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/ http://www.pophealthmetrics.com The latest articles from Population Health Metrics (ISSN 1478-7954) published by BioMed Central Background: In the United States, relatively little is known about cause of death in individuals who die prior to or after hospital discharge for acute coronary syndromes (ACS). The purpose of this report was to compare baseline patient characteristics according to whether the underlying cause of death was cardiac or non-cardiac. Methods: We linked cause of death information from Washington State death records to the Department of Veterans Affairs (VA) External Peer Review Program ACS registry. From 524 individuals who were hospitalized for ACS in veterans hospitals located in Washington State or Oregon, we identified 136 individuals who according to VA death records died during the years 2003 to 2005. Of these, 117 (86%) were found in Washington State death records. Sociodemographic variables, as well as underlying and secondary causes of death, were obtained from Washington State death records provided by the Washington State Department of Health. Clinical variables, including medical histories, presentation on admission, and in-hospital death were extracted from the VA ACS registry. Results: Somewhat surprisingly, only 52% of veterans died of cardiac causes when only the underlying cause of death was used. However, when secondary causes of death were added to the definition, the proportion that died of cardiac causes increased to 81%. Patient characteristics were similar in the two groups, although small numbers limited the ability to detect statistically significant differences. Conclusion: These preliminary findings suggest that it is important to consider secondary causes as well as the underlying one when classifying deaths as cardiac or non-cardiac. http://www.pophealthmetrics.com/content/6/1/3 Charles Maynard, Elliott Lowy, Mary McDonell and Stephan D Fihn Population Health Metrics 2008, 6:3 2008-07-23 doi:10.1186/1478-7954-6-3 Population Health Metrics 1478-7954 6 3 2008-07-23 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 and Peter Byass Population Health Metrics 2008, 6:2 2008-05-20 doi:10.1186/1478-7954-6-2 Population Health Metrics 1478-7954 6 2 2008-05-20 Background: With increasing urbanization in sub-Saharan Africa and poor economic performance, the growth of slums is unavoidable. About 71% of urban residents in Kenya live in slums. Slums are characteristically unplanned, underserved by social services, and their residents are largely underemployed and poor. Recent research shows that the urban poor fare worse than their rural counterparts on most health indicators, yet much about the health of the urban poor remains unknown. This study aims to quantify the burden of mortality of the residents in two Nairobi slums, using a Burden of Disease approach and data generated from a Demographic Surveillance System. Methods: Data from the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) collected between January 2003 and December 2005 were analysed. Core demographic events in the NUHDSS including deaths are updated three times a year; cause of death is ascertained by verbal autopsy and cause of death is assigned according to the ICD 10 classification. Years of Life Lost due to premature mortality (YLL) were calculated by multiplying deaths in each subcategory of sex, age group and cause of death, by the Global Burden of Disease standard life expectancy at that age. Results: The overall mortality burden per capita was 205 YLL/1,000 person years. Children under the age of five years had more than four times the mortality burden of the rest of the population, mostly due to pneumonia and diarrhoeal diseases. Among the population aged five years and above, HIV/AIDS and tuberculosis accounted for about 50% of the mortality burden. Conclusion: Slum residents in Nairobi have a high mortality burden from preventable and treatable conditions. It is necessary to focus on these vulnerable populations since their health outcomes are comparable to or even worse than the health outcomes of rural dwellers who are often the focus of most interventions. http://www.pophealthmetrics.com/content/6/1/1 Catherine Kyobutungi, Abdhalah Kasiira Ziraba, Alex Ezeh and Yazoumé Yé Population Health Metrics 2008, 6:1 2008-03-10 doi:10.1186/1478-7954-6-1 Population Health Metrics 1478-7954 6 1 2008-03-10 Background: In countries where routine vital registration data are scarce, Demographic Surveillance Sites (DSS: locally defined populations under longitudinal surveillance for vital events and other characteristics) and Demographic and Health Surveys (DHS: periodic national cluster samples responding to cross-sectional surveys) have become standard approaches for gathering at least some data. This paper aims to compare DSS and DHS approaches, seeing how they complement each other in the specific instance of child and adolescent mortality in Ethiopia. Methods: Data from the Butajira DSS 1987–2004 and the Ethiopia DHS rounds for 2000 and 2005 formed the basis of comparative analyses of mortality rates among those aged under 20 years, using Poisson regression models for adjusted rate ratios. Results: Patterns of mortality over time were broadly comparable using DSS and DHS approaches. DSS data were more susceptible to local epidemic variations, while DHS data tended to smooth out local variation, and be more subject to recall bias. Conclusion: Both DSS and DHS approaches to mortality surveillance gave similar overall results, but both showed method-dependent advantages and disadvantages. In many settings, this kind of joint-source data analysis could offer significant added value to results. http://www.pophealthmetrics.com/content/5/1/12 Peter Byass, Alemayehu Worku, Anders Emmelin and Yemane Berhane Population Health Metrics 2007, 5:12 2007-12-27 doi:10.1186/1478-7954-5-12 Population Health Metrics 1478-7954 5 12 2007-12-27 Background: To provide quantitative insight into current U.S. policy choices for cervical cancer prevention, we developed a model of human papillomavirus (HPV) and cervical cancer, explicitly incorporating uncertainty about the natural history of disease. Methods: We developed a stochastic microsimulation of cervical cancer that distinguishes different HPV types by their incidence, clearance, persistence, and progression. Input parameter sets were sampled randomly from uniform distributions, and simulations undertaken with each set. Through systematic reviews and formal data synthesis, we established multiple epidemiologic targets for model calibration, including age-specific prevalence of HPV by type, age-specific prevalence of cervical intraepithelial neoplasia (CIN), HPV type distribution within CIN and cancer, and age-specific cancer incidence. For each set of sampled input parameters, likelihood-based goodness-of-fit (GOF) scores were computed based on comparisons between model-predicted outcomes and calibration targets. Using 50 randomly resampled, good-fitting parameter sets, we assessed the external consistency and face validity of the model, comparing predicted screening outcomes to independent data. To illustrate the advantage of this approach in reflecting parameter uncertainty, we used the 50 sets to project the distribution of health outcomes in U.S. women under different cervical cancer prevention strategies. Results: Approximately 200 good-fitting parameter sets were identified from 1,000,000 simulated sets. Modeled screening outcomes were externally consistent with results from multiple independent data sources. Based on 50 good-fitting parameter sets, the expected reductions in lifetime risk of cancer with annual or biennial screening were 76% (range across 50 sets: 69–82%) and 69% (60–77%), respectively. The reduction from vaccination alone was 75%, although it ranged from 60% to 88%, reflecting considerable parameter uncertainty about the natural history of type-specific HPV infection. The uncertainty surrounding the model-predicted reduction in cervical cancer incidence narrowed substantially when vaccination was combined with every-5-year screening, with a mean reduction of 89% and range of 83% to 95%. Conclusion: We demonstrate an approach to parameterization, calibration and performance evaluation for a U.S. cervical cancer microsimulation model intended to provide qualitative and quantitative inputs into decisions that must be taken before long-term data on vaccination outcomes become available. This approach allows for a rigorous and comprehensive description of policy-relevant uncertainty about health outcomes under alternative cancer prevention strategies. The model provides a tool that can accommodate new information, and can be modified as needed, to iteratively assess the expected benefits, costs, and cost-effectiveness of different policies in the U.S. http://www.pophealthmetrics.com/content/5/1/11 Jeremy D Goldhaber-Fiebert, Natasha K Stout, Jesse Ortendahl, Karen M Kuntz, Sue J Goldie and Joshua A Salomon Population Health Metrics 2007, 5:11 2007-10-29 doi:10.1186/1478-7954-5-11 Population Health Metrics 1478-7954 5 11 2007-10-29 Background: A limited number of health status and health-related quality of life (HRQL) measures have been used for inter-country comparisons of population health. We compared the health of Canadians and Americans using a preference-based measure. Methods: The Joint Canada/United States Survey of Health (JCUSH) 2002–03 conducted a comprehensive cross-sectional telephone survey on the health of community-dwelling residents in Canada and the US (n = 8688). A preference-based measure, the Health Utilities Index Mark 3 (HUI3), was included in the JCUSH. Health status was analyzed for the entire population and white population only in both countries. Mean HUI3 overall scores were compared for both countries. A linear regression determinants of health model was estimated to account for differences in health between Canada and the US. Estimation with bootstraps was used to derive variance estimates that account for the survey's complex sampling design of clustering and stratification. Results: Income is associated with health in both countries. In the lowest income quintile, Canadians are healthier than Americans. At lower levels of education, again Canadians are healthier than Americans. Differences in health among subjects in the JCUSH are explained by age, gender, education, income, marital status, and country of residence. Conclusion: On average, population health in Canada and the US is similar. However, health disparities between Canadians and Americans exist at lower levels of education and income with Americans worse off. The results highlight the usefulness of continuous preference-based measures of population health such as the HUI3. http://www.pophealthmetrics.com/content/5/1/10 Ken Eng and David Feeny Population Health Metrics 2007, 5:10 2007-10-16 doi:10.1186/1478-7954-5-10 Population Health Metrics 1478-7954 5 10 2007-10-16 Background: Hospitalisation for ambulatory care sensitive conditions (ACSHs) has become a recognised tool to measure access to primary care. Timely and effective outpatient care is highly relevant to refugee populations given the past exposure to torture and trauma, and poor access to adequate health care in their countries of origin and during flight. Little is known about ACSHs among resettled refugee populations. With the aim of examining the hypothesis that people from refugee backgrounds have higher ACSHs than people born in the country of hospitalisation, this study analysed a six-year state-wide hospital discharge dataset to estimate ACSH rates for residents born in refugee-source countries and compared them with the Australia-born population. Methods: Hospital discharge data between 1 July 1998 and 30 June 2004 from the Victorian Admitted Episodes Dataset were used to assess ACSH rates among residents born in eight refugee-source countries, and compare them with the Australia-born average. Rate ratios and 95% confidence levels were used to illustrate these comparisons. Four categories of ambulatory care sensitive conditions were measured: total, acute, chronic and vaccine-preventable. Country of birth was used as a proxy indicator of refugee status. Results: When compared with the Australia-born population, hospitalisations for total and acute ambulatory care sensitive conditions were lower among refugee-born persons over the six-year period. Chronic and vaccine-preventable ACSHs were largely similar between the two population groups. Conclusion: Contrary to our hypothesis, preventable hospitalisation rates among people born in refugee-source countries were no higher than Australia-born population averages. More research is needed to elucidate whether low rates of preventable hospitalisation indicate better health status, appropriate health habits, timely and effective care-seeking behaviour and outpatient care, or overall low levels of health care-seeking due to other more pressing needs during the initial period of resettlement. It is important to unpack dimensions of health status and health care access in refugee populations through ad-hoc surveys as the refugee population is not a homogenous group despite sharing a common experience of forced displacement and violence-related trauma. http://www.pophealthmetrics.com/content/5/1/9 Ignacio Correa-Velez, Zahid Ansari, Vijaya Sundararajan, Kaye Brown and Sandra M Gifford Population Health Metrics 2007, 5:9 2007-10-03 doi:10.1186/1478-7954-5-9 Population Health Metrics 1478-7954 5 9 2007-10-03 Background: Chronic obstructive pulmonary disease (COPD) is a major but neglected public health problem. Currently 1.4% of the England population has a clinical diagnosis of COPD, but the true burden of the disease has not been known with certainty, as many cases remain undiagnosed. Methods: A mathematical model based on cross sectional data from a representative sample of the population in England (the Heath Survey for England 2001, n = 10,750) was developed allowing estimates on the prevalence of COPD (defined based on the presence of airflow obstruction) to be obtained. Logistic regression analysis was used to investigate and choose risk factors for inclusion in the model and to derive the prevalence estimates based on the strength of association between selected risk factors and the outcome COPD. The model allows the prevalence to be estimated in populations at national level and also at regional and large local areas, based on their compositions according to age, sex, smoking and ethnicity, and on area degrees of urbanisation and deprivation. We applied the model to measure the prevalence of COPD in England and in some sub-groups of the population within the country. Results: The prevalence of COPD in England is estimated as 3.1% (3.9% in men and 2.4% in women) in the population over 15 years of age, and 5.3% (6.8% in men and 3.9% in women) in 45 year-olds and over. There was a 7-fold variation in the prevalence across subgroups of the population, with lowest values in Asian women from wealthy rural areas (1.7%), and highest in black men from deprived urban areas (12.5%). Conclusion: The model can be used to estimate population prevalence of COPD from large general practices to national level, and as a tool to identify areas of high levels of unmet needs for COPD priority health actions. The results from the model highlight the importance of including variables other than age, sex and smoking, i.e. levels of deprivation, urbanisation and ethnicity, when estimating population prevalence of COPD. The model should be validated at local level and incorporated into case-finding strategies. http://www.pophealthmetrics.com/content/5/1/8 Luis C Nacul, Michael Soljak and Tom Meade Population Health Metrics 2007, 5:8 2007-09-26 doi:10.1186/1478-7954-5-8 Population Health Metrics 1478-7954 5 8 2007-09-26 Background: Among the available data provided by health information systems, data on mortality are commonly used not only as health indicators but also as socioeconomic development indices. Recognizing that in Iran accurate data on causes of death were not available, the Deputy of Health in the Ministry of Health and Medical Education (MOH&ME) established a new comprehensive system for death registration which started in one province (Bushehr) as a pilot in 1997, and was subsequently expanded to include all other provinces, except Tehran province. These data can be used to investigate the nature and extent of differences in mortality in Iran. The objective of this paper is to estimate provincial differences in the level of mortality using this death registration system. Methods: Data from the death registration system for 2004 for each province were evaluated for data completeness, and life tables were created for provinces after correction for under-enumeration of death registration. For those provinces where it was not possible to adjust the data on adult deaths by using the Brass Growth Balance method, adult mortality was predicted based on adult literacy using information from provinces with reliable data. Results: Child mortality (risk of a newborn dying before age 5, or 5q0) in 2004 varied between 47 per 1000 live births for both sexes in Sistan and Baluchistan province, and 25 per 1000 live births in Tehran and Gilan provinces. For adults, provincial differences in mortality were much greater for males than females. Adult mortality (risk of dying between ages 15 and 60, or 45q15) for females varied between 0.133 in Kerman province and 0.117 in Tehran province; for males the range was from 0.218 in Kerman to 0.149 in Tehran province. Life expectancy for females was highest in Tehran province (73.8 years) and lowest in Sistan and Baluchistan (70.9 years). For males, life expectancy ranged from 65.7 years in Sistan and Baluchistan province to 70.9 years in Tehran. Conclusion: Substantial differences in survival exist among the provinces of Iran. While the completeness of the death registration system operated by the Iranian MOH&ME appears to be acceptable in the majority of provinces, further efforts are needed to improve the quality of data on mortality in Iran, and to expand death registration to Tehran province. http://www.pophealthmetrics.com/content/5/1/7 Ardeshir Khosravi, Richard Taylor, Mohsen Naghavi and Alan D Lopez Population Health Metrics 2007, 5:7 2007-07-28 doi:10.1186/1478-7954-5-7 Population Health Metrics 1478-7954 5 7 2007-07-28 Commentary onPrevalence of chronic fatigue syndrome in metropolitan, urban, and rural GeorgiaWilliam C Reeves, James F Jones, Elizabeth Maloney, Christine Heim, David C Hoaglin, Roumiana S Boneva, Marjorie Morrissey and Rebecca Devlin http://www.pophealthmetrics.com/content/5/1/6 Peter D White Population Health Metrics 2007, 5:6 2007-06-08 doi:10.1186/1478-7954-5-6 Population Health Metrics 1478-7954 5 6 2007-06-08