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.

Extended cost-effectiveness analysis of interventions to improve uptake of diabetes services in South Africa.

The rising prevalence of diabetes in South Africa (SA), coupled with significant levels of unmet need for diagnosis and treatment, results in high rates of diabetes-associated complications. Income status is a determinant of utilization of diagnosis and treatment services, with transport costs and loss of wages being key barriers to care. A conditional cash transfer (CCT) programme, targeted to compensate for such costs, may improve service utilization. We applied extended cost-effectiveness analysis (ECEA) methods and used a Markov model to compare the costs, health benefits and financial risk protection (FRP) attributes of a CCT programme. A population was simulated, drawing from SA-specific data, which transitioned yearly through various health states, based on specific probabilities obtained from local data, over a 45-year time horizon. Costs and disability-adjusted life years (DALYs) were applied to each health state. Three CCT programme strategies were simulated and compared to a 'no programme' scenario: (1) covering diagnosis services only; (2) covering treatment services only; (3) covering both diagnosis and treatment services. Cost-effectiveness was reported as incremental net monetary benefit (INMB) using a cost-effectiveness threshold of USD3015 per DALY for SA, while FRP outcomes were reported as catastrophic health expenditure (CHE) cases averted. Distributions of the outcomes were reported by income quintile and sex. Covering both diagnosis and treatment services for the bottom two quintiles resulted in the greatest INMB (USD22 per person) and the greatest CHE cases averted. There were greater FRP benefits for women compared to men. A CCT programme covering diabetes diagnosis and treatment services was found to be cost-effective, when provided to the poorest 40% of the SA population. ECEA provides a useful platform for including equity considerations to inform priority setting and implementation policies in SA.

Hypertension care cascades and reducing inequities in cardiovascular disease in low- and middle-income countries.

Improving hypertension control in low- and middle-income countries has uncertain implications across socioeconomic groups. In this study, we simulated improvements in the hypertension care cascade and evaluated the distributional benefits across wealth quintiles in 44 low- and middle-income countries using individual-level data from nationally representative, cross-sectional surveys. We raised diagnosis (diagnosis scenario) and treatment (treatment scenario) levels for all wealth quintiles to match the best-performing country quintile and estimated the change in 10-year cardiovascular disease (CVD) risk of individuals initiated on treatment. We observed greater health benefits among bottom wealth quintiles in middle-income countries and in countries with larger baseline disparities in hypertension management. Lower-middle-income countries would see the greatest absolute benefits among the bottom quintiles under the treatment scenario (29.1 CVD cases averted per 1,000 people living with hypertension in the bottom quintile (Q1) versus 17.2 in the top quintile (Q5)), and the proportion of total CVD cases averted would be largest among the lowest quintiles in upper-middle-income countries under both diagnosis (32.0% of averted cases in Q1 versus 11.9% in Q5) and treatment (29.7% of averted cases in Q1 versus 14.0% in Q5) scenarios. Targeted improvements in hypertension diagnosis and treatment could substantially reduce socioeconomic-based inequalities in CVD burden in low- and middle-income countries.

Investing in school systems: conceptualising returns on investment across the health, education and social protection sectors.

Public policies often aim to improve welfare, economic injustice and reduce inequality, particularly in the social protection, labour, health and education sectors. While these policies frequently operate in silos, the education sphere can operate as a cross-sectoral link. Schools represent a unique locus, with globally hundreds of millions of children attending class every day. A high-profile policy example is school feeding, with over 400 million students worldwide receiving meals in schools. The benefits of harmonising interventions across sectors with a common delivery platform include economies of scale. Moreover, economic evaluation frameworks commonly used to assess policies rarely account for impact across sectors besides their primary intent. For example, school meals are often evaluated for their impact on nutrition, but they also have educational benefits, including increasing attendance and learning and incorporating smallholder farmers into corporate value chains. To address these gaps, we propose the introduction of a comprehensive value-for-money framework for investments toward school systems that acknowledges the return to a common delivery platform-schools-and the multisectoral returns (eg, education, health and nutrition, labour, social protection) emerging from the rollout of school-based programmes. Directly building on benefit-cost analysis methods, this framework could help identify interventions that yield the highest gains in human capital per budget expenditure, with direct implications for finance ministries. Given the detrimental impact of COVID-19 on schoolchildren and human capital, it is urgent to build back stronger and more sustainable welfare systems.

Economic Growth and Childhood Malnutrition in Low- and Middle-Income Countries.

Importance: Economic growth may reduce childhood malnutrition through improvements of several contributing factors, but the empirical evidence is mixed. Identifying the most important factors that contribute to child malnutrition and their associations with economic growth can inform decision-making about targeted investments to improve children's health. Objective: To assess the associations between economic growth and malnutrition, contributing factors and malnutrition, and economic growth and contributing factors of malnutrition in low- and middle-income countries (LMICs). Design, Setting, and Participants: This cross-sectional study used data from 239 Demographic and Health Surveys from January 1, 1990, to December 31, 2021. Observations included 1 138 568 children aged 0 to 35 months with valid anthropometric measures and information on contributing factors of malnutrition from 58 LMICs. Data were analyzed from May 20, 2022, to February 16, 2023. Exposure: National per-capita gross domestic product (GDP) was used as a proxy for economic growth. Main Outcomes and Measures: Six measures of childhood malnutrition were constructed: stunting (height-for-age z score <-2), underweight (weight-for-age z score <-2), wasting (weight-for-height z score <-2), overweight (weight-for-height z score >2), obesity (weight-for-height z score >3), and dietary diversity failure (consumption of less than 5 of 8 different food groups in the past 24 hours). Eighteen contributing factors of malnutrition were constructed, of which 10 were underlying determinants (eg, access to improved sanitation) and 8 were immediate determinants (eg, breastfeeding initiation). Results: A total of 1 138 568 children (mean [SD] age, 17.14 [10.26] months; 579 589 [50.9%] boys and 558 979 [49.1%] girls) were included in the analysis. Of these, 27.3% (95% CI, 27.2%-27.4%) had stunting; 25.7% (95% CI, 25.6%-25.8%), underweight; 11.2% (95% CI, 11.1%-11.2%), wasting; 3.8% (95% CI, 3.7%-3.8%), overweight; 1.1% (95% CI, 1.1%-1.1%), obesity; and 79.8% (95% CI, 79.7%-79.9%), dietary diversity failure. Per-capita GDP was weakly associated with childhood malnutrition. The odds ratios associated with a 5% increase in per-capita GDP were 0.99 (95% CI, 0.99-1.00) for stunting, 1.01 (95% CI, 1.00-1.01) for wasting, 1.00 (95% CI, 1.00-1.00) for underweight, 0.98 (95% CI, 0.98-0.98) for overweight, 0.98 (95% CI, 0.97-0.98) for obesity, and 1.03 (95% CI, 1.01-1.04) for dietary diversity failure. Although strong associations were found between many contributing factors and most outcomes for malnutrition, associations identified between per-capita GDP and these contributing factors themselves were ambiguous. Conclusions and Relevance: In this multicountry cross-sectional study, economic growth was weakly associated with childhood malnutrition and several contributing factors. To reduce child malnutrition, economic growth may need to be accompanied by more targeted investments to improve contributing factors that are strongly associated with child malnutrition, such as maternal health and education.

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.

When should global health actors prioritise more uncertain interventions?

Global health actors use economic evaluations, including cost-effectiveness analyses, to estimate the effect of different interventions they might fund. However, producing reliable cost-effectiveness estimates is difficult, meaning organisations must often choose between funding interventions for which reliable predictions of efficacy exist and those for which they do not. In practice, many organisations appear to be risk-averse, favouring more certain interventions simply because they are more certain. We argue that this practice is not justifiable. Prioritising projects backed by greater evidence might often produce greater health benefits. However, a general tendency to prefer more certain interventions will cause global health actors to overlook opportunities to help less well-studied populations, support promising but complex interventions, address the upstream causes of illness, and conduct the most important impact evaluations. We argue that global health actors should instead adopt nuanced attitudes towards uncertainty and be willing to fund highly uncertain interventions in some cases. We further describe the considerations they should take into account in rendering these judgements.

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.

Examining the computation of the underlying components of DALYs.

Disability-adjusted life years (DALYs) capture the mortality and morbidity arising from a disease: they incorporate the years of life lost (YLLs) and the years of life lived with disability (YLD) due to a disease. The relative importance of YLLs and YLDs differs across diseases. The magnitudes of YLLs and YLDs depend on parameters such as the age of onset of disease, duration of disease, the case fatality ratio and disability weight. In this paper, we examine the mathematical computation of the DALY and its underlying components, YLDs and YLLs. We aim to demonstrate under which circumstances (e.g. sets of input parameters) disease-specific YLDs and YLLs become sizeable relative to one another using the parameters of a set of diseases in low-income country settings. Researchers could then focus on understanding the key inputs that drive the relative extents of YLDs and YLLs (e.g. determine whether a detailed estimation of disability weights is essential), while maintaining DALYs as their key outcome metric consistent with disease burden assessments.

Joint distribution of child mortality and wealth across 30 sub-Saharan African countries over 2000-2019.

Background: While reductions in child mortality have been observed across sub-Saharan African countries in the last 30 years, narrowing the gaps in under-five mortality across socioeconomic groups also requires an understanding of the multiple associations between health and welfare and socioeconomic drivers. We examined the probability density distributions in under-five mortality within countries and joint pathways of under-five mortality and wealth over time. Methods: We used 69 Demographic and Health Surveys and 19 Malaria Indicator Surveys from 30 sub-Saharan African countries, with each country having at least two surveys conducted since 2000. We constructed a cross-country wealth index and estimated under-five death prevalence. We examined the pure distribution in under-five mortality prevalence and the joint probability distribution of wealth and under-five mortality prevalence over time, including the area of confidence ellipse which spanned the two dimensions of mortality and wealth and covered 75% of the mass of the joint distribution. Results: Most countries experienced decreases in under-five mortality along with increases in wealth over time. However, we observed great variations in the evolution of the joint distributions across countries over time. For instance, the areas of confidence ellipse ranged from 0.178 in Ethiopia (2000) to 1.119 in Angola (2006). The change (over time) in the area of confidence ellipses ranged from 0.010 in Tanzania to 0.844 in Angola between the 2000s and 2010s. The ranking of country performance on under-five mortality varied greatly, depending on whether performance summary indicators were based on disaggregation by wealth or on full non-disaggregated distributions. Conclusions: Our analysis points to the relevance of full distributions of health and joint distributions of health and wealth as complementary indicators of distributions of health across socioeconomic status, in assessing country performance on health.

Estimated travel time and staffing constraints to accessing the Ethiopian health care system: A two-step floating catchment area analysis.

Background: Despite large investments in the public health care system, disparities in health outcomes persist between lower- and upper-income individuals, as well as rural vs urban dwellers in Ethiopia. Evidence from Ethiopia and other low- and middle-income countries suggests that challenges in health care access may contribute to poverty in these settings. Methods: We employed a two-step floating catchment area to estimate variations in spatial access to health care and in staffing levels at health care facilities. We estimated the average travel time from the population centers of administrative areas and adjusted them with provider-to-population ratios. To test hypotheses about the role of travel time vs staffing, we applied Spearman's rank tests to these two variables against the access score to assess the significance of observed variations. Results: Among Ethiopia's 11 first-level administrative units, Addis Ababa, Dire Dawa, and Harari had the best access scores. Regions with the lowest access scores were generally poorer and more rural/pastoral. Approximately 18% of the country did not have access to a public health care facility within a two-hour walk. Our results suggest that spatial access and staffing issues both contribute to access challenges. Conclusion: Investments both in new health facilities and staffing in existing facilities will be necessary to improve health care access within Ethiopia. Because rural and low-income areas are more likely to have poor access, future strategies for expanding and strengthening the health care system should strongly emphasize equity and the role of improved access in reducing poverty.

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.

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.

Quantifying the global burden of mental disorders and their economic value.

Background: Epidemiological and economic estimates suggest that the global burden of mental disorders is considerable, both in its impacts on human health and losses to societal welfare. The availability of additional data and the emergence of new approaches present an opportunity to examine these estimates, which form a critical part in making the investment case for global mental health. Methods: This study reviews, develops, and incorporates new estimates and methods in quantifying the global burden of mental illness. Using a composite estimation approach that accounts for premature mortality due to mental disorders and additional sources of morbidity and applying a value of a statistical life approach to economic valuation, we determine global and regional estimates of the economic cost that can be associated with mental disorders, building on data from the 2019 Global Burden of Disease study. Findings: We estimate that 418 million disability-adjusted life years (DALYs) could be attributable to mental disorders in 2019 (16% of global DALYs)-a more than three-fold increase compared to conventional estimates. The economic value associated with this burden is estimated at about USD 5 trillion. At a regional level, the losses could account for between 4% of gross domestic product in Eastern sub-Saharan Africa and 8% in High-income North America. Interpretation: The burden of mental illness in terms of both health and economic losses may be much higher than previously assessed. Funding: None.

Comparative health systems analysis of differences in the catastrophic health expenditure associated with non-communicable vs communicable diseases among adults in six countries.

The growing burden of non-communicable diseases (NCDs) in low- and middle-income countries may have implications for health system performance in the area of financial risk protection, as measured by catastrophic health expenditure (CHE). We compare NCD CHE to the CHE cases caused by communicable diseases (CDs) across health systems to examine whether: (1) disease burden and CHE are linked, (2) NCD CHE disproportionately affects wealthier households and (3) whether the drivers of NCD CHE differ from the drivers of CD CHE. We used the Study on Global Aging and Adult Health survey, which captured nationally representative samples of 44 089 adults in China, Ghana, India, Mexico, Russia and South Africa. Using two-part regression and random forests, we estimated out-of-pocket spending and CHE by disease area. We compare the NCD share of CHE to the NCD share of disability-adjusted life years (DALYs) or years of life lost to disability and death. We tested for differences between NCDs and CDs in the out-of-pocket costs per visit and the number of visits occurring before spending crosses the CHE threshold. NCD CHE increased with the NCD share of DALYs except in South Africa, where NCDs caused more than 50% of CHE cases but only 30% of DALYs. A larger share of households incurred CHE due to NCDs in the lowest than the highest wealth quintile. NCD CHE cases were more likely to be caused by five or more health care visits relative to communicable disease CHE cases in Ghana (P = 0.003), India (P = 0.004) and China (P = 0.093). Health system attributes play a key mediating factor in how disease burden translates into CHE by disease. Health systems must target the specific characteristics of CHE by disease area to bolster financial risk protection as the epidemiological transition proceeds.

Priority setting in early childhood development: an analytical framework for economic evaluation of interventions.

BACKGROUND: Early childhood development (ECD) sets the foundation for healthy and successful lives with important ramifications for education, labour market outcomes and other domains of well-being. Even though a large number of interventions that promote ECD have been implemented and evaluated globally, there is currently no standardised framework that allows a comparison of the relative cost-effectiveness of these interventions. METHODS: We first reviewed the existing literature to document the main approaches that have been used to assess the relative effectiveness of interventions that promote ECD, including early parenting and at-home psychosocial stimulation interventions. We then present an economic evaluation framework that builds on these reviewed approaches and focuses on the immediate impact of interventions on motor, cognitive, language and socioemotional skills. Last, we apply our framework to compute the relative cost-effectiveness of interventions for which recent effectiveness and costing data were published. For this last part, we relied on a recently published review to obtain effect sizes documented in a consistent manner across interventions. FINDINGS: Our framework enables direct value-for-money comparison of interventions across settings. Cost-effectiveness estimates, expressed in $ per units of improvement in ECD outcomes, vary greatly across interventions. Given that estimated costs vary by orders of magnitude across interventions while impacts are relatively similar, cost-effectiveness rankings are dominated by implementation costs and the interventions with higher value for money are generally those with a lower implementation cost (eg, psychosocial interventions involving limited staff). CONCLUSIONS: With increasing attention and investment into ECD programmes, consistent assessments of the relative cost-effectiveness of available interventions are urgently needed. This paper presents a unified analytical framework to address this need and highlights the rather remarkable range in both costs and cost-effectiveness across currently available intervention strategies.

The potential distributional health and financial benefits of increased tobacco taxes in Ethiopia: Findings from a modeling study.

Ethiopia raised taxes on tobacco products in early 2020, increasing the overall price of the typical pack of cigarettes by about 67%. We quantify the potential impacts of Ethiopia's tobacco tax hike on various outcomes-life years, tax revenues, cigarette expenditures and catastrophic health expenditures (CHE). Using parameters like price elasticity of demand for cigarettes and smoking prevalence in Ethiopia from the existing literature and secondary data sources, we model the potential implications of the reform at the population level and for different wealth quintiles. We focus only on men since a small proportion of Ethiopian women smoke. Results indicate that Ethiopia's tax hike could induce a significant proportion of current smokers to quit smoking and thereby save almost eight million years of life in the current population. The reform is also likely to increase tax revenues by USD26 million in the first year after its introduction. The richest quintile will bear the greatest share of this higher tax burden and the poorest will bear the least. Additionally, deaths due to the main diseases associated with smoking will fall. This is expected to avert up to 173,000 CHE cases due to the out-of-pocket costs that would have been incurred in obtaining medical treatment. This analysis highlights that cigarette tax hikes in countries that have low smoking prevalence can reduce smoking even further, and thereby protect against the future health and financial costs of smoking. Importantly, the effects of these policies can be progressive across the income spectrum.

Economic evaluations of health system strengthening activities in low-income and middle-income country settings: a methodological systematic review.

OBJECTIVE: Health system strengthening (HSS) activities should accompany disease-targeting interventions in low/middle-income countries (LMICs). Economic evaluations provide information on how these types of investment might best be balanced but can be challenging. We conducted a systematic review to evaluate how researchers address these economic evaluation challenges. METHODS: We identified studies about economic evaluation of HSS activities in LMICs using a two-stage approach. First, we conducted a broad search to identify areas where economic evaluations of HSS activities were being conducted. Next, we selected specific interventions for more targeted literature review. We extracted study characteristics using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. Finally, we summarised authors' modelling decisions using a framework that examines how models are developed to emphasise generalisability, precision, or realism. FINDINGS: Our searches produced 1978 studies, out of which we included 36. Most studies used data from prospective trials and calculated cost-effectiveness directly from these trial inputs, rather than using simulation methods. As a group, these studies primarily emphasised precision and realism over generalisability, meaning that their results were best suited to specific settings. CONCLUSIONS: The number of included studies was small. Our findings suggest that most economic evaluations of HSS do not leverage methods like sensitivity analyses or inputs from literature review that would produce more generalisable (but potentially less precise) results. More research into how decision-makers would use economic evaluations to define the expansion path to strengthening health systems would allow for conceptualising impactful work on the economic value of HSS.

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.