The relationships between democratic experience, adult health, and cause-specific mortality in 170 countries between 1980 and 2016: an observational analysis.

BACKGROUND: Previous analyses of democracy and population health have focused on broad measures, such as life expectancy at birth and child and infant mortality, and have shown some contradictory results. We used a panel of data spanning 170 countries to assess the association between democracy and cause-specific mortality and explore the pathways connecting democratic rule to health gains. METHODS: We extracted cause-specific mortality and HIV-free life expectancy estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 and information on regime type from the Varieties of Democracy project. These data cover 170 countries and 46 years. From the Financing Global Health database, we extracted gross domestic product (GDP) per capita, also covering 46 years, and Development Assistance for Health estimates starting from 1990 and domestic health spending estimates starting from 1995. We used a diverse set of empirical methods-synthetic control, within-country variance decomposition, structural equation models, and fixed-effects regression-which together provide a robust analysis of the association between democratisation and population health. FINDINGS: HIV-free life expectancy at age 15 years improved significantly during the study period (1970-2015) in countries after they transitioned to democracy, on average by 3% after 10 years. Democratic experience explains 22·27% of the variance in mortality within a country from cardiovascular diseases, 16·53% for tuberculosis, and 17·78% for transport injuries, and a smaller percentage for other diseases included in the study. For cardiovascular diseases, transport injuries, cancers, cirrhosis, and other non-communicable diseases, democratic experience explains more of the variation in mortality than GDP. Over the past 20 years, the average country's increase in democratic experience had direct and indirect effects on reducing mortality from cardiovascular disease (-9·64%, 95% CI -6·38 to -12·90), other non-communicable diseases (-9·14%, -4·26 to -14·02), and tuberculosis (-8·93%, -2·08 to -15·77). Increases in a country's democratic experience were not correlated with GDP per capita between 1995 and 2015 (ρ=-0·1036; p=0·1826), but were correlated with declines in mortality from cardiovascular disease (ρ=-0·3873; p<0·0001) and increases in government health spending (ρ=0·4002; p<0·0001). Removal of free and fair elections from the democratic experience variable resulted in loss of association with age-standardised mortality from non-communicable diseases and injuries. INTERPRETATION: When enforced by free and fair elections, democracies are more likely than autocracies to lead to health gains for causes of mortality (eg, cardiovascular diseases and transport injuries) that have not been heavily targeted by foreign aid and require health-care delivery infrastructure. International health agencies and donors might increasingly need to consider the implications of regime type in their efforts to maximise health gains, particularly in the context of ageing populations and the growing burden of non-communicable diseases. FUNDING: Bloomberg Philanthropies and the Bill & Melinda Gates Foundation.

The overweight and obesity transition from the wealthy to the poor in low- and middle-income countries: A survey of household data from 103 countries.

BACKGROUND: In high-income countries, obesity prevalence (body mass index greater than or equal to 30 kg/m2) is highest among the poor, while overweight (body mass index greater than or equal to 25 kg/m2) is prevalent across all wealth groups. In contrast, in low-income countries, the prevalence of overweight and obesity is higher among wealthier individuals than among poorer individuals. We characterize the transition of overweight and obesity from wealthier to poorer populations as countries develop, and project the burden of overweight and obesity among the poor for 103 countries. METHODS AND FINDINGS: Our sample used 182 Demographic and Health Surveys and World Health Surveys (n = 2.24 million respondents) from 1995 to 2016. We created a standard wealth index using household assets common among all surveys and linked national wealth by country and year identifiers. We then estimated the changing probability of overweight and obesity across every wealth decile as countries' per capita gross domestic product (GDP) rises using logistic and linear fixed-effect regression models. We found that obesity rates among the wealthiest decile were relatively stable with increasing national wealth, and the changing gradient was largely due to increasing obesity prevalence among poorer populations (3.5% [95% uncertainty interval: 0.0%-8.3%] to 14.3% [9.7%-19.0%]). Overweight prevalence among the richest (45.0% [35.6%-54.4%]) and the poorest (45.5% [35.9%-55.0%]) were roughly equal in high-income settings. At $8,000 GDP per capita, the adjusted probability of being obese was no longer highest in the richest decile, and the same was true of overweight at $10,000. Above $25,000, individuals in the richest decile were less likely than those in the poorest decile to be obese, and the same was true of overweight at $50,000. We then projected overweight and obesity rates by wealth decile to 2040 for all countries to quantify the expected rise in prevalence in the relatively poor. Our projections indicated that, if past trends continued, the number of people who are poor and overweight will increase in our study countries by a median 84.4% (range 3.54%-383.4%), most prominently in low-income countries. The main limitations of this study included the inclusion of cross-sectional, self-reported data, possible reverse causality of overweight and obesity on wealth, and the lack of physical activity and food price data. CONCLUSIONS: Our findings indicate that as countries develop economically, overweight prevalence increased substantially among the poorest and stayed mostly unchanged among the wealthiest. The relative poor in upper- and lower-middle income countries may have the greatest burden, indicating important planning and targeting needs for national health programs.

Development assistance for health: past trends, associations, and the future of international financial flows for health.

BACKGROUND: Disbursements of development assistance for health (DAH) have risen substantially during the past several decades. More recently, the international community's attention has turned to other international challenges, introducing uncertainty about the future of disbursements for DAH. METHODS: We collected audited budget statements, annual reports, and project-level records from the main international agencies that disbursed DAH from 1990 to the end of 2015. We standardised and combined records to provide a comprehensive set of annual disbursements. We tracked each dollar of DAH back to the source and forward to the recipient. We removed transfers between agencies to avoid double-counting and adjusted for inflation. We classified assistance into nine primary health focus areas: HIV/AIDS, tuberculosis, malaria, maternal health, newborn and child health, other infectious diseases, non-communicable diseases, Ebola, and sector-wide approaches and health system strengthening. For our statistical analysis, we grouped these health focus areas into two categories: MDG-related focus areas (HIV/AIDS, tuberculosis, malaria, child and newborn health, and maternal health) and non-MDG-related focus areas (other infectious diseases, non-communicable diseases, sector-wide approaches, and other). We used linear regression to test for structural shifts in disbursement patterns at the onset of the Millennium Development Goals (MDGs; ie, from 2000) and the global financial crisis (impact estimated to occur in 2010). We built on past trends and associations with an ensemble model to estimate DAH through the end of 2040. FINDINGS: In 2015, US$36·4 billion of DAH was disbursed, marking the fifth consecutive year of little change in the amount of resources provided by global health development partners. Between 2000 and 2009, DAH increased at 11·3% per year, whereas between 2010 and 2015, annual growth was just 1·2%. In 2015, 29·7% of DAH was for HIV/AIDS, 17·9% was for child and newborn health, and 9·8% was for maternal health. Linear regression identifies three distinct periods of growth in DAH. Between 2000 and 2009, MDG-related DAH increased by $290·4 million (95% uncertainty interval [UI] 174·3 million to 406·5 million) per year. These increases were significantly greater than were increases in non-MDG DAH during the same period (p=0·009), and were also significantly greater than increases in the previous period (p<0·0001). Between 2000 and 2009, growth in DAH was highest for HIV/AIDS, malaria, and tuberculosis. Since 2010, DAH for maternal health and newborn and child health has continued to climb, although DAH for HIV/AIDS and most other health focus areas has remained flat or decreased. Our estimates of future DAH based on past trends and associations present a wide range of potential futures, although our mean estimate of $64·1 billion (95% UI $30·4 billion to $161·8 billion) shows an increase between now and 2040, although with a large uncertainty interval. INTERPRETATION: Our results provide evidence of two substantial shifts in DAH growth during the past 26 years. DAH disbursements increased faster in the first decade of the 2000s than in the 1990s, but DAH associated with the MDGs increased the most out of all focus areas. Since 2010, limited growth has characterised DAH and we expect this pattern to persist. Despite the fact that DAH is still growing, albeit minimally, DAH is shifting among the major health focus areas, with relatively little growth for HIV/AIDS, malaria, and tuberculosis. These changes in the growth and focus of DAH will have critical effects on health services in some low-income countries. Coordination and collaboration between donors and domestic governments is more important than ever because they have a great opportunity and responsibility to ensure robust health systems and service provision for those most in need. FUNDING: Bill & Melinda Gates Foundation.

National spending on health by source for 184 countries between 2013 and 2040.

BACKGROUND: A general consensus exists that as a country develops economically, health spending per capita rises and the share of that spending that is prepaid through government or private mechanisms also rises. However, the speed and magnitude of these changes vary substantially across countries, even at similar levels of development. In this study, we use past trends and relationships to estimate future health spending, disaggregated by the source of those funds, to identify the financing trajectories that are likely to occur if current policies and trajectories evolve as expected. METHODS: We extracted data from WHO's Health Spending Observatory and the Institute for Health Metrics and Evaluation's Financing Global Health 2015 report. We converted these data to a common purchasing power-adjusted and inflation-adjusted currency. We used a series of ensemble models and observed empirical norms to estimate future government out-of-pocket private prepaid health spending and development assistance for health. We aggregated each country's estimates to generate total health spending from 2013 to 2040 for 184 countries. We compared these estimates with each other and internationally recognised benchmarks. FINDINGS: Global spending on health is expected to increase from US$7·83 trillion in 2013 to $18·28 (uncertainty interval 14·42-22·24) trillion in 2040 (in 2010 purchasing power parity-adjusted dollars). We expect per-capita health spending to increase annually by 2·7% (1·9-3·4) in high-income countries, 3·4% (2·4-4·2) in upper-middle-income countries, 3·0% (2·3-3·6) in lower-middle-income countries, and 2·4% (1·6-3·1) in low-income countries. Given the gaps in current health spending, these rates provide no evidence of increasing parity in health spending. In 1995 and 2015, low-income countries spent $0·03 for every dollar spent in high-income countries, even after adjusting for purchasing power, and the same is projected for 2040. Most importantly, health spending in many low-income countries is expected to remain low. Estimates suggest that, by 2040, only one (3%) of 34 low-income countries and 36 (37%) of 98 middle-income countries will reach the Chatham House goal of 5% of gross domestic product consisting of government health spending. INTERPRETATION: Despite remarkable health gains, past health financing trends and relationships suggest that many low-income and lower-middle-income countries will not meet internationally set health spending targets and that spending gaps between low-income and high-income countries are unlikely to narrow unless substantive policy interventions occur. Although gains in health system efficiency can be used to make progress, current trends suggest that meaningful increases in health system resources will require concerted action. FUNDING: Bill & Melinda Gates Foundation.

US Spending on Personal Health Care and Public Health, 1996-2013.

Importance: US health care spending has continued to increase, and now accounts for more than 17% of the US economy. Despite the size and growth of this spending, little is known about how spending on each condition varies by age and across time. Objective: To systematically and comprehensively estimate US spending on personal health care and public health, according to condition, age and sex group, and type of care. Design and Setting: Government budgets, insurance claims, facility surveys, household surveys, and official US records from 1996 through 2013 were collected and combined. In total, 183 sources of data were used to estimate spending for 155 conditions (including cancer, which was disaggregated into 29 conditions). For each record, spending was extracted, along with the age and sex of the patient, and the type of care. Spending was adjusted to reflect the health condition treated, rather than the primary diagnosis. Exposures: Encounter with US health care system. Main Outcomes and Measures: National spending estimates stratified by condition, age and sex group, and type of care. Results: From 1996 through 2013, $30.1 trillion of personal health care spending was disaggregated by 155 conditions, age and sex group, and type of care. Among these 155 conditions, diabetes had the highest health care spending in 2013, with an estimated $101.4 billion (uncertainty interval [UI], $96.7 billion-$106.5 billion) in spending, including 57.6% (UI, 53.8%-62.1%) spent on pharmaceuticals and 23.5% (UI, 21.7%-25.7%) spent on ambulatory care. Ischemic heart disease accounted for the second-highest amount of health care spending in 2013, with estimated spending of $88.1 billion (UI, $82.7 billion-$92.9 billion), and low back and neck pain accounted for the third-highest amount, with estimated health care spending of $87.6 billion (UI, $67.5 billion-$94.1 billion). The conditions with the highest spending levels varied by age, sex, type of care, and year. Personal health care spending increased for 143 of the 155 conditions from 1996 through 2013. Spending on low back and neck pain and on diabetes increased the most over the 18 years, by an estimated $57.2 billion (UI, $47.4 billion-$64.4 billion) and $64.4 billion (UI, $57.8 billion-$70.7 billion), respectively. From 1996 through 2013, spending on emergency care and retail pharmaceuticals increased at the fastest rates (6.4% [UI, 6.4%-6.4%] and 5.6% [UI, 5.6%-5.6%] annual growth rate, respectively), which were higher than annual rates for spending on inpatient care (2.8% [UI, 2.8%-2.8%] and nursing facility care (2.5% [UI, 2.5%-2.5%]). Conclusions and Relevance: Modeled estimates of US spending on personal health care and public health showed substantial increases from 1996 through 2013; with spending on diabetes, ischemic heart disease, and low back and neck pain accounting for the highest amounts of spending by disease category. The rate of change in annual spending varied considerably among different conditions and types of care. This information may have implications for efforts to control US health care spending.

Global, regional, and national levels and causes of maternal mortality during 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.

BACKGROUND: The fifth Millennium Development Goal (MDG 5) established the goal of a 75% reduction in the maternal mortality ratio (MMR; number of maternal deaths per 100,000 livebirths) between 1990 and 2015. We aimed to measure levels and track trends in maternal mortality, the key causes contributing to maternal death, and timing of maternal death with respect to delivery. METHODS: We used robust statistical methods including the Cause of Death Ensemble model (CODEm) to analyse a database of data for 7065 site-years and estimate the number of maternal deaths from all causes in 188 countries between 1990 and 2013. We estimated the number of pregnancy-related deaths caused by HIV on the basis of a systematic review of the relative risk of dying during pregnancy for HIV-positive women compared with HIV-negative women. We also estimated the fraction of these deaths aggravated by pregnancy on the basis of a systematic review. To estimate the numbers of maternal deaths due to nine different causes, we identified 61 sources from a systematic review and 943 site-years of vital registration data. We also did a systematic review of reports about the timing of maternal death, identifying 142 sources to use in our analysis. We developed estimates for each country for 1990-2013 using Bayesian meta-regression. We estimated 95% uncertainty intervals (UIs) for all values. FINDINGS: 292,982 (95% UI 261,017-327,792) maternal deaths occurred in 2013, compared with 376,034 (343,483-407,574) in 1990. The global annual rate of change in the MMR was -0·3% (-1·1 to 0·6) from 1990 to 2003, and -2·7% (-3·9 to -1·5) from 2003 to 2013, with evidence of continued acceleration. MMRs reduced consistently in south, east, and southeast Asia between 1990 and 2013, but maternal deaths increased in much of sub-Saharan Africa during the 1990s. 2070 (1290-2866) maternal deaths were related to HIV in 2013, 0·4% (0·2-0·6) of the global total. MMR was highest in the oldest age groups in both 1990 and 2013. In 2013, most deaths occurred intrapartum or postpartum. Causes varied by region and between 1990 and 2013. We recorded substantial variation in the MMR by country in 2013, from 956·8 (685·1-1262·8) in South Sudan to 2·4 (1·6-3·6) in Iceland. INTERPRETATION: Global rates of change suggest that only 16 countries will achieve the MDG 5 target by 2015. Accelerated reductions since the Millennium Declaration in 2000 coincide with increased development assistance for maternal, newborn, and child health. Setting of targets and associated interventions for after 2015 will need careful consideration of regions that are making slow progress, such as west and central Africa. FUNDING: Bill & Melinda Gates Foundation.

Sources and Focus of Health Development Assistance, 1990-2014.

IMPORTANCE: The governments of high-income countries and private organizations provide billions of dollars to developing countries for health. This type of development assistance can have a critical role in ensuring that life-saving health interventions reach populations in need. OBJECTIVES: To identify the amount of development assistance that countries and organizations provided for health and to determine the health areas that received these funds. EVIDENCE REVIEW: Budget, revenue, and expenditure data on the primary agencies and organizations (n = 38) that provided resources to developing countries (n = 146-183, depending on the year) for health from 1990 through 2014 were collected. For each channel (the international agency or organization that directed the resources toward the implementing institution or government), the source and recipient of the development assistance were determined and redundant accounting of the same dollar, which occurs when channels transfer funds among each other, was removed. This research derived the flow of resources from source to intermediary channel to recipient. Development assistance for health (DAH) was divided into 11 mutually exclusive health focus areas, such that every dollar of development assistance was assigned only 1 health focus area. FINDINGS: Since 1990, $458.0 billion of development assistance has been provided to maintain or improve health in developing countries. The largest source of funding was the US government, which provided $143.1 billion between 1990 and 2014, including $12.4 billion in 2014. Of resources that originated with the US government, 70.6% were provided through US government agencies, and 41.0% were allocated for human immunodeficiency virus (HIV)/AIDS. The second largest source of development assistance for health was private philanthropic donors, including the Bill and Melinda Gates Foundation and other private foundations, which provided $69.9 billion between 1990 and 2014, including $6.2 billion in 2014. These resources were provided primarily through private foundations and nongovernmental organizations and were allocated for a diverse set of health focus areas. Since 1990, 28.0% of all DAH was allocated for maternal health and newborn and child health; 23.2% for HIV/AIDS, 4.3% for malaria, 2.8% for tuberculosis, and 1.5% for noncommunicable diseases. Between 2000 and 2010, DAH increased 11.3% annually. However, since 2010, total DAH has not increased as substantially. CONCLUSIONS AND RELEVANCE: Funding for health in developing countries has increased substantially since 1990, with a focus on HIV/AIDS, maternal health, and newborn and child health. Funding from the US government has played a substantial role in this expansion. Funding for noncommunicable diseases has been limited. Understanding how funding patterns have changed across time and the priorities of sources of international funding across distinct channels, recipients, and health focus areas may help identify where funding gaps persist and where cost-effective interventions could save lives.

Addressing 6 challenges in generative AI for digital health: A scoping review.

Generative artificial intelligence (AI) can exhibit biases, compromise data privacy, misinterpret prompts that are adversarial attacks, and produce hallucinations. Despite the potential of generative AI for many applications in digital health, practitioners must understand these tools and their limitations. This scoping review pays particular attention to the challenges with generative AI technologies in medical settings and surveys potential solutions. Using PubMed, we identified a total of 120 articles published by March 2024, which reference and evaluate generative AI in medicine, from which we synthesized themes and suggestions for future work. After first discussing general background on generative AI, we focus on collecting and presenting 6 challenges key for digital health practitioners and specific measures that can be taken to mitigate these challenges. Overall, bias, privacy, hallucination, and regulatory compliance were frequently considered, while other concerns around generative AI, such as overreliance on text models, adversarial misprompting, and jailbreaking, are not commonly evaluated in the current literature.

Democracies Linked To Greater Universal Health Coverage Compared With Autocracies, Even In An Economic Recession.

Despite widespread recognition that universal health coverage is a political choice, the roles that a country's political system plays in ensuring essential health services and minimizing financial risk remain poorly understood. Identifying the political determinants of universal health coverage is important for continued progress, and understanding the roles of political systems is particularly valuable in a global economic recession, which tests the continued commitment of nations to protecting their health of its citizens and to shielding them from financial risk. We measured the associations that democracy has with universal health coverage and government health spending in 170 countries during the period 1990-2019. We assessed how economic recessions affect those associations (using synthetic control methods) and the mechanisms connecting democracy with government health spending and universal health coverage (using machine learning methods). Our results show that democracy is positively associated with universal health coverage and government health spending and that this association is greatest for low-income countries. Free and fair elections were the mechanism primarily responsible for those positive associations. Democracies are more likely than autocracies to maintain universal health coverage, even amid economic recessions, when access to affordable, effective health services matters most.

Autocratisation and universal health coverage: synthetic control study.

OBJECTIVE: To assess the relation between autocratisation-substantial decreases in democratic traits (free and fair elections, freedom of civil and political association, and freedom of expression)-and countries' population health outcomes and progress toward universal health coverage (UHC). DESIGN: Synthetic control analysis. SETTING AND COUNTRY SELECTION: Global sample of countries for all years from 1989 to 2019, split into two categories: 17 treatment countries that started autocratising during 2000 to 2010, and 119 control countries that never autocratised from 1989 to 2019. The treatment countries comprised low and middle income nations and represent all world regions except North America and western Europe. A weighted combination of control countries was used to construct synthetic controls for each treatment country. This statistical method is especially well suited to population level studies when random assignment is infeasible and sufficiently similar comparators are not available. The method was originally developed in economics and political science to assess the impact of policies and events, and it is now increasingly used in epidemiology. MAIN OUTCOME MEASURES: HIV-free life expectancy at age 5 years, UHC effective coverage index (0-100 point scale), and out-of-pocket spending on health per capita. All outcome variables are for the period 1989 to 2019. RESULTS: Autocratising countries underperformed for all three outcome variables in the 10 years after the onset of autocratisation, despite some improvements in life expectancy, UHC effective coverage index, and out-of-pocket spending on health. On average, HIV-free life expectancy at age 5 years increased by 2.2% (from 64.7 to 66.1 years) during the 10 years after the onset of autocratisation. This study estimated that it would have increased by 3.5% (95% confidence interval 3.3% to 3.6%, P<0.001) (from 64.7 to 66.9 years) in the absence of autocratisation. On average, the UHC effective coverage index increased by 11.9% (from 42.5 to 47.6 points) during the 10 years after the onset of autocratisation. This study estimated that it would have increased by 20.2% (95% confidence interval 19.6% to 21.2%, P<0.001) (from 42.5 to 51.1 points) in the absence of autocratisation. Finally, on average, out-of-pocket spending on health per capita increased by 10.0% (from $4.00 (£3.1; €3.4) to $4.4, log transformed) during the 10 years after the onset of autocratisation. This study estimated that it would have increased by only 4.4% (95% confidence interval 3.9% to 4.6%, P<0.001) (from $4.0 to $4.2, log transformed) in the absence of autocratisation. CONCLUSIONS: Autocratising countries had worse than estimated life expectancy, effective health service coverage, and levels of out-of-pocket spending on health. These results suggest that the noticeable increase in the number of countries that are experiencing democratic erosion in recent years is hindering population health gains and progress toward UHC. Global health institutions will need to adjust their policy recommendations and activities to obtain the best possible results in those countries with a diminishing democratic incentive to provide quality healthcare to populations.

The financing gaps framework: using need, potential spending and expected spending to allocate development assistance for health.

As growth in development assistance for health levels off, development assistance partners must make allocation decisions within tighter budget constraints. Furthermore, with the advent of comprehensive and comparable burden of disease and health financing estimates, empirical evidence can increasingly be used to direct funding to those most in need. In our 'financing gaps framework', we propose a new approach for harnessing information to make decisions about health aid. The framework was designed to be forward-looking, goal-oriented, versatile and customizable to a range of organizational contexts and health aims. Our framework brings together expected health spending, potential health spending and spending need, to orient financing decisions around international health targets. As an example of how the framework could be applied, we develop a case study, focused on global goals for child health. The case study harnesses data from the Global Burden of Disease 2013 Study, Financing Global Health 2015, the WHO Global Health Observatory and National Health Accounts. Funding flows are tied to progress toward the Sustainable Development Goal's target for reductions in under-five mortality. The flexibility and comprehensiveness of our framework makes it adaptable for use by a diverse set of governments, donors, policymakers and other stakeholders. The framework can be adapted to short- or long-run time frames, cross-country or subnational scales, and to a number of specific health focus areas. Depending on donor preferences, the framework can be deployed to incentivize local investments in health, ensuring the long-term sustainability of health systems in low- and middle-income countries, while also furnishing international support for progress toward global health goals.

Lower-Income Countries That Face The Most Rapid Shift In Noncommunicable Disease Burden Are Also The Least Prepared.

Demographic and epidemiological changes are shifting the disease burden from communicable to noncommunicable diseases in lower-income countries. Within a generation, the share of disease burden attributed to noncommunicable diseases in some poor countries will exceed 80 percent, rivaling that of rich countries, but this burden is likely to affect much younger people in poorer countries. The health systems of lower-income countries are unprepared for this change. We examined the shift to noncommunicable diseases and estimated preparedness for the shift by ranking 172 nations using a health system capacity index for noncommunicable disease. We project that the countries with the greatest increases in the share of disease burden attributable to noncommunicable disease over the next twenty-five years will also be the least prepared for the change, as they ranked low on our capacity index and are expected to have the smallest increases in national health spending. National governments and donors must invest more in preparing the health systems of lower-income countries for the dramatic shift to noncommunicable diseases and in reducing modifiable noncommunicable disease risks.

Correction: Random-Effects, Fixed-Effects and the within-between Specification for Clustered Data in Observational Health Studies: A Simulation Study.

[This corrects the article DOI: 10.1371/journal.pone.0110257.].

Understanding The Relationships Between Noncommunicable Diseases, Unhealthy Lifestyles, And Country Wealth.

The amount of international aid given to address noncommunicable diseases is minimal. Most of it is directed to wealthier countries and focuses on the prevention of unhealthy lifestyles. Explanations for the current direction of noncommunicable disease aid include that these are diseases of affluence that benefit from substantial research and development into their treatment in high-income countries and are better addressed through domestic tax and policy measures to reduce risk-factor prevalence than through aid programs. This study assessed these justifications. First, we examined the relationships among premature adult mortality, defined as the probability that a person who has lived to the age of fifteen will die before the age of sixty from noncommunicable diseases; the major risk factors for these diseases; and country wealth. Second, we compared noncommunicable and communicable diseases prevalent in poor and wealthy countries alike, and their respective links to economic development. Last, we examined the respective roles that wealth and risk prevention have played in countries that achieved substantial reductions in premature mortality from noncommunicable diseases. Our results support greater investment in cost-effective noncommunicable disease preventive care and treatment in poorer countries and a higher priority for reducing key risk factors, particularly tobacco use.

Random-effects, fixed-effects and the within-between specification for clustered data in observational health studies: a simulation study.

BACKGROUND: When unaccounted-for group-level characteristics affect an outcome variable, traditional linear regression is inefficient and can be biased. The random- and fixed-effects estimators (RE and FE, respectively) are two competing methods that address these problems. While each estimator controls for otherwise unaccounted-for effects, the two estimators require different assumptions. Health researchers tend to favor RE estimation, while researchers from some other disciplines tend to favor FE estimation. In addition to RE and FE, an alternative method called within-between (WB) was suggested by Mundlak in 1978, although is utilized infrequently. METHODS: We conduct a simulation study to compare RE, FE, and WB estimation across 16,200 scenarios. The scenarios vary in the number of groups, the size of the groups, within-group variation, goodness-of-fit of the model, and the degree to which the model is correctly specified. Estimator preference is determined by lowest mean squared error of the estimated marginal effect and root mean squared error of fitted values. RESULTS: Although there are scenarios when each estimator is most appropriate, the cases in which traditional RE estimation is preferred are less common. In finite samples, the WB approach outperforms both traditional estimators. The Hausman test guides the practitioner to the estimator with the smallest absolute error only 61% of the time, and in many sample sizes simply applying the WB approach produces smaller absolute errors than following the suggestion of the test. CONCLUSIONS: Specification and estimation should be carefully considered and ultimately guided by the objective of the analysis and characteristics of the data. The WB approach has been underutilized, particularly for inference on marginal effects in small samples. Blindly applying any estimator can lead to bias, inefficiency, and flawed inference.

Global health development assistance remained steady in 2013 but did not align with recipients' disease burden.

Tracking development assistance for health for low- and middle-income countries gives policy makers information about spending patterns and potential improvements in resource allocation. We tracked the flows of development assistance and explored the relationship between national income, disease burden, and assistance. We estimated that development assistance for health reached US$31.3 billion in 2013. Increased assistance from the Global Fund to Fight AIDS, Tuberculosis, and Malaria; the GAVI Alliance; and bilateral agencies in the United Kingdom helped raise funding to the highest level to date. The largest portion of health assistance targeted HIV/AIDS (25 percent); 20 percent targeted maternal, newborn, and child health. Disease burden and economic development were significantly associated with development assistance for health, but many countries received considerably more or less aid than these indicators predicted. Five countries received more than five times their expected amount of health aid, and seven others received less than one-fifth their expected funding. The lack of alignment between disease burden, income, and funding reveals the potential for improvement in resource allocation.