http://www.pophealthmetrics.com The latest research articles published by Population Health Metrics 2012-01-13T00:00:00Z 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 PDF 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 PDF 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 Background: Public health surveillance is often concerned with the analysis of health outcomes over small areas. Funnel plots have been proposed as a useful tool for assessing and visualizing surveillance data, but their full utility has not been appreciated (for example, in the incorporation and interpretation of risk factors). Methods: We investigate a way to simultaneously focus funnel plot analyses on direct policy implications while visually incorporating model fit and the effects of risk factors. Health survey data representing modifiable and nonmodifiable risk factors are used in an analysis of 2007 small area motor vehicle mortality rates in Alberta, Canada. Results: Small area variations in motor vehicle mortality in Alberta were well explained by the suite of modifiable and nonmodifiable risk factors. Funnel plots of raw rates and of risk adjusted rates lead to different conclusions; the analysis process highlights opportunities for intervention as risk factors are incorporated into the model. Maps based on funnel plot methods identify areas worthy of further investigation. Conclusions: Funnel plots provide a useful tool to explore small area data and to routinely incorporate covariate relationships in surveillance analyses. The exploratory process has at each step a direct and useful policy-related result. Dealing thoughtfully with statistical overdispersion is a cornerstone to fully understanding funnel plots. http://www.pophealthmetrics.com/content/9/1/58 Douglas Dover Donald Schopflocher Population Health Metrics 2011, null:58 2011-11-10T00:00:00Z doi:10.1186/1478-7954-9-58 /content/figures/1478-7954-9-58-toc.gif Population Health Metrics 1478-7954 ${item.volume} 58 2011-11-10T00:00:00Z XML Background: Nutrition and mortality surveys are the main tools whereby evidence on the health status of populations affected by disasters and armed conflict is quantified and monitored over time. Several reviews have consistently revealed a lack of rigor in many surveys. We describe an algorithm for analyzing nutritional and mortality survey reports to identify a comprehensive range of errors that may result in sampling, response, or measurement biases and score quality. We apply the algorithm to surveys conducted in Darfur, Sudan. Methods: We developed an algorithm based on internationally agreed upon methods and best practices. Penalties are attributed for a list of errors, and an overall score is built from the summation of penalties accrued by the survey as a whole. To test the algorithm reproducibility, it was independently applied by three raters on 30 randomly selected survey reports. The algorithm was further applied to more than 100 surveys conducted in Darfur, Sudan. Results: The Intra Class Correlation coefficient was 0.79 for mortality surveys and 0.78 for nutrition surveys. The overall median quality score and range of about 100 surveys conducted in Darfur were 0.60 (0.12-0.93) and 0.675 (0.23-0.86) for mortality and nutrition surveys, respectively. They varied between the organizations conducting the surveys, with no major trend over time. Conclusion: Our study suggests that it is possible to systematically assess quality of surveys and reveals considerable problems with the quality of nutritional and particularly mortality surveys conducted in the Darfur crisis. http://www.pophealthmetrics.com/content/9/1/57 Claudine Prudhon Xavier de Radigues Nancy Dale Francesco Checchi Population Health Metrics 2011, null:57 2011-11-09T00:00:00Z doi:10.1186/1478-7954-9-57 /content/figures/1478-7954-9-57-toc.gif Population Health Metrics 1478-7954 ${item.volume} 57 2011-11-09T00:00:00Z XML Background: No recent national studies have provided incidence data for obesity, nor have they examined the association between incidence and selected risk factors. The purpose of this study is to examine the incidence of obesity (body mass index [BMI] ≥ 30.0 kg/m2) and extreme obesity (BMI ≥ 40.0 kg/m2) among US adults and to determine variations across socio-demographic characteristics and behavioral factors. Methods: We used a weighted sample of 401,587 US adults from the 2009 Behavioral Risk Factor Surveillance System. Incidence calculations were based on respondent's height and current and previous weights. Logistic regression was used to examine associations between incidence and selected socio-demographic characteristics and behavioral factors. Results: The overall crude incidences of obesity and extreme obesity in 2009 were 4% and 0.7% per year, respectively. In our multivariable analyses that controlled for baseline body mass index, the incidences of obesity and extreme obesity decreased significantly with increasing levels of education. Incidences were significantly higher among young adults, women, and adults who did not participate in any leisure-time physical activity. Incidence was lowest among non-Hispanic whites. Conclusions: The high incidence of obesity underscores the importance of implementing effective policy and environmental strategies in the general population. Given the significant variations in incidence within the subgroups, public health officials should prioritize younger adults, women, minorities, and adults with lower education as the targets for these efforts. http://www.pophealthmetrics.com/content/9/1/56 Liping Pan David Freedman Cathleen Gillespie Sohyun Park Bettylou Sherry Population Health Metrics 2011, null:56 2011-10-17T00:00:00Z doi:10.1186/1478-7954-9-56 /content/figures/1478-7954-9-56-toc.gif Population Health Metrics 1478-7954 ${item.volume} 56 2011-10-17T00:00:00Z XML Background: 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. 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. Results: 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.DiscussionManagement 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. http://www.pophealthmetrics.com/content/9/1/55 Farshad Farzadfar Goodarz Danaei Hengameh Namdaritabar Julie Rajaratnam Jacob Marcus Ardeshir Khosravi Siamak Alikhani Christopher Murray Majid Ezzati Population Health Metrics 2011, null:55 2011-10-11T00:00:00Z doi:10.1186/1478-7954-9-55 /content/figures/1478-7954-9-55-toc.gif Population Health Metrics 1478-7954 ${item.volume} 55 2011-10-11T00:00:00Z XML Background: Appalachia is a region of the United States noted for the poverty and poor health outcomes of its residents. Residents of the poorest Appalachian counties have a high prevalence of diabetes and risk factors (obesity, low income, low education, etc.) for type 2 diabetes. However, diabetes prevalence exceeds what these risk factors alone explain. Based on this, the history of poor health outcomes in Appalachia, and personally observed high rates of childhood obesity and lack of concern about prediabetes, we speculated that people in Appalachia with diagnosed diabetes might tend to be diagnosed younger than their non-Appalachian counterparts. Methods: We used data from the Behavioral Risk Factor Surveillance System (2006-2008). We compared age at diagnosis among counties by Appalachian Regional Commission-defined level of economic development. To account for risk differences, we constructed a model for average age at diagnosis of diabetes, adjusting for county economic development, obesity, income, sedentary lifestyle, and other covariates.FindingsAfter adjustment for risk factors for diabetes, people in distressed or at-risk counties (the least economically developed) had their diabetes diagnosed two to three years younger than comparable people in non-Appalachian counties. No significant differences between non-Appalachian counties and Appalachian counties at higher levels of economic development remained after adjusting. Conclusions: People in distressed and at-risk counties have poor access to care, and are unlikely to develop diabetes at the same age as their non-Appalachian counterparts but be diagnosed sooner. Therefore, people in distressed and at-risk counties are likely developing diabetes at younger ages. We recommend that steps to reduce health disparities between the poorest Appalachian counties and non-Appalachian counties be considered. http://www.pophealthmetrics.com/content/9/1/54 Lawrence Barker Robert Gerzoff Richard Crespo Molly Shrewsberry Population Health Metrics 2011, null:54 2011-09-30T00:00:00Z doi:10.1186/1478-7954-9-54 /content/figures/1478-7954-9-54-toc.gif Population Health Metrics 1478-7954 ${item.volume} 54 2011-09-30T00:00:00Z XML Background: Many studies have documented the bias in body mass index (BMI) determined from self-reported data on height and weight, but few have examined the change in bias over time. Methods: Using data from large, nationally-representative population health surveys, we examined change in bias in height and weight reporting among Australian adults between 1995 and 2008. Our study dataset included 9,635 men and women in 1995 and 9,141 in 2007-2008. We investigated the determinants of the bias and derived correction equations using 2007-2008 data, which can be applied when only self-reported anthropometric data are available. Results: In 1995, self-reported BMI (derived from height and weight) was 1.2 units (men) and 1.4 units (women) lower than measured BMI. In 2007-2008, there was still underreporting, but the amount had declined to 0.6 units (men) and 0.7 units (women) below measured BMI. The major determinants of reporting error in 2007-2008 were age, sex, measured BMI, and education of the respondent. Correction equations for height and weight derived from 2007-2008 data and applied to self-reported data were able to adjust for the bias and were accurate across all age and sex strata. Conclusions: The diminishing reporting bias in BMI in Australia means that correction equations derived from 2007-2008 data may not be transferable to earlier self-reported data. Second, predictions of future overweight and obesity in Australia based on trends in self-reported information are likely to be inaccurate, as the change in reporting bias will affect the apparent increase in self-reported obesity prevalence. http://www.pophealthmetrics.com/content/9/1/53 Alison Hayes Philip Clarke Tom Lung Population Health Metrics 2011, null:53 2011-09-25T00:00:00Z doi:10.1186/1478-7954-9-53 /content/figures/1478-7954-9-53-toc.gif Population Health Metrics 1478-7954 ${item.volume} 53 2011-09-25T00:00:00Z XML