Mapping Plasmodium falciparum Mortality in Africa between 1990 and 2015.

BACKGROUND: Malaria control has not been routinely informed by the assessment of subnational variation in malaria deaths. We combined data from the Malaria Atlas Project and the Global Burden of Disease Study to estimate malaria mortality across sub-Saharan Africa on a grid of 5 km2 from 1990 through 2015. METHODS: We estimated malaria mortality using a spatiotemporal modeling framework of geolocated data (i.e., with known latitude and longitude) on the clinical incidence of malaria, coverage of antimalarial drug treatment, case fatality rate, and population distribution according to age. RESULTS: Across sub-Saharan Africa during the past 15 years, we estimated that there was an overall decrease of 57% (95% uncertainty interval, 46 to 65) in the rate of malaria deaths, from 12.5 (95% uncertainty interval, 8.3 to 17.0) per 10,000 population in 2000 to 5.4 (95% uncertainty interval, 3.4 to 7.9) in 2015. This led to an overall decrease of 37% (95% uncertainty interval, 36 to 39) in the number of malaria deaths annually, from 1,007,000 (95% uncertainty interval, 666,000 to 1,376,000) to 631,000 (95% uncertainty interval, 394,000 to 914,000). The share of malaria deaths among children younger than 5 years of age ranged from more than 80% at a rate of death of more than 25 per 10,000 to less than 40% at rates below 1 per 10,000. Areas with high malaria mortality (>10 per 10,000) and low coverage (<50%) of insecticide-treated bed nets and antimalarial drugs included much of Nigeria, Angola, and Cameroon and parts of the Central African Republic, Congo, Guinea, and Equatorial Guinea. CONCLUSIONS: We estimated that there was an overall decrease of 57% in the rate of death from malaria across sub-Saharan Africa over the past 15 years and identified several countries in which high rates of death were associated with low coverage of antimalarial treatment and prevention programs. (Funded by the Bill and Melinda Gates Foundation and others.).

Mapping under-5 and neonatal mortality in Africa, 2000-15: a baseline analysis for the Sustainable Development Goals.

BACKGROUND: During the Millennium Development Goal (MDG) era, many countries in Africa achieved marked reductions in under-5 and neonatal mortality. Yet the pace of progress toward these goals substantially varied at the national level, demonstrating an essential need for tracking even more local trends in child mortality. With the adoption of the Sustainable Development Goals (SDGs) in 2015, which established ambitious targets for improving child survival by 2030, optimal intervention planning and targeting will require understanding of trends and rates of progress at a higher spatial resolution. In this study, we aimed to generate high-resolution estimates of under-5 and neonatal all-cause mortality across 46 countries in Africa. METHODS: We assembled 235 geographically resolved household survey and census data sources on child deaths to produce estimates of under-5 and neonatal mortality at a resolution of 5 × 5 km grid cells across 46 African countries for 2000, 2005, 2010, and 2015. We used a Bayesian geostatistical analytical framework to generate these estimates, and implemented predictive validity tests. In addition to reporting 5 × 5 km estimates, we also aggregated results obtained from these estimates into three different levels-national, and subnational administrative levels 1 and 2-to provide the full range of geospatial resolution that local, national, and global decision makers might require. FINDINGS: Amid improving child survival in Africa, there was substantial heterogeneity in absolute levels of under-5 and neonatal mortality in 2015, as well as the annualised rates of decline achieved from 2000 to 2015. Subnational areas in countries such as Botswana, Rwanda, and Ethiopia recorded some of the largest decreases in child mortality rates since 2000, positioning them well to achieve SDG targets by 2030 or earlier. Yet these places were the exception for Africa, since many areas, particularly in central and western Africa, must reduce under-5 mortality rates by at least 8·8% per year, between 2015 and 2030, to achieve the SDG 3.2 target for under-5 mortality by 2030. INTERPRETATION: In the absence of unprecedented political commitment, financial support, and medical advances, the viability of SDG 3.2 achievement in Africa is precarious at best. By producing under-5 and neonatal mortality rates at multiple levels of geospatial resolution over time, this study provides key information for decision makers to target interventions at populations in the greatest need. In an era when precision public health increasingly has the potential to transform the design, implementation, and impact of health programmes, our 5 × 5 km estimates of child mortality in Africa provide a baseline against which local, national, and global stakeholders can map the pathways for ending preventable child deaths by 2030. FUNDING: Bill & Melinda Gates Foundation.

Under-5 mortality in 2851 Chinese counties, 1996-2012: a subnational assessment of achieving MDG 4 goals in China.

BACKGROUND: In the past two decades, the under-5 mortality rate in China has fallen substantially, but progress with regards to the Millennium Development Goal (MDG) 4 at the subnational level has not been quantified. We aimed to estimate under-5 mortality rates in mainland China for the years 1970 to 2012. METHODS: We estimated the under-5 mortality rate for 31 provinces in mainland China between 1970 and 2013 with data from censuses, surveys, surveillance sites, and disease surveillance points. We estimated under-5 mortality rates for 2851 counties in China from 1996 to 2012 with the reported child mortality numbers from the Annual Report System on Maternal and Child Health. We used a small area mortality estimation model, spatiotemporal smoothing, and Gaussian process regression to synthesise data and generate consistent provincial and county-level estimates. We compared progress at the county level with what was expected on the basis of income and educational attainment using an econometric model. We computed Gini coefficients to study the inequality of under-5 mortality rates across counties. FINDINGS: In 2012, the lowest provincial level under-5 mortality rate in China was about five per 1000 livebirths, lower than in Canada, New Zealand, and the USA. The highest provincial level under-5 mortality rate in China was higher than that of Bangladesh. 29 provinces achieved a decrease in under-5 mortality rates twice as fast as the MDG 4 target rate; only two provinces will not achieve MDG 4 by 2015. Although some counties in China have under-5 mortality rates similar to those in the most developed nations in 2012, some have similar rates to those recorded in Burkina Faso and Cameroon. Despite wide differences, the inter-county Gini coefficient has been decreasing. Improvement in maternal education and the economic boom have contributed to the fall in child mortality; more than 60% of the counties in China had rates of decline in under-5 mortality rates significantly faster than expected. Fast reduction in under-5 mortality rates have been recorded not only in the Han population, the dominant ethnic majority in China, but also in the minority populations. All top ten minority groups in terms of population sizes have experienced annual reductions in under-5 mortality rates faster than the MDG 4 target at 4.4%. INTERPRETATION: The reduction of under-5 mortality rates in China at the country, provincial, and county level is an extraordinary success story. Reductions of under-5 mortality rates faster than 8.8% (twice MDG 4 pace) are possible. Extremely rapid declines seem to be related to public policy in addition to socioeconomic progress. Lessons from successful counties should prove valuable for China to intensify efforts for those with unacceptably high under-5 mortality rates. FUNDING: National "Twelfth Five-Year" Plan for Science and Technology Support, National Health and Family Planning Commission of The People's Republic of China, Program for Changjiang Scholars and Innovative Research Team in University, the National Institute on Aging, and the Bill & Melinda Gates Foundation.

Changes in health in England, with analysis by English regions and areas of deprivation, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.

BACKGROUND: In the Global Burden of Disease Study 2013 (GBD 2013), knowledge about health and its determinants has been integrated into a comparable framework to inform health policy. Outputs of this analysis are relevant to current policy questions in England and elsewhere, particularly on health inequalities. We use GBD 2013 data on mortality and causes of death, and disease and injury incidence and prevalence to analyse the burden of disease and injury in England as a whole, in English regions, and within each English region by deprivation quintile. We also assess disease and injury burden in England attributable to potentially preventable risk factors. England and the English regions are compared with the remaining constituent countries of the UK and with comparable countries in the European Union (EU) and beyond. METHODS: We extracted data from the GBD 2013 to compare mortality, causes of death, years of life lost (YLLs), years lived with a disability (YLDs), and disability-adjusted life-years (DALYs) in England, the UK, and 18 other countries (the first 15 EU members [apart from the UK] and Australia, Canada, Norway, and the USA [EU15+]). We extended elements of the analysis to English regions, and subregional areas defined by deprivation quintile (deprivation areas). We used data split by the nine English regions (corresponding to the European boundaries of the Nomenclature for Territorial Statistics level 1 [NUTS 1] regions), and by quintile groups within each English region according to deprivation, thereby making 45 regional deprivation areas. Deprivation quintiles were defined by area of residence ranked at national level by Index of Multiple Deprivation score, 2010. Burden due to various risk factors is described for England using new GBD methodology to estimate independent and overlapping attributable risk for five tiers of behavioural, metabolic, and environmental risk factors. We present results for 306 causes and 2337 sequelae, and 79 risks or risk clusters. FINDINGS: Between 1990 and 2013, life expectancy from birth in England increased by 5·4 years (95% uncertainty interval 5·0-5·8) from 75·9 years (75·9-76·0) to 81·3 years (80·9-81·7); gains were greater for men than for women. Rates of age-standardised YLLs reduced by 41·1% (38·3-43·6), whereas DALYs were reduced by 23·8% (20·9-27·1), and YLDs by 1·4% (0·1-2·8). For these measures, England ranked better than the UK and the EU15+ means. Between 1990 and 2013, the range in life expectancy among 45 regional deprivation areas remained 8·2 years for men and decreased from 7·2 years in 1990 to 6·9 years in 2013 for women. In 2013, the leading cause of YLLs was ischaemic heart disease, and the leading cause of DALYs was low back and neck pain. Known risk factors accounted for 39·6% (37·7-41·7) of DALYs; leading behavioural risk factors were suboptimal diet (10·8% [9·1-12·7]) and tobacco (10·7% [9·4-12·0]). INTERPRETATION: Health in England is improving although substantial opportunities exist for further reductions in the burden of preventable disease. The gap in mortality rates between men and women has reduced, but marked health inequalities between the least deprived and most deprived areas remain. Declines in mortality have not been matched by similar declines in morbidity, resulting in people living longer with diseases. Health policies must therefore address the causes of ill health as well as those of premature mortality. Systematic action locally and nationally is needed to reduce risk exposures, support healthy behaviours, alleviate the severity of chronic disabling disorders, and mitigate the effects of socioeconomic deprivation. FUNDING: Bill & Melinda Gates Foundation and Public Health England.

Impact of traffic, poverty and facility ownership on travel time to emergency care in Nairobi, Kenya.

BACKGROUND: In many low and middle-income countries (LMICs), timely access to emergency healthcare services is limited. In urban settings, traffic can have a significant impact on travel time, leading to life-threatening delays for time-sensitive injuries and medical emergencies. In this study, we examined travel times to hospitals in Nairobi, Kenya, one of the largest and most congested cities in the developing world. METHODS: We used a network approach to estimate average minimum travel times to different types of hospitals (e.g. ownership and level of care) in Nairobi under both congested and uncongested traffic conditions. We also examined the correlation between travel time and socioeconomic status. RESULTS: We estimate the average minimum travel time during uncongested traffic conditions to any level 4 health facility (primary hospitals) or above in Nairobi to be 4.5 min (IQR 2.5-6.1). Traffic added an average of 9.0 min (a 200% increase). In uncongested conditions, we estimate an average travel time of 7.9 min (IQR 5.1-10.4) to level 5 facilities (secondary hospitals) and 11.6 min (IQR 8.5-14.2) to Kenyatta National Hospital, the only level 6 facility (tertiary hospital) in the country. Traffic congestion added an average of 13.1 and 16.0 min (166% and 138% increase) to travel times to level 5 and level 6 facilities, respectively. For individuals living below the poverty line, we estimate that preferential use of public or faith-based facilities could increase travel time by as much as 65%. CONCLUSION: Average travel times to health facilities capable of providing emergency care in Nairobi are quite low, but traffic congestion double or triple estimated travel times. Furthermore, we estimate significant disparities in timely access to care for those individuals living under the poverty line who preferentially seek care in public or faith-based facilities.

Moving from Malaria Burden Reduction toward Elimination: An Evaluation of Mass Drug Administration in Southern Province, Zambia.

From December 2014 to February 2016, a cluster randomized controlled trial was carried out in 60 health facility catchment areas along Lake Kariba in Zambia's Southern Province. The trial sought to evaluate the impact of four rounds of a mass drug administration (MDA) intervention with dihydroartemisinin-piperaquine (DHAP) or focal MDA with DHAP at the household level compared with a control population that received the standard of care. This study was the first randomized controlled trial with DHAP for MDA in sub-Saharan Africa and was conducted through a collaboration between the National Malaria Elimination Programme in the Zambian Ministry of Health, the PATH Malaria Control and Elimination Partnership in Africa, and the Center for Applied Malaria Research and Evaluation at Tulane University. This article serves as an introduction to a collection of articles designed to explore different aspects of the intervention. By describing the recent history of malaria control in Zambia leading up to the trial-from the scale-up of point-of-care diagnosis and treatment, vector control, and indoor residual spraying early in the twenty-first century, to the efforts made to sustain the gains achieved with that approach-it provides a rationale for the implementation of a trial that has informed a new national strategic plan and solidified malaria elimination as Zambia's national goal.

Impact of traffic, poverty and facility ownership on travel time to emergency care in Nairobi, Kenya

Background: In many low and middle-income countries (LMICs), timely access to emergency healthcare services is limited. In urban settings, traffic can have a significant impact on travel time, leading to life-threatening delays for time-sensitive injuries and medical emergencies. In this study, we examined travel times to hospitals in Nairobi, Kenya, one of the largest and most congested cities in the developing world. Methods: We used a network approach to estimate average minimum travel times to different types of hospitals (e.g. ownership and level of care) in Nairobi under both congested and uncongested traffic conditions. We also examined the correlation between travel time and socioeconomic status. Results: We estimate the average minimum travel time during uncongested traffic conditions to any level 4 health facility (primary hospitals) or above in Nairobi to be 4.5 min (IQR 2.5­6.1). Traffic added an average of 9.0 min (a 200% increase). In uncongested conditions, we estimate an average travel time of 7.9 min (IQR 5.1­10.4) to level 5 facilities (secondary hospitals) and 11.6 min (IQR 8.5­14.2) to Kenyatta National Hospital, the only level 6 facility (tertiary hospital) in the country. Traffic congestion added an average of 13.1 and 16.0 min (166% and 138% increase) to travel times to level 5 and level 6 facilities, respectively. For individuals living below the poverty line, we estimate that preferential use of public or faith-based facilities could increase travel time by as much as 65%. Conclusion: Average travel times to health facilities capable of providing emergency care in Nairobi are quite low, but traffic congestion double or triple estimated travel times. Furthermore, we estimate significant disparities in timely access to care for those individuals living under the poverty line who preferentially seek care in public or faith-based facilities