http://www.pophealthmetrics.com The most accessed research articles published by Population Health Metrics 2012-01-13T00:00:00Z 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: The United States health care debate has focused on the nation's uniquely high rates of lack of insurance and poor health outcomes relative to other high-income countries. Large disparities in health outcomes are well-documented in the US, but the most recent assessment of county disparities in mortality is from 1999. It is critical to tracking progress of health reform legislation to have an up-to-date assessment of disparities in life expectancy across counties. US disparities can be seen more clearly in the context of how progress in each county compares to international trends. Methods: We use newly released mortality data by age, sex, and county for the US from 2000 to 2007 to compute life tables separately for each sex, for all races combined, for whites, and for blacks. We propose, validate, and apply novel methods to estimate recent life tables for small areas to generate up-to-date estimates. Life expectancy rates and changes in life expectancy for counties are compared to the life expectancies across nations in 2000 and 2007. We calculate the number of calendar years behind each county is in 2000 and 2007 compared to an international life expectancy time series. Results: Across US counties, life expectancy in 2007 ranged from 65.9 to 81.1 years for men and 73.5 to 86.0 years for women. When compared against a time series of life expectancy in the 10 nations with the lowest mortality, US counties range from being 15 calendar years ahead to over 50 calendar years behind for men and 16 calendar years ahead to over 50 calendar years behind for women. County life expectancy for black men ranges from 59.4 to 77.2 years, with counties ranging from seven to over 50 calendar years behind the international frontier; for black women, the range is 69.6 to 82.6 years, with counties ranging from eight to over 50 calendar years behind. Between 2000 and 2007, 80% (men) and 91% (women) of American counties fell in standing against this international life expectancy standard. Conclusions: The US has extremely large geographic and racial disparities, with some communities having life expectancies already well behind those of the best-performing nations. At the same time, relative performance for most communities continues to drop. Efforts to address these issues will need to tackle the leading preventable causes of death. http://www.pophealthmetrics.com/content/9/1/16 Sandeep Kulkarni Alison Levin-Rector Majid Ezzati Christopher Murray Population Health Metrics 2011, null:16 2011-06-15T00:00:00Z doi:10.1186/1478-7954-9-16 US life expectancy in a global context As the world's healthiest nations enjoy increasingly longer life spans, people living in most of the United States – especially women – are falling further behind with each decade. /content/figures/1478-7954-9-16-toc.gif Population Health Metrics 1478-7954 ${item.volume} 16 2011-06-15T00:00:00Z XML Background: People with diabetes can suffer from diverse complications that seriously erode quality of life. Diabetes, costing the United States more than $174 billion per year in 2007, is expected to take an increasingly large financial toll in subsequent years. Accurate projections of diabetes burden are essential to policymakers planning for future health care needs and costs. Methods: Using data on prediabetes and diabetes prevalence in the United States, forecasted incidence, and current US Census projections of mortality and migration, the authors constructed a series of dynamic models employing systems of difference equations to project the future burden of diabetes among US adults. A three-state model partitions the US population into no diabetes, undiagnosed diabetes, and diagnosed diabetes. A four-state model divides the state of "no diabetes" into high-risk (prediabetes) and low-risk (normal glucose) states. A five-state model incorporates an intervention designed to prevent or delay diabetes in adults at high risk. Results: The authors project that annual diagnosed diabetes incidence (new cases) will increase from about 8 cases per 1,000 in 2008 to about 15 in 2050. Assuming low incidence and relatively high diabetes mortality, total diabetes prevalence (diagnosed and undiagnosed cases) is projected to increase from 14% in 2010 to 21% of the US adult population by 2050. However, if recent increases in diabetes incidence continue and diabetes mortality is relatively low, prevalence will increase to 33% by 2050. A middle-ground scenario projects a prevalence of 25% to 28% by 2050. Intervention can reduce, but not eliminate, increases in diabetes prevalence. Conclusions: These projected increases are largely attributable to the aging of the US population, increasing numbers of members of higher-risk minority groups in the population, and people with diabetes living longer. Effective strategies will need to be undertaken to moderate the impact of these factors on national diabetes burden. Our analysis suggests that widespread implementation of reasonably effective preventive interventions focused on high-risk subgroups of the population can considerably reduce, but not eliminate, future increases in diabetes prevalence. http://www.pophealthmetrics.com/content/8/1/29 James Boyle Theodore Thompson Edward Gregg Lawrence Barker David Williamson Population Health Metrics 2010, null:29 2010-10-22T00:00:00Z doi:10.1186/1478-7954-8-29 Diabetes incidence set to rise Statistics show that diabetes prevalence is set to rise in the US adult population to 33% by 2050 and so implementation of preventative measures needs to be considered. /content/figures/1478-7954-8-29-toc.gif Population Health Metrics 1478-7954 ${item.volume} 29 2010-10-22T00:00:00Z XML Background: Current US surveillance data provide estimates of diabetes using laboratory tests at the national level as well as self-reported data at the state level. Self-reported diabetes prevalence may be biased because respondents may not be aware of their risk status. Our objective was to estimate the prevalence of diagnosed and undiagnosed diabetes by state. Methods: We estimated undiagnosed diabetes prevalence as a function of a set of health system and sociodemographic variables using a logistic regression in the National Health and Nutrition Examination Survey (2003-2006). We applied this relationship to identical variables from the Behavioral Risk Factor Surveillance System (2003-2007) to estimate state-level prevalence of undiagnosed diabetes by age group and sex. We assumed that those who report being diagnosed with diabetes in both surveys are truly diabetic. Results: The prevalence of diabetes in the U.S. was 13.7% among men and 11.7% among women ≥ 30 years. Age-standardized diabetes prevalence was highest in Mississippi, West Virginia, Louisiana, Texas, South Carolina, Alabama, and Georgia (15.8 to 16.6% for men and 12.4 to 14.8% for women). Vermont, Minnesota, Montana, and Colorado had the lowest prevalence (11.0 to 12.2% for men and 7.3 to 8.4% for women). Men in all states had higher diabetes prevalence than women. The absolute prevalence of undiagnosed diabetes, as a percent of total population, was highest in New Mexico, Texas, Florida, and California (3.5 to 3.7 percentage points) and lowest in Montana, Oklahoma, Oregon, Alaska, Vermont, Utah, Washington, and Hawaii (2.1 to 3 percentage points). Among those with no established diabetes diagnosis, being obese, being Hispanic, not having insurance and being ≥ 60 years old were significantly associated with a higher risk of having undiagnosed diabetes. Conclusion: Diabetes prevalence is highest in the Southern and Appalachian states and lowest in the Midwest and the Northeast. Better diabetes diagnosis is needed in a number of states. http://www.pophealthmetrics.com/content/7/1/16 Goodarz Danaei Ari Friedman Shefali Oza Christopher Murray Majid Ezzati Population Health Metrics 2009, null:16 2009-09-25T00:00:00Z doi:10.1186/1478-7954-7-16 Improving diabetes diagnosis in US states Prevalence of undiagnosed and total diabetes, determined from analysis of US national blood tests and state survey data, indicate that some states are under diagnosing this condition. /content/figures/1478-7954-7-16-toc.gif Population Health Metrics 1478-7954 ${item.volume} 16 2009-09-25T00:00:00Z XML Background: The Multidimensional Fatigue Inventory (MFI-20) was developed in 1995. Since then, it has been widely used in cancer research and cancer-related illnesses but has never been validated in fatiguing illnesses or in a large US population-selected sample. In this study, we sought to examine the reliability and validity of the MFI-20 in the population of the state of Georgia, USA. Further, we assessed whether the MFI-20 could serve as a complementary diagnostic tool in chronically fatigued and unwell populations. Methods: The data derive from a cross-sectional population-based study investigating the prevalence of chronic fatigue syndrome (CFS) in Georgia. The study sample was comprised of three diagnostic groups: CFS-like (292), chronically unwell (269), and well (222). Participants completed the MFI-20 along with several other measures of psychosocial functioning, including the Medical Outcomes Survey Short Form-36 (SF-36), the Zung Self-Rating Depression Scale (SDS), and the Spielberger State-Trait Anxiety Inventory (STAI). We assessed the five MFI-20 subscales using several criteria: inter-item correlations, corrected item-total correlations, internal consistency reliability (Cronbach's alpha coefficients), construct validity, discriminant (known-group) validity, floor/ceiling effects, and convergent validity through correlations with the SF-36, SDS, and STAI instruments. Results: Averaged inter-item correlations ranged from 0.38 to 0.61, indicating no item redundancy. Corrected item-total correlations for all MFI-20 subscales were greater than 0.30, and Cronbach's alpha coefficients achieved an acceptable level of 0.70. No significant floor/ceiling effect was observed. Factor analysis demonstrated factorial complexity. The MFI-20 also distinguished clearly between three diagnostic groups on all subscales. Furthermore, correlations with depression (SDS), anxiety (STAI), and functional impairment (SF-36) demonstrated strong convergent validity. Conclusions: This study provides support for the MFI-20 as a valuable tool when used in chronically unwell and well populations. It also suggests that the MFI-20 could serve as a complementary diagnostic tool in fatiguing illnesses, such as CFS. http://www.pophealthmetrics.com/content/7/1/18 Jin-Mann Lin Dana Brimmer Elizabeth Maloney Ernestina Nyarko Rhonda BeLue William Reeves Population Health Metrics 2009, null:18 2009-12-15T00:00:00Z doi:10.1186/1478-7954-7-18 /content/figures/1478-7954-7-18-toc.gif Population Health Metrics 1478-7954 ${item.volume} 18 2009-12-15T00:00:00Z XML Background: Verbal autopsy methods are critically important for evaluating the leading causes of death in populations without adequate vital registration systems. With a myriad of analytical and data collection approaches, it is essential to create a high quality validation dataset from different populations to evaluate comparative method performance and make recommendations for future verbal autopsy implementation. This study was undertaken to compile a set of strictly defined gold standard deaths for which verbal autopsies were collected to validate the accuracy of different methods of verbal autopsy cause of death assignment. Methods: Data collection was implemented in six sites in four countries: Andhra Pradesh, India; Bohol, Philippines; Dar es Salaam, Tanzania; Mexico City, Mexico; Pemba Island, Tanzania; and Uttar Pradesh, India. The Population Health Metrics Research Consortium (PHMRC) developed stringent diagnostic criteria including laboratory, pathology, and medical imaging findings to identify gold standard deaths in health facilities as well as an enhanced verbal autopsy instrument based on World Health Organization (WHO) standards. A cause list was constructed based on the WHO Global Burden of Disease estimates of the leading causes of death, potential to identify unique signs and symptoms, and the likely existence of sufficient medical technology to ascertain gold standard cases. Blinded verbal autopsies were collected on all gold standard deaths. Results: Over 12,000 verbal autopsies on deaths with gold standard diagnoses were collected (7,836 adults, 2,075 children, 1,629 neonates, and 1,002 stillbirths). Difficulties in finding sufficient cases to meet gold standard criteria as well as problems with misclassification for certain causes meant that the target list of causes for analysis was reduced to 34 for adults, 21 for children, and 10 for neonates, excluding stillbirths. To ensure strict independence for the validation of methods and assessment of comparative performance, 500 test-train datasets were created from the universe of cases, covering a range of cause-specific compositions. Conclusions: This unique, robust validation dataset will allow scholars to evaluate the performance of different verbal autopsy analytic methods as well as instrument design. This dataset can be used to inform the implementation of verbal autopsies to more reliably ascertain cause of death in national health information systems. http://www.pophealthmetrics.com/content/9/1/27 Christopher Murray Alan Lopez Robert Black Ramesh Ahuja Said Mohd Ali Abdullah Baqui Lalit Dandona Emily Dantzer Vinita Das Usha Dhingra Arup Dutta Wafaie Fawzi Abraham Flaxman Sara Gomez Bernardo Hernandez Rohina Joshi Henry Kalter Aarti Kumar Vishwajeet Kumar Rafael Lozano Marilla Lucero Saurabh Mehta Bruce Neal Summer Lockett Ohno Rajendra Prasad Devarsetty Praveen Zul Premji Dolores Ramirez-Villalobos Hazel Remolador Ian Riley Minerva Romero Mwanaidi Said Diozele Sanvictores Sunil Sazawal Veronica Tallo Population Health Metrics 2011, null:27 2011-08-04T00:00:00Z doi:10.1186/1478-7954-9-27 /content/figures/1478-7954-9-27-toc.gif Population Health Metrics 1478-7954 ${item.volume} 27 2011-08-04T00:00:00Z XML Background: InterVA is a widely disseminated tool for cause of death attribution using information from verbal autopsies. Several studies have attempted to validate the concordance and accuracy of the tool, but the main limitation of these studies is that they compare cause of death as ascertained through hospital record review or hospital discharge diagnosis with the results of InterVA. This study provides a unique opportunity to assess the performance of InterVA compared to physician-certified verbal autopsies (PCVA) and alternative automated methods for analysis. Methods: Using clinical diagnostic gold standards to select 12,542 verbal autopsy cases, we assessed the performance of InterVA on both an individual and population level and compared the results to PCVA, conducting analyses separately for adults, children, and neonates. Following the recommendation of Murray et al., we randomly varied the cause composition over 500 test datasets to understand the performance of the tool in different settings. We also contrasted InterVA with an alternative Bayesian method, Simplified Symptom Pattern (SSP), to understand the strengths and weaknesses of the tool. Results: Across all age groups, InterVA performs worse than PCVA, both on an individual and population level. On an individual level, InterVA achieved a chance-corrected concordance of 24.2% for adults, 24.9% for children, and 6.3% for neonates (excluding free text, considering one cause selection). On a population level, InterVA achieved a cause-specific mortality fraction accuracy of 0.546 for adults, 0.504 for children, and 0.404 for neonates. The comparison to SSP revealed four specific characteristics that lead to superior performance of SSP. Increases in chance-corrected concordance are attained by developing cause-by-cause models (2%), using all items as opposed to only the ones that mapped to InterVA items (7%), assigning probabilities to clusters of symptoms (6%), and using empirical as opposed to expert probabilities (up to 8%). Conclusions: Given the widespread use of verbal autopsy for understanding the burden of disease and for setting health intervention priorities in areas that lack reliable vital registrations systems, accurate analysis of verbal autopsies is essential. While InterVA is an affordable and available mechanism for assigning causes of death using verbal autopsies, users should be aware of its suboptimal performance relative to other methods. http://www.pophealthmetrics.com/content/9/1/50 Rafael Lozano Michael Freeman Spencer James Benjamin Campbell Alan Lopez Abraham Flaxman Christopher Murray the Population Health Metrics Research Consortium (phmrc) Population Health Metrics 2011, null:50 2011-08-05T00:00:00Z doi:10.1186/1478-7954-9-50 /content/figures/1478-7954-9-50-toc.gif Population Health Metrics 1478-7954 ${item.volume} 50 2011-08-05T00:00:00Z XML Background: Population-based sample surveys and sentinel surveillance methods are commonly used as substitutes for more widespread health and demographic monitoring and intervention studies in resource-poor settings. Such methods have been criticised as only being worthwhile if the results can be extrapolated to the surrounding 100-fold population. With an emphasis on measuring mortality, this study explores the extent to which choice of sampling method affects the representativeness of 1% sample data in relation to various demographic and health parameters in a rural, developing-country setting. Methods: Data from a large community based census and health survey conducted in rural Burkina Faso were used as a basis for modelling. Twenty 1% samples incorporating a range of health and demographic parameters were drawn at random from the overall dataset for each of seven different sampling procedures at two different levels of local administrative units. Each sample was compared with the overall 'gold standard' survey results, thus enabling comparisons between the different sampling procedures. Results: All sampling methods and parameters tested performed reasonably well in representing the overall population. Nevertheless, a degree of variation could be observed both between sampling approaches and between different parameters, relating to their overall distribution in the total population. Conclusion: Sample surveys are able to provide useful demographic and health profiles of local populations. However, various parameters being measured and their distribution within the sampling unit of interest may not all be best represented by a particular sampling method. It is likely therefore that compromises may have to be made in choosing a sampling strategy, with costs, logistics the intended use of the data being important considerations. http://www.pophealthmetrics.com/content/6/1/2 Edward Fottrell Peter Byass Population Health Metrics 2008, null:2 2008-05-20T00:00:00Z doi:10.1186/1478-7954-6-2 /content/figures/1478-7954-6-2-toc.gif Population Health Metrics 1478-7954 ${item.volume} 2 2008-05-20T00:00:00Z XML Reliable and comparable analysis of risks to health is key for preventing disease and injury. Causal attribution of morbidity and mortality to risk factors has traditionally been conducted in the context of methodological traditions of individual risk factors, often in a limited number of settings, restricting comparability.In this paper, we discuss the conceptual and methodological issues for quantifying the population health effects of individual or groups of risk factors in various levels of causality using knowledge from different scientific disciplines. The issues include: comparing the burden of disease due to the observed exposure distribution in a population with the burden from a hypothetical distribution or series of distributions, rather than a single reference level such as non-exposed; considering the multiple stages in the causal network of interactions among risk factor(s) and disease outcome to allow making inferences about some combinations of risk factors for which epidemiological studies have not been conducted, including the joint effects of multiple risk factors; calculating the health loss due to risk factor(s) as a time-indexed "stream" of disease burden due to a time-indexed "stream" of exposure, including consideration of discounting; and the sources of uncertainty. http://www.pophealthmetrics.com/content/1/1/1 Christopher Murray Majid Ezzati Alan Lopez Anthony Rodgers Stephen Vander Hoorn Population Health Metrics 2003, null:1 2003-04-14T00:00:00Z doi:10.1186/1478-7954-1-1 /content/figures/1478-7954-1-1-toc.gif Population Health Metrics 1478-7954 ${item.volume} 1 2003-04-14T00:00:00Z 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 PDF