http://www.pophealthmetrics.com The latest research articles published by Population Health Metrics 2010-03-04T00:00:00Z This is an RSS newsfeed from BioMed Central It is intended to be used with an RSS reader. For more information about RSS newsfeeds from BioMed Central, visit http://www.biomedcentral.com/info/about/rss/ Background: Alcohol consumption is a major risk factor in the global burden of disease, with overall volume of exposure as the principal underlying dimension. Two main sources of data on volume of alcohol exposure are available: surveys and per capita consumption derived from routine statistics such as taxation. As both sources have significant problems, this paper presents an approach that triangulates information from both sources into disaggregated estimates in line with the overall level of per capita consumption. Methods: A modeling approach was applied to the US using data from a large and representative survey, the National Epidemiologic Survey on Alcohol and Related Conditions. Different distributions (log-normal, gamma, Weibull) were used to model consumption among drinkers in subgroups defined by sex, age, and ethnicity. The gamma distribution was used to shift the fitted distributions in line with the overall volume as derived from per capita estimates. Implications for alcohol-attributable fractions were presented, using liver cirrhosis as an example. Results: The triangulation of survey data with aggregated per capita consumption data proved feasible and allowed for modeling of alcohol exposure disaggregated by sex, age, and ethnicity. These models can be used in combination with risk relations for burden of disease calculations. Sensitivity analyses showed that the gamma distribution chosen yielded very similar results in terms of fit and alcohol-attributable mortality as the other tested distributions. Conclusions: Modeling alcohol consumption via the gamma distribution was feasible. To further refine this approach, research should focus on the main assumptions underlying the approach to explore differences between volume estimates derived from surveys and per capita consumption figures. http://www.pophealthmetrics.com/content/8/1/3 Jurgen Rehm Tara Kehoe Gerrit Gmel Fred Stinson Bridget Grant Gerhard Gmel Population Health Metrics 2010, 8:3 2010-03-04T00:00:00Z doi:10.1186/1478-7954-8-3 Population Health Metrics 1478-7954 8 3 2010-03-04T00:00:00Z PDF Background: This paper examines the specificity and sensitivity of a breath carbon monoxide (BCO) test and optimum BCO cutoff level for validating self-reported tobacco smoking in Indigenous Australians in Arnhem Land, Northern Territory (NT). Methods: In a sample of 400 people (≥16 years) interviewed about tobacco use in three communities, both self-reported smoking and BCO data were recorded for 309 study participants. Of these, 249 reported smoking tobacco within the preceding 24 hours, and 60 reported they had never smoked or had not smoked tobacco for ≥6 months. The sample was opportunistically recruited using quotas to reflect age and gender balances in the communities where the combined Indigenous populations comprised 1,104 males and 1,215 females (≥16 years). Local Indigenous research workers assisted researchers in interviewing participants and facilitating BCO tests using a portable hand-held analyzer. Results: A BCO cutoff of ≥7 parts per million (ppm) provided good agreement between self-report and BCO (96.0% sensitivity, 93.3% specificity). An alternative cutoff of ≥5 ppm increased sensitivity from 96.0% to 99.6% with no change in specificity (93.3%). With data for two self-reported nonsmokers who also reported that they smoked cannabis removed from the analysis, specificity increased to 96.6%. Conclusion: In these disadvantaged Indigenous populations, where data describing smoking are few, testing for BCO provides a practical, noninvasive, and immediate method to validate self-reported smoking. In further studies of tobacco smoking in these populations, cannabis use should be considered where self-reported nonsmokers show high BCO. http://www.pophealthmetrics.com/content/8/1/2 David MacLaren Katherine Conigrave Jan Robertson Rowena Ivers Sandra Eades Alan Clough Population Health Metrics 2010, 8:2 2010-02-20T00:00:00Z doi:10.1186/1478-7954-8-2 Population Health Metrics 1478-7954 8 2 2010-02-20T00:00:00Z XML Background: Malaria in India has been difficult to measure. Mortality and morbidity are not comprehensively reported, impeding efforts to track changes in disease burden. However, a set of blood measures has been collected regularly by the National Malaria Control Program in most districts since 1958. Methods: Here, we use principal components analysis to combine these measures into a single index, the Summary Index of Malaria Surveillance (SIMS), and then test its temporal and geographic stability using subsets of the data. Results: The SIMS correlates positively with all its individual components and with external measures of mortality and morbidity. It is highly consistent and stable over time (1995-2005) and regions of India. It includes measures of both vivax and falciparum malaria, with vivax dominant at lower transmission levels and falciparum dominant at higher transmission levels, perhaps due to ecological specialization of the species. Conclusions: This measure should provide a useful tool for researchers looking to summarize geographic or temporal trends in malaria in India, and can be readily applied by administrators with no mathematical or scientific background. We include a spreadsheet that allows simple calculation of the index for researchers and local administrators. Similar principles are likely applicable worldwide, though further validation is needed before using the SIMS outside India. http://www.pophealthmetrics.com/content/8/1/1 Alan Cohen Neeraj Dhingra Raju Jotkar Peter Rodriguez Vinod Sharma Prabhat Jha Population Health Metrics 2010, 8:1 2010-02-11T00:00:00Z doi:10.1186/1478-7954-8-1 Population Health Metrics 1478-7954 8 1 2010-02-11T00:00:00Z PDF ObjectivesThe aim of this study was to describe the temporal determinants of meningitis incidence in the population living in the Tehran metropolis. Methods: All cases of meningitis reported to health districts throughout the Tehran metropolis from 1999 to 2005 were abstracted from patient files. Referral cases (patients who did not reside in the Tehran metropolis) were excluded. For each year, sex- and age-specific incidences were estimated. Temporality and its determinants were analyzed using Poisson regression. Results: Age-specific incidence is highest among males younger than 5 years of age at 10.2 cases per 100,000 population per year. The lowest incidence was among females aged 30 to 40 years at 0.72 cases per 100,000 population per year, with an overall male-to-female incidence ratio of 2.1. The temporal analysis showed seasonality, with a higher risk of meningitis in spring at a rate ratio of 1.31 with a 95% confidence interval (CI) of 1.20 to 1.41 and in autumn (rate ratio = 1.16, 95% CI 1.06, 1.27). For periodicity, we found a peak of occurrence around the years 2000 and 2003. Conclusion: The epidemiology of meningitis in Iran follows similar patterns of age, sex, and seasonality distribution as found in other countries and populations. http://www.pophealthmetrics.com/content/7/1/19 Alireza Mosavi-Jarrahi Abdolreza Esteghamati Freshteh Asgari Mohammadali Heidarnia Yasamin Mousavi-Jarrahi Mohammadmehdi Goya Population Health Metrics 2009, 7:19 2009-12-23T00:00:00Z doi:10.1186/1478-7954-7-19 Population Health Metrics 1478-7954 7 19 2009-12-23T00:00:00Z 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, 7:18 2009-12-15T00:00:00Z doi:10.1186/1478-7954-7-18 Population Health Metrics 1478-7954 7 18 2009-12-15T00:00:00Z XML Background: Chronic fatigue syndrome (CFS) is defined by self-reported symptoms. There are no diagnostic signs or laboratory markers, and the pathophysiology remains inchoate. In part, difficulties identifying and replicating biomarkers and elucidating the pathophysiology reflect the heterogeneous nature of the syndromic illness CFS. We conducted this analysis of people from defined metropolitan, urban, and rural populations to replicate our earlier empirical delineation of medically unexplained chronic fatigue and CFS into discrete endophenotypes. Both the earlier and current analyses utilized quantitative measures of functional impairment and symptoms as well as laboratory data. This study and the earlier one enrolled participants from defined populations and measured the internal milieu, which differentiates them from studies of clinic referrals that examine only clinical phenotypes. Methods: This analysis evaluated 386 women identified in a population-based survey of chronic fatigue and unwellness in metropolitan, urban, and rural populations of the state of Georgia, USA. We used variables previously demonstrated to effectively delineate endophenotypes in an attempt to replicate identification of these endophenotypes. Latent class analyses were used to derive the classes, and these were compared and contrasted to those described in the previous study based in Wichita, Kansas. Results: We identified five classes in the best fit analysis. Participants in Class 1 (25%) were polysymptomatic, with sleep problems and depressed mood. Class 2 (24%) was also polysymptomatic, with insomnia and depression, but participants were also obese with associated metabolic strain. Class 3 (20%) had more selective symptoms but was equally obese with metabolic strain. Class 4 (20%) and Class 5 (11%) consisted of nonfatigued, less symptomatic individuals, Class 4 being older and Class 5 younger. The classes were generally validated by independent variables. People with CFS fell equally into Classes 1 and 2. Similarities to the Wichita findings included the same four main defining variables of obesity, sleep problems, depression, and the multiplicity of symptoms. Four out of five classes were similar across both studies. Conclusion: These data support the hypothesis that chronic medically unexplained fatigue is heterogeneous and can be delineated into discrete endophenotypes that can be replicated. The data do not support the current perception that CFS represents a unique homogeneous disease and suggests broader criteria may be more explanatory. This replication suggests that delineation of endophenotypes of CFS and associated ill health may be necessary in order to better understand etiology and provide more patient-focused treatments. http://www.pophealthmetrics.com/content/7/1/17 Eric Aslakson Ute Vollmer-Conna William Reeves Peter White Population Health Metrics 2009, 7:17 2009-10-05T00:00:00Z doi:10.1186/1478-7954-7-17 Population Health Metrics 1478-7954 7 17 2009-10-05T00:00:00Z 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, 7:16 2009-09-25T00:00:00Z doi:10.1186/1478-7954-7-16 Population Health Metrics 1478-7954 7 16 2009-09-25T00:00:00Z XML Background: Large-scale epidemiological studies commonly use self-reported weights and heights to determine weight status. Validity of such self-reported data has been assessed primarily in Western populations in developed countries, although its use is widespread in developing countries. We examine the validity of obesity based on self-reported data in an Asian developing country, and derive improved obesity prevalence estimates using the "reduced BMI threshold" method. Methods: Self-reported and measured heights and weights were obtained from 741 students attending an open university in Thailand (mean age 34 years). Receiver operator characteristic techniques were applied to derive "reduced BMI thresholds." Results: Height was over-reported by a mean of 1.54 cm (SD 2.23) in men and 1.33 cm (1.84) in women. Weight was under-reported by 0.93 kg (3.47) in men and 0.62 kg (2.14) in women. Sensitivity and specificity for determining obesity (Thai BMI threshold 25 kg/m2) using self-reported data were 74.2% and 97.3%, respectively, for men and 71.9% and 100% for women. For men, reducing the BMI threshold to 24.5 kg/m2 increased the estimated obesity prevalence based on self-reports from 29.1% to 33.8% (true prevalence was 36.9%). For women, using a BMI threshold of 24.4 kg/m2, the improvement was from 12.0% to 15.9% (true prevalence 16.7%). Conclusion: Young educated Thais under-report weight and over-report height in ways similar to their counterparts in developed countries. Simple adjustments to BMI thresholds will overcome these reporting biases for estimation of obesity prevalence. Our study suggests that self-reported weights and heights can provide economical and valid measures of weight status in high school-educated populations in developing countries. http://www.pophealthmetrics.com/content/7/1/15 Lynette Lim Sam-ang Seubsman Adrian Sleigh Population Health Metrics 2009, 7:15 2009-09-25T00:00:00Z doi:10.1186/1478-7954-7-15 Population Health Metrics 1478-7954 7 15 2009-09-25T00:00:00Z XML Background: Some of the Census Enumeration Areas' (CEA) information may help planning the sample of population studies but it can also be used for some analyses that require information that is more difficult to obtain at the individual or household level, such as income. This paper verifies if the income information of CEA can be used as a proxy for household income in a household survey. Methods: A population-based survey conducted from January to December 2003 obtained data from a probabilistic sample of 1,734 households of Niterói, Rio de Janeiro, Brazil. Uniform semi-association models were adjusted in order to obtain information about the agreement/disagreement structure of data. The distribution of nutritional status categories of the population of Niterói according to income quintiles was performed using both CEA- and household-level income measures and then compared using Wald statistics for homogeneity. Body mass index was calculated using body mass and stature data measured in the households and then used to define nutritional status categories according to the World Health Organization. All estimates and statistics were calculated accounting for the structural information of the sample design and a significance level lower than 5% was adopted. Results: The classification of households in the quintiles of household income was associated with the classification of these households in the quintiles of CEA income. The distribution of the nutritional status categories in all income quintiles did not differ significantly according to the source of income information (household or CEA) used in the definition of quintiles. Conclusion: The structure of agreement/disagreement between quintiles of the household's monthly per capita income and quintiles of the head-of-household's mean nominal monthly income of the CEA, as well as the results produced by these measures when they were associated with the nutritional status of the population, showed that the CEA's income information can be used when income information at the individual or household levels is not available. http://www.pophealthmetrics.com/content/7/1/14 Fabio Gomes Mauricio Vasconcellos Luiz Anjos Population Health Metrics 2009, 7:14 2009-09-22T00:00:00Z doi:10.1186/1478-7954-7-14 Population Health Metrics 1478-7954 7 14 2009-09-22T00:00:00Z XML Background: Time trends in infant mortality for the 20th century show a curvilinear pattern that most demographers have assumed to be approximately exponential. Virtually all cross-country comparisons and time series analyses of infant mortality have studied the logarithm of infant mortality to account for the curvilinear time trend. However, there is no evidence that the log transform is the best fit for infant mortality time trends. Methods: We use maximum likelihood methods to determine the best transformation to fit time trends in infant mortality reduction in the 20th century and to assess the importance of the proper transformation in identifying the relationship between infant mortality and gross domestic product (GDP) per capita. We apply the Box Cox transform to infant mortality rate (IMR) time series from 18 countries to identify the best fitting value of lambda for each country and for the pooled sample. For each country, we test the value of λ against the null that λ = 0 (logarithmic model) and against the null that λ = 1 (linear model). We then demonstrate the importance of selecting the proper transformation by comparing regressions of ln(IMR) on same year GDP per capita against Box Cox transformed models. Results: Based on chi-squared test statistics, infant mortality decline is best described as an exponential decline only for the United States. For the remaining 17 countries we study, IMR decline is neither best modelled as logarithmic nor as a linear process. Imposing a logarithmic transform on IMR can lead to bias in fitting the relationship between IMR and GDP per capita. Conclusion: The assumption that IMR declines are exponential is enshrined in the Preston curve and in nearly all cross-country as well as time series analyses of IMR data since Preston's 1975 paper, but this assumption is seldom correct. Statistical analyses of IMR trends should assess the robustness of findings to transformations other than the log transform. http://www.pophealthmetrics.com/content/7/1/13 David Bishai Marjorie Opuni Population Health Metrics 2009, 7:13 2009-08-23T00:00:00Z doi:10.1186/1478-7954-7-13 Population Health Metrics 1478-7954 7 13 2009-08-23T00:00:00Z XML