Designing verbal autopsy studies

The trend and pattern of adult mortality in South-Central Ethiopia: analysis using the 2008-2019 data from Butajira Health and Demographic Surveillance System

Background

Understanding context-specific temporal trends in mortality is essential for setting health policy priorities.

Objective

To investigate the trends and distribution of deaths due to communicable and non-communicable diseases and external causes in South-Central Ethiopia.

Method

All adult deaths captured by the Butajira Health and Demographic Surveillance System between January 2008 and December 2019 were included. A verbal autopsy method of collecting cause of death data was used. Physician review and a computerised algorithm, InterVA, were used to determine the cause of death. Coding was undertaken using the World Health Organization's International Classification of Diseases. Trends in adult mortality rate and proportional mortality were estimated by major cause of death categories. Significant trends were analysed using the Mann–Kendall statistical test with a significance set at P < 0.05. Deaths were also disaggregated by age, sex, and residence.

Results

There were 1,612 deaths in 279,681 person-years; 811 (50.3%) were females. The median age at death was 65 years. The proportional adult mortality and adult mortality rates (per 1000 person-years) attributed to communicable diseases, non-communicable diseases, and external causes were 31.1%, 58.9%, and 6.0%, and 1.9, 3.4, and 0.4, respectively. Adult mortality due to communicable diseases showed a declining trend (tau, the measure of the strength and direction of association, = −0.52; P < 0.05), whereas the trend increased for non-communicable diseases (tau = 0.67, P < 0.05) and external causes (tau = 0.29, P > 0.05). Moreover, death rates were pronounced in the 65+ age group and rural areas but comparable among males and females.

Conclusion

The trend in deaths due to communicable diseases declined but increased for non-communicable diseases and external causes with significant public health burdens. These findings will provide essential input in formulating health policy reforms to reduce premature mortality.

Validation of physician certified verbal autopsy using conventional autopsy: a large study of adult non-external causes of death in a metropolitan area in Brazil

Background

Reliable mortality data are essential for the development of public health policies. In Brazil, although there is a well-consolidated universal system for mortality data, the quality of information on causes of death (CoD) is not even among Brazilian regions, with a high proportion of ill-defined CoD. Verbal autopsy (VA) is an alternative to improve mortality data. This study aimed to evaluate the performance of an adapted and reduced version of VA in identifying the underlying causes of non-forensic deaths, in São Paulo, Brazil. This is the first time that a version of the questionnaire has been validated considering the autopsy as the gold standard.

Methods

The performance of a physician-certified verbal autopsy (PCVA) was evaluated considering conventional autopsy (macroscopy plus microscopy) as gold standard, based on a sample of 2060 decedents that were sent to the Post-Mortem Verification Service (SVOC-USP). All CoD, from the underlying to the immediate, were listed by both parties, and ICD-10 attributed by a senior coder. For each cause, sensitivity and chance corrected concordance (CCC) were computed considering first the underlying causes attributed by the pathologist and PCVA, and then any CoD listed in the death certificate given by PCVA. Cause specific mortality fraction accuracy (CSMF-accuracy) and chance corrected CSMF-accuracy were computed to evaluate the PCVA performance at the populational level.

Results

There was substantial variability of the sensitivities and CCC across the causes. Well-known chronic diseases with accurate diagnoses that had been informed by physicians to family members, such as various cancers, had sensitivities above 40% or 50%. However, PCVA was not effective in attributing Pneumonia, Cardiomyopathy and Leukemia/Lymphoma as underlying CoD. At populational level, the PCVA estimated cause specific mortality fractions (CSMF) may be considered close to the fractions pointed by the gold standard. The CSMF-accuracy was 0.81 and the chance corrected CSMF-accuracy was 0.49.

Conclusions

The PCVA was efficient in attributing some causes individually and proved effective in estimating the CSMF, which indicates that the method is useful to establish public health priorities.

BAYESIAN FACTOR MODELS FOR PROBABILISTIC CAUSE OF DEATH ASSESSMENT WITH VERBAL AUTOPSIES

The distribution of deaths by cause provides crucial information for public health planning, response and evaluation. About 60% of deaths globally are not registered or given a cause, limiting our ability to understand disease epidemiology. Verbal autopsy (VA) surveys are increasingly used in such settings to collect information on the signs, symptoms and medical history of people who have recently died. This article develops a novel Bayesian method for estimation of population distributions of deaths by cause using verbal autopsy data. The proposed approach is based on a multivariate probit model where associations among items in questionnaires are flexibly induced by latent factors. Using the Population Health Metrics Research Consortium labeled data that include both VA and medically certified causes of death, we assess performance of the proposed method. Further, we estimate important questionnaire items that are highly associated with causes of death. This framework provides insights that will simplify future data.

Integrating community-based verbal autopsy into civil registration and vital statistics (CRVS): system-level considerations

Background: Reliable and representative cause of death (COD) statistics are essential to inform public health policy, respond to emerging health needs, and document progress towards Sustainable Development Goals. However, less than one-third of deaths worldwide are assigned a cause. Civil registration and vital statistics (CRVS) systems in low- and lower-middle-income countries are failing to provide timely, complete and accurate vital statistics, and it will still be some time before they can provide physician-certified COD for every death.

Proposals: Verbal autopsy (VA) is a method to ascertain the probable COD and, although imperfect, it is the best alternative in the absence of medical certification. There is extensive experience with VA in research settings but only a few examples of its use on a large scale. Data collection using electronic questionnaires on mobile devices and computer algorithms to analyse responses and estimate probable COD have increased the potential for VA to be routinely applied in CRVS systems. However, a number of CRVS and health system integration issues should be considered in planning, piloting and implementing a system-wide intervention such as VA. These include addressing the multiplicity of stakeholders and sub-systems involved, integration with existing CRVS work processes and information flows, linking VA results to civil registration records, information technology requirements and data quality assurance.

Conclusions: Integrating VA within CRVS systems is not simply a technical undertaking. It will have profound system-wide effects that should be carefully considered when planning for an effective implementation. This paper identifies and discusses the major system-level issues and emerging practices, provides a planning checklist of system-level considerations and proposes an overview for how VA can be integrated into routine CRVS systems.

How the news media activate public expression and influence national agendas

We demonstrate that exposure to the news media causes Americans to take public stands on specific issues, join national policy conversations, and express themselves publicly-all key components of democratic politics-more often than they would otherwise. After recruiting 48 mostly small media outlets, we chose groups of these outlets to write and publish articles on subjects we approved, on dates we randomly assigned. We estimated the causal effect on proximal measures, such as website pageviews and Twitter discussion of the articles' specific subjects, and distal ones, such as national Twitter conversation in broad policy areas. Our intervention increased discussion in each broad policy area by ~62.7% (relative to a day's volume), accounting for 13,166 additional posts over the treatment week, with similar effects across population subgroups.

Probabilistic Cause-of-death Assignment using Verbal Autopsies

In regions without complete-coverage civil registration and vital statistics systems there is uncertainty about even the most basic demographic indicators. In such regions the majority of deaths occur outside hospitals and are not recorded. Worldwide, fewer than one-third of deaths are assigned a cause, with the least information available from the most impoverished nations. In populations like this, verbal autopsy (VA) is a commonly used tool to assess cause of death and estimate cause-specific mortality rates and the distribution of deaths by cause. VA uses an interview with caregivers of the decedent to elicit data describing the signs and symptoms leading up to the death. This paper develops a new statistical tool known as InSilicoVA to classify cause of death using information acquired through VA. InSilicoVA shares uncertainty between cause of death assignments for specific individuals and the distribution of deaths by cause across the population. Using side-by-side comparisons with both observed and simulated data, we demonstrate that InSilicoVA has distinct advantages compared to currently available methods.

How much do the physician review and InterVA model agree in determining causes of death? A comparative analysis of deaths in rural Ethiopia

Background

Despite it is costly, slow and non-reproducible process, physician review (PR) is a commonly used method to interpret verbal autopsy data. However, there is a growing interest to adapt a new automated and internally consistent method called InterVA. This study evaluated the level of agreement in determining causes of death between PR and the InterVA model.

Methods

Verbal autopsy data for 434 cases collected between September 2009 and November 2012, were interpreted using both PR and the InterVA model. Cohen’s kappa statistic (κ) was used to compare the level of chance corrected case-by-case agreement in the diagnosis reached by the PR and InterVA model.

Results

Both methods gave comparable cause specific mortality fractions of communicable diseases (36.6 % by PR and 36.2 % by the model), non-communicable diseases (31.1 % by PR and 38.2 % by the model) and accidents/injuries (12.9 % by PR and 10.1 % by the model). The level of case-by-case chance corrected concordance between the two methods was 0.33 (95 % CI for κ = 0.29–0.34). The highest and lowest agreements were seen for accidents/injuries and non-communicable diseases; with κ = 0.75 and κ = 0.37, respectively.

Conclusion

If the InterVA were used in place of the existing PR process, the overall diagnosis would be fairly similar. The methods had better agreement in important public health diseases like; TB, perinatal causes, and pneumonia/sepsis; and lower in cardiovascular diseases and neoplasms. Therefore, both methods need to be validated against a gold-standard diagnosis of death.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-015-2032-7) contains supplementary material, which is available to authorized users.

How much does a verbal autopsy based mortality surveillance system cost in rural India?

Objective

This paper aims to determine the cost of establishing and sustaining a verbal-autopsy based mortality surveillance system in rural India.

Materials and Methods

Deaths occurring in 45 villages (population 185,629) were documented over a 4-year period from 2003–2007 by 45 non-physician healthcare workers (NPHWs) trained in data collection using a verbal autopsy tool. Causes of death were assigned by 2 physicians for the first year and by one physician for the subsequent years. Costs were calculated for training of interviewers and physicians, data collection, verbal autopsy analysis, project management and infrastructure. Costs were divided by the number of deaths and the population covered in the year.

Results

Verbal-autopsies were completed for 96.7% (5786) of all deaths (5895) recorded. The annual cost in year 1 was INR 1,133,491 (USD 24,943) and the total cost per death was INR 757 (USD 16.66). These costs included training of NPHWs and physician reviewers Rs 67,025 (USD 1474), data collection INR 248,400 (USD 5466), dual physician review for cause of death assignment INR 375,000 (USD 8252), and project management INR 341,724 (USD 7520). The average annual cost to run the system each year was INR 822,717 (USD18104) and the cost per death was INR 549 (USD 12) for the next 3 years. Costs were reduced by using single physician review and shortened re-training sessions. The annual cost of running a surveillance system was INR 900,410 (USD 19814).

Discussion

This study provides detailed empirical evidence of the costs involved in running a mortality surveillance site using verbal-autopsy.

Mortality in Cambodia: an 18-month prospective community-based surveillance of all-age deaths using verbal autopsies

To estimate the 2009-2010 death rates, causes, and patterns of mortality in rural Cambodia, we conducted active, population-based death surveillance in 25 rural villages of Cambodia from March 2009 to August 2010. Among the population of 28,053 under surveillance, 280 deaths were reported and explored by physician-certified verbal autopsies, using the International Classification of Diseases 10, yielding an overall mortality rate (MR) of 6.7/1000 persons-year (95% CI 5.74-7.68). The MR was 39.1/1000 live births for those younger than 5 years old. Infants accounted for 5.4% of all deaths. In children younger than 5 years, infectious and parasitic diseases were the leading causes of death. In children 5 to 14 years, 3 out of 4 deaths were due to injuries. Adult deaths were mainly attributed to noncommunicable diseases (52%). We conclude that this rural population is facing a substantial burden of noncommunicable diseases while still struggling with infectious diseases, respiratory diseases in particular.

Applying the InterVA-4 model to determine causes of death in rural Ethiopia

BACKGROUND

In Ethiopia, most deaths take place at home and routine certification of cause of death by physicians is lacking. As a result, reliable cause of death (CoD) data are often not available. Recently, a computerized method for interpretation of verbal autopsy (VA) data, called InterVA, has been developed and used. It calculates the probability of a set of CoD given the presence of circumstances, signs, and symptoms reported during VA interviews. We applied the InterVA model to describe CoD in a rural population of Ethiopia.

OBJECTIVE

VA data for 436/599 (72.7%) deaths that occurred during 2010-2011 were included. InterVA-4 was used to interpret the VA data into probable cause of death. Cause-specific mortality fraction was used to describe frequency of occurrence of death from specific causes.

RESULTS

InterVA-4 was able to give likely cause(s) of death for 401/436 of the cases (92.0%). Overall, 35.0% of the total deaths were attributed to communicable diseases, and 30.7% to chronic non-communicable diseases. Tuberculosis (12.5%) and acute respiratory tract infections (10.4%) were the most frequent causes followed by neoplasms (9.6%) and diseases of circulatory system (7.2%).

CONCLUSION

InterVA-4 can produce plausible estimates of the major public health problems that can guide public health interventions. We encourage further validation studies, in local settings, so that InterVA can be integrated into national health surveys.

Performance of four computer-coded verbal autopsy methods for cause of death assignment compared with physician coding on 24,000 deaths in low- and middle-income countries

BACKGROUND

Physician-coded verbal autopsy (PCVA) is the most widely used method to determine causes of death (CODs) in countries where medical certification of death is uncommon. Computer-coded verbal autopsy (CCVA) methods have been proposed as a faster and cheaper alternative to PCVA, though they have not been widely compared to PCVA or to each other.

METHODS

We compared the performance of open-source random forest, open-source tariff method, InterVA-4, and the King-Lu method to PCVA on five datasets comprising over 24,000 verbal autopsies from low- and middle-income countries. Metrics to assess performance were positive predictive value and partial chance-corrected concordance at the individual level, and cause-specific mortality fraction accuracy and cause-specific mortality fraction error at the population level.

RESULTS

The positive predictive value for the most probable COD predicted by the four CCVA methods averaged about 43% to 44% across the datasets. The average positive predictive value improved for the top three most probable CODs, with greater improvements for open-source random forest (69%) and open-source tariff method (68%) than for InterVA-4 (62%). The average partial chance-corrected concordance for the most probable COD predicted by the open-source random forest, open-source tariff method and InterVA-4 were 41%, 40% and 41%, respectively, with better results for the top three most probable CODs. Performance generally improved with larger datasets. At the population level, the King-Lu method had the highest average cause-specific mortality fraction accuracy across all five datasets (91%), followed by InterVA-4 (72% across three datasets), open-source random forest (71%) and open-source tariff method (54%).

CONCLUSIONS

On an individual level, no single method was able to replicate the physician assignment of COD more than about half the time. At the population level, the King-Lu method was the best method to estimate cause-specific mortality fractions, though it does not assign individual CODs. Future testing should focus on combining different computer-coded verbal autopsy tools, paired with PCVA strengths. This includes using open-source tools applied to larger and varied datasets (especially those including a random sample of deaths drawn from the population), so as to establish the performance for age- and sex-specific CODs.

DodaLab: an urban health and demographic surveillance site, the first three years in Hanoi, Vietnam

RATIONALE

Health and demographic surveillance sites (HDSSs) are important sources for health planning and policy in many low and middle income countries. Almost all HDSSs are in rural settings. The article aims to present the experiences and some concrete results for the first three years of operation of an urban HDSS in Hanoi, Vietnam, and discuss advantages and disadvantages of conducting health studies in HDSSs.

DESIGN, POPULATION AND SAMPLE SIZE

The DodaLab urban HDSS was established in 2007 in three communes at different economic levels in Dong Da district, Hanoi, Vietnam. Demographic, social and economic information about 10,000 households and their 37,000 persons was obtained through household interviews. Quarterly follow-up was initiated to provide information about vital events, birth, death and migration. A new household survey was undertaken in 2009. The existing rural HDSS FilaBavi, started in 1999, with 12,000 households and 52,000 persons, was used as the blueprint.

CONCLUSIONS

It was possible to establish and run an urban HDSS with experiences from the rural site. The urban and rural contexts are different and demographically, economically and socially complex, but the use of HDSSs can facilitate research beyond very simplified models for comparisons. General statements about external validity of results from the HDSS cannot be made. This issue has to be considered specifically in every situation as an integral part of the research so that the results can be made useful outside the researched HDSS and in performing relevant comparisons.

Birth attendants as perinatal verbal autopsy respondents in low- and middle-income countries: a viable alternative?

OBJECTIVE

To assess the feasibility of using birth attendants instead of bereaved mothers as perinatal verbal autopsy respondents.

METHODS

Verbal autopsy interviews for early neonatal deaths and stillbirths were conducted separately among mothers (reference standard) and birth attendants in 38 communities in four developing countries. Concordance between maternal and attendant responses was calculated for all questions, for categories of questions and for individual questions. The sensitivity and specificity of individual questions with the birth attendant as respondent were assessed.

FINDINGS

For early neonatal deaths, concordance across all questions was 94%. Concordance was at least 95% for more than half the questions on maternal medical history, birth attendance and neonate characteristics. Concordance on any given question was never less than 80%. Sensitivity and specificity varied across individual questions, more than 80% of which had a sensitivity of at least 80% and a specificity of at least 90%. For stillbirths, concordance across all questions was 93%. Concordance was 95% or greater more than half the time for questions on birth attendance, site of delivery and stillborn characteristics. Sensitivity and specificity varied across individual questions. Over 60% of the questions had a sensitivity of at least 80% and over 80% of them had a specificity of at least 90%. Overall, the causes of death established through verbal autopsy were similar, regardless of respondent.

CONCLUSION

Birth attendants can substitute for bereaved mothers as verbal autopsy respondents. The questions in existing harmonized verbal autopsy questionnaires need further refinement, as their sensitivity and specificity differ widely.

Classifying perinatal mortality using verbal autopsy: is there a role for nonphysicians?

Background

Because of a physician shortage in many low-income countries, the use of nonphysicians to classify perinatal mortality (stillbirth and early neonatal death) using verbal autopsy could be useful.

Objective

To determine the extent to which underlying perinatal causes of deaths assigned by nonphysicians in Guatemala, Pakistan, Zambia, and the Democratic Republic of the Congo using a verbal autopsy method are concordant with underlying perinatal cause of death assigned by physician panels.

Methods

Using a train-the-trainer model, 13 physicians and 40 nonphysicians were trained to determine cause of death using a standardized verbal autopsy training program. Subsequently, panels of two physicians and individual nonphysicians from this trained cohort independently reviewed verbal autopsy data from a sample of 118 early neonatal deaths and 134 stillbirths. With the cause of death assigned by the physician panel as the reference standard, sensitivity, specificity, positive and negative predictive values, and cause-specific mortality fractions were calculated to assess nonphysicians' coding responses. Robustness criteria to assess how well nonphysicians performed were used.

Results

Causes of early neonatal death and stillbirth assigned by nonphysicians were concordant with physician-assigned causes 47% and 57% of the time, respectively. Tetanus filled robustness criteria for early neonatal death, and cord prolapse filled robustness criteria for stillbirth.

Conclusions

There are significant differences in underlying cause of death as determined by physicians and nonphysicians even when they receive similar training in cause of death determination. Currently, it does not appear that nonphysicians can be used reliably to assign underlying cause of perinatal death using verbal autopsy.

Verbal autopsy-based cause-specific mortality trends in rural KwaZulu-Natal, South Africa, 2000-2009

Background

The advent of the HIV pandemic and the more recent prevention and therapeutic interventions have resulted in extensive and rapid changes in cause-specific mortality rates in sub-Saharan Africa, and there is demand for timely and accurate cause-specific mortality data to steer public health responses and to evaluate the outcome of interventions. The objective of this study is to describe cause-specific mortality trends based on verbal autopsies conducted on all deaths in a rural population in KwaZulu-Natal, South Africa, over a 10-year period (2000-2009).

Methods

The study used population-based mortality data collected by a demographic surveillance system on all resident and nonresident members of 12,000 households. Cause of death was determined by verbal autopsy based on the standard INDEPTH/WHO verbal autopsy questionnaire. Cause of death was assigned by physician review and the Bayesian-based InterVA program.

Results

There were 11,281 deaths over 784,274 person-years of observation of 125,658 individuals between Jan. 1, 2000 and Dec. 31, 2009. The cause-specific mortality fractions (CSMF) for the population as a whole were: HIV-related (including tuberculosis), 50%; other communicable diseases, 6%; noncommunicable lifestyle-related conditions, 15%; other noncommunicable diseases, 2%; maternal, perinatal, nutritional, and congenital causes, 1%; injury, 8%; indeterminate causes, 18%. Over the course of the 10 years of observation, the CSMF of HIV-related causes declined from a high of 56% in 2002 to a low of 39% in 2009 with the largest decline starting in 2004 following the introduction of an antiretroviral treatment program into the population. The all-cause age-standardized mortality rate (SMR) declined over the same period from a high of 174 (95% confidence interval [CI]: 165, 183) deaths per 10,000 person-years observed (PYO) in 2003 to a low of 116 (95% CI: 109, 123) in 2009. The decline in the SMR is predominantly due to a decline in the HIV-related SMR, which declined in the same period from 96 (95% CI: 89, 102) to 45 (95% CI: 40, 49) deaths per 10,000 PYO.

There was substantial agreement (79% kappa = 0.68 (95% CI: 0.67, 0.69)) between physician coding and InterVA coding at the burden of disease group level.

Conclusions

Verbal autopsy based methods enabled the timely measurement of changing trends in cause-specific mortality to provide policymakers with the much-needed information to allocate resources to appropriate health interventions.

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

Background

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.

Methods

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.

Results

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.

Conclusions

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.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.