Showing papers in "Population Health Metrics in 2012"

National, regional, and global trends in adult overweight and obesity prevalences

Abstract: Overweight and obesity prevalence are commonly used for public and policy communication of the extent of the obesity epidemic, yet comparable estimates of trends in overweight and obesity prevalence by country are not available. We estimated trends between 1980 and 2008 in overweight and obesity prevalence and their uncertainty for adults 20 years of age and older in 199 countries and territories. Data were from a previous study, which used a Bayesian hierarchical model to estimate mean body mass index (BMI) based on published and unpublished health examination surveys and epidemiologic studies. Here, we used the estimated mean BMIs in a regression model to predict overweight and obesity prevalence by age, country, year, and sex. The uncertainty of the estimates included both those of the Bayesian hierarchical model and the uncertainty due to cross-walking from mean BMI to overweight and obesity prevalence. The global age-standardized prevalence of obesity nearly doubled from 6.4% (95% uncertainty interval 5.7-7.2%) in 1980 to 12.0% (11.5-12.5%) in 2008. Half of this rise occurred in the 20 years between 1980 and 2000, and half occurred in the 8 years between 2000 and 2008. The age-standardized prevalence of overweight increased from 24.6% (22.7-26.7%) to 34.4% (33.2-35.5%) during the same 28-year period. In 2008, female obesity prevalence ranged from 1.4% (0.7-2.2%) in Bangladesh and 1.5% (0.9-2.4%) in Madagascar to 70.4% (61.9-78.9%) in Tonga and 74.8% (66.7-82.1%) in Nauru. Male obesity was below 1% in Bangladesh, Democratic Republic of the Congo, and Ethiopia, and was highest in Cook Islands (60.1%, 52.6-67.6%) and Nauru (67.9%, 60.5-75.0%). Globally, the prevalence of overweight and obesity has increased since 1980, and the increase has accelerated. Although obesity increased in most countries, levels and trends varied substantially. These data on trends in overweight and obesity may be used to set targets for obesity prevalence as requested at the United Nations high-level meeting on Prevention and Control of NCDs.

Modeling causes of death: an integrated approach using CODEm

Abstract: Data on causes of death by age and sex are a critical input into health decision-making Priority setting in public health should be informed not only by the current magnitude of health problems but by trends in them However, cause of death data are often not available or are subject to substantial problems of comparability We propose five general principles for cause of death model development, validation, and reporting We detail a specific implementation of these principles that is embodied in an analytical tool - the Cause of Death Ensemble model (CODEm) - which explores a large variety of possible models to estimate trends in causes of death Possible models are identified using a covariate selection algorithm that yields many plausible combinations of covariates, which are then run through four model classes The model classes include mixed effects linear models and spatial-temporal Gaussian Process Regression models for cause fractions and death rates All models for each cause of death are then assessed using out-of-sample predictive validity and combined into an ensemble with optimal out-of-sample predictive performance Ensemble models for cause of death estimation outperform any single component model in tests of root mean square error, frequency of predicting correct temporal trends, and achieving 95% coverage of the prediction interval We present detailed results for CODEm applied to maternal mortality and summary results for several other causes of death, including cardiovascular disease and several cancers CODEm produces better estimates of cause of death trends than previous methods and is less susceptible to bias in model specification We demonstrate the utility of CODEm for the estimation of several major causes of death

Determining the best population-level alcohol consumption model and its impact on estimates of alcohol-attributable harms

Abstract: The goals of our study are to determine the most appropriate model for alcohol consumption as an exposure for burden of disease, to analyze the effect of the chosen alcohol consumption distribution on the estimation of the alcohol Population- Attributable Fractions (PAFs), and to characterize the chosen alcohol consumption distribution by exploring if there is a global relationship within the distribution. To identify the best model, the Log-Normal, Gamma, and Weibull prevalence distributions were examined using data from 41 surveys from Gender, Alcohol and Culture: An International Study (GENACIS) and from the European Comparative Alcohol Study. To assess the effect of these distributions on the estimated alcohol PAFs, we calculated the alcohol PAF for diabetes, breast cancer, and pancreatitis using the three above-named distributions and using the more traditional approach based on categories. The relationship between the mean and the standard deviation from the Gamma distribution was estimated using data from 851 datasets for 66 countries from GENACIS and from the STEPwise approach to Surveillance from the World Health Organization. The Log-Normal distribution provided a poor fit for the survey data, with Gamma and Weibull distributions providing better fits. Additionally, our analyses showed that there were no marked differences for the alcohol PAF estimates based on the Gamma or Weibull distributions compared to PAFs based on categorical alcohol consumption estimates. The standard deviation of the alcohol distribution was highly dependent on the mean, with a unit increase in alcohol consumption associated with a unit increase in the mean of 1.258 (95% CI: 1.223 to 1.293) (R2 = 0.9207) for women and 1.171 (95% CI: 1.144 to 1.197) (R2 = 0. 9474) for men. Although the Gamma distribution and the Weibull distribution provided similar results, the Gamma distribution is recommended to model alcohol consumption from population surveys due to its fit, flexibility, and the ease with which it can be modified. The results showed that a large degree of variance of the standard deviation of the alcohol consumption Gamma distribution was explained by the mean alcohol consumption, allowing for alcohol consumption to be modeled through a Gamma distribution using only average consumption.

Household food access and child malnutrition: results from the eight-country MAL-ED study

Abstract: Background: Stunting results from decreased food intake, poor diet quality, and a high burden of early childhood infections, and contributes to significant morbidity and mortality worldwide. Although food insecurity is an important determinant of child nutrition, including stunting, development of universal measures has been challenging due to cumbersome nutritional questionnaires and concerns about lack of comparability across populations. We investigate the relationship between household food access, one component of food security, and indicators of nutritional status in early childhood across eight country sites. Methods: We administered a socioeconomic survey to 800 households in research sites in eight countries, including a recently validated nine-item food access insecurity questionnaire, and obtained anthropometric measurements from children aged 24 to 60 months. We used multivariable regression models to assess the relationship between household food access insecurity and anthropometry in children, and we assessed the invariance of that relationship across country sites. Results: Average age of study children was 41 months. Mean food access insecurity score (range: 0–27) was 5.8, and varied from 2.4 in Nepal to 8.3 in Pakistan. Across sites, the prevalence of stunting (42%) was much higher than the prevalence of wasting (6%). In pooled regression analyses, a 10-point increase in food access insecurity score was associated with a 0.20 SD decrease in height-for-age Z score (95% CI 0.05 to 0.34 SD; p = 0.008). A likelihood ratio test for heterogeneity revealed that this relationship was consistent across countries (p = 0.17). Conclusions: Our study provides evidence of the validity of using a simple household food access insecurity score to investigate the etiology of childhood growth faltering across diverse geographic settings. Such a measure could be used to direct interventions by identifying children at risk of illness and death related to malnutrition.

Measuring infertility in populations: constructing a standard definition for use with demographic and reproductive health surveys

Abstract: Background: Infertility is a significant disability, yet there are no reliable estimates of its global prevalence. Studies on infertility prevalence define the condition inconsistently, rendering the comparison of studies or quantitative summaries of the literature difficult. This study analyzed key components of infertility to develop a definition that can be consistently applied to globally available household survey data. Methods: We proposed a standard definition of infertility and used it to generate prevalence estimates using 53 Demographic and Health Surveys (DHS). The analysis was restricted to the subset of DHS that contained detailed fertility information collected through the reproductive health calendar. We performed sensitivity analyses for key components of the definition and used these to inform our recommendations for each element of the definition. Results: Exposure type (couple status, contraceptive use, and intent), exposure time, and outcomes were key elements of the definition that we proposed. Our definition produced estimates that ranged from 0.6% to 3.4% for primary infertility and 8.7% to 32.6% for secondary infertility. Our sensitivity analyses showed that using an exposure measure of five years is less likely to misclassify fertile unions as infertile. Additionally, using a current, rather than continuous, measure of contraceptive use over five years resulted in a median relative error in secondary infertility of 20.7% (interquartile range of relative error [IQR]: 12.6%-26.9%), while not incorporating intent produced a corresponding error in secondary infertility of 58.2% (IQR: 44.3%-67.9%). Conclusions: In order to estimate the global burden of infertility, prevalence estimates using a consistent definition need to be generated. Our analysis provided a recommended definition that could be applied to widely available global household data. We also summarized potential biases that should be considered when making estimates of infertility prevalence using household survey data.

Developing a comprehensive time series of GDP per capita for 210 countries from 1950 to 2015

Abstract: Income has been extensively studied and utilized as a determinant of health. There are several sources of income expressed as gross domestic product (GDP) per capita, but there are no time series that are complete for the years between 1950 and 2015 for the 210 countries for which data exist. It is in the interest of population health research to establish a global time series that is complete from 1950 to 2015. We collected GDP per capita estimates expressed in either constant US dollar terms or international dollar terms (corrected for purchasing power parity) from seven sources. We applied several stages of models, including ordinary least-squares regressions and mixed effects models, to complete each of the seven source series from 1950 to 2015. The three US dollar and four international dollar series were each averaged to produce two new GDP per capita series. Nine complete series from 1950 to 2015 for 210 countries are available for use. These series can serve various analytical purposes and can illustrate myriad economic trends and features. The derivation of the two new series allows for researchers to avoid any series-specific biases that may exist. The modeling approach used is flexible and will allow for yearly updating as new estimates are produced by the source series. GDP per capita is a necessary tool in population health research, and our development and implementation of a new method has allowed for the most comprehensive known time series to date.

Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation

Abstract: The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models. Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites. In this paper we discuss the deficiencies of existing spatial population datasets and their limitations on epidemiological analyses. We review sources of detailed, contemporary, freely available and relevant spatial demographic data focusing on low income regions where such data are often sparse and highlight the value of incorporating these through a set of examples of their application in disease studies. Moreover, the importance of acknowledging, measuring, and accounting for uncertainty in spatial demographic datasets is outlined. Finally, a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications is put forward.

Systematic review of general burden of disease studies using disability-adjusted life years

Abstract: To systematically review the methodology of general burden of disease studies. Three key questions were addressed: 1) what was the quality of the data, 2) which methodological choices were made to calculate disability adjusted life years (DALYs), and 3) were uncertainty and risk factor analyses performed? Furthermore, DALY outcomes of the included studies were compared. Burden of disease studies (1990 to 2011) in international peer-reviewed journals and in grey literature were identified with main inclusion criteria being multiple-cause studies that quantified the burden of disease as the sum of the burden of all distinct diseases expressed in DALYs. Electronic database searches included Medline (PubMed), EMBASE, and Web of Science. Studies were collated by study population, design, methods used to measure mortality and morbidity, risk factor analyses, and evaluation of results. Thirty-one studies met the inclusion criteria of our review. Overall, studies followed the Global Burden of Disease (GBD) approach. However, considerable variation existed in disability weights, discounting, age-weighting, and adjustments for uncertainty. Few studies reported whether mortality data were corrected for missing data or underreporting. Comparison with the GBD DALY outcomes by country revealed that for some studies DALY estimates were of similar magnitude; others reported DALY estimates that were two times higher or lower. Overcoming “error” variation due to the use of different methodologies and low-quality data is a critical priority for advancing burden of disease studies. This can enlarge the detection of true variation in DALY outcomes between populations or over time.

Impact of diabetes mellitus on life expectancy and health-adjusted life expectancy in Canada

Abstract: The objectives of this study were to estimate life expectancy (LE) and health-adjusted life expectancy (HALE) for Canadians with and without diabetes and to evaluate the impact of diabetes on population health using administrative and survey data. Mortality data from the Canadian Chronic Disease Surveillance System (2004 to 2006) and Health Utilities Index data from the Canadian Community Health Survey (2000 to 2005) were used. Life table analysis was applied to calculate LE, HALE, and their confidence intervals using the Chiang and the adapted Sullivan methods. LE and HALE were significantly lower among people with diabetes than for people without the disease. LE and HALE for females without diabetes were 85.0 and 73.3 years, respectively (males: 80.2 and 70.9 years). Diabetes was associated with a loss of LE and HALE of 6.0 years and 5.8 years, respectively, for females, and 5.0 years and 5.3 years, respectively, for males, living with diabetes at 55 years of age. The overall gains in LE and HALE after the hypothetical elimination of prevalent diagnosed diabetes cases in the population were 1.4 years and 1.2 years, respectively, for females, and 1.3 years for both LE and HALE for males. The results of the study confirm that diabetes is an important disease burden in Canada impacting the female and male populations differently. The methods can be used to calculate LE and HALE for other chronic conditions, providing useful information for public health researchers and policymakers.

Global burden of injuries attributable to alcohol consumption in 2004: a novel way of calculating the burden of injuries attributable to alcohol consumption

Abstract: Alcohol consumption is a major risk factor for injuries; however, international data on this burden are limited. This article presents new methods to quantify the burden of injuries attributable to alcohol consumption and quantifies the number of deaths, potential years of life lost (PYLL), and disability-adjusted life years (DALYs) lost from injuries attributable to alcohol consumption for 2004. Data on drinking indicators were obtained from the Comparative Risk Assessment study. Data on mortality, PYLL, and DALYs for injuries were obtained from the World Health Organization. Alcohol-attributable fractions were calculated based on a new risk modeling methodology, which accounts for average and heavy drinking occasions. 95% confidence intervals (CIs) were calculated using a Monte Carlo simulation method. In 2004, 851,900 (95% CI: 419,400 to 1,282,500) deaths, 19,051,000 (95% CI: 9,767,000 to 28,243,000) PYLL, and 21,688,000 (95% CI: 11,097,000 to 32,385,000) DALYs for people 15 years and older were due to injuries attributable to alcohol consumption. With respect to the total number of deaths, harms to others were responsible for 15.1% of alcohol-attributable injury deaths, 14.5% of alcohol-attributable injury PYLL, and 11.35% of alcohol-attributable injury DALYs. The overall burden of injuries attributable to alcohol consumption corresponds to 17.3% of all injury deaths, 16.7% of all PYLL, and 13.6% of all DALYs caused by injuries, or 1.4% of all deaths, 2.0% of all PYLL, and 1.4% of all DALYs in 2004. The novel methodology described in this article to calculate the burden of injuries attributable to alcohol consumption improves on previous methodology by more accurately calculating the burden of injuries attributable to one’s own drinking, and for the first time, calculates the burden of injuries attributable to the alcohol consumption of others. The burden of injuries attributable to alcohol consumption is large and is entirely avoidable, and policies and strategies to reduce it are recommended.

Incidence, prevalence, and hybrid approaches to calculating disability-adjusted life years

Abstract: When disability-adjusted life years are used to measure the burden of disease on a population in a time interval, they can be calculated in several different ways: from an incidence, pure prevalence, or hybrid perspective. I show that these calculation methods are not equivalent and discuss some of the formal difficulties each method faces. I show that if we don’t discount the value of future health, there is a sense in which the choice of calculation method is a mere question of accounting. Such questions can be important, but they don’t raise deep theoretical concerns. If we do discount, however, choice of calculation method can change the relative burden attributed to different conditions over time. I conclude by recommending that studies involving disability-adjusted life years be explicit in noting what calculation method is being employed and in explaining why that calculation method has been chosen.

Estimating global mortality from potentially foodborne diseases: an analysis using vital registration data

Abstract: Foodborne diseases (FBD) comprise a large part of the global mortality burden, yet the true extent of their impact remains unknown. The present study utilizes multiple regression with the first attempt to use nonhealth variables to predict potentially FBD mortality at the country level. Vital registration (VR) data were used to build a multiple regression model incorporating nonhealth variables in addition to traditionally used health indicators. This model was subsequently used to predict FBD mortality rates for all countries of the World Health Organization classifications AmrA, AmrB, EurA, and EurB. Statistical modeling strongly supported the inclusion of nonhealth variables in a multiple regression model as predictors of potentially FBD mortality. Six variables were included in the final model: percent irrigated land, average calorie supply from animal products, meat production in metric tons, adult literacy rate, adult HIV/AIDS prevalence, and percent of deaths under age 5 caused by diarrheal disease. Interestingly, nonhealth variables were not only more robust predictors of mortality than health variables but also remained significant when adding additional health variables into the analysis. Mortality rate predictions from our model ranged from 0.26 deaths per 100,000 (Netherlands) to 15.65 deaths per 100,000 (Honduras). Reported mortality rates of potentially FBD from VR data lie within the 95% prediction interval for the majority of countries (37/39) where comparison was possible. Nonhealth variables appear to be strong predictors of potentially FBD mortality at the country level and may be a powerful tool in the effort to estimate the global mortality burden of FBD. The views expressed in this document are solely those of the authors and do not represent the views of the World Health Organization.

The contribution of educational inequalities to lifespan variation

Abstract: Studies of socioeconomic inequalities in mortality consistently point to higher death rates in lower socioeconomic groups. Yet how these between-group differences relate to the total variation in mortality risk between individuals is unknown. We used data assembled and harmonized as part of the Eurothine project, which includes census-based mortality data from 11 European countries. We matched this to national data from the Human Mortality Database and constructed life tables by gender and educational level. We measured variation in age at death using Theil's entropy index, and decomposed this measure into its between- and within-group components. The least-educated groups lived between three and 15 years fewer than the highest-educated groups, the latter having a more similar age at death in all countries. Differences between educational groups contributed between 0.6% and 2.7% to total variation in age at death between individuals in Western European countries and between 1.2% and 10.9% in Central and Eastern European countries. Variation in age at death is larger and differs more between countries among the least-educated groups. At the individual level, many known and unknown factors are causing enormous variation in age at death, socioeconomic position being only one of them. Reducing variations in age at death among less-educated people by providing protection to the vulnerable may help to reduce inequalities in mortality between socioeconomic groups.

Predicting mortality with biomarkers: a population-based prospective cohort study for elderly Costa Ricans

Abstract: Background: Little is known about adult health and mortality relationships outside high-income nations, partly because few datasets have contained biomarker data in representative populations. Our objective is to determine the prognostic value of biomarkers with respect to total and cardiovascular mortality in an elderly population of a middle-income country, as well as the extent to which they mediate the effects of age and sex on mortality. Methods: This is a prospective population-based study in a nationally representative sample of elderly Costa Ricans. Baseline interviews occurred mostly in 2005 and mortality follow-up went through December 2010. Sample size after excluding observations with missing values: 2,313 individuals and 564 deaths. Main outcome: prospective death rate ratios for 22 baseline biomarkers, which were estimated with hazard regression models. Results: Biomarkers significantly predict future death above and beyond demographic and self-reported health conditions. The studied biomarkers account for almost half of the effect of age on mortality. However, the sex gap in mortality became several times wider after controlling for biomarkers. The most powerful predictors were simple physical tests: handgrip strength, pulmonary peak flow, and walking speed. Three blood tests also predicted prospective mortality: C-reactive protein (CRP), glycated hemoglobin (HbA1c), and dehydroepiandrosterone sulfate (DHEAS). Strikingly, high blood pressure (BP) and high total cholesterol showed little or no predictive power. Anthropometric measures also failed to show significant mortality effects. Conclusions: This study adds to the growing evidence that blood markers for CRP, HbA1c, and DHEAS, along with organ-specific functional reserve indicators (handgrip, walking speed, and pulmonary peak flow), are valuable tools for identifying vulnerable elderly. The results also highlight the need to better understand an anomaly noted previously in other settings: despite the continued medical focus on drugs for BP and cholesterol, high levels of BP and cholesterol have little predictive value of mortality in this elderly population.

Measured and modeled personal and environmental NO2 exposure

Abstract: Measured or modeled levels of outdoor air pollution are being used as proxies for individual exposure in a growing number of epidemiological studies. We studied the accuracy of such approaches, in comparison with measured individual levels, and also combined modeled levels for each subject’s workplace with the levels at their residence to investigate the influence of living and working in different places on individual exposure levels. A GIS-based dispersion model and an emissions database were used to model concentrations of NO2 at the subject’s residence. Modeled levels were then compared with measured levels of NO2. Personal exposure was also modeled based on levels of NO2 at the subject’s residence in combination with levels of NO2 at their workplace during working hours. There was a good agreement between measured facade levels and modeled residential NO2 levels (rs = 0.8, p > 0.001); however, the agreement between measured and modeled outdoor levels and measured personal exposure was poor with overestimations at low levels and underestimation at high levels (rs = 0.5, p > 0.001 and rs = 0.4, p > 0.001) even when compensating for workplace location (rs = 0.4, p > 0.001). Modeling residential levels of NO2 proved to be a useful method of estimating facade concentrations. However, the agreement between outdoor levels (both modeled and measured) and personal exposure was, although significant, rather poor even when compensating for workplace location. These results indicate that personal exposure cannot be fully approximated by outdoor levels and that differences in personal activity patterns or household characteristics should be carefully considered when conducting exposure studies. This is an important finding that may help to correct substantial bias in epidemiological studies.

The influence of measurement error on calibration, discrimination, and overall estimation of a risk prediction model

Abstract: Self-reported height and weight are commonly collected at the population level; however, they can be subject to measurement error. The impact of this error on predicted risk, discrimination, and calibration of a model that uses body mass index (BMI) to predict risk of diabetes incidence is not known. The objective of this study is to use simulation to quantify and describe the effect of random and systematic error in self-reported height and weight on the performance of a model for predicting diabetes. Two general categories of error were examined: random (nondirectional) error and systematic (directional) error on an algorithm relating BMI in kg/m2 to probability of developing diabetes. The cohort used to develop the risk algorithm was derived from 23,403 Ontario residents that responded to the 1996/1997 National Population Health Survey linked to a population-based diabetes registry. The data and algorithm were then simulated to allow for estimation of the impact of these errors on predicted risk using the Hosmer-Lemeshow goodness-of-fit χ2 and C-statistic. Simulations were done 500 times with sample sizes of 9,177 for males and 10,618 for females. Simulation data successfully reproduced discrimination and calibration generated from population data. Increasing levels of random error in height and weight reduced the calibration and discrimination of the model. Random error biased the predicted risk upwards whereas systematic error biased predicted risk in the direction of the bias and reduced calibration; however, it did not affect discrimination. This study demonstrates that random and systematic errors in self-reported health data have the potential to influence the performance of risk algorithms. Further research that quantifies the amount and direction of error can improve model performance by allowing for adjustments in exposure measurements.

Health states for schizophrenia and bipolar disorder within the Global Burden of Disease 2010 Study

Abstract: A comprehensive revision of the Global Burden of Disease (GBD) study is expected to be completed in 2012. This study utilizes a broad range of improved methods for assessing burden, including closer attention to empirically derived estimates of disability. The aim of this paper is to describe how GBD health states were derived for schizophrenia and bipolar disorder. These will be used in deriving health state-specific disability estimates. A literature review was first conducted to settle on a parsimonious set of health states for schizophrenia and bipolar disorder. A second review was conducted to investigate the proportion of schizophrenia and bipolar disorder cases experiencing these health states. These were pooled using a quality-effects model to estimate the overall proportion of cases in each state. The two schizophrenia health states were acute (predominantly positive symptoms) and residual (predominantly negative symptoms). The three bipolar disorder health states were depressive, manic, and residual. Based on estimates from six studies, 63% (38%-82%) of schizophrenia cases were in an acute state and 37% (18%-62%) were in a residual state. Another six studies were identified from which 23% (10%-39%) of bipolar disorder cases were in a manic state, 27% (11%-47%) were in a depressive state, and 50% (30%-70%) were in a residual state. This literature review revealed salient gaps in the literature that need to be addressed in future research. The pooled estimates are indicative only and more data are required to generate more definitive estimates. That said, rather than deriving burden estimates that fail to capture the changes in disability within schizophrenia and bipolar disorder, the derived proportions and their wide uncertainty intervals will be used in deriving disability estimates.

Health, well-being, and measuring the burden of disease

Abstract: This essay asks whether the global burden of diseases, injuries, and risk factors (GBD) should be measured in terms of their consequences for health, as maintained by most of those who are attempting to measure the GBD, or in terms of their consequences for well-being, as argued by John Broome. It answers that the burden of disease should be understood in terms of the consequences of disease for health, and it defends the wider efforts to measure health by those who are in other ways skeptical of the project of measuring the GBD.

Sentinel site community surveillance of mortality and nutritional status in southwestern Central African Republic, 2010

Abstract: Background During 2010, a community-based, sentinel site prospective surveillance system measured mortality, acute malnutrition prevalence, and the coverage of a Medecins Sans Frontieres (MSF) intervention in four sous-prefectures of Lobaye prefecture in southwestern Central African Republic. We describe this surveillance system and its evaluation.

Causes of death in Tonga: quality of certification and implications for statistics

Abstract: Detailed cause of death data by age group and sex are critical to identify key public health issues and target interventions appropriately. In this study the quality of local routinely collected cause of death data from medical certification is reviewed, and a cause of death profile for Tonga based on amended data is presented. Medical certificates of death for all deaths in Tonga for 2001 to 2008 and medical records for all deaths in the main island Tongatapu for 2008 were sought from the national hospital. Cause of death data for 2008 were reviewed for quality through (a) a review of current tabulation procedures and (b) a medical record review. Data from each medical record were extracted and provided to an independent medical doctor to assign cause of death, with underlying cause from the medical record tabulated against underlying cause from the medical certificate. Significant associations in reporting patterns were evaluated and final cause of death for each case in 2008 was assigned based on the best quality information from the medical certificate or medical record. Cause of death data from 2001 to 2007 were revised based on findings from the evaluation of certification of the 2008 data and added to the dataset. Proportional mortality was calculated and applied to age- and sex-specific mortality for all causes from 2001 to 2008. Cause of death was tabulated by age group and sex, and age-standardized (all ages) mortality rates for each sex by cause were calculated. Reported tabulations of cause of death in Tonga are of immediate cause, with ischemic heart disease and diabetes underrepresented. In the majority of cases the reported (immediate) cause fell within the same broad category as the underlying cause of death from the medical certificate. Underlying cause of death from the medical certificate, attributed to neoplasms, diabetes, and cardiovascular disease were assigned to other underlying causes by the medical record review in 70% to 77% of deaths. Of the 28 (6.5%) deaths attributed to nonspecific or unknown causes on the medical certificate, 17 were able to be attributed elsewhere following review of the medical record. Final cause of death tabulations for 2001 to 2008 demonstrate that noncommunicable diseases are leading adult mortality, and age-standardized rates for cardiovascular diseases, neoplasms, and diabetes increased significantly between 2001 to 2004 and 2005 to 2008. Cause of death data for 2001 to 2008 show increasing cause-specific mortality (deaths per 100,000) from 2001-2004 to 2005-2008 from cardiovascular (194-382 to 423-644 in 2005-2008 for males and 108-227 to 194-321 for females) and other noncommunicable diseases that cannot be accounted for by changes in the age structure of the population. Mortality from diabetes for 2005 to 2008 is estimated at 94 to 222 deaths per 100,000 population for males and 98 to 190 for females (based on the range of plausible all-cause mortality estimates) compared with 2008 estimates from the global burden of disease study of 40 (males) and 53 (females) deaths per 100,000 population. Certification of death was generally found to be the most reliable data on cause of death in Tonga available for Tonga, with 93% of the final assigned causes following review of the 2008 data matching those listed on the medical certificate of death. Cause of death data available in Tonga can be improved by routinely tabulating data by underlying cause and ensuring contributory causes are not recorded in Part I of the certificate during data entry to the database. There is significantly more data on cause of death available in Tonga than are routinely reported or known to international agencies.

Mortality trends in Tonga: an assessment based on a synthesis of local data

Abstract: Accurate measures of mortality level by age group, gender, and region are critical for health planning and evaluation. These are especially required for a country like Tonga, which has limited resources and works extensively with international donors. Mortality levels in Tonga were examined through an assessment of available published information and data available from the four routine death reporting systems currently in operation. Available published data on infant mortality rate (IMR) and life expectancy (LE) in Tonga were sought through direct contact with the Government of Tonga and relevant international and regional organizations. Data sources were assessed for reliability and plausibility of estimates on the basis of method of estimation, original source of data, and data consistency. Unreliable sources were censored from further analysis and remaining data analysed for trends. Mortality data for 2001 to 2009 were obtained from both the Health Information System (based on medical certificates of death) and the Civil Registry. Data from 2005 to 2009 were also obtained from the Reproductive Health System of the Ministry of Health (MoH) (based on community nursing reports), and for 2005–2008, data were also obtained from the Prime Minister’s office. Records were reconciled to create a single list of unique deaths and IMR and life tables calculated. Completeness of the reconciled data was examined using the Brass growth-balance method and capture-recapture analysis using two and three sources. Published IMR estimates varied significantly through to the late 1990s when most estimates converge to a narrower range between 10 and 20 deaths per 1,000 live births. Findings from reconciled data were consistent with this range, and did not demonstrate any significant trend over 2001 to 2009. Published estimates of LE from 2000 onwards varied from 65 to 75 years for males and 68 to 74 years for females, with most clustered around 70 to 71 for males and 72 to 73 for females. Reconciled empirical data for 2005 to 2009 produce an estimate of LE of 65.2 years (95% confidence interval [CI]: 64.6 - 65.8) for males and 69.6 years (95% CI: 69.0 – 70.2) for females, which are several years lower than published MoH and census estimates. Adult mortality (15 to 59 years) is estimated at 26.7% for males and 19.8% for females. Analysis of reporting completeness suggests that even reconciled data are under enumerated, and these estimates place the plausible range of LE between 60.4 to 64.2 years for males and 65.4 to 69.0 years for females, with adult mortality at 28.6% to 36.3% and 20.9% to 27.7%, respectively. The level of LE at a relatively low IMR and high adult mortality suggests that non-communicable diseases are having a profound limiting effect on health status in Tonga. There has been a sustained history of incomplete and erroneous mortality estimates for Tonga. The findings highlight the critical need to reconcile existing data sources and integrate reporting systems more fully to ensure all deaths in Tonga are captured and the importance of local empirical data in monitoring trends in mortality.

OBAYA (obesity and adverse health outcomes in young adults): feasibility of a population-based multiethnic cohort study using electronic medical records

Abstract: Background: Although obesity is a risk factor for many chronic diseases, we have only limited knowledge of the magnitude of these associations in young adults. A multiethnic cohort of young adults was established to close current knowledge gaps; cohort demographics, cohort retention, and the potential influence of migration bias were investigated. Methods: For this population-based cross-sectional study, demographics, and measured weight and height were extracted from electronic medical records of 1,929,470 patients aged 20 to 39 years enrolled in two integrated health plans in California from 2007 to 2009. Results: The cohort included about 84.4% of Kaiser Permanente California members in this age group who had a medical encounter during the study period and represented about 18.2% of the underlying population in the same age group in California. The age distribution of the cohort was relatively comparable to the underlying population in California Census 2010 population, but the proportion of women and ethnic/racial minorities was slightly higher. The three-year retention rate was 68.4%. Conclusion: These data suggest the feasibility of our study for medium-term follow-up based on sufficient membership retention rates. While nationwide 6% of young adults are extremely obese, we know little to adequately quantify the health burden attributable to obesity, especially extreme obesity, in this age group. This cohort of young adults provides a unique opportunity to investigate associations of obesity-related factors and risk of cancer in a large multiethnic population.

A critical re-evaluation of the regression model specification in the US D1 EQ-5D value function

Abstract: Background The EQ-5D is a generic health-related quality of life instrument (five dimensions with three levels, 243 health states), used extensively in cost-utility/cost-effectiveness analyses EQ-5D health states are assigned values on a scale anchored in perfect health (1) and death (0) The dominant procedure for defining values for EQ-5D health states involves regression modeling These regression models have typically included a constant term, interpreted as the utility loss associated with any movement away from perfect health The authors of the United States EQ-5D valuation study replaced this constant with a variable, D1, which corresponds to the number of impaired dimensions beyond the first The aim of this study was to illustrate how the use of the D1 variable in place of a constant is problematic

Looking at the smoking epidemic through the lens of population pyramids: sociodemographic patterns of smoking in Italy, 1983 to 2005

Abstract: Background Surveillance systems often present data by means of summary measures, like age-standardised rates. In this study, we aimed at comparing information derived from commonly used measures of smoking with that presented in modified population pyramids (PPs), using the example of the diffusion of smoking in Italy over the past two decades.