Christopher Jl Murray

Publications of Christopher Jl Murray

• Modeling causes of death: an integrated approach using CODEm.

  Authors: Kyle J Foreman, Rafael Lozano, Alan D Lopez, Christopher Jl Murray

  Population health metrics. 01/2012; 10(1):1.

  ABSTRACT: 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 magnitudeABSTRACT: 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.

• Effectiveness of diabetes and hypertension management by rural primary health-care workers (Behvarz workers) in Iran: a nationally representative observational study.

  Authors: Farshad Farzadfar, Christopher J L Murray, Emmanuela Gakidou, Thomas Bossert, Hengameh Namdaritabar, Siamak Alikhani, Ghobad Moradi, Alireza Delavari, Hamidreza Jamshidi, Majid Ezzati

  Lancet. 12/2011; 379(9810):47-54.

  Non-communicable diseases and their risk factors are leading causes of disease burden in Iran and other middle-income countries. Little evidence exists for whether the primary health-care system canNon-communicable diseases and their risk factors are leading causes of disease burden in Iran and other middle-income countries. Little evidence exists for whether the primary health-care system can effectively manage non-communicable diseases and risk factors at the population level. Our aim was to examine the effectiveness of the Iranian rural primary health-care system (the Behvarz system) in the management of diabetes and hypertension, and to assess whether the effects depend on the number of health-care workers in the community. We used individual-level data from the 2005 Non-Communicable Disease Surveillance Survey (NCDSS) for fasting plasma glucose (FPG) and systolic blood pressure (SBP), body-mass index, medication use, and sociodemographic variables. Data for Behvarz-worker and physician densities were from the 2006 Population and Housing Census and the 2005 Outpatient Care Centre Mapping Survey. We assessed the effectiveness of treatment on FPG and SBP, and associations between FPG or SBP and Behvarz-worker density with two statistical approaches: a mixed-effects regression analysis of the full NCDSS sample adjusting for individual-level and community-level covariates and an analysis that estimated average treatment effect on data balanced with propensity score matching. NCDSS had data for 65,619 individuals aged 25 years or older (11,686 of whom in rural areas); of these, 64,694 (11,521 in rural areas) had data for SBP and 50,202 (9337 in rural areas) had data for FPG. Nationally, 39·2% (95% CI 37·7 to 40·7) of individuals with diabetes and 35·7% (34·9 to 36·5) of those with hypertension received treatment, with higher treatment coverage in women than in men and in urban areas than in rural areas. Treatment lowered mean FPG by an estimated 1·34 mmol/L (0·58 to 2·10) in rural areas and 0·21 mmol/L (-0·15 to 0·56) in urban areas. Individuals in urban areas with hypertension who received treatment had 3·8 mm Hg (3·1 to 4·5) lower SBP than they would have had if they had not received treatment; the treatment effect was 2·5 mm Hg (1·1 to 3·9) lower FPG in rural areas. Each additional Behvarz worker per 1000 adults was associated with a 0·09 mmol/L (0·01 to 0·18) lower district-level average FPG (p=0·02); for SBP this effect was 0·53 mm Hg (-0·44 to 1·50; p=0·28). Our findings were not sensitive to the choice of statistical method. Primary care systems with trained community health-care workers and well established guidelines can be effective in non-communicable disease prevention and management. Iran's primary care system should expand the number and scope of its primary health-care worker programmes to also address blood pressure and to improve performance in areas with few primary care personnel. None.

• Progress towards Millennium Development Goals 4 and 5 on maternal and child mortality: an updated systematic analysis.

  Authors: Rafael Lozano, Haidong Wang, Kyle J Foreman, Julie Knoll Rajaratnam, Mohsen Naghavi, Jake R Marcus, Laura Dwyer-Lindgren, Katherine T Lofgren, David Phillips, Charles Atkinson, Alan D Lopez, Christopher J L Murray

  Lancet. 09/2011; 378(9797):1139-65.

  With 4 years until 2015, it is essential to monitor progress towards Millennium Development Goals (MDGs) 4 and 5. Although estimates of maternal and child mortality were published in 2010, an updateWith 4 years until 2015, it is essential to monitor progress towards Millennium Development Goals (MDGs) 4 and 5. Although estimates of maternal and child mortality were published in 2010, an update of estimates is timely in view of additional data sources that have become available and new methods developed. Our 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. We update the analyses of the progress towards MDGs 4 and 5 from 2010 with additional surveys, censuses, vital registration, and verbal autopsy data. For children, we estimate early neonatal (0-6 days), late neonatal (7-28 days), postneonatal (29-364 days), childhood (ages 1-4 years), and under-5 mortality. We use an improved model for estimating mortality by age under 5 years. For maternal mortality, our updated analysis includes greater than 1000 additional site-years of data. We tested a large set of alternative models for maternal mortality; we used an ensemble model based on the models with the best out-of-sample predictive validity to generate new estimates from 1990 to 2011. Under-5 deaths have continued to decline, reaching 7·2 million in 2011 of which 2·2 million were early neonatal, 0·7 million late neonatal, 2·1 million postneonatal, and 2·2 million during childhood (ages 1-4 years). Comparing rates of decline from 1990 to 2000 with 2000 to 2011 shows that 106 countries have accelerated declines in the child mortality rate in the past decade. Maternal mortality has also continued to decline from 409,100 (uncertainty interval 382,900-437,900) in 1990 to 273,500 (256,300-291,700) deaths in 2011. We estimate that 56,100 maternal deaths in 2011 were HIV-related deaths during pregnancy. Based on recent trends in developing countries, 31 countries will achieve MDG 4, 13 countries MDG 5, and nine countries will achieve both. Even though progress on reducing maternal and child mortality in most countries is accelerating, most developing countries will take many years past 2015 to achieve the targets of the MDGs 4 and 5. Similarly, although there continues to be progress on maternal mortality the pace is slow, without any overall evidence of acceleration. Immediate concerted action is needed for a large number of countries to achieve MDG 4 and MDG 5. Bill & Melinda Gates Foundation.

• Why is Japanese life expectancy so high?

  Authors: Christopher J L Murray

  Lancet. 08/2011; 378(9797):1124-5.

• Falling behind: life expectancy in US counties from 2000 to 2007 in an international context.

  Authors: Sandeep C Kulkarni, Alison Levin-Rector, Majid Ezzati, Christopher Jl Murray

  Population health metrics. 06/2011; 9(1):16.

  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 disparitiesABSTRACT: 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.

• Development assistance for health: trends and prospects.

  Authors: Christopher J L Murray, Brent Anderson, Roy Burstein, Katherine Leach-Kemon, Matthew Schneider, Annette Tardif, Raymond Zhang

  Lancet. 04/2011; 378(9785):8-10.

• Exposing misclassified HIV/AIDS deaths in South Africa.

  Authors: Jeanette Kurian Birnbaum, Christopher Jl Murray, Rafael Lozano

  Bulletin of the World Health Organization. 04/2011; 89(4):278-85.

  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 dataTo 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. 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. 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. 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.

• Management of diabetes and associated cardiovascular risk factors in seven countries: a comparison of data from national health examination surveys.

  Authors: Emmanuela Gakidou, Leslie Mallinger, Jesse Abbott-Klafter, Ramiro Guerrero, Salvador Villalpando, Ruy Lopez Ridaura, Wichai Aekplakorn, Mohsen Naghavi, Stephen Lim, Rafael Lozano, Christopher J L Murray

  Bulletin of the World Health Organization. 03/2011; 89(3):172-83.

  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 socioeconomicTo 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. 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. 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. 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.

• Improving the public health utility of global cardiovascular mortality data: the rise of ischemic heart disease.

  Authors: Ryan M Ahern, Rafael Lozano, Mohsen Naghavi, Kyle Foreman, Emmanuela Gakidou, Christopher Jl Murray

  Population health metrics. 03/2011; 9:8.

  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 proposeHigh-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.

• Verbal autopsy: advancing science, facilitating application.

  Authors: Christopher Jl Murray, Alan D Lopez, Kenji Shibuya, Rafael Lozano

  Population health metrics. 01/2011; 9:18.

• Performance of the Tariff Method: validation of a simple additive algorithm for analysis of verbal autopsies.

  Authors: Spencer L James, Abraham D Flaxman, Christopher Jl Murray

  Population health metrics. 01/2011; 9:31.

  ABSTRACT: Verbal autopsies provide valuable information for studying mortality patterns in populations that lack reliable vital registration data. Methods for transforming verbal autopsy results intoABSTRACT: 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.

• Population Health Metrics Research Consortium gold standard verbal autopsy validation study: design, implementation, and development of analysis datasets.

  Authors: Christopher Jl Murray, Alan D Lopez, Robert Black, Ramesh Ahuja, Said Mohd Ali, Abdullah Baqui, Lalit Dandona, Emily Dantzer, Vinita Das, Usha Dhingra [......] Devarsetty Praveen, Zul Premji, Dolores Ramírez-Villalobos, Hazel Remolador, Ian Riley, Minerva Romero, Mwanaidi Said, Diozele Sanvictores, Sunil Sazawal, Veronica Tallo

  Population health metrics. 01/2011; 9:27.

  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 dataABSTRACT: 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.

• Direct estimation of cause-specific mortality fractions from verbal autopsies: multisite validation study using clinical diagnostic gold standards.

  Authors: Abraham D Flaxman, Alireza Vahdatpour, Spencer L James, Jeanette K Birnbaum, Christopher Jl Murray

  Population health metrics. 01/2011; 9:35.

  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-specificABSTRACT: 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.

• Simplified Symptom Pattern Method for verbal autopsy analysis: multisite validation study using clinical diagnostic gold standards.

  Authors: Christopher Jl Murray, Spencer L James, Jeanette K Birnbaum, Michael K Freeman, Rafael Lozano, Alan D Lopez

  Population health metrics. 01/2011; 9:30.

  ABSTRACT: 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 verbalABSTRACT: 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. 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. 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. 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.

• Performance of physician-certified verbal autopsies: multisite validation study using clinical diagnostic gold standards.

  Authors: Rafael Lozano, Alan D Lopez, Charles Atkinson, Mohsen Naghavi, Abraham D Flaxman, Christopher Jl Murray

  Population health metrics. 01/2011; 9:32.

  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 performanceABSTRACT: 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.

• Random forests for verbal autopsy analysis: multisite validation study using clinical diagnostic gold standards.

  Authors: Abraham D Flaxman, Alireza Vahdatpour, Sean Green, Spencer L James, Christopher Jl Murray

  Population health metrics. 01/2011; 9:29.

  ABSTRACT: 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.ABSTRACT: 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). 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). 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. 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.

• Robust metrics for assessing the performance of different verbal autopsy cause assignment methods in validation studies.

  Authors: Christopher Jl Murray, Rafael Lozano, Abraham D Flaxman, Alireza Vahdatpour, Alan D Lopez

  Population health metrics. 01/2011; 9:28.

  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,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.

• Performance of InterVA for assigning causes of death to verbal autopsies: multisite validation study using clinical diagnostic gold standards.

  Authors: Rafael Lozano, Michael K Freeman, Spencer L James, Benjamin Campbell, Alan D Lopez, Abraham D Flaxman, Christopher Jl Murray

  Population health metrics. 01/2011; 9:50.

  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 theABSTRACT: 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.

• National and subnational mortality effects of metabolic risk factors and smoking in Iran: a comparative risk assessment.

  Authors: Farshad Farzadfar, Goodarz Danaei, Hengameh Namdaritabar, Julie Knoll Rajaratnam, Jacob R Marcus, Ardeshir Khosravi, Siamak Alikhani, Christopher Jl Murray, Majid Ezzati

  Population health metrics. 01/2011; 9(1):55.

  ABSTRACT: 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 glucoseABSTRACT: 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. We used data from the Non-Communicable Disease Surveillance Survey to estimate means and standard deviations for the metabolic risk factors, nationally and by region. Lung cancer mortality was used to measure cumulative exposure to smoking. We used data from the death registration system to estimate age-, sex-, and disease-specific numbers of deaths in 2005, adjusted for incompleteness using demographic methods. We used systematic reviews and meta-analyses of epidemiologic studies to obtain the effect of risk factors on disease-specific mortality. We estimated deaths and life expectancy loss attributable to risk factors using the comparative risk assessment framework. In 2005, high SBP was responsible for 41,000 (95% uncertainty interval: 38,000, 44,000) deaths in men and 39,000 (36,000, 42,000) deaths in women in Iran. High FPG, BMI, and TC were responsible for about one-third to one-half of deaths attributable to SBP in men and/or women. Smoking was responsible for 9,000 deaths among men and 2,000 among women. If SBP were reduced to optimal levels, life expectancy at birth would increase by 3.2 years (2.6, 3.9) and 4.1 years (3.2, 4.9) in men and women, respectively; the life expectancy gains ranged from 1.1 to 1.8 years for TC, BMI, and FPG. SBP was also responsible for the largest number of deaths in every region, with age-standardized attributable mortality ranging from 257 to 333 deaths per 100,000 adults in different regions. Management of blood pressure through diet, lifestyle, and pharmacological interventions should be a priority in Iran. Interventions for other metabolic risk factors and smoking can also improve population health.

• Public financing of health in developing countries: a cross-national systematic analysis.

  Authors: Chunling Lu, Matthew T Schneider, Paul Gubbins, Katherine Leach-Kemon, Dean Jamison, Christopher J L Murray

  Lancet. 04/2010; 375(9723):1375-87.

  Government spending on health from domestic sources is an important indicator of a government's commitment to the health of its people, and is essential for the sustainability of health programmes.Government spending on health from domestic sources is an important indicator of a government's commitment to the health of its people, and is essential for the sustainability of health programmes. We aimed to systematically analyse all data sources available for government spending on health in developing countries; describe trends in public financing of health; and test the extent to which they were related to changes in gross domestic product (GDP), government size, HIV prevalence, debt relief, and development assistance for health (DAH) to governmental and non-governmental sectors. We did a systematic analysis of all data sources available for government expenditures on health as agent (GHE-A) in developing countries, including government reports and databases from WHO and the International Monetary Fund (IMF). GHE-A consists of domestically and externally financed public health expenditures. We assessed the quality of these sources and used multiple imputation to generate a complete sequence of GHE-A. With these data and those for DAH to governments, we estimated government spending on health from domestic sources. We used panel-regression methods to estimate the association between government domestic spending on health and GDP, government size, HIV prevalence, debt relief, and DAH disbursed to governmental and non-governmental sectors. We tested the robustness of our conclusions using various models and subsets of countries. In all developing countries, public financing of health in constant US$ from domestic sources increased by nearly 100% (IMF 120%; WHO 88%) from 1995 to 2006. Overall, this increase was the product of rising GDP, slight decreases in the share of GDP spent by government, and increases in the share of government spending on health. At the country level, while shares of government expenditures to health increased in many regions, they decreased in many sub-Saharan African countries. The statistical analysis showed that DAH to government had a negative and significant effect on domestic government spending on health such that for every US$1 of DAH to government, government health expenditures from domestic resources were reduced by $0.43 (p=0) to $1.14 (p=0). However, DAH to the non-governmental sector had a positive and significant effect on domestic government health spending. Both results were robust to multiple specifications and subset analyses. Other factors, such as debt relief, had no detectable effect on domestic government health spending. To address the negative effect of DAH on domestic government health spending, we recommend strong standardised monitoring of government health expenditures and government spending in other health-related sectors; establishment of collaborative targets to maintain or increase the share of government expenditures going to health; investment in the capacity of developing countries to effectively receive and use DAH; careful assessment of the risks and benefits of expanded DAH to non-governmental sectors; and investigation of the use of global price subsidies or product transfers as mechanisms for DAH. Bill & Melinda Gates Foundation.