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Showing papers by "Christopher J L Murray published in 2011"


Journal ArticleDOI
TL;DR: More policy attention is needed to strengthen established health-system responses to reduce breast and cervical cancer, especially in developing countries.

932 citations


Journal ArticleDOI
TL;DR: The aim was to update previous estimates of maternal and child mortality using better data and more robust methods to provide the best available evidence for tracking progress on MDGs 4 and 5.

870 citations


Journal ArticleDOI
TL;DR: This unique, robust validation dataset will allow scholars to evaluate the performance of different verbal autopsy analytic methods as well as instrument design and can be used to inform the implementation of verbal autopsies to more reliably ascertain cause of death in national health information systems.
Abstract: 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. 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. 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. 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.

166 citations


Journal ArticleDOI
TL;DR: Findings from a multi-country analysis of household survey data on the association between possession of insecticide-treated mosquito nets and child mortality and parasitemia suggest scale-up of net coverage was associated with a substantial reduction in childhood mortality and in Parasitemia prevalence.
Abstract: Background: Several sub-Saharan African countries have rapidly scaled up the number of households that own insecticidetreated mosquito nets (ITNs). Although the efficacy of ITNs in trials has been shown, evidence on their impact under routine conditions is limited to a few countries and the extent to which the scale-up of ITNs has improved population health remains uncertain. Methods and Findings: We used matched logistic regression to assess the individual-level association between household ITN ownership or use in children under 5 years of age and the prevalence of parasitemia among children using six malaria indicator surveys (MIS) and one demographic and health survey. We used Cox proportional hazards models to assess the relationship between ITN household ownership and child mortality using 29 demographic and health surveys. The pooled relative reduction in parasitemia prevalence from random effects meta-analysis associated with household ownership of at least one ITN was 20% (95% confidence interval [CI] 3%–35%; I 2 = 73.5%, p,0.01 for I 2 value). Sleeping under an ITN was associated with a pooled relative reduction in parasitemia prevalence in children of 24% (95% CI 1%–42%; I 2 = 79.5%, p,0.001 for I 2 value). Ownership of at least one ITN was associated with a pooled relative reduction in mortality between 1 month and 5 years of age of 23% (95% CI 13–31%; I 2 = 25.6%, p.0.05 for I 2 value). Conclusions: Our findings across a number of sub-Saharan African countries were highly consistent with results from previous clinical trials. These findings suggest that the recent scale-up in ITN coverage has likely been accompanied by significant reductions in child mortality and that additional health gains could be achieved with further increases in ITN coverage in populations at risk of malaria. Please see later in the article for the Editors’ Summary.

163 citations


Journal ArticleDOI
TL;DR: The US has extremely large geographic and racial disparities, with some communities having life expectancies already well behind those of the best-performing nations, and relative performance for most communities continues to drop.
Abstract: 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. 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. 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. 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.

146 citations


Journal ArticleDOI
TL;DR: In this article, the authors examined the effectiveness of the health system response to the challenge of diabetes across different settings and explore the inequalities in diabetes care that are attributable to socioeconomic factors.
Abstract: OBJECTIVE: To examine the effectiveness of the health system response to the challenge of diabetes across different settings and explore the inequalities in diabetes care that are attributable to socioeconomic factors. METHODS: We used nationally representative health examination surveys from Colombia, England, the Islamic Republic of Iran, Mexico, Scotland, Thailand and the United States of America to obtain data on diagnosis, treatment and control of hyperglycaemia, arterial hypertension and hypercholesterolaemia among individuals with diabetes. Using logistic regression, we explored the socioeconomic determinants of diagnosis and effective case management. FINDINGS: A substantial proportion of individuals with diabetes remain undiagnosed and untreated, both in developed and developing countries. The figures range from 24% of the women in Scotland and the USA to 62% of the men in Thailand. The proportion of individuals with diabetes reaching treatment targets for blood glucose, arterial blood pressure and serum cholesterol was very low, ranging from 1% of male patients in Mexico to about 12% in the United States. Income and education were not found to be significantly related to the rates of diagnosis and treatment anywhere except in Thailand, but in the three countries with available data insurance status was a strong predictor of diagnosis and effective management, especially in the United States. CONCLUSION: There are many missed opportunities to reduce the burden of diabetes through improved control of blood glucose levels and improved diagnosis and treatment of arterial hypertension and hypercholesterolaemia. While no large socioeconomic inequalities were noted in the management of individuals with diabetes, financial access to care was a strong predictor of diagnosis and management.

128 citations


Journal ArticleDOI
TL;DR: The RF Method outperformed the PCVA method in terms of chance-corrected concordance and CSMF accuracy for adult and child VA with and without H CE and for neonatal VA without HCE and is recommended as the technique of choice for analyzing past and current verbal autopsies.
Abstract: Background: Computer-coded verbal autopsy (CCVA) is a promising alternative to the standard approach of physician-certified verbal autopsy (PCVA), because of its high speed, low cost, and reliability. This study introduces a new CCVA technique and validates its performance using defined clinical diagnostic criteria as a gold standard for a multisite sample of 12,542 verbal autopsies (VAs). Methods: The Random Forest (RF) Method from machine learning (ML) was adapted to predict cause of death by training random forests to distinguish between each pair of causes, and then combining the results through a novel ranking technique. We assessed quality of the new method at the individual level using chance-corrected concordance and at the population level using cause-specific mortality fraction (CSMF) accuracy as well as linear regression. We also compared the quality of RF to PCVA for all of these metrics. We performed this analysis separately for adult, child, and neonatal VAs. We also assessed the variation in performance with and without household recall of health care experience (HCE). Results: For all metrics, for all settings, RF was as good as or better than PCVA, with the exception of a nonsignificantly lower CSMF accuracy for neonates with HCE information. With HCE, the chance-corrected concordance of RF was 3.4 percentage points higher for adults, 3.2 percentage points higher for children, and 1.6 percentage points higher for neonates. The CSMF accuracy was 0.097 higher for adults, 0.097 higher for children, and 0.007 lower for neonates. Without HCE, the chance-corrected concordance of RF was 8.1 percentage points higher than PCVA for adults, 10.2 percentage points higher for children, and 5.9 percentage points higher for neonates. The CSMF accuracy was higher for RF by 0.102 for adults, 0.131 for children, and 0.025 for neonates. Conclusions: We found that our RF Method outperformed the PCVA method in terms of chance-corrected concordance and CSMF accuracy for adult and child VA with and without HCE and for neonatal VA without HCE. It is also preferable to PCVA in terms of time and cost. Therefore, we recommend it as the technique of choice for analyzing past and current verbal autopsies.

104 citations


Journal ArticleDOI
TL;DR: Verbal autopsies can be an efficient means of obtaining cause of death data, and Tariff provides an intuitive, reliable method for generating individual cause assignment and CSMFs from verbal autopsy data.
Abstract: Verbal autopsies provide valuable information for studying mortality patterns in populations that lack reliable vital registration data. Methods for transforming verbal autopsy results into meaningful information for health workers and policymakers, however, are often costly or complicated to use. We present a simple additive algorithm, the Tariff Method (termed Tariff), which can be used for assigning individual cause of death and for determining cause-specific mortality fractions (CSMFs) from verbal autopsy data. Tariff calculates a score, or "tariff," for each cause, for each sign/symptom, across a pool of validated verbal autopsy data. The tariffs are summed for a given response pattern in a verbal autopsy, and this sum (score) provides the basis for predicting the cause of death in a dataset. We implemented this algorithm and evaluated the method's predictive ability, both in terms of chance-corrected concordance at the individual cause assignment level and in terms of CSMF accuracy at the population level. The analysis was conducted separately for adult, child, and neonatal verbal autopsies across 500 pairs of train-test validation verbal autopsy data. Tariff is capable of outperforming physician-certified verbal autopsy in most cases. In terms of chance-corrected concordance, the method achieves 44.5% in adults, 39% in children, and 23.9% in neonates. CSMF accuracy was 0.745 in adults, 0.709 in children, and 0.679 in neonates. Verbal autopsies can be an efficient means of obtaining cause of death data, and Tariff provides an intuitive, reliable method for generating individual cause assignment and CSMFs. The method is transparent and flexible and can be readily implemented by users without training in statistics or computer science.

100 citations



Journal ArticleDOI
TL;DR: Results show that physician coding for cause of death assignment may not be as robust as previously thought and highlight the importance and urgency of developing better methods to more reliably analyze past and future verbal autopsies to obtain the highest quality mortality data from populations without reliable death certification.
Abstract: Physician review of a verbal autopsy (VA) and completion of a death certificate remains the most widely used approach for VA analysis. This study provides new evidence about the performance of physician-certified verbal autopsy (PCVA) using defined clinical diagnostic criteria as a gold standard for a multisite sample of 12,542 VAs. The study was also designed to analyze issues related to PCVA, such as the impact of a second physician reader on the cause of death assigned, the variation in performance with and without household recall of health care experience (HCE), and the importance of local information for physicians reading VAs. The certification was performed by 24 physicians. The assignment of VA was random and blinded. Each VA was certified by one physician. Half of the VAs were reviewed by a different physician with household recall of health care experience included. The completed death certificate was processed for automated ICD-10 coding of the underlying cause of death. PCVA was compared to gold standard cause of death assignment based on strictly defined clinical diagnostic criteria that are part of the Population Health Metrics Research Consortium (PHMRC) gold standard verbal autopsy study. For individual cause assignment, the overall chance-corrected concordance for PCVA against the gold standard cause of death is less than 50%, with substantial variability by cause and physician. Physicians assign the correct cause around 30% of the time without HCE, and addition of HCE improves performance in adults to 45% and slightly higher in children to 48%. Physicians estimate cause-specific mortality fractions (CSMFs) with considerable error for adults, children, and neonates. Only for neonates for a cause list of six causes with HCE is accuracy above 0.7. In all three age groups, CSMF accuracy improves when household recall of health care experience is available. Results show that physician coding for cause of death assignment may not be as robust as previously thought. The time and cost required to initially collect the verbal autopsies must be considered in addition to the analysis, as well as the impact of diverting physicians from servicing immediate health needs in a population to review VAs. All of these considerations highlight the importance and urgency of developing better methods to more reliably analyze past and future verbal autopsies to obtain the highest quality mortality data from populations without reliable death certification.

94 citations


Journal ArticleDOI
TL;DR: This study introduced standardised dispositions for tracking development assistance for health, and integrated standardised statements, tax returns, and other data from the Organisation for Economic Co-operation and Development’s Creditor Reporting System, UN health agencies, the World Bank, the Global Fund to Fight AIDS, Tuberculosis and Malaria, the GAVI Alliance, foundations, and non-governmental organisations.

Journal ArticleDOI
TL;DR: Age-adjusted death rate increases for ischemic heart disease in low- and middle-income countries, such as Argentina and South Africa, highlight the rise of the cardiovascular epidemic in regions where public health efforts have historically focused on infectious diseases.
Abstract: High-quality, cause-specific mortality data are critical for effective health policy. Yet vague cause of death codes, such as heart failure, are highly prevalent in global mortality data. We propose an empirical method correcting mortality data for the use of heart failure as an underlying cause of death. We performed a regression analysis stratified by sex, age, and country development status on all available ICD-10 mortality data, consisting of 142 million deaths across 838 country-years. The analysis yielded predicted fractions with which to redistribute heart failure-attributed deaths to the appropriate underlying causes of death. Age-adjusted death rates and rank causes of death before and after correction were calculated. Heart failure accounts for 3.1% of all deaths in the dataset. Ischemic heart disease has the highest redistribution proportion for ages 15-49 and 50+ in both sexes and country development levels, causing gains in age-adjusted death rates in both developed and developing countries. COPD and hypertensive heart disease also make significant rank gains. Reproductive-aged women in developing country-years yield the most diverse range of heart failure causes. Ischemic heart disease becomes the No. 1 cause of death in several developed countries, including France and Japan, underscoring the cardiovascular epidemic in high-income countries. Age-adjusted death rate increases for ischemic heart disease in low- and middle-income countries, such as Argentina and South Africa, highlight the rise of the cardiovascular epidemic in regions where public health efforts have historically focused on infectious diseases. This method maximizes the use of available data, providing better evidence on major causes of death to inform policymakers in allocating finite resources.

Journal ArticleDOI
TL;DR: In this article, 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 sub-national, using representative data and comparable methods.
Abstract: 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.

Journal ArticleDOI
TL;DR: The results suggest that Avahan had a beneficial effect in reducing HIV prevalence at the population level over 5 years of programme implementation in some of the states, and support investment in well planned and managed HIV prevention programmes in low-income and middle-income countries.

Journal ArticleDOI
TL;DR: Chance-corrected concordance and CSMF accuracy assessed across a large number of test datasets with widely varying CSMF composition provide a robust strategy for this assessment of verbal autopsy methods.
Abstract: Verbal autopsy (VA) is an important method for obtaining cause of death information in settings without vital registration and medical certification of causes of death. An array of methods, including physician review and computer-automated methods, have been proposed and used. Choosing the best method for VA requires the appropriate metrics for assessing performance. Currently used metrics such as sensitivity, specificity, and cause-specific mortality fraction (CSMF) errors do not provide a robust basis for comparison. We use simple simulations of populations with three causes of death to demonstrate that most metrics used in VA validation studies are extremely sensitive to the CSMF composition of the test dataset. Simulations also demonstrate that an inferior method can appear to have better performance than an alternative due strictly to the CSMF composition of the test set. VA methods need to be evaluated across a set of test datasets with widely varying CSMF compositions. We propose two metrics for assessing the performance of a proposed VA method. For assessing how well a method does at individual cause of death assignment, we recommend the average chance-corrected concordance across causes. This metric is insensitive to the CSMF composition of the test sets and corrects for the degree to which a method will get the cause correct due strictly to chance. For the evaluation of CSMF estimation, we propose CSMF accuracy. CSMF accuracy is defined as one minus the sum of all absolute CSMF errors across causes divided by the maximum total error. It is scaled from zero to one and can generalize a method's CSMF estimation capability regardless of the number of causes. Performance of a VA method for CSMF estimation by cause can be assessed by examining the relationship across test datasets between the estimated CSMF and the true CSMF. With an increasing range of VA methods available, it will be critical to objectively assess their performance in assigning cause of death. Chance-corrected concordance and CSMF accuracy assessed across a large number of test datasets with widely varying CSMF composition provide a robust strategy for this assessment.

Journal ArticleDOI
TL;DR: Adjusting for cause of death misclassification can improve death registration data and provide empirical estimates of HIV/AIDS deaths that may be useful in assessing estimates from demographic models.
Abstract: OBJECTIVE: To quantify the deaths from human immunodeficiency virus (HIV) infection or acquired immunodeficiency syndrome (AIDS) that are misattributed to other causes in South Africa's death registration data and to adjust for this bias. METHODS: Deaths in the World Health Organization's mortality database were distributed among 48 mutually exclusive causes. For each cause, age- and sex-specific global death rates were compared with the average rate among people aged 65-69, 70-74 and 75-79 years to generate "relative" global death rates. Relative rates were also computed for South Africa alone. Differences between global and South African relative death rates were used to identify the causes to which deaths from HIV/AIDS were misattributed in South Africa and quantify the HIV/AIDS deaths misattributed to each. These deaths were then reattributed to HIV/AIDS. FINDINGS: In South Africa, deaths from HIV/AIDS are often misclassified as being caused by 14 other conditions. Whereas in 1996-2006 deaths attributed to HIV/AIDS accounted for 2.0-2.5% of all registered deaths in South Africa, our analysis shows that the true cause-specific mortality fraction rose from 19% (uncertainty range: 7-28%) to 48% (uncertainty range: 38-50%) over that period. More than 90% of HIV/AIDS deaths were found to have been misattributed to other causes during 1996-2006. CONCLUSION: Adjusting for cause of death misclassification, a simple procedure that can be carried out in any country, can improve death registration data and provide empirical estimates of HIV/AIDS deaths that may be useful in assessing estimates from demographic models.

Journal ArticleDOI
TL;DR: 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.
Abstract: 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. 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. 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%). 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.

Journal ArticleDOI
TL;DR: The Simplified Symptom Pattern Method for verbal autopsy can yield reliable and reasonably accurate results for both individual cause of death assignment and for determining cause-specific mortality fractions.
Abstract: Background: Verbal autopsy can be a useful tool for generating cause of death data in data-sparse regions around the world. The Symptom Pattern (SP) Method is one promising approach to analyzing verbal autopsy data, but it has not been tested rigorously with gold standard diagnostic criteria. We propose a simplified version of SP and evaluate its performance using verbal autopsy data with accompanying true cause of death. Methods: We investigated specific parameters in SP’s Bayesian framework that allow for its optimal performance in both assigning individual cause of death and in determining cause-specific mortality fractions. We evaluated these outcomes of the method separately for adult, child, and neonatal verbal autopsies in 500 different population constructs of verbal autopsy data to analyze its ability in various settings. Results: We determined that a modified, simpler version of Symptom Pattern (termed Simplified Symptom Pattern, or SSP) performs better than the previously-developed approach. Across 500 samples of verbal autopsy testing data, SSP achieves a median cause-specific mortality fraction accuracy of 0.710 for adults, 0.739 for children, and 0.751 for neonates. In individual cause of death assignment in the same testing environment, SSP achieves 45.8% chance-corrected concordance for adults, 51.5% for children, and 32.5% for neonates. Conclusions: The Simplified Symptom Pattern Method for verbal autopsy can yield reliable and reasonably accurate results for both individual cause of death assignment and for determining cause-specific mortality fractions. The method demonstrates that verbal autopsies coupled with SSP can be a useful tool for analyzing mortality patterns and determining individual cause of death from verbal autopsy data.

Journal ArticleDOI
TL;DR: This work applied the King and Lu method to adult, child, and neonatal VA datasets from the Population Health Metrics Research Consortium gold standard verbal autopsy validation study, finding that KL would only be the preferred technique when the cause list is short and individual-level predictions are not needed.
Abstract: Verbal autopsy (VA) is used to estimate the causes of death in areas with incomplete vital registration systems. The King and Lu method (KL) for direct estimation of cause-specific mortality fractions (CSMFs) from VA studies is an analysis technique that estimates CSMFs in a population without predicting individual-level cause of death as an intermediate step. In previous studies, KL has shown promise as an alternative to physician-certified verbal autopsy (PCVA). However, it has previously been impossible to validate KL with a large dataset of VAs for which the underlying cause of death is known to meet rigorous clinical diagnostic criteria. We applied the KL method to adult, child, and neonatal VA datasets from the Population Health Metrics Research Consortium gold standard verbal autopsy validation study, a multisite sample of 12,542 VAs where gold standard cause of death was established using strict clinical diagnostic criteria. To emulate real-world populations with varying CSMFs, we evaluated the KL estimations for 500 different test datasets of varying cause distribution. We assessed the quality of these estimates in terms of CSMF accuracy as well as linear regression and compared this with the results of PCVA. KL performance is similar to PCVA in terms of CSMF accuracy, attaining values of 0.669, 0.698, and 0.795 for adult, child, and neonatal age groups, respectively, when health care experience (HCE) items were included. We found that the length of the cause list has a dramatic effect on KL estimation quality, with CSMF accuracy decreasing substantially as the length of the cause list increases. We found that KL is not reliant on HCE the way PCVA is, and without HCE, KL outperforms PCVA for all age groups. Like all computer methods for VA analysis, KL is faster and cheaper than PCVA. Since it is a direct estimation technique, though, it does not produce individual-level predictions. KL estimates are of similar quality to PCVA and slightly better in most cases. Compared to other recently developed methods, however, KL would only be the preferred technique when the cause list is short and individual-level predictions are not needed.

Journal ArticleDOI
TL;DR: Nayu Ikeda and colleagues provide a careful analysis of the cause of death and risk factor data to investigate potential causes for Japan’s decline in mortality since World War 2 and argue that public health programmes to promote salt reduction and primary care management of high blood pressure with antihypertensives were instrumental in bringing down stroke mortality.

Journal ArticleDOI
TL;DR: This thematic series of Population Health Metrics clearly shows that automated methods for VA are more accurate, faster, and cheaper than traditional physician review, and represents a substantial increase in knowledge about the comparative performance of various methods to assign causes of death.
Abstract: Editorial Critical information on population health is needed to inform planning, resource allocation, program implementation, monitoring, and evaluation. One of the key descriptors of a population’s health is information about causes of death. Since many countries lack complete vital registration systems with medical certification of deaths, cause of death information is often missing. Verbal autopsy (VA) can be used to determine individuals’ causes of death and cause-specific mortality fractions in populations without a complete vital registration system. A standard VA instrument paired with easy-to-implement and reliable analytic methods could help bridge significant gaps in information about causes of death, particularly in resource-poor settings. A great deal of research has been conducted in the past several decades about VA and its application in the field, particularly in research settings, but some traditional methods of implementation and analysis can be costly, time-consuming, and potentially of varying quality. Verbal autopsies can now be analyzed using a much wider array of innovative techniques, most of which will be less expensive and yield higher quality results than current practice. What has been missing from the field of verbal autopsy is a collection of the most up-to-date research to help decision-makers choose the best and most cost-effective VA techniques to identify causes of death in their populations. This thematic series of Population Health Metrics, “Verbal autopsy: innovations, applications, opportunities,” was developed in response to this need. The research published in this thematic series emerged from the “Global Congress on Verbal Autopsy: State of the Science,” held in Bali, Indonesia, in February 2011. The conference was co-sponsored by the Institute for Health Metrics and Evaluation, the University of Queensland School of Population Health, and Population Health Metrics. The Congress convened the global research and policy community who currently work with VA data, or who could greatly benefit from doing so. The conference inspired vibrant discussions about critical aspects of VA, including instrument design, analysis methods, and the potential use of VA in national health information systems. By convening a wide array of participants with different perspectives, a greater exchange of ideas, collaboration, and intellectual innovation was encouraged to advance the use and understanding of VA as a mechanism for gathering valuable information about causes of death in populations. The innovative research presented at the conference has motivated the creation of a community of scientists, policymakers, and practitioners dedicated to furthering this important field of population health. In an effort to promote and disseminate the key research breakthroughs discussed at the Global Congress, we are publishing this thematic series. After peer review, 24 papers and eight commentaries were accepted for publication. The innovations in VA detailed in these papers represent a substantial increase in knowledge about the comparative performance of various methods to assign causes of death, from applications of methods used in current practice, including physician review, to a rigorous validation of new automated methods with significant potential for future application in routine national and research data collection platforms. We expect that this thematic series of Population Health Metrics will provide an opportunity for informed discussion and debate and hopefully will stimulate the widespread application of VA where it is needed. This collection of research clearly shows that automated methods for VA are more accurate, faster, and cheaper than traditional physician review. Scientific innovation has taken VA from infancy to maturity. While methods innovation will and must continue, we hope that this thematic series will stimulate debate, * Correspondence: cjlm@u.washington.edu Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave, Suite 600, Seattle, WA 98121, USA Full list of author information is available at the end of the article Murray et al. Population Health Metrics 2011, 9:18 http://www.pophealthmetrics.com/content/9/1/18



01 Jan 2011
TL;DR: It is proposed that increased NK cell numbers and increased IFN-y production are the mediators of this effect and the mechanisms and clinical significance of NK regulation on human IgG2 serum concentrations are addressed.
Abstract: summary, rIL-2 results in significant decreases in serum IgG2 concentrations. We propose that increased NK cell numbers and increased IFN-y production are the mediators of this effect. Future studies in our SCID mouse model and in patients receiving IL-2 will address the mechanisms and clinical significance of NK regulation on human IgG2 serum concentrations. REFERENCES I. Isakson PC, Pure E, Vitteta ES, Krammer PH: T cell-derived B cell differentiation factor(s). Effect on the isotype switch of murine B cells. J Exp Med 155:734, 1982 2. Coffman RL, Carty J: T cell activity that enhances polyclonal IgE production and its inhibition by interferon-?. J Immunol 136:949, 1986 3. Snapper CM, Paul WE: Interferon-y and B cell stimulatory factor- 1 reciprocally regulate Ig isotype production. Science 236:944, 1987 4. Shurmans S, Heusser CH, Qin HY, Merino J, Brighouse G, Lambert PH: In vivo effects of anti-IL-4 monoclonal antibody on neonatal induction of tolerance and on an associated autoimmune syndrome. J Immunol 145:2465, 1990