Cause-specific Mortality Fractions Research Articles

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.

2000 Cause of death amongst frail and non-frail older people post-hospital admission in northern Tanzania

Abstract Introduction Verbal autopsy (VA) is a tool used to determine cause of death (COD) in regions lacking routine medical certification. Automated algorithms are widely used to interpret VA data. This study aimed to investigate potential differences in COD between frail and non-frail older people in northern Tanzania. Method This work forms part of a longitudinal study investigating the clinical outcomes of 308 consecutive adults aged ≥60 years following admission to four hospitals in northern Tanzania. Frailty status was established on admission using the Clinical Frailty Scale (CFS) and dichotomised with CFS ≥5 indicating frailty. For participants who passed away in the 10-12 months following admission, VA data were collected through telephone interview using the 2022 World Health Organization (WHO) VA instrument. COD estimates were established using the SmartVA-Analyze program, implementing the Tariff 2.0 Method. Results After a mean follow-up period of 10.8 (±0.9) months, VA data were available for 69 participants. At admission screening, 51 (73.9%) were frail, while 18 (26.1%) were non-frail. SmartVA produced COD estimates for 42 (60.9%) participants, while 27 (39.1%) remained undetermined. Cause Specific Mortality Fractions (CSMFs) for non-communicable disease were higher for those with frailty than the non-frail group (70.6% vs. 54.0%, respectively). An undetermined COD was more likely in those with frailty. Of the 27 undetermined COD, 22 (81.5%) were attributed to frail individuals and 5 (18.5%) to non-frail. These undetermined COD represent 43.1% of frailty-related deaths and 27.8% of non-frailty-related deaths. Conclusions Older people with frailty living in Tanzania are more likely to die from non-communicable diseases compared to non-frail older people. Although employing SmartVA to analyse VA data from this population was feasible, it faced challenges ascribing COD to all participants. This limitation may be due to the multi-morbidity often present in older populations where multiple factors combine to cause mortality.

Causes of death among women of reproductive age during the war in Tigray, Ethiopia.

In resource-limited countries with weak healthcare systems, women of reproductive age are particularly vulnerable during times of conflict. In Tigray, Ethiopia, where a war broke out on 04 November 2020, there is a lack of information on causes of death (CoD) among women of reproductive age. This study aims to determine the underlying CoD among women of reproductive age during the armed conflict in Tigray. This community-based survey was carried out in six Tigray zones, excluding the western zone for security reasons. We used a multistage stratified cluster sampling method to select the smallest administrative unit known as Tabiya. Data were collected using a standardized 2022 WHO Verbal Autopsy (VA) tool. The collected data were analyzed using the InterVA model using R analytic software. The study reported both group-based and cause-specific mortality fractions. A total of 189,087 households were screened and 832 deaths were identified among women of reproductive age. The Global Burden of Disease classification showed that infectious and maternal disorders were the leading CoD, accounting for 42.9% of all deaths. External causes contributed to 26.4% of fatalities, where assault accounted for 13.2% of the deaths. Maternal deaths made up 30.0% of the overall mortality rate. HIV/AIDS was the primary CoD, responsible for 13.2% of all deaths and 54.0% of infectious causes. Other significant causes included obstetric hemorrhage (11.7%) and other and unspecified cardiac disease (6.6%). The high proportion of infectious diseases related CoD, including HIV/AIDS, as well as the occurrence of uncommon external CoD among women, such as assault, and a high proportion of maternal deaths are likely the result of the impact of war in the region. This highlights the urgent need for targeted interventions to address these issues and prioritize sexual and reproductive health as well as maternal health in Tigray.

Community perception of causes of death using verbal autopsy for diabetes mellitus in Saudi Arabia.

Diabetes mellitus is a serious global health issue which significantly impacts public health and socioeconomic development. Exploring how the community perceives the causes of death and their associated risk factors is crucial for public health. This study combines verbal autopsy (VA) with the Type 2 Diabetes Mellitus (T2DM) register to explore community perceptions of causes of death and associated influential factors in Makkah province, Saudi Arabia. 302 VA interviews were conducted with relatives or caregivers of deceased who died between 2018 and 2021 based on T2DM medical register from Alnoor Specialist Hospital in Makkah City, Saudi Arabia. Cause-specific mortality fractions (CSMFs) obtained from the VA using the InterVA-5 model were utilized to assess community perception. We used a multivariable logistic regression model to determine factors influencing community perceptions of causes of death. Lin's CCC with 95% CI was used to analyze the concordance for the CSMFs from verbal autopsy causes of death (VACoD) as a presumed reference standard and family-reported causes of death (FRCoD). The outcomes of this study demonstrate a generally broad spectrum of community perceived mortalities, with some critical misconceptions based on the type of death and other vital events like marital status, with an overall CCC of 0.60 (95% CI: 0.20-1.00; p = 003). The study findings demonstrate that community perception is weak if the deceased was male compared to female (aOR: 0.52; 95% CI: 0.26-1.03) and if the deceased was > = 80 years compared to 34-59 years (aOR: 0.48; 95% CI: 0.16-1.38), but it significantly improves among married compared to single (aOR: 2.13; 95% CI: 1.02-4.42). Exploring community perception of causes of death is crucial as it provides valuable insights into the community's understanding, beliefs, and concerns regarding mortality. Higher or lower community perception is attributed to how people may perceive risk factors associated with the causes of death, which can guide public health planning and interventional programs. The study findings further emphasize the need to employ robust and standardized VA methods within the routine medical services for a systemized assessment of families' reported causes of death.

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

ABSTRACT 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.

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

IntroductionUddanam is an agricultural area with a high burden of chronic kidney disease of unknown aetiology (CKDu). Despite reports of many deaths due to CKD in the lay press, the exact contribution of CKD to deaths remains uncertain as most deaths occur outside medical care. MethodsWe used SmartVA automated verbal autopsy (VA) tool to ascertain the cause-specific mortality fractions amongst a 2419 subject-strong general population cohort in Uddanam between 2018-2022. VA interviews were conducted twice with the family members of the deceased. ResultsA 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%). ConclusionThis 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.

Further evidence of misclassification of the injury deaths in South Africa: When will the barriers to accurate injury death statistics be removed?

Contrary to the World Health Organization's internationally recommended medical certificate of cause of death, the South African (SA) death notification form (DNF) does not allow for the reporting of the manner of death to permit accurate coding of external causes of injury deaths. To describe the injury cause-of-death profile from forensic pathology records collected for the National Cause-of-Death Validation (NCoDV) Project and compare it with profiles from other sources of injury mortality data. In particular, the recording of firearm use in homicides is compared between sources. The NCoDV Project was a cross-sectional study of deaths that occurred during a fixed period in 2017 and 2018, from a nationally representative sample of 27 health subdistricts in SA. Trained fieldworkers scanned forensic records for all deaths investigated at the forensic mortuaries serving the sampled subdistricts during the study period. Forensic practitioners reviewed the records and completed a medical certificate of cause of death for each decedent. Causes of death were coded to the International Statistical Classification of Diseases, 10th revision (ICD-10), using Iris automated coding software. Cause-specific mortality fractions for injury deaths were compared with Injury Mortality Survey 2017 (IMS 2017) and Statistics South Africa 2017 (Stats SA 2017) datasets. The cause profile for all firearm-related deaths was compared between the three datasets. A total of 5 315 records were available for analysis. Males accounted for 77.6% of cases, and most decedents were aged between 25 and 44 years. Homicide was the leading cause of death (34.7%), followed by transport injuries (32.6%) and suicide (14.7%). This injury cause profile was similar to IMS 2017 but differed markedly from the official statistics, which showed markedly lower proportions of these three causes (15.0%, 11.6% and 0.7%, respectively), and a much higher proportion of other unintentional causes. Investigation of firearm-related deaths revealed that most were homicides in NCoDV 2017/18 (88.5%) and IMS 2017 (93.1%), while in the Stats SA 2017 data, 98.7% of firearm deaths were classified as accidental. Approximately 7% of firearm-related deaths were suicides in NCoDV 2017/18 and IMS 2017, with only 0.3% in Stats SA 2017. The official cause-of-death data for injuries in SA in 2017 differed substantially from findings from the NCoDV 2017/18 study and IMS 2017. Accurate data sources would ensure that public health interventions are designed to reduce the high injury burden. Inclusion of the manner of death on the DNF, as is recommended internationally, is critically important to enable more accurate, reliable and valid reporting of the injury profile.

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

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. 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. 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. 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.

Multi-Cause Calibration of Verbal Autopsy-Based Cause-Specific Mortality Estimates of Children and Neonates in Mozambique.

The Countrywide Mortality Surveillance for Action platform is collecting verbal autopsy (VA) records from a nationally representative sample in Mozambique. These records are used to estimate the national and subnational cause-specific mortality fractions (CSMFs) for children (1-59 months) and neonates (1-28 days). Cross-tabulation of VA-based cause-of-death (COD) determination against that from the minimally invasive tissue sampling (MITS) from the Child Health and Mortality Prevention project revealed important misclassification errors for all the VA algorithms, which if not accounted for will lead to bias in the estimates of CSMF from VA. A recently proposed Bayesian VA-calibration method is used that accounts for this misclassification bias and produces calibrated estimates of CSMF. Both the VA-COD and the MITS-COD can be multi-cause (i.e., suggest more than one probable COD for some of the records). To fully use this probabilistic COD data, we use the multi-cause VA calibration. Two different computer-coded VA algorithms are considered-InSilicoVA and EAVA-and the final CSMF estimates are obtained using an ensemble calibration that uses data from both the algorithms. The calibrated estimates consistently offer a better fit to the data and reveal important changes in the CSMF for both children and neonates in Mozambique after accounting for VA misclassification bias.

Cause-Specific Mortality Fraction (CSMF) of adult mortality in Butajira, South Central Ethiopia.

Cause- and context-specific mortality data are imperative to understand the extent of health problems in low-income settings, where national death registration and cause of death identification systems are at a rudimentary stage. Aiming to estimate cause-specific mortality fractions, adult (15+ years) deaths between January 2008 and April 2020 were extracted from the Butajira health and demographic surveillance system electronic database. The physician review and a computerized algorithm, InterVA (Interpreting Verbal Autopsy), methods were used to assign the likely causes of death from January 2008 to April 2017 (the first) and May 2017 to April 2020 (the second) phase of the surveillance period, respectively. Initially, adult mortality rates per 1000py across sex and age were summarized. A total of 1,625 deaths were captured in 280, 461 person-years, with an overall mortality rate of 5.8 (95%CI: 5.5, 6.0) per 1000py. Principally, mortality fractions for each specific cause of death were estimated, and for 1,571 deaths, specific causes were determined. During the first phase, the leading cause of death was tuberculosis (13.6%), followed by hypertension (6.6%) and chronic liver disease (5.9%). During the second phase, digestive neoplasms (17.3%), tuberculosis (12.1%), and stroke (9.4%) were the leading causes of death, respectively. Moreover, tuberculosis was higher among persons aged 50+ (15.0%), males (13.8%), and in rural areas (14.1%) during the first phase. Hypertensive diseases were higher among females (7.9%) and in urbanities. In the second phase, digestive neoplasms were higher in the age group of 50-64 years (25.4%) and females (19.0%), and stroke was higher in older adults (65+) (10%) and marginally higher among males (9.7%). Our results showed that tuberculosis and digestive neoplasms were the most common causes of death. Hence, prevention, early detection, and management of cases at all levels of the existing healthcare system should be prioritized to avert premature mortality.

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

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. 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. 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). 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.

Assessing the Diagnostic Accuracy of Physicians for Home Death Certification in Shanghai: Application of SmartVA.

Approximately 30% of deaths in Shanghai either occur at home or are not medically attended. The recorded cause of death (COD) in these cases may not be reliable. We applied the Smart Verbal Autopsy (VA) tool to assign the COD for a representative sample of home deaths certified by 16 community health centers (CHCs) from three districts in Shanghai, from December 2017 to June 2018. The results were compared with diagnoses from routine practice to ascertain the added value of using SmartVA. Overall, cause-specific mortality fraction (CSMF) accuracy improved from 0.93 (93%) to 0.96 after the application of SmartVA. A comparison with a “gold standard (GS)” diagnoses obtained from a parallel medical record review investigation found that 86.3% of the initial diagnoses made by the CHCs were assigned the correct COD, increasing to 90.5% after the application of SmartVA. We conclude that routine application of SmartVA is not indicated for general use in CHCs, although the tool did improve diagnostic accuracy for residual causes, such as other or ill-defined cancers and non-communicable diseases.

Mortality and Years of Life Lost due to Burn Injury Among Older Iranian People; a Cross-Sectional study

Introduction:The mortality of burn injury is a serious health problem among older people. The present study aimed to determine the epidemiological characteristics of burn mortality and Years of Life Lost (YLLs) among people aged ≥ 60. Methods:The National and Subnational Burden of Disease (NASBOD) study includes population-based cross-sectional data from the death registration system of Iran and those recorded by the cemeteries of Tehran and Esfahan were used in this study. Spatio-temporal and Gaussian process regression models were applied to estimate rates and trends of mortality and cause-specific mortality fractions. YLLs were calculated using Iranian life expectancy and the number of deaths. Results:The mortality rate for 1990 and 2015 was 17.4 and 4.5 per 100,000, respectively. From 1990 through 2015, the annual percentage of change in burn mortality rate was -6.1% in females and -4.4% in males. During 2015, there were 326 deaths following burns in people aged 60+ with 4586 person YLLs, and in 1990 there were 523 deaths with 4862 person-YLLs. The male-female ratio for 1990 and 2015 were 0.80 and 0.88, respectively. The age-standardized mortality rate was higher than 8.5 per 100,000 in border provinces in 2015. The provinces with better socioeconomic situations, such as Tehran, had a lower mortality rate than poor provinces, such as Sistan va Baluchistan.Conclusion:Although burn mortality in old people decreased in those 26 years, it is still high compared to high-income countries. Continued efforts to increase preventive measures and adequate access to quality care, especially in border provinces, is suggested.

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

BackgroundReliable 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.MethodsThe 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.ResultsThere 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.ConclusionsThe 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.

Assessment of the quality of the vital registration system for under-5 mortality in Yucatan, Mexico

IntroductionVital registration is an important element in health information systems which can inform policy and strengthen health systems. Mexico has a well-functioning vital registration system; however, there is still room for improvement, especially for deaths of children under 5. This study assesses the quality of the vital registration system in capturing deaths and evaluates the quality of cause of death certification in under-5 deaths in Yucatan, Mexico.MethodsWe collected information on under-5 deaths that occurred in 2015 and 2016 in Yucatan, Mexico. We calculated the Vital Statistics Performance Index (VSPI) to have a general assessment of the vital registration performance. We examined the agreement between vital registration records and medical records at the individual and population levels using the chance-corrected concordance (CCC) and cause-specific mortality fraction (CSMF) accuracy as quality metrics.ResultsWe identified 966 records from the vital registry for all under-5 deaths, and 390 were linked to medical records of deaths occurring at public hospitals. The Yucatan vital registration system captured 94.8% of the expected under-5 deaths, with an overall VSPI score of 87.2%. Concordance between underlying cause of death listed in the vital registry and the cause determined by the medical record review varied substantially across causes, with a mean overall chance-corrected concordance across causes of 6.9% for neonates and 46.9% for children. Children had the highest concordance for digestive diseases, and neonates had the highest concordance for meningitis/sepsis. At the population level, the CSMF accuracy for identifying the underlying cause listed was 35.3% for neonates and 67.7% for children.ConclusionsAlthough the vital registration system has overall good performance, there are still problems in information about causes of death for children under 5 that are related mostly to certification of the causes of death. The accuracy of information can vary substantially across age groups and causes, with causes reported for neonates being generally less reliable than those for older children. Results highlight the need to implement strategies to improve the certification of causes of death in this population.

Are cause of death data for Shanghai fit for purpose? A retrospective study of medical records

ObjectivesTo assess the quality of cause of death reporting in Shanghai for both hospital and home deaths.Design and settingMedical records review (MRR) to independently establish a reference data set against...

Automatic Verbal Autopsy Classification Using Multinomial Logistic Regression Classifier by Using Recursive Feature Elimination

Verbal autopsy is one of the finest medical process to identify automatically the cause of a death afore medical ascendant entities will certify it. Identifying the exact cause is intricate and fuzzy in nature. The dataset with an exact cause of death is a paramount implement for every country to make the presage about the life style and medical facilities available to the people. Multinomial logistic regression was utilized in our study to relegate the exact cause of death. We used standard datasets like PHMRC and Matlab which were potentially accepted in medical field. The reason to utilize the Multinomial logistic Regression is that most of the dataset is consisting of 0 and 1 values which betoken the presence and absence of value in the attribute. We used three standard metrics like the sensitivity, Chance Corrected Concordance (CCC) and Cause-specific mortality fraction (CSMF) for a comparison of our model with precedent models like Insilico VA, Tariff and InterVA-4. Computed results show that proposed model is better than the precedent models.

Generating cause of death information to inform health policy: implementation of an automated verbal autopsy system in the Solomon Islands

BackgroundGood quality cause of death (COD) information is fundamental for formulating and evaluating public health policy; yet most deaths in developing countries, including the Solomon Islands, occur at home without medical certification of cause of death (MCCOD). As a result, COD data in such contexts are often of limited use for policy and planning. Verbal autopsies (VAs) are a cost-effective way of generating reliable COD information in populations lacking comprehensive MCCOD coverage, but this method has not previously been applied in the Solomon Islands. This study describes the establishment of a VA system to estimate the cause specific mortality fractions (CSMFs) for community deaths that are not medically certified in the Solomon Islands.MethodsAutomated VA methods (SmartVA) were introduced into the Solomon Islands in 2016. Trained data collectors (nurses) conducted VAs on eligible deaths to December 2020 using electronic tablet devices and VA responses were analysed using the Tariff 2.0 automated diagnostic algorithm. CSMFs were generated for both non-inpatient deaths in hospitals (i.e. ‘dead on/by arrival’) and community deaths.ResultsVA was applied to 914 adolescent-and-adult deaths with a median (IQR) age of 62 (45–75) years, 61% of whom were males. A specific COD could be diagnosed for more than 85% of deaths. The leading causes of death for both sexes combined were: ischemic heart disease (16.3%), stroke (13.5%), diabetes (8.1%), pneumonia (5.7%) and chronic-respiratory disease (4.8%). Stroke was the top-ranked cause for females, and ischaemic heart disease the leading cause for males. The CSMFs from the VAs were similar to Global Burden of Disease (GBD) estimates. Overall, non-communicable diseases (NCDs) accounted for 73% of adult deaths; communicable, maternal and nutritional conditions 15%, and injuries 12%. Six of the ten leading causes reported for facility deaths in the Solomon Islands were also identified as leading causes of community deaths based on the VA diagnoses.ConclusionsNCDs are the leading cause of adult deaths in the Solomon Islands. Automated VA methods are an effective means of generating reliable COD information for community deaths in the Solomon Islands and should be routinely incorporated into the national mortality surveillance system.

Validation of SmartVA using conventional autopsy: A study of adult deaths in Brazil

Accurate cause of death data are essential to guide health policy. However, mortality surveillance is limited in many low-income countries. In such settings, verbal autopsy (VA) is increasingly used to provide population-level cause of death data. VAs are now widely interpreted using the automated algorithms SmartVA and InterVA. Here we use conventional autopsy as the gold standard to validate SmartVA methodology. This study included adult deaths from natural causes in São Paulo and Recife for which conventional autopsy was indicated. VA was conducted with a relative of the deceased using an amended version of the SmartVA instrument to suit the local context. Causes of death from VA were produced using the SmartVA-Analyze program. Physician coded verbal autopsy (PCVA), conducted on the same questionnaires, and Global Burden of Disease Study data were used as additional comparators. Cause of death data were grouped into 10 broad causes for the validation due to the real-world utility of VA lying in identifying broad population cause of death patterns. The study included 2,060 deaths in São Paulo and 1,079 in Recife. The cause specific mortality fractions (CSMFs) estimated using SmartVA were broadly similar to conventional autopsy for: cardiovascular diseases (46.8% vs 54.0%, respectively), cancers (10.6% vs 11.4%), infections (7.0% vs 10.4%) and chronic respiratory disease (4.1% vs 3.7%), causes accounting for 76.1% of the autopsy dataset. The SmartVA CSMF estimates were lower than autopsy for "Other NCDs" (7.8% vs 14.6%) and higher for diabetes (13.0% vs 6.6%). CSMF accuracy of SmartVA compared to autopsy was 84.5%. CSMF accuracy for PCVA was 93.0%. The results suggest that SmartVA can, with reasonable accuracy, predict the broad cause of death groups important to assess a population's epidemiological transition. VA remains a useful tool for understanding causes of death where medical certification is not possible.

Estimating the mortality burden of large scale mining projects-Evidence from a prospective mortality surveillance study in Tanzania.

We set up a mortality surveillance system around two of the largest gold mines in Tanzania between February 2019 and February 2020 to estimate the mortality impact of gold mines. Death circumstances were collected using a standardized verbal autopsy tool, and causes of death were assigned using the InSilicoVA algorithm. We compared cause-specific mortality fractions in mining communities with other subnational data as well as national estimates. Within mining communities, we estimated mortality risks of mining workers relative to other not working at mines. At the population level, mining communities had higher road-traffic injuries (RTI) (risk difference (RD): 3.1%, Confidence Interval (CI): 0.4%, 5.9%) and non-HIV infectious disease mortality (RD: 5.6%, CI: 0.8%, 10.3%), but lower burden of HIV mortality (RD: -5.9%, CI: -10.2%, -1.6%). Relative to non-miners living in the same communities, mining workers had over twice the mortality risk (relative risk (RR): 2.09, CI: 1.57, 2.79), with particularly large increases for death due to RTIs (RR: 14.26, CI: 4.95, 41.10) and other injuries (RR:10.10, CI: 3.40, 30.02). Our results shows that gold mines continue to be associated with a large mortality burden despite major efforts to ensure the safety in mining communities. Given that most of the additional mortality risk appears to be related to injuries programs targeting these specific risks seem most desirable.