can be examined by analyzing the distribution of age at death. METHODS: We sourced period-specific death counts for 18 OECD countries over 1900-2020 from the Human Mortality Database. We studied the evolution of country-year-specific distributions of age at death, with an examination of the lower and upper tails of these distributions. For each country-year, we extracted the 1st, 5th, 10th, 90th, 95th and 99th percentiles of the age-at-death distribution. We then computed the corresponding shares of longevity-the sum of the ages weighted by the age-at-death distribution as a fraction of the sum of the ages weighted by the distribution-for each percentile. For example, for the 10th percentile, this would correspond to how much longevity accrues to the bottom 10% of the age-at-death distribution in a given country-year. RESULTS: We expose a characterization of the age-at-death distribution across populations with a focus on the lower and upper tails of the distribution. Our metrics, specifically the gap measures in age and share across the 10th and 90th percentiles of the distribution, enable a systematic comparison of national performances, which yields information supplementary to the cross-country differences commonly pointed by traditional indicators of life expectancy and coefficient of variation. CONCLUSIONS: Examining the tails of age-at-death distributions can help characterize the comparative situations of the better- and worse-off individuals across nations, similarly to depictions of income distributions in economics.

Estimating the health impact of vaccination against ten pathogens in 98 low-income and middle-income countries from 2000 to 2030: a modelling study.

BACKGROUND: The past two decades have seen expansion of childhood vaccination programmes in low-income and middle-income countries (LMICs). We quantify the health impact of these programmes by estimating the deaths and disability-adjusted life-years (DALYs) averted by vaccination against ten pathogens in 98 LMICs between 2000 and 2030. METHODS: 16 independent research groups provided model-based disease burden estimates under a range of vaccination coverage scenarios for ten pathogens: hepatitis B virus, Haemophilus influenzae type B, human papillomavirus, Japanese encephalitis, measles, Neisseria meningitidis serogroup A, Streptococcus pneumoniae, rotavirus, rubella, and yellow fever. Using standardised demographic data and vaccine coverage, the impact of vaccination programmes was determined by comparing model estimates from a no-vaccination counterfactual scenario with those from a reported and projected vaccination scenario. We present deaths and DALYs averted between 2000 and 2030 by calendar year and by annual birth cohort. FINDINGS: We estimate that vaccination of the ten selected pathogens will have averted 69 million (95% credible interval 52-88) deaths between 2000 and 2030, of which 37 million (30-48) were averted between 2000 and 2019. From 2000 to 2019, this represents a 45% (36-58) reduction in deaths compared with the counterfactual scenario of no vaccination. Most of this impact is concentrated in a reduction in mortality among children younger than 5 years (57% reduction [52-66]), most notably from measles. Over the lifetime of birth cohorts born between 2000 and 2030, we predict that 120 million (93-150) deaths will be averted by vaccination, of which 58 million (39-76) are due to measles vaccination and 38 million (25-52) are due to hepatitis B vaccination. We estimate that increases in vaccine coverage and introductions of additional vaccines will result in a 72% (59-81) reduction in lifetime mortality in the 2019 birth cohort. INTERPRETATION: Increases in vaccine coverage and the introduction of new vaccines into LMICs have had a major impact in reducing mortality. These public health gains are predicted to increase in coming decades if progress in increasing coverage is sustained. FUNDING: Gavi, the Vaccine Alliance and the Bill & Melinda Gates Foundation.

Estimating and Comparing Health and Financial Risk Protection Outcomes in Economic Evaluations.

OBJECTIVES: Improving health and financial risk protection (FRP, the prevention of medical impoverishment) and their distributions is a major objective of national health systems. Explicitly describing FRP and disaggregated (eg, across socioeconomic groups) impact of health interventions in economic evaluations can provide decision makers with a broader set of health and financial outcomes to compare and prioritize interventions against each other. METHODS: We propose methods to synthesize such a broader set of outcomes by estimating and comparing the distributions in both health and FRP benefits procured by health interventions. We build on benefit-cost analysis frameworks and utility-based models, and we illustrate our methods with the case study of universal public finance (financing by government regardless of whom an intervention is targeting) of disease treatment in a low- and middle-income country setting. RESULTS: Two key findings seem to emerge: FRP is critical when diseases are less lethal (eg, case fatality rates <1% or so), and quantitative valuation of inequality aversion across income groups matters greatly. We recommend the use of numerous sensitivity analyses and that all distributional health and financial outcomes be first presented in a disaggregated form (before potential subsequent aggregation). CONCLUSIONS: Estimation approaches such as the one we propose provide explicit disaggregated considerations of equity, FRP, and poverty impact for the development of health sector policies, with high relevance for population-based preventive measures.

Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities.

OBJECTIVES: Clinical artificial intelligence (AI) is a novel technology, and few economic evaluations have focused on it to date. Before its wider implementation, it is important to highlight the aspects of AI that challenge traditional health technology assessment methods. METHODS: We used an existing broad value framework to assess potential ways AI can provide good value for money. We also developed a rubric of how economic evaluations of AI should vary depending on the case of its use. RESULTS: We found that the measurement of core elements of value-health outcomes and cost-are complicated by AI because its generalizability across different populations is often unclear and because its use may necessitate reconfigured clinical processes. Clinicians' productivity may improve when AI is used. If poorly implemented though, AI may also cause clinicians' workload to increase. Some AI has been found to exacerbate health disparities. Nevertheless, AI may promote equity by expanding access to medical care and, when properly trained, providing unbiased diagnoses and prognoses. The approach to assessment of AI should vary based on its use case: AI that creates new clinical possibilities can improve outcomes, but regulation and evidence collection may be difficult; AI that extends clinical expertise can reduce disparities and lower costs but may result in overuse; and AI that automates clinicians' work can improve productivity but may reduce skills. CONCLUSIONS: The potential uses of clinical AI create challenges for health technology assessment methods originally developed for pharmaceuticals and medical devices. Health economists should be prepared to examine data collection and methods used to train AI, as these may impact its future value.

The economic value of changing mortality risk in low- and middle-income countries: a systematic breakdown by cause of death.

BACKGROUND: We develop a framework for quantifying monetary values associated with changes in disease-specific mortality risk in low- and middle-income countries to help quantify trade-offs involved in investing in mortality reduction due to one disease versus another. METHODS: We monetized the changes in mortality risk for communicable and non-communicable diseases (CD and NCD, respectively) between 2017 and 2030 for low-income, lower-middle-income, and upper-middle-income countries (LICs, LMICs, and UMICs, respectively). We modeled three mortality trajectories ("base-case", "high-performance", and "low-performance") using Global Burden of Disease study forecasts and estimated disease-specific mortality risk changes relative to the base-case. We assigned monetary values to changes in mortality risk using value of a statistical life (VSL) methods and conducted multiple sensitivity analyses. RESULTS: In terms of NCDs, the absolute monetary value associated with changing mortality risk was highest for cardiovascular diseases in older age groups. For example, being on the low-performance trajectory relative to the base-case in 2030 was valued at $9100 (95% uncertainty range $6800; $11,400), $28,300 ($24,200; $32,400), and $30,300 ($27,200; $33,300) for females aged 70-74 years in LICs, LMICs, and UMICs, respectively. Changing the mortality rate from the base-case to the high-performance trajectory was associated with high monetary value for CDs as well, especially among younger age groups. Estimates were sensitive to assumptions made in calculating VSL. CONCLUSIONS: Our framework provides a priority setting paradigm to best allocate investments toward the health sector and enables intersectoral comparisons of returns on investments from health interventions.

Balancing health and financial protection in health benefit package design.

Policymakers face difficult choices over which health interventions to publicly finance. We developed an approach to health benefits package design that accommodates explicit tradeoffs between improvements in health and provision of financial risk protection (FRP). We designed a mathematical optimization model to balance gains in health and FRP across candidate interventions when publicly financed. The optimal subset of interventions selected for inclusion was determined with bi-criterion integer programming conditional on a budget constraint. The optimal set of interventions to publicly finance in a health benefits package varied according to whether the objective for optimization was population health benefits or FRP. When both objectives were considered jointly, the resulting optimal essential benefits package depended on the weights placed on the two objectives. In the Sustainable Development Goals era, smart spending toward universal health coverage is essential. Mathematical optimization provides a quantitative framework for policymakers to design health policies and select interventions that jointly prioritize multiple objectives with explicit financial constraints.

Questioning the regressivity of tobacco taxes: a distributional accounting impact model of increased tobacco taxation.

BACKGROUND: Tobacco taxes, as with other 'sin taxes', are generally regarded as a highly cost-effective mechanism to reduce consumption but are often considered by policymakers to be regressive, undermining efforts to fully implement them at levels recommended by the WHO due to concerns of fairness. We aim to demonstrate whether there are circumstances in which the impacts of additional tobacco taxes are not regressive, using a standard income-share accounting definition of tax burden. METHODS AND FINDINGS: We apply mathematical modelling and explore the hypothetical distributions in the net change in tobacco taxes and cigarette expenditures by income group, following an increase in tobacco taxation. The hypothetical distribution per income group of additional taxes and cigarette expenditures borne by individuals following tobacco tax hikes was calculated with respect to a selection of parameters including: the change in the retail price of cigarettes, the price elasticity of demand for tobacco, smoking prevalence, cigarette consumption and individual income. We determine the range of hypothetical parameter values for which increased tobacco taxation should not be considered to penalise the poorest income groups when examining marginal cigarette consumption expenditures and using an accounting definition of tax burden. CONCLUSIONS: Our findings question the doctrine that tobacco taxes are uniformly regressive from a standard income-share accounting view and point to the importance of the specific features of tax policy to shape a progressive approach to tobacco taxation: tobacco tax increases are less likely to be regressive when accompanied by a broad framework of demand-side measures that enhance the capacity of low-income smokers to quit tobacco use.

Nationally and regionally representative analysis of 1.65 million children aged under 5 years using a child-based human development index: A multi-country cross-sectional study.

BACKGROUND: Education and health are both constituents of human capital that enable people to earn higher wages and enhance people's capabilities. Human capabilities may lead to fulfilling lives by enabling people to achieve a valuable combination of human functionings-i.e., what people are able to do or be as a result of their capabilities. A better understanding of how these different human capabilities are produced together could point to opportunities to help jointly reduce the wide disparities in health and education across populations. METHODS AND FINDINGS: We use nationally and regionally representative individual-level data from Demographic and Health Surveys (DHS) for 55 low- and middle-income countries (LMICs) to examine patterns in human capabilities at the national and regional levels, between 2000 and 2017 (N = 1,657,194 children under age 5). We graphically analyze human capabilities, separately for each country, and propose a novel child-based Human Development Index (HDI) based on under-five survival, maternal educational attainment, and measures of a child's household wealth. We normalize the range of each component using data on the minimum and maximum values across countries (for national comparisons) or first-level administrative units within countries (for subnational comparisons). The scores that can be generated by the child-based HDI range from 0 to 1. We find considerable heterogeneity in child health across countries as well as within countries. At the national level, the child-based HDI ranged from 0.140 in Niger (with mean across first-level administrative units = 0.277 and standard deviation [SD] 0.114) to 0.755 in Albania (with mean across first-level administrative units = 0.603 and SD 0.089). There are improvements over time overall between the 2000s and 2010s, although this is not the case for all countries included in our study. In Cambodia, Malawi, and Nigeria, for instance, under-five survival improved over time at most levels of maternal education and wealth. In contrast, in the Philippines, we found relatively few changes in under-five survival across the development spectrum and over time. In these countries, the persistent location of geographical areas of poor child health across both the development spectrum and time may indicate within-country poverty traps. Limitations of our study include its descriptive nature, lack of information beyond first- and second-level administrative units, and limited generalizability beyond the countries analyzed. CONCLUSIONS: This study maps patterns and trends in human capabilities and is among the first, to our knowledge, to introduce a child-based HDI at the national and subnational level. Areas of chronic deprivation may indicate within-country poverty traps and require alternative policy approaches to improving child health in low-resource settings.

Spatial distribution and characteristics of HIV clusters in Ethiopia.

OBJECTIVES: Ethiopia's HIV prevalence has decreased by 75% in the past 20 years with the implementation of antiretroviral therapy, but HIV transmission continues in high-risk clusters. Identifying the spatial and temporal trends, and epidemiologic correlates, of these clusters can lead to targeted interventions. METHODS: We used biomarker and survey data from the 2005, 2011 and 2016 Ethiopia Demographic and Health Surveys (DHS). The spatial-temporal distribution of HIV was estimated using the Kulldorff spatial scan statistic, a likelihood-based method for determining clustering. Significant clusters (P < 0.05) were identified and compared based on HIV risk factors to non-cluster areas. RESULTS: In 2005, 2011 and 2016, respectively, 219, 568 and 408 individuals tested positive for HIV. Four HIV clusters were identified, representing 17% of the total population and 43% of all HIV cases. The clusters were centred around Addis Ababa (1), Afar (2), Dire Dawa (3) and Gambella (4). Cluster 1 had higher rates of unsafe injections (4.9% vs. 2.2%, P < 0.001) and transactional sex (6.0% vs. 1.6%, P < 0.001) than non-cluster regions, but more male circumcision (98.5% vs. 91.3%, P < 0.001). Cluster 2 had higher levels of transactional sex (4.9% vs. 1.6%, P < 0.01), but lower levels of unsafe injections (0.8% vs. 2.2%, P < 0.01). Cluster 3 had fewer individuals with> 1 sexual partner (0% vs. 1.7%, P < 0.001) and more male circumcision (100% vs. 91.3%, P < 0.001). Cluster 4 had less male circumcision (59.1% vs. 91.3%, P < 0.01). CONCLUSIONS: In Ethiopia, geographic HIV clusters are driven by different risk factors. Decreasing the HIV burden requires targeted interventions.

OBJECTIFS: La prévalence du VIH en Ethiopie a diminué de 75% au cours des 20 dernières années avec l'implémentation du traitement antirétroviral, mais la transmission du VIH se poursuit dans les grappes à haut risque. L'identification des tendances spatiales et temporelles et des corrélations épidémiologiques de ces grappes peut mener à des interventions ciblées. MÉTHODES: Nous avons utilisé des biomarqueurs et des données d'enquête provenant des Surveillances Démographiques et de Santé (SDS) en Ethiopie de 2005, 2011 et 2016. La distribution spatiotemporelle du VIH a été estimée à l'aide de la statistique de balayage spatial de Kulldorff, une méthode basée sur la probabilité de déterminer des regroupements. Des grappes significatives (P < 0.05) ont été identifiées et comparées sur base des facteurs de risque du VIH dans les zones sans regroupements. RÉSULTATS: En 2005, 2011 et 2016, respectivement, 219, 568 et 408 personnes ont été testées positives pour le VIH. Quatre grappes de VIH ont été identifiées, représentant 17% de la population totale et 43% de tous les cas de VIH. Les grappes étaient centrées sur Addis-Abeba (1), Afar (2), Dire Dawa (3) et Gambella(4). La grappe 1 avait des taux plus élevés d'injections à risque (4,9% contre 2,2%, P < 0.001) et de rapports sexuels transactionnels (6,0% contre 1,6%, P < 0.001) que les régions sans regroupement, mais plus de circoncisions masculines (98,5% contre 91,3%, p <0,001). La grappe 2 avait des taux plus élevés de rapports sexuels transactionnels (4,9% contre 1,6%, P < 0.01), mais des taux inférieurs d'injections à risque (0,8% contre 2,2%, P < 0.01). La grappe 3 avait moins d'individus avec >1 partenaire sexuel (0% contre 1,7%, P < 0.001) et plus de circoncisions masculines (100% contre 91,3%, P < 0.001). La grappe 4 avait moins de circoncisions masculines (59,1% contre 91,3%, P < 0.01). CONCLUSIONS: En Ethiopie, les grappes géographiques du VIH sont guidées par différents facteurs de risque. La réduction de la charge du VIH nécessite des interventions ciblées.

Health gains and financial risk protection afforded by public financing of selected malaria interventions in Ethiopia: an extended cost-effectiveness analysis.

BACKGROUND: Malaria is a public health burden and a major cause for morbidity and mortality in Ethiopia. Malaria also places a substantial financial burden on families and Ethiopia's national economy. Economic evaluations, with evidence on equity and financial risk protection (FRP), are therefore essential to support decision-making for policymakers to identify best buys amongst possible malaria interventions. The aim of this study is to estimate the expected health and FRP benefits of universal public financing of key malaria interventions in Ethiopia. METHODS: Using extended cost-effectiveness analysis (ECEA), the potential health and FRP benefits were estimated, and their distributions across socio-economic groups, of publicly financing a 10% coverage increase in artemisinin-based combination therapy (ACT), long-lasting insecticide-treated bed nets (LLIN), indoor residual spraying (IRS), and malaria vaccine (hypothetical). RESULTS: ACT, LLIN, IRS, and vaccine would avert 358, 188, 107 and 38 deaths, respectively, each year at a net government cost of $5.7, 16.5, 32.6, and 5.1 million, respectively. The annual cost of implementing IRS would be two times higher than that of the LLIN interventions, and would be the main driver of the total costs. The averted deaths would be mainly concentrated in the poorest two income quintiles. The four interventions would eliminate about $4,627,800 of private health expenditures, and the poorest income quintiles would see the greatest FRP benefits. ACT and LLINs would have the largest impact on malaria-related deaths averted and FRP benefits. CONCLUSIONS: ACT, LLIN, IRS, and vaccine interventions would bring large health and financial benefits to the poorest households in Ethiopia.

Comparative Distributional Impact of Routine Immunization and Supplementary Immunization Activities in Delivery of Measles Vaccine in Low- and Middle-Income Countries.

OBJECTIVES: In many countries, measles disproportionately affects poorer households. To achieve equitable delivery, national immunization programs can use 2 main delivery platforms: routine immunization and supplementary immunization activities (SIAs). The objective of this article is to use data concerning measles vaccination coverage delivered via routine and SIA strategies to make inferences about the associated equity impact. METHODS: We relied on Demographic and Health Survey and Multiple Indicator Cluster Surveys multi-country survey data to conduct a comparative analysis of routine and SIA measles vaccination status of children by wealth quintile. We estimated the value of the angle, θ, for the ratio of the difference between coverage levels of adjacent wealth quintiles by using the arc-tangent formula. For each country/year observation, we averaged the θ estimates into one summary measurement, defined as the "equity impact number." RESULTS: Across 20 countries, the equity impact number summarized across wealth quintiles was greater (and hence less equitable) for routine delivery than for SIAs in the survey rounds (years) during, before, and after an SIA about 65% of the time. The equity impact numbers for routine measles vaccination averaged across wealth quintiles were usually greater than for SIA measles vaccination across country-year observations. CONCLUSIONS: This analysis examined how different measles vaccine delivery platforms can affect equity. It can serve to elucidate the impact of immunization and public health programs in terms of comparing horizontal to vertical delivery efforts and in reducing health inequalities in global and country-level decision-making.

The Scale-Up of the Global Surgical Workforce: Can Estimates be Achieved by 2030?

BACKGROUND: The Lancet Commission on Global Surgery showed that countries with surgeon, anesthetist, and obstetrician (SAO) densities of 20-40 SAO/100,000 population were associated with improved health outcomes and recommended a global surgical workforce scale-up by 2030. Whether countries would be able to achieve such scale-up efforts in that time-frame is unknown. METHODS: A differential equation model was used to estimate the growth rate and number of SAO necessary for each country to reach the aforementioned SAO densities. Workforce data from Mexico and India were used to estimate achievable rates of SAO scale-up for middle- and low-income countries, respectively. Secular surgical growth rates were estimated to demonstrate what might occur without dedicated scale-up efforts. RESULTS: To reach at least 20 SAO/100,000 population in all countries by 2030, over 808 thousand SAO need to be trained by 2030. To reach at least 40 SAO/100,000 population, over 2.1 million SAO need to be trained. If countries adopt a scale-up rate similar to Mexico's previously achieved rate of scale-up, 66% of countries would have 20 SAO/100,000 population by 2030. If countries adopt a scale-up rate similar to India's previously achieved rate of scale-up, 56% would have 20 SAO/100,000 population by 2030. CONCLUSION: With dedicated efforts in surgical workforce scale-up, significant gains in SAO density can be made worldwide. However, without intervention, many countries are unlikely to improve their current workforce densities. Investments in workforce scale-up are likely to yield workforce gains that mirror current resource states.

Is cycle network expansion cost-effective? A health economic evaluation of cycling in Oslo.

BACKGROUND: Expansion of designated cycling networks increases cycling for transport that, in turn, increases physical activity, contributing to improvement in public health. This paper aims to determine whether cycle-network construction in a large city is cost-effective when compared to the status-quo. We developed a cycle-network investment model (CIM) for Oslo and explored its impact on overall health and wellbeing resulting from the increased physical activity. METHODS: First, we applied a regression technique on cycling data from 123 major European cities to model the effect of additional cycle-networks on the share of cyclists. Second, we used a Markov model to capture health benefits from increased cycling for people starting to ride cycle at the age of 30 over the next 25 years. All health gains were measured in quality-adjusted life years (QALYs). Costs were estimated in US dollars. Other data to populate the model were derived from a comprehensive literature search of epidemiological and economic evaluation studies. Uncertainty was assessed using deterministic and probabilistic sensitivity analyses. RESULTS: Our regression analysis reveals that a 100 km new cycle network construction in Oslo city would increase cycling share by 3%. Under the base-case assumptions, where the benefits of the cycle-network investment relating to increased physical activity are sustained over 25 years, the predicted average increases in costs and QALYs per person are $416 and 0.019, respectively. Thus, the incremental costs are $22,350 per QALY gained. This is considered highly cost-effective in a Norwegian setting. CONCLUSIONS: The results support the use of CIM as part of a public health program to improve physical activity and consequently avert morbidity and mortality. CIM is affordable and has a long-term effect on physical activity that in turn has a positive impact on health improvement.

Toward health system strengthening in low- and middle-income countries: insights from mathematical modeling of drug supply chains.

BACKGROUND: Global health priority setting increasingly focuses on understanding the functioning of health systems and on how they can be strengthened. Beyond vertical programs, health systems research should examine system-wide delivery platforms (e.g. health facilities) and operational elements (e.g. supply chains) as primary units of study and evaluation. METHODS: We use dynamical system methods to develop a simple analytical model for the supply chain of a low-income country's health system. In doing so, we emphasize the dynamic links that integrate the supply chain within other elements of the health system; and we examine how the evolution over time of such connections would affect drug delivery, following the implementation of selected interventions (e.g. enhancing road networks, expanding workforce). We also test feedback loops and forecasts to study the potential impact of setting up a digital system for tracking drug delivery to prevent drug stockout and expiration. RESULTS: Numerical simulations that capture a range of supply chain scenarios demonstrate the impact of different health system strengthening interventions on drug stock levels within health facilities. Our mathematical modeling also points to how implementing a digital drug tracking system could help anticipate and prevent drug stockout and expiration. CONCLUSION: Our mathematical model of drug supply chain delivery represents an important component toward the development of comprehensive quantitative frameworks that aim at describing health systems as complex dynamical systems. Such models can help predict how investments in system-wide interventions, like strengthening drug supply chains in low-income settings, may improve population health outcomes.