Population Health Metrics

Measuring the Global Burden of Disease (GBD) has been the key to verifying the evolution of health indicators worldwide. We analyse subnational GBD data for Brazil in order to monitor the performance of the Brazilian states in the last 28 years on their progress towards meeting the health-related SDGs. As part of the GBD study, we assessed the 41 health-related indicators from the SDGs in Brazil at the subnational level for all the 26 Brazilian states and the Federal District from 1990 to 2017. The GBD group has rescaled all worldwide indicators from 0 to 100, assuming that for each one of them, the worst value among all countries and overtime is 0, and the best is 100. They also estimate the overall health-related SDG index as a function of all previously estimated health indicators and the SDI index (Socio-Demographic Index) as a function of per capita income, average schooling in the population aged 15 years or over, and total fertility rate under the age of 25 (TFU25). From 1990 to 2017, most subnational health-related SDGs, the SDG and SDI indexes improved considerable in most Brazilian states. The observed differences in SDG indicators within Brazilian states, including HIV incidence and health worker density, increased over time. In 2017, health-related indicators that achieved good results globally included the prevalence of child wasting, NTD, household air pollution, conflict mortality, skilled birth attendance, use of modern contraceptive methods, vaccine coverage, and health worker density, but poor results were observed for child overweight and homicide rates. The high rates of overweight, alcohol consumption, and smoking prevalence found in the historically richest regions (i.e., the South and Southeast), contrast with the high rates of tuberculosis, maternal, neonatal, and under-5 mortality and WASH-related mortality found in the poorer regions (i.e., the North and Northeast). The majority of Brazil’s health-related SDG indicators have substantially improved over the past 28 years. However, inequalities in health among the Brazilian states and regions remain noticeable negatively affecting the Brazilian population, which can contribute to Brazil not achieving the SDG 2030 targets.

It is widely recognized that there are multiple risk factors for early-life mortality. In practice most interventions to curb early-life mortality target births based on a single risk factor, such as poverty. However, most premature deaths are not from the targeted group. Thus interventions target many births that are at not at high risk and miss many births at high risk. Using data from the second wave of Demographic and Health Surveys from India and a hierarchical Bayesian model, we estimate infant mortality risk for 73.320 infants in India as a function of 4 risk factors. We show how this information can be used to improve program targeting. We compare our novel approach against common programs that target groups based on a single risk factor. A conventional approach that targets mothers in the lowest quintile of income correctly identifies only 30% of infant deaths. By contrast, using four risk factors simultaneously we identify a group of births of the same size that includes 57% of all deaths. Using the 2012 census to translate these percentages into numbers, there were 25.642.200 births in 2012 and 4.4% died before the age of one. Our approach correctly identifies 643.106 of 1.128.257 infant deaths while poverty only identifies 338.477 infant deaths. Our approach considerably improves program targeting by identifying more infant deaths than the usual approach that targets births based on a single risk factor. This leads to more efficient program targeting. This is particularly useful in developing countries, where resources are lacking and needs are high.

The purpose of this study is to validate the Pulvers silhouette showcard as a measure of weight status in a population in the African region. This tool is particularly beneficial when scarce resources do not allow for direct anthropometric measurements due to limited survey time or lack of measurement technology in face-to-face general-purpose surveys or in mailed, online, or mobile device-based surveys. A cross-sectional study was conducted in the Republic of Seychelles with a sample of 1240 adults. We compared self-reported body sizes measured by Pulvers’ silhouette showcards to four measurements of body size and adiposity: body mass index (BMI), body fat percent measured, waist circumference, and waist to height ratio. The accuracy of silhouettes as an obesity indicator was examined using sex-specific receiver operator curve (ROC) analysis and the reliability of this tool to detect socioeconomic gradients in obesity was compared to BMI-based measurements. Our study supports silhouette body size showcards as a valid and reliable survey tool to measure self-reported body size and adiposity in an African population. The mean correlation coefficients of self-reported silhouettes with measured BMI were 0.80 in men and 0.81 in women (P < 0.001). The silhouette showcards also showed high accuracy for detecting obesity as per a BMI ≥ 30 (Area under curve, AUC: 0.91/0.89, SE: 0.01), which was comparable to other measured adiposity indicators: fat percent (AUC: 0.94/0.94, SE: 0.01), waist circumference (AUC: 0.95/0.94, SE: 0.01), and waist to height ratio (AUC: 0.95/0.94, SE: 0.01) amongst men and women, respectively. The use of silhouettes in detecting obesity differences among different socioeconomic groups resulted in similar magnitude, direction, and significance of association between obesity and socioeconomic status as when using measured BMI. This study highlights the validity and reliability of silhouettes as a survey tool for measuring obesity in a population in the African region. The ease of use and cost-effectiveness of this tool makes it an attractive alternative to measured BMI in the design of non-face-to-face online- or mobile device-based surveys as well as in-person general-purpose surveys of obesity in social sciences, where limited resources do not allow for direct anthropometric measurements.

Based on the global predictions majority of deaths will be collectively caused by cancer, cardiovascular diseases, and traffic accidents over the coming 25 years. In planning future national health policy actions, inter – regional assessments play an important role. The purpose of the study was to analyze similarities and differences in premature mortality between Serbia, EURO A, EURO B, and EURO C regions in 2000. Mortality and premature mortality patterns were analysed according to cause of death, by gender and seven age intervals. The study results are presented in relative (%) and absolute terms (age-specific and age-standardized death rates per 100,000 population, and age-standardized rates of years of life lost – YLL per 1,000). Direct standardization of rates was undertaken using the standard population of Europe. The inter-regional comparison was based on a calculation of differences in YLL structures and with a ratio of age-standardized YLL rates per 1,000. A multivariate generalized linear model was used to explore mortality of Serbia and Europe sub-regions with ln age-specific death rates. The dissimilarity was achieved with a p ≤ 0.05. According to the mortality pattern, Serbia was similar to EURO B, but with a lower average YLL per death case. YLL patterns indicated similarities between Serbia and EURO A, while SRR YLL had similarities between Serbia and EURO B. Compared to all Europe sub-regions, Serbia had a major excess of premature mortality in neoplasms and diabetes mellitus. Serbia had lost more years of life than EURO A due to cardiovascular, genitourinary diseases, and intentional injuries. Yet, Serbia was not as burdened with communicable diseases and injuries as were EURO B and EURO C. With a premature mortality pattern, Serbia is placed in the middle position of the Europe triangle. The main excess of YLL in Serbia was due to cardiovascular, malignant diseases, and diabetes mellitus. The results may be used for assessment of unacceptable social risks resulting from health inequalities. Within intentions to reduce an unfavourable premature mortality gap, it is necessary to reconsider certain local polices and practices as well as financial and human resources incorporated in the prevention of disease and injury burden.

Through application of the verbal autopsy (VA) approach, trained fieldworkers collect information about the probable cause of death (COD) by using a standardized questionnaire to interview family members who were present at the time of death. The physician-certified VA (PCVA), an independent review of this questionnaire data by up to three physicians trained in VA coding, is currently recommended by the World Health Organization (WHO) and is widely used in the INDEPTH Network. Even given its appropriateness in these contexts, a large percentage of causes of death assigned by VAs remains undetermined. As physicians often do not agree upon a final COD classification, there remains substantial room to improve the standard VA method, potentially leading to a reduction in physician discordance in COD coding. We present an extension of the current method of PCVA and compare it to the standard WHO-recommended procedure. We used VA data collected in the Nouna Health and Demographic Surveillance Site (NHDSS) between 2009 and 2010 using a locally-adapted version of an INDEPTH standard verbal autopsy questionnaire. Until 2009, physicians in the NHDSS followed the WHO method (Method 1). As an extension of Method 1, starting in 2010, the use of a panel of physicians was added to the coding process in the case where a third physician's final conclusions resulted in an undetermined COD (Method 2). Two independent samples of VA questionnaires were compared for the year 2009 (using Method 1) and the year 2010 (using Method 2). The WHO-recommended method used for 2009 yielded a high level of undetermined CODs, where the final coding was "undetermined" in 50.8% of all questionnaires due to disagreement among participating physicians (Method 1). By introducing a panel of physicians in 2010 for cases where the principal physicians disagreed on the cause of death, the revised method significantly reduced the proportion of undetermined CODs to 1.5% (Method 2). As the extended method of PCVA significantly improved the accuracy of the VA procedure, we suggest the adoption of this method for those countries where alternatives like computer-based VA coding are not available. Based on the results of our study, further research should be pursued.

We examine the association between family structure and children’s health care utilization, barriers to health care access, health, and schooling and cognitive outcomes and assess whether socioeconomic status (SES) accounts for those family structure differences. We advance prior research by focusing on understudied but increasingly common family structures including single father families and five different family structures that include grandparents. Our data on United States children aged birth through 17 (unweighted N = 198,864) come from the 1997–2013 waves of the National Health Interview Survey, a nationally representative, publicly available, household-based sample. We examine 17 outcomes across nine family structures, including married couple, cohabiting couple, single mother, and single father families, with and without grandparents, and skipped-generation families that include children and grandparents but not parents. The SES measures include family income, home ownership, and parents’ or grandparents’ (depending on who is in the household) employment and education. Compared to children living with married couples, children in single mother, extended single mother, and cohabiting couple families average poorer outcomes, but children in single father families sometimes average better health outcomes. The presence of grandparents in single parent, cohabiting, or married couple families does not buffer children from adverse outcomes. SES only partially explains family structure disparities in children’s well-being. All non-married couple family structures are associated with some adverse outcomes among children, but the degree of disadvantage varies across family structures. Efforts to understand and improve child well-being might be most effective if they recognize the increasing diversity in children’s living arrangements.

Despite a comprehensive ban on cultivation, manufacture, distribution, and sale of tobacco products since 2004, two nationwide surveys conducted in 2012 and 2013 reported high tobacco use in Bhutan. National Health Survey 2012 reported that 4 % of the population aged 15–75 years used smoked tobacco and about 48 % used smokeless tobacco. Similarly, Global Youth Tobacco Survey (GYTS) of Bhutan reported tobacco use prevalence of 30.3 % in 2013. However, factors associated with this high tobacco use were not systematically studied. Hence, we assessed the prevalence of tobacco use and its associated sociodemographic, behavioral, and environmental factors. This cross-sectional analytical study used secondary data collected in a nationally representative Non-communicable Disease Risk Factors Surveillance STEPS Survey 2014 conducted among Bhutanese adults (18–69 years). The survey included a total of 2820 adults; selected using multistage stratified cluster sampling. Weighted analysis was done to calculate the prevalence of tobacco use. Unadjusted and adjusted prevalence ratios were calculated using log binomial regression. The prevalence of current overall tobacco use was 24.8 % (95 % CI: 21.4–28.3) and that of smoked, smokeless, and dual forms (smoked and smokeless forms) were 7.4 % (95 % CI: 5.8–9.0), 19.7 % (95 % CI: 16.5–22.9), and 2.3 % (95 % CI: 1.8–2.9), respectively. Significantly higher prevalence of tobacco use in all forms was found among males, younger age groups, and alcohol users. The prevalence of smoked form was higher in urban areas compared to rural areas (11 % vs 6 %; aPR 1.8, 95 % CI: 1.5–2.0). Among individuals who reported having a non-communicable disease, the prevalence of smoked tobacco use was significantly lower than those who did not have disease (3.5 % vs. 8.3 %; aPR 0.5, 95 % CI: 0.3–0.9). Exposure to health warnings was protective for current tobacco use and smokeless tobacco use, while exposure to tobacco warnings through the media was helpful among smokers and overall tobacco users. Despite a comprehensive ban on tobacco, tobacco use was high in Bhutan, especially the smokeless form. Males, younger age groups, and alcohol users should be targeted with behavioral interventions along the stricter implementation of tobacco control measures.

Mortality from cardiovascular and other chronic diseases has increased in Iran. Our aim was to estimate the effects of smoking and high systolic blood pressure (SBP), fasting plasma glucose (FPG), total cholesterol (TC), and high body mass index (BMI) on mortality and life expectancy, nationally and subnationally, using representative data and comparable methods. We used data from the Non-Communicable Disease Surveillance Survey to estimate means and standard deviations for the metabolic risk factors, nationally and by region. Lung cancer mortality was used to measure cumulative exposure to smoking. We used data from the death registration system to estimate age-, sex-, and disease-specific numbers of deaths in 2005, adjusted for incompleteness using demographic methods. We used systematic reviews and meta-analyses of epidemiologic studies to obtain the effect of risk factors on disease-specific mortality. We estimated deaths and life expectancy loss attributable to risk factors using the comparative risk assessment framework. In 2005, high SBP was responsible for 41,000 (95% uncertainty interval: 38,000, 44,000) deaths in men and 39,000 (36,000, 42,000) deaths in women in Iran. High FPG, BMI, and TC were responsible for about one-third to one-half of deaths attributable to SBP in men and/or women. Smoking was responsible for 9,000 deaths among men and 2,000 among women. If SBP were reduced to optimal levels, life expectancy at birth would increase by 3.2 years (2.6, 3.9) and 4.1 years (3.2, 4.9) in men and women, respectively; the life expectancy gains ranged from 1.1 to 1.8 years for TC, BMI, and FPG. SBP was also responsible for the largest number of deaths in every region, with age-standardized attributable mortality ranging from 257 to 333 deaths per 100,000 adults in different regions. Management of blood pressure through diet, lifestyle, and pharmacological interventions should be a priority in Iran. Interventions for other metabolic risk factors and smoking can also improve population health.

The expanded definition of liver-related deaths includes a wide range of etiologies and sequelae. We compared the changes in liver-related mortality by etiology and sequelae for different age groups between 2008 and 2018 in the USA using both underlying and multiple cause of death (UCOD and MCOD) data. We extracted mortality data from the CDC WONDER. Both the absolute (rate difference) and relative (rate ratio and 95% confidence intervals) changes were calculated to quantify the magnitude of change using the expanded definition of liver-related mortality. Using the expanded definition including secondary liver cancer and according to UCOD data, we identified 68,037 liver-related deaths among people aged 20 years and above in 2008 (29 per 100,000) and this increased to 90,635 in 2018 (33 per 100,000), a 13% increase from 2008 to 2018. However, according to MCOD data, the number of deaths was 113,219 (48 per 100,000) in 2008 and increased to 161,312 (58 per 100,000) in 2018, indicating a 20% increase. The increase according to MCOD was mainly due to increase in alcoholic liver disease and secondary liver cancer (liver metastasis) for each age group and hepatitis C virus (HCV) and primary liver cancer among decedents aged 65–74 years. The direction of mortality change (increasing or decreasing) was similar in UCOD and MCOD data in most etiologies and sequelae, except secondary liver cancer. However, the extent of change differed between UCOD and MCOD data.

The study of the seasonal variation of disease is receiving increasing attention from health researchers. Available statistical tests for seasonality typically indicate the presence or absence of statistically significant seasonality but do not provide a meaningful measure of its strength. We propose the coefficient of determination of the autoregressive regression model fitted to the data ( ) as a measure for quantifying the strength of the seasonality. The performance of the proposed statistic is assessed through a simulation study and using two data sets known to demonstrate statistically significant seasonality: atrial fibrillation and asthma hospitalizations in Ontario, Canada. The simulation results showed the power of the in adequately quantifying the strength of the seasonality of the simulated observations for all models. In the atrial fibrillation and asthma datasets, while the statistical tests such as Bartlett's Kolmogorov-Smirnov (BKS) and Fisher's Kappa support statistical evidence of seasonality for both, the quantifies the strength of that seasonality. Corroborating the visual evidence that asthma is more conspicuously seasonal than atrial fibrillation, the calculated for atrial fibrillation indicates a weak to moderate seasonality ( = 0.44, 0.28 and 0.45 for both genders, males and females respectively), whereas for asthma, it indicates a strong seasonality ( = 0.82, 0.78 and 0.82 for both genders, male and female respectively). For the purposes of health services research, evidence of the statistical presence of seasonality is insufficient to determine the etiologic, clinical and policy relevance of findings. Measurement of the strength of the seasonal effect, as can be determined using the technique, is also important in order to provide a robust sense of seasonality.