http://www.pophealthmetrics.com The latest research articles published by Population Health Metrics 2012-05-16T00:00:00Z 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.KeywordsPopulation, epidemiology, demography, disease mapping http://www.pophealthmetrics.com/content/10/1/8 Andrew Tatem Susana Adamo Nita Bharti Clara Burgert Marcia Castro Audrey Dorelien Gunther Fink Catherine Linard John Mendelsohn Livia Montana Mark Montgomery Andrew Nelson Abdisalan Noor Deepa Pindolia Greg Yetman Deborah Balk Population Health Metrics 2012, null:8 2012-05-16T00:00:00Z doi:10.1186/1478-7954-10-8 /content/figures/1478-7954-10-8-toc.gif Population Health Metrics 1478-7954 ${item.volume} 8 2012-05-16T00:00:00Z PDF 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.KeywordsLife expectancy, health-adjusted life expectancy, diabetes mellitus, health utility index, summary measure of population health http://www.pophealthmetrics.com/content/10/1/7 Lidia Loukine Chris Waters Bernard Choi Joellyn Ellison Population Health Metrics 2012, null:7 2012-04-24T00:00:00Z doi:10.1186/1478-7954-10-7 /content/figures/1478-7954-10-7-toc.gif Population Health Metrics 1478-7954 ${item.volume} 7 2012-04-24T00:00:00Z PDF Background: 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. Methods: 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. Results: 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. Conclusions: 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. http://www.pophealthmetrics.com/content/10/1/6 Tara Kehoe Gerrit Gmel Kevin Shield Gerhard Gmel Jurgen Rehm Population Health Metrics 2012, null:6 2012-04-10T00:00:00Z doi:10.1186/1478-7954-10-6 /content/figures/1478-7954-10-6-toc.gif Population Health Metrics 1478-7954 ${item.volume} 6 2012-04-10T00:00:00Z XML Background: 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. Methods: 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. Results: 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. Conclusions: 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.DisclaimerThe views expressed in this document are solely those of the authors and do not represent the views of the World Health Organization. http://www.pophealthmetrics.com/content/10/1/5 Laura Hanson Elizabeth Zahn Sommer Wild Dorte Dopfer James Scott Claudia Stein Population Health Metrics 2012, null:5 2012-03-16T00:00:00Z doi:10.1186/1478-7954-10-5 /content/figures/1478-7954-10-5-toc.gif Population Health Metrics 1478-7954 ${item.volume} 5 2012-03-16T00:00:00Z XML Background: 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. Methods: 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. Results: 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.DiscussionCertification 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. http://www.pophealthmetrics.com/content/10/1/4 Karen Carter Sione Hufanga Chalapati Rao Sione Akauola Alan Lopez Rasika Rampitage Richard Taylor Population Health Metrics 2012, null:4 2012-03-05T00:00:00Z doi:10.1186/1478-7954-10-4 /content/figures/1478-7954-10-4-toc.gif Population Health Metrics 1478-7954 ${item.volume} 4 2012-03-05T00:00:00Z PDF Background: 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. Methods: 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. Results: 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. Conclusions: 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. http://www.pophealthmetrics.com/content/10/1/3 Alyson van Raalte Anton Kunst Olle Lundberg Mall Leinsalu Pekka Martikainen Barbara Artnik Patrick Deboosere Irina Stirbu Bogdan Wojtyniak Johan Mackenbach Population Health Metrics 2012, null:3 2012-02-16T00:00:00Z doi:10.1186/1478-7954-10-3 /content/figures/1478-7954-10-3-toc.gif Population Health Metrics 1478-7954 ${item.volume} 3 2012-02-16T00:00:00Z XML 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. Methods: We compared the original D1 regression model with a mathematically equivalent model with a constant term. Comparisons included implications for the magnitude and statistical significance of the coefficients, multicollinearity (variance inflation factors, or VIFs), number of calculation steps needed to determine tariff values, and consequences for tariff interpretation. Results: Using the D1 variable in place of a constant shifted all dummy variable coefficients away from zero by the value of the constant, greatly increased the multicollinearity of the model (maximum VIF of 113.2 vs. 21.2), and increased the mean number of calculation steps required to determine health state values.DiscussionUsing the D1 variable in place of a constant constitutes an unnecessary complication of the model, obscures the fact that at least two of the main effect dummy variables are statistically nonsignificant, and complicates and biases interpretation of the tariff algorithm. http://www.pophealthmetrics.com/content/10/1/2 Kim Rand-Hendriksen Liv Augestad Fredrik Dahl Population Health Metrics 2012, null:2 2012-01-13T00:00:00Z doi:10.1186/1478-7954-10-2 /content/figures/1478-7954-10-2-toc.gif Population Health Metrics 1478-7954 ${item.volume} 2 2012-01-13T00:00:00Z XML Background: 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. Methods: 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. Results: 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. Conclusions: 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. http://www.pophealthmetrics.com/content/10/1/1 Kyle Foreman Rafael Lozano Alan Lopez Christopher Murray Population Health Metrics 2012, null:1 2012-01-06T00:00:00Z doi:10.1186/1478-7954-10-1 /content/figures/1478-7954-10-1-toc.gif Population Health Metrics 1478-7954 ${item.volume} 1 2012-01-06T00:00:00Z XML Background: Type 2 diabetes is associated with low socioeconomic position (SEP) in high-income countries. Despite the important role of SEP in the development of many diseases, no socioeconomic indicator was included in the Comparative Risk Assessment (CRA) module of the Global Burden of Disease study. We therefore aimed to illustrate an example by estimating the burden of type 2 diabetes in Sweden attributed to lower educational levels as a measure of SEP using the methods applied in the CRA. Methods: To include lower educational levels as a risk factor for type 2 diabetes, we pooled relevant international data from a recent systematic review to measure the association between type 2 diabetes incidence and lower educational levels. We also collected data on the distribution of educational levels in the Swedish population using comparable criteria for educational levels as identified in the international literature. Population attributable fractions (PAF) were estimated and applied to the burden of diabetes estimates from the Swedish burden of disease database for men and women in the separate age groups (30-44, 45-59, 60-69, 70-79, and 80+ years). Results: The PAF estimates showed that 17.2% of the diabetes burden in men and 20.1% of the burden in women were attributed to lower educational levels in Sweden when combining all age groups. The burden was, however, most pronounced in the older age groups (70-79 and 80+), where lower educational levels contributed to 22.5% to 24.5% of the diabetes burden in men and 27.8% to 32.6% in women. Conclusions: There is a considerable burden of type 2 diabetes attributed to lower educational levels in Sweden, and socioeconomic indicators should be considered to be incorporated in the CRA. http://www.pophealthmetrics.com/content/9/1/60 Emilie Agardh Anna Sidorchuk Johan Hallqvist Rickard Ljung Stefan Peterson Tahereh Moradi Peter Allebeck Population Health Metrics 2011, null:60 2011-12-16T00:00:00Z doi:10.1186/1478-7954-9-60 /content/figures/1478-7954-9-60-toc.gif Population Health Metrics 1478-7954 ${item.volume} 60 2011-12-16T00:00:00Z XML This study compares two methods for testing fertility trends and fertility stalls using Demographic and Health Surveys data. The first method is based on linear regression and uses the equivalence of period and cohort estimates with the same cumulative fertility at age 40, the same number of births, and the same distribution of women by parity. The second method is based on logistic regression. It assumes that the age pattern of fertility is constant over short periods of time. Both methods were applied to fertility trends in several African countries (Ghana, Kenya, Madagascar, Nigeria, Rwanda, Senegal, Tanzania, and Zambia). The two methods were found to predict similar values of cumulative fertility, to produce consistent slopes, to document fertility trends the same way, and to characterize fertility stalls with similar statistical evidence. They can also be used to refute apparent fertility stalls obtained when comparing two point estimates from two successive surveys. http://www.pophealthmetrics.com/content/9/1/59 Michel Garenne Population Health Metrics 2011, null:59 2011-12-08T00:00:00Z doi:10.1186/1478-7954-9-59 /content/figures/1478-7954-9-59-toc.gif Population Health Metrics 1478-7954 ${item.volume} 59 2011-12-08T00:00:00Z XML