Shefali Oza

Global, regional, and national causes of child mortality in 2000-13, with projections to inform post-2015 priorities: an updated systematic analysis

BACKGROUND Trend data for causes of child death are crucial to inform priorities for improving child survival by and beyond 2015. We report child mortality by cause estimates in 2000-13, and cause-specific mortality scenarios to 2030 and 2035. METHODS We estimated the distributions of causes of child mortality separately for neonates and children aged 1-59 months. To generate cause-specific mortality fractions, we included new vital registration and verbal autopsy data. We used vital registration data in countries with adequate registration systems. We applied vital registration-based multicause models for countries with low under-5 mortality but inadequate vital registration, and updated verbal autopsy-based multicause models for high mortality countries. We used updated numbers of child deaths to derive numbers of deaths by causes. We applied two scenarios to derive cause-specific mortality in 2030 and 2035. FINDINGS Of the 6·3 million children who died before age 5 years in 2013, 51·8% (3·257 million) died of infectious causes and 44% (2·761 million) died in the neonatal period. The three leading causes are preterm birth complications (0·965 million [15·4%, uncertainty range (UR) 9·8-24·5]; UR 0·615-1·537 million), pneumonia (0·935 million [14·9%, 13·0-16·8]; 0·817-1·057 million), and intrapartum-related complications (0·662 million [10·5%, 6·7-16·8]; 0·421-1·054 million). Reductions in pneumonia, diarrhoea, and measles collectively were responsible for half of the 3·6 million fewer deaths recorded in 2013 versus 2000. Causes with the slowest progress were congenital, preterm, neonatal sepsis, injury, and other causes. If present trends continue, 4·4 million children younger than 5 years will still die in 2030. Furthermore, sub-Saharan Africa will have 33% of the births and 60% of the deaths in 2030, compared with 25% and 50% in 2013, respectively. INTERPRETATION Our projection results provide concrete examples of how the distribution of child causes of deaths could look in 15-20 years to inform priority setting in the post-2015 era. More evidence is needed about shifts in timing, causes, and places of under-5 deaths to inform child survival agendas by and beyond 2015, to end preventable child deaths in a generation, and to count and account for every newborn and every child. FUNDING Bill & Melinda Gates Foundation.

Global, regional, and national causes of under-5 mortality in 2000–15: an updated systematic analysis with implications for the Sustainable Development Goals

Summary Background Despite remarkable progress in the improvement of child survival between 1990 and 2015, the Millennium Development Goal (MDG) 4 target of a two-thirds reduction of under-5 mortality rate (U5MR) was not achieved globally. In this paper, we updated our annual estimates of child mortality by cause to 2000–15 to reflect on progress toward the MDG 4 and consider implications for the Sustainable Development Goals (SDG) target for child survival. Methods We increased the estimation input data for causes of deaths by 43% among neonates and 23% among 1–59-month-olds, respectively. We used adequate vital registration (VR) data where available, and modelled cause-specific mortality fractions applying multinomial logistic regressions using adequate VR for low U5MR countries and verbal autopsy data for high U5MR countries. We updated the estimation to use Plasmodium falciparum parasite rate in place of malaria index in the modelling of malaria deaths; to use adjusted empirical estimates instead of modelled estimates for China; and to consider the effects of pneumococcal conjugate vaccine and rotavirus vaccine in the estimation. Findings In 2015, among the 5·9 million under-5 deaths, 2·7 million occurred in the neonatal period. The leading under-5 causes were preterm birth complications (1·055 million [95% uncertainty range (UR) 0·935–1·179]), pneumonia (0·921 million [0·812 −1·117]), and intrapartum-related events (0·691 million [0·598 −0·778]). In the two MDG regions with the most under-5 deaths, the leading cause was pneumonia in sub-Saharan Africa and preterm birth complications in southern Asia. Reductions in mortality rates for pneumonia, diarrhoea, neonatal intrapartum-related events, malaria, and measles were responsible for 61% of the total reduction of 35 per 1000 livebirths in U5MR in 2000–15. Stratified by U5MR, pneumonia was the leading cause in countries with very high U5MR. Preterm birth complications and pneumonia were both important in high, medium high, and medium child mortality countries; whereas congenital abnormalities was the most important cause in countries with low and very low U5MR. Interpretation In the SDG era, countries are advised to prioritise child survival policy and programmes based on their child cause-of-death composition. Continued and enhanced efforts to scale up proven life-saving interventions are needed to achieve the SDG child survival target. Funding Bill & Melinda Gates Foundation, WHO.

The promise of prevention: the effects of four preventable risk factors on national life expectancy and life expectancy disparities by race and county in the United States.

Majid Ezzati and colleagues examine the contribution of a set of risk factors (smoking, high blood pressure, elevated blood glucose, and adiposity) to socioeconomic disparities in life expectancy in the US population.

Trends and Cardiovascular Mortality Effects of State-Level Blood Pressure and Uncontrolled Hypertension in the United States

Background— Blood pressure is an important risk factor for cardiovascular disease and mortality and has lifestyle and healthcare determinants that vary across states. Only self-reported hypertension status is measured at the state level in the United States. Our aim was to estimate levels and trends in state-level mean systolic blood pressure (SBP), the prevalence of uncontrolled systolic hypertension, and cardiovascular mortality attributable to all levels of higher-than-optimal SBP. Methods and Results— We estimated the relationship between actual SBP/uncontrolled hypertension and self-reported hypertension, use of blood pressure medication, and a set of health system and sociodemographic variables in the nationally representative National Health and Nutrition Examination Survey. We applied this relationship to identical variables from the Behavioral Risk Factor Surveillance System to estimate state-specific mean SBP and uncontrolled hypertension. We used the comparative risk assessment methods to estimate cardiovascular mortality attributable to higher-than-optimal SBP. In 2001–2003, age-standardized uncontrolled hypertension prevalence was highest in the District of Columbia, Mississippi, Louisiana, Alabama, Texas, Georgia, and South Carolina (18% to 21% for men and 24% to 26% for women) and lowest in Vermont, Minnesota, Connecticut, New Hampshire, Iowa, and Colorado (15% to 16% for men and ≈21% for women). Women had a higher prevalence of uncontrolled hypertension than men in every state by 4 (Arizona) to 7 (Kansas) percentage points. In the 1990s, uncontrolled hypertension in women increased the most in Idaho and Oregon (by 6 percentage points) and the least in the District of Columbia and Mississippi (by 3 percentage points). For men, the worst-performing states were New Mexico and Louisiana (decrease of 0.6 and 1.3 percentage points), and the best-performing states were Vermont and Indiana (decrease of 4 and 3 percentage points). Age-standardized cardiovascular mortality attributable to higher-than-optimal SBP ranged from 200 to 220 per 100 000 (Minnesota and Massachusetts) to 360 to 370 per 100 000 (District of Columbia and Mississippi) for women and from 210 per 100 000 (Colorado and Utah) to 370 per 100 000 (Mississippi) and 410 per 100 000 (District of Columbia) for men. Conclusions— Lifestyle and pharmacological interventions for lowering blood pressure are particularly needed in the South and Appalachia, and with emphasis on control among women. Self-reported data on hypertension diagnosis from the Behavioral Risk Factor Surveillance System can be used to obtain unbiased state-level estimates of blood pressure and uncontrolled hypertension as benchmarks for priority setting and for designing and evaluating intervention programs.

Are Americans Feeling Less Healthy? The Puzzle of Trends in Self-rated Health

Although self-rated health is proposed for use in public health monitoring, previous reports on US levels and trends in self-rated health have shown ambiguous results. This study presents a comprehensive comparative analysis of responses to a common self-rated health question in 4 national surveys from 1971 to 2007: the National Health and Nutrition Examination Survey, Behavioral Risk Factor Surveillance System, National Health Interview Survey, and Current Population Survey. In addition to variation in the levels of self-rated health across surveys, striking discrepancies in time trends were observed. Whereas data from the Behavioral Risk Factor Surveillance System demonstrate that Americans were increasingly likely to report “fair” or “poor” health over the last decade, those from the Current Population Survey indicate the opposite trend. Subgroup analyses revealed that the greatest inconsistencies were among young respondents, Hispanics, and those without a high school education. Trends in “fair” or “poor” ratings were more inconsistent than trends in “excellent” ratings. The observed discrepancies elude simple explanations but suggest that self-rated health may be unsuitable for monitoring changes in population health over time. Analyses of socioeconomic disparities that use self-rated health may be particularly vulnerable to comparability problems, as inconsistencies are most pronounced among the lowest education group. More work is urgently needed on robust and comparable approaches to tracking population health.

Diabetes Prevalence and Diagnosis in US states: Analysis of Health Surveys

BackgroundCurrent US surveillance data provide estimates of diabetes using laboratory tests at the national level as well as self-reported data at the state level. Self-reported diabetes prevalence may be biased because respondents may not be aware of their risk status. Our objective was to estimate the prevalence of diagnosed and undiagnosed diabetes by state.MethodsWe estimated undiagnosed diabetes prevalence as a function of a set of health system and sociodemographic variables using a logistic regression in the National Health and Nutrition Examination Survey (2003-2006). We applied this relationship to identical variables from the Behavioral Risk Factor Surveillance System (2003-2007) to estimate state-level prevalence of undiagnosed diabetes by age group and sex. We assumed that those who report being diagnosed with diabetes in both surveys are truly diabetic.ResultsThe prevalence of diabetes in the U.S. was 13.7% among men and 11.7% among women ≥ 30 years. Age-standardized diabetes prevalence was highest in Mississippi, West Virginia, Louisiana, Texas, South Carolina, Alabama, and Georgia (15.8 to 16.6% for men and 12.4 to 14.8% for women). Vermont, Minnesota, Montana, and Colorado had the lowest prevalence (11.0 to 12.2% for men and 7.3 to 8.4% for women). Men in all states had higher diabetes prevalence than women. The absolute prevalence of undiagnosed diabetes, as a percent of total population, was highest in New Mexico, Texas, Florida, and California (3.5 to 3.7 percentage points) and lowest in Montana, Oklahoma, Oregon, Alaska, Vermont, Utah, Washington, and Hawaii (2.1 to 3 percentage points). Among those with no established diabetes diagnosis, being obese, being Hispanic, not having insurance and being ≥ 60 years old were significantly associated with a higher risk of having undiagnosed diabetes.ConclusionDiabetes prevalence is highest in the Southern and Appalachian states and lowest in the Midwest and the Northeast. Better diabetes diagnosis is needed in a number of states.

Neonatal cause-of-death estimates for the early and late neonatal periods for 194 countries: 2000–2013

Abstract Objective To estimate cause-of-death distributions in the early (0–6 days of age) and late (7–27 days of age) neonatal periods, for 194 countries between 2000 and 2013. Methods For 65 countries with high-quality vital registration, we used each country’s observed early and late neonatal proportional cause distributions. For the remaining 129 countries, we used multinomial logistic models to estimate these distributions. For countries with low child mortality we used vital registration data as inputs and for countries with high child mortality we used neonatal cause-of-death distribution data from studies in similar settings. We applied cause-specific proportions to neonatal death estimates from the United Nations Inter-agency Group for Child Mortality Estimation, by country and year, to estimate cause-specific risks and numbers of deaths. Findings Over time, neonatal deaths decreased for most causes. Of the 2.8 million neonatal deaths in 2013, 0.99 million deaths (uncertainty range: 0.70–1.31) were estimated to be caused by preterm birth complications, 0.64 million (uncertainty range: 0.46–0.84) by intrapartum complications and 0.43 million (uncertainty range: 0.22–0.66) by sepsis and other severe infections. Preterm birth (40.8%) and intrapartum complications (27.0%) accounted for most early neonatal deaths while infections caused nearly half of late neonatal deaths. Preterm birth complications were the leading cause of death in all regions of the world. Conclusion The neonatal cause-of-death distribution differs between the early and late periods and varies with neonatal mortality rate level. To reduce neonatal deaths, effective interventions to address these causes must be incorporated into policy decisions.

How many deaths are attributable to smoking in the United States? Comparison of methods for estimating smoking-attributable mortality when smoking prevalence changes.

BACKGROUND The number of smoking-attributable deaths is commonly estimated using current and former smoking prevalences or lung cancer mortality as an indirect metric of cumulative population smoking. Neither method accounts for differences in the timing with which relative risks (RRs) for different diseases change following smoking initiation and cessation. We aimed to develop a method to account for time-dependent RRs. METHODS We used birth cohort lung cancer mortality and its change over time to characterize time-varying cumulative smoking exposure. We analyzed data from the American Cancer Society Cancer Prevention Study II to estimate RRs for disease-specific mortality associated with current and former smoking, and change in RRs over time after cessation. RESULTS When lung cancer was used to measure cumulative smoking exposure, 254,700 male and 227,000 female deaths were attributed to smoking in the US in 2005. A modified method in which RRs for different diseases decreased at different rates after cessation yielded similar but slightly lower estimates [251,900 (male) and 221,100 (female)]. The lowest estimates resulted from the method based on smoking prevalence [225,800 (male) and 163,700 (female)]. CONCLUSIONS Although all methods estimated a large number of smoking attributable deaths, future efforts should account for temporal changes in smoking prevalence and in accumulation/reversibility of disease-specific risks.

Estimation of daily risk of neonatal death, including the day of birth, in 186 countries in 2013: a vital-registration and modelling-based study

BACKGROUND The days immediately after birth are the most risky for human survival, yet neonatal mortality risks are generally not reported by day. Early neonatal deaths are sometimes under-reported or might be misclassified by day of death or as stillbirths. We modelled daily neonatal mortality risk and estimated the proportion of deaths on the day of birth and in week 1 for 186 countries in 2013. METHODS We reviewed data from vital registration (VR) and demographic and health surveys for information on the timing of neonatal deaths. For countries with high-quality VR we used the data as reported. For countries without high-quality VR data, we applied an exponential model to data from 206 surveys in 79 countries (n=50,396 deaths) to estimate the proportions of neonatal deaths per day and used bootstrap sampling to develop uncertainty estimates. FINDINGS 57 countries (n=122,757 deaths) had high-quality VR, and modelled data were used for 129 countries. The proportion of deaths on the day of birth (day 0) and within week 1 varied little by neonatal mortality rate, income, or region. 1·00 million (36.3%) of all neonatal deaths occurred on day 0 (uncertainty range 0·94 million to 1·05 million), and 2·02 million (73.2%) in the first week (uncertainty range 1·99 million to 2·05 million). Sub-Saharan Africa had the highest risk of neonatal death and, therefore, had the highest risk of death on day 0 (11·2 per 1000 livebirths); the highest number of deaths on day 0 was seen in southern Asia (n=392,300). INTERPRETATION The risk of early neonatal death is very high across a range of countries and contexts. Cost-effective and feasible interventions to improve neonatal and maternity care could save many lives. FUNDING Save the Childrens Saving Newborn Lives programme.

Ebola Virus Disease in Children, Sierra Leone, 2014–2015

Children died rapidly, more than half in Ebola holding units before transfer to treatment units.