Spencer L. James

India's Janani Suraksha Yojana, a conditional cash transfer programme to increase births in health facilities: an impact evaluation

BACKGROUND In 2005, with the goal of reducing the numbers of maternal and neonatal deaths, the Government of India launched Janani Suraksha Yojana (JSY), a conditional cash transfer scheme, to incentivise women to give birth in a health facility. We independently assessed the effect of JSY on intervention coverage and health outcomes. METHODS We used data from the nationwide district-level household surveys done in 2002-04 and 2007-09 to assess receipt of financial assistance from JSY as a function of socioeconomic and demographic characteristics; and used three analytical approaches (matching, with-versus-without comparison, and differences in differences) to assess the effect of JSY on antenatal care, in-facility births, and perinatal, neonatal, and maternal deaths. FINDINGS Implementation of JSY in 2007-08 was highly variable by state-from less than 5% to 44% of women giving birth receiving cash payments from JSY. The poorest and least educated women did not always have the highest odds of receiving JSY payments. JSY had a significant effect on increasing antenatal care and in-facility births. In the matching analysis, JSY payment was associated with a reduction of 3.7 (95% CI 2.2-5.2) perinatal deaths per 1000 pregnancies and 2.3 (0.9-3.7) neonatal deaths per 1000 livebirths. In the with-versus-without comparison, the reductions were 4.1 (2.5-5.7) perinatal deaths per 1000 pregnancies and 2.4 (0.7-4.1) neonatal deaths per 1000 livebirths. INTERPRETATION The findings of this assessment are encouraging, but they also emphasise the need for improved targeting of the poorest women and attention to quality of obstetric care in health facilities. Continued independent monitoring and evaluations are important to measure the effect of JSY as financial and political commitment to the programme intensifies. FUNDING Bill & Melinda Gates Foundation.

The global burden of viral hepatitis from 1990 to 2013: findings from the Global Burden of Disease Study 2013

BACKGROUND With recent improvements in vaccines and treatments against viral hepatitis, an improved understanding of the burden of viral hepatitis is needed to inform global intervention strategies. We used data from the Global Burden of Disease (GBD) Study to estimate morbidity and mortality for acute viral hepatitis, and for cirrhosis and liver cancer caused by viral hepatitis, by age, sex, and country from 1990 to 2013. METHODS We estimated mortality using natural history models for acute hepatitis infections and GBDs cause-of-death ensemble model for cirrhosis and liver cancer. We used meta-regression to estimate total cirrhosis and total liver cancer prevalence, as well as the proportion of cirrhosis and liver cancer attributable to each cause. We then estimated cause-specific prevalence as the product of the total prevalence and the proportion attributable to a specific cause. Disability-adjusted life-years (DALYs) were calculated as the sum of years of life lost (YLLs) and years lived with disability (YLDs). FINDINGS Between 1990 and 2013, global viral hepatitis deaths increased from 0·89 million (95% uncertainty interval [UI] 0·86-0·94) to 1·45 million (1·38-1·54); YLLs from 31·0 million (29·6-32·6) to 41·6 million (39·1-44·7); YLDs from 0·65 million (0·45-0·89) to 0·87 million (0·61-1·18); and DALYs from 31·7 million (30·2-33·3) to 42·5 million (39·9-45·6). In 2013, viral hepatitis was the seventh (95% UI seventh to eighth) leading cause of death worldwide, compared with tenth (tenth to 12th) in 1990. INTERPRETATION Viral hepatitis is a leading cause of death and disability worldwide. Unlike most communicable diseases, the absolute burden and relative rank of viral hepatitis increased between 1990 and 2013. The enormous health loss attributable to viral hepatitis, and the availability of effective vaccines and treatments, suggests an important opportunity to improve public health. FUNDING Bill & Melinda Gates Foundation.

Using verbal autopsy to measure causes of death: the comparative performance of existing methods

BackgroundMonitoring progress with disease and injury reduction in many populations will require widespread use of verbal autopsy (VA). Multiple methods have been developed for assigning cause of death from a VA but their application is restricted by uncertainty about their reliability.MethodsWe investigated the validity of five automated VA methods for assigning cause of death: InterVA-4, Random Forest (RF), Simplified Symptom Pattern (SSP), Tariff method (Tariff), and King-Lu (KL), in addition to physician review of VA forms (PCVA), based on 12,535 cases from diverse populations for which the true cause of death had been reliably established. For adults, children, neonates and stillbirths, performance was assessed separately for individuals using sensitivity, specificity, Kappa, and chance-corrected concordance (CCC) and for populations using cause specific mortality fraction (CSMF) accuracy, with and without additional diagnostic information from prior contact with health services. A total of 500 train-test splits were used to ensure that results are robust to variation in the underlying cause of death distribution.ResultsThree automated diagnostic methods, Tariff, SSP, and RF, but not InterVA-4, performed better than physician review in all age groups, study sites, and for the majority of causes of death studied. For adults, CSMF accuracy ranged from 0.764 to 0.770, compared with 0.680 for PCVA and 0.625 for InterVA; CCC varied from 49.2% to 54.1%, compared with 42.2% for PCVA, and 23.8% for InterVA. For children, CSMF accuracy was 0.783 for Tariff, 0.678 for PCVA, and 0.520 for InterVA; CCC was 52.5% for Tariff, 44.5% for PCVA, and 30.3% for InterVA. For neonates, CSMF accuracy was 0.817 for Tariff, 0.719 for PCVA, and 0.629 for InterVA; CCC varied from 47.3% to 50.3% for the three automated methods, 29.3% for PCVA, and 19.4% for InterVA. The method with the highest sensitivity for a specific cause varied by cause.ConclusionsPhysician review of verbal autopsy questionnaires is less accurate than automated methods in determining both individual and population causes of death. Overall, Tariff performs as well or better than other methods and should be widely applied in routine mortality surveillance systems with poor cause of death certification practices.

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

BackgroundComputer-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).MethodsThe 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).ResultsFor 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.ConclusionsWe 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.

Developing a comprehensive time series of GDP per capita for 210 countries from 1950 to 2015

BackgroundIncome has been extensively studied and utilized as a determinant of health. There are several sources of income expressed as gross domestic product (GDP) per capita, but there are no time series that are complete for the years between 1950 and 2015 for the 210 countries for which data exist. It is in the interest of population health research to establish a global time series that is complete from 1950 to 2015.MethodsWe collected GDP per capita estimates expressed in either constant US dollar terms or international dollar terms (corrected for purchasing power parity) from seven sources. We applied several stages of models, including ordinary least-squares regressions and mixed effects models, to complete each of the seven source series from 1950 to 2015. The three US dollar and four international dollar series were each averaged to produce two new GDP per capita series.Results and discussionNine complete series from 1950 to 2015 for 210 countries are available for use. These series can serve various analytical purposes and can illustrate myriad economic trends and features. The derivation of the two new series allows for researchers to avoid any series-specific biases that may exist. The modeling approach used is flexible and will allow for yearly updating as new estimates are produced by the source series.ConclusionGDP per capita is a necessary tool in population health research, and our development and implementation of a new method has allowed for the most comprehensive known time series to date.

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

BackgroundVerbal 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.MethodsWe investigated specific parameters in SPs 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.ResultsWe 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.ConclusionsThe 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.

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

BackgroundVerbal 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.MethodsWe 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.ResultsKL 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.ConclusionsLike 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.

Validating estimates of prevalence of non-communicable diseases based on household surveys: the symptomatic diagnosis study

BackgroundEasy-to-collect epidemiological information is critical for the more accurate estimation of the prevalence and burden of different non-communicable diseases around the world. Current measurement is restricted by limitations in existing measurement systems in the developing world and the lack of biometry tests for non-communicable diseases. Diagnosis based on self-reported signs and symptoms (“Symptomatic Diagnosis,” or SD) analyzed with computer-based algorithms may be a promising method for collecting timely and reliable information on non-communicable disease prevalence. The objective of this study was to develop and assess the performance of a symptom-based questionnaire to estimate prevalence of non-communicable diseases in low-resource areas.MethodsAs part of the Population Health Metrics Research Consortium study, we collected 1,379 questionnaires in Mexico from individuals who suffered from a non-communicable disease that had been diagnosed with gold standard diagnostic criteria or individuals who did not suffer from any of the 10 target conditions. To make the diagnosis of non-communicable diseases, we selected the Tariff method, a technique developed for verbal autopsy cause of death calculation. We assessed the performance of this instrument and analytical techniques at the individual and population levels.ResultsThe questionnaire revealed that the information on health care experience retrieved achieved 66.1% (95% uncertainty interval [UI], 65.6–66.5%) chance corrected concordance with true diagnosis of non-communicable diseases using health care experience and 0.826 (95% UI, 0.818–0.834) accuracy in its ability to calculate fractions of different causes. SD is also capable of outperforming the current estimation techniques for conditions estimated by questionnaire-based methods.ConclusionsSD is a viable method for producing estimates of the prevalence of non-communicable diseases in areas with low health information infrastructure. This technology can provide higher-resolution prevalence data, more flexible data collection, and potentially individual diagnoses for certain conditions.

Ensemble modelling in verbal autopsy: the Popular Voting method

Abstract Background Verbal autopsy (VA) is a highly valuable tool for assessing causes of death in resource-limited settings without medically certified death certificates. The Population Health Metrics Research Consortium (PHMRC) collected 12 535 VAs in four countries for which the true cause of death was reliably known. This project led to the development of three new computer algorithms to determine cause of death from these VAs, all of which predict underlying cause of death more accurately than the status quo: physician review. Concurrently, ensemble models, or blends of well-performing models, have been shown to have favourable predictive validity and have begun to be implemented in global health metrics settings. Methods We developed a simple ensemble model based on the three top performing PHMRC methods: the Simplified Symptom Pattern (SSP), the Tariff, and the Random Forest (RF). This ensemble method functions at the individual-record level, examining the predicted cause of death from the three component models and selecting cause of death by a simple majority (Popular Voting). Sensitivity analyses revealed that selecting the prediction made by RF in cases where all three models disagreed was preferable, and this ensemble method was adapted accordingly. Findings The Popular Voting method performed better in cause-specific mortality fraction accuracy than did any individual model alone for adults, children, and neonates, and performed better in chance-corrected concordance than did any individual model except SSP in adults. The three component models disagreed in 16% of all cases, and unanimously agreed in 47% of cases. Interpretation As VA continues to be an effective source of data for estimating cause of death, accurate and inexpensive methods for analysing VA interview responses are increasingly important. The recent development of the three highly accurate PHMRC computational models allows for the option of a meta-model such as the ensemble introduced here. This ensemble model for VA achieves superior performance, and could be applied to other VA samples to accurately assess the relative mortality burden from a variety of diseases and injuries. Funding Population Health Metrics Research Consortium.

Can computers measure the chronic disease burden using survey questionnaires? The Symptomatic Diagnosis Study

Abstract Background Measuring the burden of chronic conditions in the developing world is a critical global health challenge. Scarce resources and the lack of biometry tests for chronic conditions such as depression and arthritis limit current measurement. Computer-based diagnosis based on self-reported signs and symptoms (symptomatic diagnosis) is a promising method for more accurately measuring the chronic disease burden. Methods As part of the Population Health Metrics Research Consortium (PHMRC) study, we collected 1379 questionnaires in Mexico from individuals who had a chronic condition that had been diagnosed with gold-standard diagnostic criteria or individuals who did not have any of the ten target conditions. Our primary analytical goal was to develop an algorithm to accurately diagnose chronic conditions. To this end, we tested methods previously developed for verbal autopsy. We analysed the performance of each method and compared performance to existing epidemiological measurement techniques. Findings The top-performing method is capable of achieving 68% concordance with gold-standard diagnosis. Concordance ranged from approximately 90% for depression, angina pectoris, and cirrhosis, to 40% for osteoarthritis and vision loss. The prevalence fraction of each condition could be measured with less than 3% absolute error. These findings roughly parallel validated verbal autopsy methods at an expectedly higher performance level. Interpretation Symptomatic diagnosis outperforms current techniques and is a viable method for measuring the burden of chronic diseases in areas with low health information infrastructure. It is a critical global health challenge to better characterise the epidemiology of chronic conditions in these areas, and is a powerful, unique solution capable of collecting an array of prevalence data in a single survey. This technology can provide myriad benefits to the field of epidemiology, including higher-resolution prevalence data, flexible data collection with rapid interpretability, and individual diagnosis for certain conditions. Funding The Population Health Metrics Research Consortium funded the data collection as part of a Gates Grand Challenges in Global Health initiative (GC-13).