Validation of malaria-attributed deaths using verbal autopsy studies: a systematic review.

BACKGROUND: Malaria contributes substantially to the persistent burden of child deaths in sub-Saharan Africa. Accurate and comprehensive malaria mortality data are crucial to monitor the progress in reducing malaria incidence and mortality. Verbal Autopsy (VA) ascertains the cause of death despite its limitations leading to misclassification errors. Minimally Invasive Tissue Sampling (MITS) is being conducted in some settings as an alternative to Complete Diagnostic Autopsy (CDA). The present study examines the validity of malaria-related deaths comparing VA diagnoses with those obtained through MITS and/or CDA. METHODS: A comprehensive literature search for original studies in English language using Ovid MEDLINE, Ovid Embase, CINAHL via EBSCO, Scopus, The Cochrane Library via Wiley, Google Scholar and searching the MITS Surveillance Alliance papers was carried out. The reference period was January 1, 1990-March 31, 2024. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were adopted. RESULTS: Among 71 articles identified in the databases, 21 matched the eligibility criteria. Qualitative syntheses showed that malaria Cause Specific Mortality Fractions (CSMFs) across various studies ranged from 2 to 31%. Plasmodium falciparum was mostly responsible for these deaths and the most common complications were anaemia and cerebral malaria. The sensitivity and specificity of the VA validation studies ranged from 18.4% to 33% and from 86.6% to 97%, respectively, and there was a high level of misclassification for both InSilico and Expert Algorithm VA for malaria compared to MITS. The overall concordance rates between MITS and CDA diagnoses ranged from 68 to 90%, with the highest concordance seen in deaths due to infectious diseases and malignant tumours. Clinical data increased diagnostic coincidence between MITS blind to clinical data and the gold standard CDA by 11%. CONCLUSIONS: The comprehensive review finds that MITS demonstrated better accuracy compared to VA in diagnosing malaria-attributed deaths, particularly in hospital settings. The high specificity of malaria in VA diagnosis suggests population-based estimates of the proportion of deaths due to malaria are broadly plausible.

Estimation of cause-specific mortality in Rakai, Uganda, using verbal autopsy 1999-2019.

BACKGROUND: There are scant data on the causes of adult deaths in sub-Saharan Africa. We estimated the level and trends in adult mortality, overall and by different causes, in rural Rakai, Uganda, by age, sex, and HIV status. OBJECTIVES: To estimate and analyse adult cause-specific mortality trends in Rakai, Uganda. METHODOLOGY: Mortality information by cause, age, sex, and HIV status was recorded in the Rakai Community Cohort study using verbal autopsy interviews, HIV serosurveys, and residency data. We estimated the average number of years lived in adulthood. Using demographic decomposition methods, we estimated the contribution of each cause of death to adult mortality based on the average number of years lived in adulthood. RESULTS: Between 1999 and 2019, 63082 adults (15-60 years) were censused, with 1670 deaths registered. Of these, 1656 (99.2%) had completed cause of death data from verbal autopsy. The crude adult death rate was 5.60 (95% confidence interval (CI): 5.33-5.87) per 1000 person-years of observation (pyo). The crude death rate decreased from 11.41 (95% CI: 10.61-12.28) to 3.27 (95% CI: 2.89-3.68) per 1000 pyo between 1999-2004 and 2015-2019. The average number of years lived in adulthood increased in people living with HIV and decreased in HIV-negative individuals between 2000 and 2019. Communicable diseases, primarily HIV and Malaria, had the biggest decreases, which improved the average number of years lived by approximately extra 12 years of life in females and 6 years in males. There were increases in deaths due to non-communicable diseases and external causes, which reduced the average number of years lived in adulthood by 2.0 years and 1.5 years in females and males, respectively. CONCLUSION: There has been a significant decline in overall mortality from 1999 to 2019, with the greatest decline seen in people living with HIV since the availability of antiretroviral therapy in 2004. By 2020, the predominant causes of death among females were non-communicable diseases, with external causes of death dominating in males.

Main findings: There are significant declines in mortality in people living with HIV. However, mortality in HIV-negative people increased due to non-communicable diseases in females, and injuries and external causes of death among males.Added knowledge: In this HIV-endemic area, decreasing adult mortality has been documented over the last 20 years. This paper benchmarks the changes in cause-specific mortality in this area.Global health impact for policy action: As in many African countries, more effort is needed to reduce mortality for non-communicable diseases, injuries, and external causes of death as these seem to have been neglected.

Mortality from non-communicable diseases and associated risk factors in Zambia; analysis of the sample vital registration with verbal autopsy 2015/2016.

BACKGROUND: Non-communicable diseases (NCDs) are the world's growing cause of preventable illness, disability, morbidity, and mortality which account for 71% of deaths. The aim of this study was to determine the factors associated with mortality from NCDs among persons aged 15 years and above in Zambia. METHODOLOGY: The study used data from Sample Vital Registration with Verbal Autopsy (SAVVY) 2015/16 (Zambia). A total of 3529 Verbal Autopsy were completed in the study, with only 2599 of death where among people aged 15 years and above. Three-level data analysis was applied; univariate analysis, bivariate analysis, and multivariate analysis (binary logistic regression). FINDINGS: The overall number of deaths from NCDs was 28.81%. Stratified analysis by gender showed that deaths from NCDs were higher among women (32.60%) as compared to men (26.25%). Among all persons, dying from NCDs was associated with tobacco use, age, and education. Tobacco use was negatively associated with mortality from NCDs (adjusted odds ratio [aOR] = 0.68; 95% confidence interval [CI]: 0.48-0.98). Age was positively associated with the odds of dying from NCDs among persons aged 45-59 years (aOR = 3.87, 95% CI: 2.13-7.01), 60-74 years (aOR = 12.05, 95% CI: 6.44-22.55), and 75 + years (aOR = 15.16, 95% CI: 7.93-28.97). The likelihood of dying from NCDs was higher among persons with secondary education as compared to those with no education (aOR = 1.93, 95% CI: 1.11-3.33). CONCLUSION: The findings from this study suggest that public health interventions targeting NCDs need to consider behavioural factors, especially tobacco use which exposes people to second-hand smoke. We also recommend large-scale national-level studies to further examine the contribution of each factor leading to mortality from NCDs.

CKD is the Major Cause of Death in Uddanam: A Population-Representative Study Using Smart Verbal Autopsy.

Introduction: Uddanam is an agricultural area with a high burden of chronic kidney disease of unknown etiology (CKDu). Despite reports of many deaths due to CKD in the lay press, the exact contribution of CKD to deaths remains uncertain because most deaths occur outside medical care. Methods: We used SmartVA automated verbal autopsy tool to ascertain the cause-specific mortality fractions among a 2419 subject-strong general population cohort of adult subjects in Uddanam between 2018 and 2022. Verbal autopsy interviews were conducted twice with the family members of the deceased. Results: A total of 133 deaths were recorded, giving a crude death rate of 5.5%, 10 times higher than that recorded in national surveys. CKD was responsible for 45% of all deaths, followed by ischemic heart disease (15%) and respiratory disease (6%). Conclusion: This study confirms CKD as the leading cause of mortality in this high CKD burden area and provides crucial data for public health decision-making and resource allocation.

Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy.

Determining causes of deaths (CODs) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of interviewing relatives of a deceased person about symptoms of the deceased in the period leading to the death, often resulting in multivariate binary responses. While statistical methods have been devised for estimating the cause-specific mortality fractions (CSMFs) for a study population, continued expansion of VA to new populations (or "domains") necessitates approaches that recognize between-domain differences while capitalizing on potential similarities. In this article, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a prespecified rooted weighted tree. Given a cause, we use latent class models to characterize the conditional distributions of the responses that may vary by domain. We specify a logistic stick-breaking Gaussian diffusion process prior along the tree for class mixing weights with node-specific spike-and-slab priors to pool information between the domains in a data-driven way. The posterior inference is conducted via a scalable variational Bayes algorithm. Simulation studies show that the domain adaptation enabled by the proposed method improves CSMF estimation and individual COD assignment. We also illustrate and evaluate the method using a validation dataset. The article concludes with a discussion of limitations and future directions.

Maternal mortality during war time in Tigray, Ethiopia: A community-based study.

OBJECTIVE: This study seeks to examine the impact of war on maternal mortality following an exacerbation in the dynamics of inequality in maternal health caused by the continuing conflict. DESIGN: Community-based cross-sectional study. SETTING: Tigray region of Northern Ethiopia, between November 2020 and May 2022. POPULATION: This study surveyed a total of 189 087 households from six of the seven zones of Tigray in 121 tabiyas from 31 districts selected. A multistage cluster sampling technique was used to select the districts and tabiyas. METHODS: The study was conducted in two phases. In the first phase, reproductive-age deaths that occurred during the study period were screened. In the second phase, verbal autopsies were conducted at the screened households. MAIN OUTCOME MEASURES: Maternal mortality ratio level and cause-specific mortality. RESULTS: The results of the study showed that the maternal mortality ratio was 840 (95% CI 739-914) per 100 000 live births. Haemorrhage, 107 (42.8%), pregnancy-induced hypertension, 21 (8.4%), and accidents, 14 (5.6%), were the main causes of mortality. Additionally, 203 (81.2%) of the mothers died outside of a health facility. CONCLUSIONS: This study has shown a higher maternal mortality ratio following the dynamics of the Tigray war, as compared with the pre-war level of 186/100 000. Furthermore, potentially many of the pregnancy-related deaths could have been prevented with access to preventive and emergency services. Given the destruction and looting of many facilities, the restoration and improvement of the Tigray health system must take precedence.

Pregnancy-related mortality up to 1 year postpartum in sub-Saharan Africa: an analysis of verbal autopsy data from six countries.

OBJECTIVE: To compare the causes of death for women who died during pregnancy and within the first 42 days postpartum with those of women who died between >42 days and within 1 year postpartum. DESIGN: Open population cohort (Health and Demographic Surveillance Systems). SETTING: Ten Health and Demographic Surveillance Systems (HDSS) in The Gambia, Kenya, Malawi, Tanzania, Ethiopia and South Africa. POPULATION: 2114 deaths which occurred within 1 year of the end of pregnancy where a verbal autopsy interview was conducted from 2000 to 2019. METHODS: InterVA5 and InSilicoVA verbal autopsy algorithms were used to attribute the most likely underlying cause of death, which were grouped according to adapted International Classification of Diseases-Maternal Mortality categories. Multinomial regression was used to compare differences in causes of deaths within 42 days versus 43-365 days postpartum adjusting for HDSS and time period (2000-2009 and 2010-2019). MAIN OUTCOME MEASURES: Cause of death and the verbal autopsy Circumstances of Mortality Categories (COMCATs). RESULTS: Of 2114 deaths, 1212 deaths occurred within 42 days postpartum and 902 between 43 and 365 days postpartum. Compared with deaths within 42 days, deaths from HIV and TB, other infectious diseases, and non-communicable diseases constituted a significantly larger proportion of late pregnancy-related deaths beyond 42 days postpartum, and health system failures were important in the circumstances of those deaths. The contribution of HIV and TB to deaths beyond 42 days postpartum was greatest in Southern Africa. The causes of pregnancy-related mortality within and beyond 42 days postpartum did not change significantly between 2000-2009 and 2010-2019. CONCLUSIONS: Cause of death data from the extended postpartum period are critical to inform prevention. The dominance of HIV and TB, other infectious and non-communicable diseases to (late) pregnancy-related mortality highlights the need for better integration of non-obstetric care with ante-, intra- and postpartum care in high-burden settings.

Accuracy of the Verbal Autopsy questionnaire in the diagnosis of COVID-19 deaths in a Brazilian capital

ABSTRACT The Verbal Autopsy (VA) is a questionnaire about the circumstances surrounding a death. It was widely used in Brazil to assist in postmortem diagnoses and investigate excess mortality during the Coronavirus Disease 2019 (COVID-19) pandemic. This study aimed to determine the accuracy of investigating acute respiratory distress syndrome (ARDS) using VA. This is a cross-sectional study with prospective data collected from January 2020 to August 2021 at the Death Verification Service of Sao Luis city, Brazil. VA was performed for suspected COVID-19 deaths, and one day of the week was randomly chosen to collect samples from patients without suspected COVID-19. Two swabs were collected after death and subjected to reverse transcription-polymerase chain reaction (RT-PCR) for SARS-CoV-2 detection. Of the 250 cases included, the VA questionnaire identified COVID-19-related ARDS in 67.2% (52.98% were positive for COVID-19). The sensitivity of the VA questionnaire was 0.53 (0.45-0.61), the specificity was 0.75 (0.64-0.84), the positive predictive value was 0.81 (0.72-0.88), and the negative predictive value was 0.44 (0.36-0.53). The VA had a lower-than-expected accuracy for detecting COVID-19 deaths; however, because it is an easily accessible and cost-effective tool, it can be combined with more accurate methods to improve its performance.

Agreement between cause of death assignment by computer-coded verbal autopsy methods and physician coding of verbal autopsy interviews in South Africa.

BACKGROUND: The South African national cause of death validation (NCODV 2017/18) project collected a national sample of verbal autopsies (VA) with cause of death (COD) assignment by physician-coded VA (PCVA) and computer-coded VA (CCVA). OBJECTIVE: The performance of three CCVA algorithms (InterVA-5, InSilicoVA and Tariff 2.0) in assigning a COD was compared with PCVA (reference standard). METHODS: Seven performance metrics assessed individual and population level agreement of COD assignment by age, sex and place of death subgroups. Positive predictive value (PPV), sensitivity, overall agreement, kappa, and chance corrected concordance (CCC) assessed individual level agreement. Cause-specific mortality fraction (CSMF) accuracy and Spearman's rank correlation assessed population level agreement. RESULTS: A total of 5386 VA records were analysed. PCVA and CCVAs all identified HIV/AIDS as the leading COD. CCVA PPV and sensitivity, based on confidence intervals, were comparable except for HIV/AIDS, TB, maternal, diabetes mellitus, other cancers, and some injuries. CCVAs performed well for identifying perinatal deaths, road traffic accidents, suicide and homicide but poorly for pneumonia, other infectious diseases and renal failure. Overall agreement between CCVAs and PCVA for the top single cause (48.2-51.6) indicated comparable weak agreement between methods. Overall agreement, for the top three causes showed moderate agreement for InterVA (70.9) and InSilicoVA (73.8). Agreement based on kappa (-0.05-0.49)and CCC (0.06-0.43) was weak to none for all algorithms and groups. CCVAs had moderate to strong agreement for CSMF accuracy, with InterVA-5 highest for neonates (0.90), Tariff 2.0 highest for adults (0.89) and males (0.84), and InSilicoVA highest for females (0.88), elders (0.83) and out-of-facility deaths (0.85). Rank correlation indicated moderate agreement for adults (0.75-0.79). CONCLUSIONS: Whilst CCVAs identified HIV/AIDS as the leading COD, consistent with PCVA, there is scope for improving the algorithms for use in South Africa.

Erratum: Evaluation of methods for assigning causes of death from verbal autopsies in India.

[This corrects the article DOI: 10.3389/fdata.2023.1197471.].

Verbal autopsy to assess causes of mortality among the economically productive age group in the tribal region of Melghat, central India.

Background & Objectives: Verbal autopsy (VA) is the systematic and retrospective inquiry (from relatives) about the symptoms of an illness prior to death. In tribal India, 67-75 per cent of deaths occur at home with an unknown cause of death (CoD). Hence, the aim of this study was to determine the CoD in the 16-60 yr age group utilizing VA. Methods: A prospective, community based longitudinal study was conducted in 32 tribal villages in the Melghat region of Maharashtra, between 2004 and 2020. Number of deaths and VAs in 16-60 yr age group were collected by village health workers (VHWs) and supervisors, verified by five different persons (internal-external) and cross-checked by three VA interpretation trained physicians. A modified version of WHO VA was used. Cause-specific mortality fractions were calculated. Results: Of the 1011 deaths recorded, mortality in males was significantly higher than females (P<0.001). A total of 763 VAs were conducted which revealed that tuberculosis was the leading CoD, followed by jaundice, heart diseases, diarrhoea, central nervous system infections and suicide. Suicides were significantly more common among males than in females (P=0.046). Significantly, more deaths occurred during the monsoon (P=0.002), especially diarrhoeal deaths (P=0.024). Interpretation & conclusions: The findings of this study suggest that, in Indian tribal areas, infectious diseases are the leading causes of morbidity and one of the major causes of deaths in economically productive age group. Intensified VHW-mediated interventions are required to reduce the premature deaths.

Cause of Death estimation from Verbal Autopsies: Is the Open Response redundant or synergistic?

Civil registration and vital statistics systems capture birth and death events to compile vital statistics and to provide legal rights to citizens. Vital statistics are a key factor in promoting public health policies and the health of the population. Medical certification of cause of death is the preferred source of cause of death information. However, two thirds of all deaths worldwide are not captured in routine mortality information systems and their cause of death is unknown. Verbal autopsy is an interim solution for estimating the cause of death distribution at the population level in the absence of medical certification. A Verbal Autopsy (VA) consists of an interview with the relative or the caregiver of the deceased. The VA includes both Closed Questions (CQs) with structured answer options, and an Open Response (OR) consisting of a free narrative of the events expressed in natural language and without any pre-determined structure. There are a number of automated systems to analyze the CQs to obtain cause specific mortality fractions with limited performance. We hypothesize that the incorporation of the text provided by the OR might convey relevant information to discern the CoD. The experimental layout compares existing Computer Coding Verbal Autopsy methods such as Tariff 2.0 with other approaches well suited to the processing of structured inputs as is the case of the CQs. Next, alternative approaches based on language models are employed to analyze the OR. Finally, we propose a new method with a bi-modal input that combines the CQs and the OR. Empirical results corroborated that the CoD prediction capability of the Tariff 2.0 algorithm is outperformed by our method taking into account the valuable information conveyed by the OR. As an added value, with this work we made available the software to enable the reproducibility of the results attained with a version implemented in R to make the comparison with Tariff 2.0 evident.

Evaluation of methods for assigning causes of death from verbal autopsies in India.

Background: Physician-coded verbal autopsy (PCVA) is the most widely used method to determine causes of death (COD) in countries where medical certification of death is low. Computer-coded verbal autopsy (CCVA), an alternative method to PCVA for assigning the COD is considered to be efficient and cost-effective. However, the performance of CCVA as compared to PCVA is yet to be established in the Indian context. Methods: We evaluated the performance of PCVA and three CCVA methods i.e., InterVA 5, InSilico, and Tariff 2.0 on verbal autopsies done using the WHO 2016 VA tool on 2,120 reference standard cases developed from five tertiary care hospitals of Delhi. PCVA methodology involved dual independent review with adjudication, where required. Metrics to assess performance were Cause Specific Mortality Fraction (CSMF), sensitivity, positive predictive value (PPV), CSMF Accuracy, and Kappa statistic. Results: In terms of the measures of the overall performance of COD assignment methods, for CSMF Accuracy, the PCVA method achieved the highest score of 0.79, followed by 0.67 for Tariff_2.0, 0.66 for Inter-VA and 0.62 for InSilicoVA. The PCVA method also achieved the highest agreement (57%) and Kappa scores (0.54). The PCVA method showed the highest sensitivity for 15 out of 20 causes of death. Conclusion: Our study found that the PCVA method had the best performance out of all the four COD assignment methods that were tested in our study sample. In order to improve the performance of CCVA methods, multicentric studies with larger sample sizes need to be conducted using the WHO VA tool.

How valid are proxy assessment of mental health and sleep comorbidities of patients with epilepsy using standardized questionnaires?

BACKGROUND: The study aimed to determine the level of agreement between patients with epilepsy and their proxies when assessing psychiatric comorbidities, sleep disorders, and medication adherence using standardized questionnaires. METHODS: This agreement study is an ancillary analysis of the PRERIES study, a matched case-control study exploring SUDEP risk factors. Controls aged 15 years and older, with active epilepsy or in remission for less than 5 years were recruited between 01/01/2011 and 03/31/2019. An interview was carried out by a trained psychologist on both the patient and a proxy-respondent. During these independent interviews, the following comorbidities were explored: psychiatric comorbidities using the MINI, the STAI- Y2 and NDDI-E scales, sleep disorders with the SDQ-SA and Epworth scales and medication adherence. Level of agreement between patient and their proxy was estimated using Gwet's AC1&2. RESULTS: Among the 107 patient-proxy dyads recruited, proxy respondents were mainly family members (65.4%) or spouses (30.8%). Exploration of present major depression showed excellent agreement at 0.81 [0.65;0.97], as well as exploration of dysthymia at 0.96 [0.61;1]. Suicidal risk evaluation had a lesser agreement at 0.77 [0.60;0.94]. Agreement on anxiety was moderate 0.5 [0.38;0.62]. For sleep disorder, SDQ-SA presented a better agreement than the Epworth questionnaire with respectively 0.73 [0.51;0.95] and 0.45 [0.26;0.63]. For medication adherence, the overall agreement rate was excellent (0.90 [0.78;1]). CONCLUSION: Exploration of potential risk factors through families can give valuable and relatively robust information, especially if the respondent lives with the patient, and should be retrieved, when possible, in usual clinical setting.

Role of verbal autopsy in cancer registration: A mixed-methods study from the population-based cancer registry of Northern India.

OBJECTIVES: To describe utilisation of verbal autopsy as one of the data collection approaches in cancer registration in an Indian setting. We aimed to estimate the proportion and epidemiological characteristics of malignancies identified by the Varanasi population-based cancer registry (PBCR) using verbal autopsy between 2017 and 2019 and to develop a thematic network for implementing verbal autopsy. METHODS: This was a cross-sectional mixed-methods study. Quantitative methods were applied to analyse information from PBCR proforma of the verbal autopsy-confirmed cancers; qualitative methods were applied to evaluate verbal autopsy conducted by field staff from key informants. In-depth interviews of field staff for the challenges and potential solutions during verbal autopsy were assessed. RESULTS: Of 6466 registered cancers, 1103 (17.1%) were verbal autopsy-confirmed cancers, which had no other source of information. The majority of verbal autopsy cases were from vulnerable populations who were aged >50 years (721, 65.4%), female (607, 55.1%), from rural backgrounds (853, 77.3%), illiterate or just able to read and write (636, 57.7%), and from lower and middle-income groups (823, 74.6%). Verbal autopsy helped provide information about symptoms and site of disease, diagnostic and treatment details, and disease status. Major challenges during verbal autopsy described by field staff were incomplete cancer treatment, destruction of medical records and non-cooperation by the community and lack of support from the local workforce as cancer is not notifiable. CONCLUSION: Verbal autopsy helped identify cancers that would have been missed during active case finding from available resources. The majority of verbal autopsy-confirmed patients belonged to vulnerable populations. Non-cooperation from community and local health systems was major challenge during verbal autopsy. Developing robust cancer awareness, patient navigation, and social support programmes will strengthen verbal autopsy. Integration of standardised and reproducible methods of verbal autopsy in cancer registry and digitalization of health information, especially in limited-resource settings with weak vital registration, will facilitate completeness in cancer registration.

Ascertainment of causes of neonatal death using verbal autopsy in Rishikesh, Uttarakhand: A cross-sectional study.

Background: India shares a huge burden of the total number of global neonatal deaths. The neonatal mortality rate in Uttarakhand is higher in comparison to the national rate. Understanding the causes and contextual factors that contribute to neonatal deaths is critical for developing a health programme and policy. Therefore, this study was aimed to ascertain causes of neonatal deaths using verbal autopsy in Rishikesh, Uttarakhand. Material and Methods: A community-based cross-sectional study was conducted in Rishikesh, Uttarakhand from July 2018 to June 2019. The study participants were mothers or primary caregivers of deceased neonates. The verbal autopsy tool of the National Health Mission, Government of India was used to collect data. All neonatal deaths from July 2018 to June 2019 were included in this study. The cause of neonatal death was ascertained by two independent doctors. Results: A total of 23 neonatal deaths could be traced during July 2018 through June 2019 in Rishikesh. One death was excluded from the study. Sixty-eight percent (15/22) of neonatal deaths occurred in the first week of life. Major causes of neonatal deaths were perinatal asphyxia (7/22), congenital malformation (6/22), and prematurity (4/22). Most (9/22) of the delays were in making a decision to seek medical care for neonates. Conclusion: The major causes of neonatal deaths were perinatal asphyxia, congenital malformation, and prematurity. Most of the deaths occurred during the early neonatal period.

Assessment of the underlying causes of adult deaths using a short version of verbal autopsy in Xaiyabouli Province, Lao People's Democratic Republic.

BACKGROUND: In developing countries, it is difficult to collect the data of the underlying cause of death (UCOD), especially when a death does not occur in a health facility. This study aimed to develop a short version of verbal autopsy (VA) and identify the UCOD of adults in Lao People's Democratic Republic (Lao PDR). METHODS: A short version of VA for deaths outside health facilities was developed. This study included all deaths of people aged 15 years old or older in Xaiyabouli Province in 2020. Socio-demographic factors, place of death, and UCOD of the deceased were collected from health facilities or from family members using a questionnaire including the short VA form. UCOD was compared between home deaths and hospital deaths, between the age group of 15-59 years old and the age group ≥ 60 years old, and between males and females. RESULTS: Of all the 1,235 deaths included in this study, 1,012 deaths (81.9%) occured at home and 223 deaths (18.1%) at hospitals. The most common UCOD was senility (13.3%), followed by heart/renal failure (10.5%), pneumonia (9.6%) and traffic accident (7.1%). Compared to hospital deaths, home deaths had more people who were females, 75 years old or older, and Lao-Tai. Home deaths had more deaths than hospital deaths due to accident/injury (16.0% vs. 8.1%), tumor (4.7% vs. 1.8%), and senility (16.2% vs. 0%); fewer deaths due to heart/renal disease (15.1% vs. 32.3%), respiratory disease (12.2% vs. 18.8%), liver/gastro-intestine disease (5.3% vs. 9.0%), and infection (3.1% vs. 14.3%). The age group of 15-59 years had more deaths in the categories of accident/injury (28.1% vs. 4.4%), liver/gastro-intestine disease (8.1% vs. 4.4%), infection (7.2% vs. 3.5%), and tumor (6.0% vs. 2.8%). Males had more deaths due to tumor (5.2% vs. 3.0%) and fewer natural deaths (11.2% vs. 15.9%) than females. CONCLUSIONS: The major UCOD category was heart/renal disease in the adult generation in Xaiyabouli Province. Cost-effective interventions based on the multisectoral noncommunicable disease prevention plan should be appropriately implemented. Mortality surveillance using the short VA tool should be conducted for all home deaths in Lao PDR.

Value of Verbal Autopsy in a Fragile Setting: Reported versus Estimated Community Deaths Associated with COVID-19, Banadir, Somalia.

BACKGROUND: Accurate mortality data associated with infectious diseases such as coronavirus disease 2019 (COVID-19) are often unavailable in countries with fragile health systems such as Somalia. We compared officially reported COVID-19 deaths in Somalia with COVID-19 deaths estimated using verbal autopsy. METHODS: We interviewed relatives of deceased persons to collect information on symptoms, cause, and place of death. We compared these data with officially reported data and estimated the positive and negative predictive values of verbal autopsy. RESULTS: We identified 530 deaths during March-October 2020. We classified 176 (33.2%) as probable COVID-19 deaths. Most deaths (78.5%; 416/530) occurred at home and 144 (34.6%) of these were attributed to COVID-19. The positive predictive value of verbal autopsy was lower for home deaths (22.3%; 95% CI: 15.7-30.1%) than for hospital deaths (32.3%; 95% CI: 16.7-51.4%). The negative predictive value was higher: 97.8% (95% CI: 95.0-99.3%) for home deaths and 98.4% (95% CI: 91.5-100%) for hospital deaths. Conclusions Verbal autopsy has acceptable predictive value to estimate COVID-19 deaths where disease prevalence is high and can provide data on the COVID-19 burden in countries with low testing and weak mortality surveillance where home deaths may be missed.

Population cause of death estimation using verbal autopsy methods in large-scale field trials of maternal and child health: lessons learned from a 20-year research collaboration in Central Ghana.

Low and middle-income countries continue to use Verbal autopsies (VAs) as a World Health Organisation-recommended method to ascertain causes of death in settings where coverage of vital registration systems is not yet comprehensive. Whilst the adoption of VA has resulted in major improvements in estimating cause-specific mortality in many settings, well documented limitations have been identified relating to the standardisation of the processes involved. The WHO has invested significant resources into addressing concerns in some of these areas; there however remains enduring challenges particularly in operationalising VA surveys for deaths amongst women and children, challenges which have measurable impacts on the quality of data collected and on the accuracy of determining the final cause of death. In this paper we describe some of our key experiences and recommendations in conducting VAs from over two decades of evaluating seminal trials of maternal and child health interventions in rural Ghana. We focus on challenges along the entire VA pathway that can impact on the success rates of ascertaining the final cause of death, and lessons we have learned to optimise the procedures. We highlight our experiences of the value of the open history narratives in VAs and the training and skills required to optimise the quality of the information collected. We describe key issues in methods for ascertaining cause of death and argue that both automated and physician-based methods can be valid depending on the setting. We further summarise how increasingly popular information technology methods may be used to facilitate the processes described. Verbal autopsy is a vital means of increasing the coverage of accurate mortality statistics in low- and middle-income settings, however operationalisation remains problematic. The lessons we share here in conducting VAs within a long-term surveillance system in Ghana will be applicable to researchers and policymakers in many similar settings.

Leading causes of deaths in the mortality transition in Papua New Guinea: evidence from the Comprehensive Health and Epidemiological Surveillance System.

BACKGROUND: Changing causes of deaths in the mortality transition in Papua New Guinea (PNG) are poorly understood. This study analysed community-level data to identify leading causes of death in the population and variations across age groups and sexes, urban-rural sectors and provinces. METHOD: Mortality surveillance data were collected from 2018-20 as part of the Comprehensive Health and Epidemiological Surveillance System (CHESS), using the World Health Organization 2016 verbal autopsy (VA) instrument. Data from 926 VA interviews were analysed, using the InterVA-5 cause of death analytical tool to assign specific causes of death among children (0-14 years), those of working age (15-64 years) and the elderly (65+ years). RESULT: Nearly 50% of the total deaths were attributed to non-communicable diseases (NCDs), followed by infectious and parasitic diseases (35%), injuries and external causes (11%) and maternal and neonatal deaths (4%). Leading causes of death among children were acute respiratory tract infections (ARTIs) and diarrhoeal diseases, each contributing to 13% of total deaths. Among the working population, tuberculosis (TB) contributed to 12% of total deaths, followed by HIV/AIDS (11%). TB- and HIV/AIDS-attributed deaths were highest in the age group 25-34 years, at 20% and 18%, respectively. These diseases killed more females of working age (n = 79, 15%) than males (n = 52, 8%). Among the elderly, the leading causes of death were ARTIs (13%) followed by digestive neoplasms (10%) and acute cardiac diseases (9%). CONCLUSION: The variations in leading causes of death across the populations in PNG suggest diversity in mortality transition. This requires different strategies to address specific causes of death in particular populations.