Dataset Information

Causes of death in two rural demographic surveillance sites in Bangladesh, 2004-2010: automated coding of verbal autopsies using InterVA-4.

ABSTRACT: Population-based information on causes of death (CoD) by age, sex, and area is critical for countries with limited resources to identify and address key public health issues. This study analysed the demographic surveillance and verbal autopsy (VA) data to estimate age- and sex-specific mortality rates and cause-specific mortality fractions in two well-defined rural populations within the demographic surveillance system in Abhoynagar and Mirsarai subdistricts, located in different climatic zones.During 2004-2010, the sample demographic surveillance system registered 1,384 deaths in Abhoynagar and 1,847 deaths in Mirsarai. Trained interviewers interviewed the main caretaker of the deceased with standard VA questionnaires to record signs and symptoms of diseases or conditions that led to death and health care experiences before death. The computer-automated InterVA-4 method was used to analyse VAs to determine probable CoD.Age- and sex-specific death rates revealed a higher neonatal mortality rate in Abhoynagar than Mirsarai, and death rates and sex ratios of male to female death rates were higher in the ages after infancy. Communicable diseases (CDs) accounted for 16.7% of all deaths in Abhoynagar and 21.2% in Mirsarai--the difference was due mostly to more deaths from acute respiratory infections, pneumonia, and tuberculosis in Mirsarai. Non-communicable diseases (NCDs) accounted for 56.2 and 55.3% of deaths in each subdistrict, respectively, with leading causes being stroke (16.5-19.3%), neoplasms (13.2% each), cardiac diseases (8.9-11.6%), chronic obstructive pulmonary diseases (5.1-6.3%), diseases of the digestive system (3.1-4.1%), and diabetes (2.8-3.5%), together accounting for 49.2-51.2% points of the NCD deaths in the two subdistricts. Injury and other external causes accounted for another 7.5-7.7% deaths, with self-harm being higher among females in Abhoynagar.The computer-automated coding of VA to determine CoD reconfirmed that NCDs were the leading CoD with some differences between the sites. Incorporating VA into the national sample vital registration system can help policy makers to identify the leading CoDs for public health planning.

SUBMITTER: Alam N

PROVIDER: S-EPMC4220132 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Causes of death in two rural demographic surveillance sites in Bangladesh, 2004-2010: automated coding of verbal autopsies using InterVA-4.

Alam Nurul N Chowdhury Hafizur R HR Das Subhash C SC Ashraf Ali A Streatfield P Kim PK

Global health action 20141029

<h4>Objective</h4>Population-based information on causes of death (CoD) by age, sex, and area is critical for countries with limited resources to identify and address key public health issues. This study analysed the demographic surveillance and verbal autopsy (VA) data to estimate age- and sex-specific mortality rates and cause-specific mortality fractions in two well-defined rural populations within the demographic surveillance system in Abhoynagar and Mirsarai subdistricts, located in differe ...[more]

PMID: 25377334

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Project description:This study used the InterVA-4 computerised model to assign probable cause of death (CoD) to verbal autopsies (VAs) generated from two rural areas, with a difference in health service provision, within the Matlab Health and Demographic Surveillance site (HDSS). This study aimed to compare CoD by gender, as well as discussing possible factors which could influence differences in the distribution of CoD between the two areas.Data for this study came from the Matlab the HDSS maintained by the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) since 1966. In late 1977, icddr,b divided HDSS and implemented a high-quality maternal, newborn and child health and family planning (MNCH-FP) services project in one half, called the icddr,b service area (SA), in addition to the usual public and private MNCH-FP services that serve the other half, called the government SA. HDSS field workers registered 12,144 deaths during 2003-2010, and trained interviewers obtained VA for 98.9% of them. The probabilistic model InterVA-4 probabilistic model (version 4.02) was used to derive probable CoD from VA symptoms. Cause-specific mortality rates and fractions were compared across gender and areas. Appropriate statistical tests were applied for significance testing.Mortality rates due to neonatal causes and communicable diseases (CDs) were lower in the icddr,b SA than in the government SA, where mortality rates due to non-communicable diseases (NCDs) were lower. Cause-specific mortality fractions (CSMFs) due to CDs (23.2% versus 18.8%) and neonatal causes (7.4% versus 6%) were higher in the government SA, whereas CSMFs due to NCDs were higher (58.2% versus 50.7%) in the icddr,b SA. The rank-order of CSMFs by age group showed marked variations, the largest category being acute respiratory infection/pneumonia in infancy, injury in 1-4 and 5-14 years, neoplasms in 15-49 and 50-64 years, and stroke in 65+ years.Automated InterVA-4 coding of VA to determine probable CoD revealed the difference in the structure of CoD between areas with prominence of NCDs in both areas. Such information can help local planning of health services for prevention and management of disease burden.

Project description:BackgroundMaternal education is universally recognised as a major factor in positive societal indicators (health, wellbeing, overall education, etc.) and a country's growth and economic sustainability, yet the underlying factors contributing to maternal education have not been widely investigated, especially in developing countries.ObjectiveThis study investigated the 15-year trend of maternal education in Bangladesh (2004-2018) to identify the factors contributing to maternal education.MethodThis study used publicly available cross-data from five consecutive Bangladesh Demographic and Health Surveys (2004, 2007, 2011, 2014 and 2018). Level of maternal education was grouped as 'no education', 'incomplete primary', 'complete primary', 'incomplete secondary', 'complete secondary' and 'higher education' (reference group). The differences in factors/exposure variables suspected to contribute to maternal education were measured for these groups for 2004-2018, and a survey multinomial logistic regression was performed to estimate the explanatory value of these factors.ResultsFrom 2004-2018, there was a 62% gross reduction of the no education group and a 61% gross increase in the higher education group. A gross increase was also observed for complete secondary (49%), incomplete secondary (39%) and complete primary education (14%). In multivariate analysis, in rural areas, in 2018, the probability of a woman being in the complete primary, incomplete primary or no education groups was increased (adjusted relative risk ratio: 1.21, 1.40 and 1.59), compared to 2004 (0.73, 1.09, 1.12), respectively. From 2004-2018, the factor of no television watching reduced the probability of maternal education levels. Having a husband/partner who had no education increased the probability of a woman's education level. The probability of all maternal education levels decreased across all wealth index groups.ConclusionThe data suggest that average maternal education level in Bangladesh increased from 2004-2018. However, an integrated effort is required to improve factors associated with maternal education to both increase maternal education and Bangladesh's long-term sustainability.

Project description:BackgroundInterVA is a widely disseminated tool for cause of death attribution using information from verbal autopsies. Several studies have attempted to validate the concordance and accuracy of the tool, but the main limitation of these studies is that they compare cause of death as ascertained through hospital record review or hospital discharge diagnosis with the results of InterVA. This study provides a unique opportunity to assess the performance of InterVA compared to physician-certified verbal autopsies (PCVA) and alternative automated methods for analysis.MethodsUsing clinical diagnostic gold standards to select 12,542 verbal autopsy cases, we assessed the performance of InterVA on both an individual and population level and compared the results to PCVA, conducting analyses separately for adults, children, and neonates. Following the recommendation of Murray et al., we randomly varied the cause composition over 500 test datasets to understand the performance of the tool in different settings. We also contrasted InterVA with an alternative Bayesian method, Simplified Symptom Pattern (SSP), to understand the strengths and weaknesses of the tool.ResultsAcross all age groups, InterVA performs worse than PCVA, both on an individual and population level. On an individual level, InterVA achieved a chance-corrected concordance of 24.2% for adults, 24.9% for children, and 6.3% for neonates (excluding free text, considering one cause selection). On a population level, InterVA achieved a cause-specific mortality fraction accuracy of 0.546 for adults, 0.504 for children, and 0.404 for neonates. The comparison to SSP revealed four specific characteristics that lead to superior performance of SSP. Increases in chance-corrected concordance are attained by developing cause-by-cause models (2%), using all items as opposed to only the ones that mapped to InterVA items (7%), assigning probabilities to clusters of symptoms (6%), and using empirical as opposed to expert probabilities (up to 8%).ConclusionsGiven the widespread use of verbal autopsy for understanding the burden of disease and for setting health intervention priorities in areas that lack reliable vital registrations systems, accurate analysis of verbal autopsies is essential. While InterVA is an affordable and available mechanism for assigning causes of death using verbal autopsies, users should be aware of its suboptimal performance relative to other methods.

Project description:BackgroundFollowing data access and storage concerns, Government of India transferred the management of its Sample Registration System (SRS) based mortality surveillance (formerly known as the Million Death Study) to an Indian agency. This paper introduces the new system, challenges it faced and its vision for future.MethodsThe All India Institute of Medical Sciences (AIIMS), New Delhi, the new nodal agency, established the "Mortality in India Established through Verbal Autopsy" (MINErVA) platform with state level partners across India in November 2017. The network in its first three years has undertaken capacity building of supervisors conducting verbal autopsy under the SRS, established a panel of trained physician reviewers and developed three IT-based platforms for training, quality control and coding. Coding of VA forms started from January 2015 onwards, and the cause specific mortality fractions (CSMF) of the first 14 185 adult verbal autopsy (VA) records for 2015 were compared with earlier published data for 2010-2013 to check for continuity of system performance.ResultsThe network consists of 25 institutions and a panel of 676 trained physician reviewers. 916 supervisors have been trained in conducting verbal autopsies. More than 75 000 VA forms have been coded to date. The median time taken for finalizing cause of death on the coding platform is 37 days. The level of physician agreement (67%) and proportion of VA forms requiring adjudication (12%) are consistent with published literature. Preliminary CSMF estimates for 2015 were comparable with those for 2010-2013 and identified same top ten causes of death. In addition to the delay, two major challenges identified for coding were language proficiency of physician reviewers vis-à-vis language of narratives and quality of verbal autopsies. While an initial strategic decision was made to consolidate the system to ensure continuity, future vision of the network is to move towards technology-based solutions including electronic data capture of VAs and its analysis and improving the use of mortality data in decision making.ConclusionMINErVA network is now fully functional and is moving towards achieving global standards. It provides valuable lessons for other developing countries to establish their own mortality surveillance systems.

Project description:BackgroundIn much of sub-Saharan Africa, health facilities serve as the primary source of routine vital statistics. These passive surveillance systems, however, are plagued by infrequent and unreliable reporting and do not capture events that occur outside of the formal health sector. Verbal autopsies (VA) have been utilized to estimate the burden and causes of mortality where civil registration and vital statistics systems are weak, but VAs have not been widely employed in national surveillance systems. In response, we trained lay community health workers (CHW) in a rural sub-county of western Uganda to conduct VA interviews in order to assess the feasibility of leveraging CHW to measure the burden of disease in resource limited settings.Methods and findingsTrained CHWs conducted a cross-sectional survey of the 36 villages comprising the Bugoye sub-county to identify all deaths occurring in the prior year. The sub county has an estimated population of 50,249, approximately one-quarter of whom are children under 5 years of age (25.3%). When an eligible death was reported, CHWs administered a WHO 2014 VA questionnaire, the results of which were analyzed using the InterVA-4 tool. To compare the findings of the CHW survey to existing surveillance systems, study staff reviewed inpatient registers from neighboring referral health facilities in an attempt to match recorded deaths to those identified by the survey. Overall, CHWs conducted high quality VA interviews on direct observation, identifying 230 deaths that occurred within the sub-county, including 77 (33.5%) among children under five years of age. More than half of the deaths (123 of 230, 53.5%) were reported to have occurred outside a health facility and thus would not be captured by passive surveillance. More than two-thirds (73 of 107, 68.2%) of facility deaths took place in one of three nearby hospitals, yet only 35 (47.9%) were identified on our review of inpatient registers. Consistent with previous VA studies, the leading causes of death among children under five years of age were malaria (19.5%), prematurity (19.5%), and neonatal pneumonia (15.6%). while among adults, HIV/AIDS-related deaths illness (13.6%), pulmonary tuberculosis (11.4%) and malaria (8.6%) were the leading causes of death. No child deaths identified from inpatient registers listed HIV/AIDS as a cause of death despite 8 deaths (10.4%) attributed to HIV/AIDS as determined by VA.ConclusionsLay CHWs are able to conduct high quality VA interviews to capture critical information that can be analyzed using standard methodologies to provide a more complete estimate of the burden and causes of mortality. Similar approaches can be scaled to improve the measurement of vital statistics in order to facilitate appropriate public health interventions in rural areas of sub-Saharan Africa.

Project description:BackgroundDeaths in developing countries often occur outside health facilities, making it extremely difficult to gather reliable cause of death (COD) information. Automated COD assignment using a verbal autopsy instrument (VAI) has been proposed as a reliable and cost-effective alternative to traditional physician-certified verbal autopsy, but its performance is still being evaluated. The purpose of this study was to compare the similarity of diagnosis by Medical Assistants (MA) in the Matlab Health and Demographic Surveillance System (HDSS) with the SmartVA Analyze 1.2 (Tariff 2.0) diagnosis.MethodsThis study took place between January 2011 and April 2014 in Matlab, Bangladesh. MA with 3 years of medical training assigned COD to Matlab residents by reviewing the information collected using the Population Health Metrics Research Consortium (PHMRC) long-form VAI. Smart VA Analyze 1.2 automatically assigned COD using the same questionnaire. COD agreement and cause-specific mortality fractions (CSMFs) were compared for MA and Tariff.ResultsOf the 4969 verbal autopsy cases reviewed, 4328 were adults, 296 were children, and 345 were neonates. Cohen's kappa was 0.38 (0.36, 0.40) for adults, 0.43 (0.38, 0.49) for children, and 0.27 (0.22, 0.33) for neonates. For adults, the top two COD for MA were stroke (29.6%) and ischemic heart diseases (IHD) (14.2%) and for Tariff these were stroke (32.0%) and IHD (14.0%). For children, the top two COD for MA were drowning (33.5%) and pneumonia (13.2%) and for Tariff these were also drowning (36.8%) and pneumonia (12.4%). For neonates, the top two COD for MA were birth asphyxia (41.2%) and meningitis/sepsis (22.3%) and for Tariff these were birth asphyxia (37.0%) and preterm delivery (30.9%).ConclusionThe CSMFs for Tariff and MA showed very close agreement across all age categories but some differences were observed for neonate preterm delivery and meningitis/sepsis. Given the known advantages of automated methods over physician certified verbal autopsy, the SmartVA software, incorporating the shortened VAI questionnaire and Tariff 2.0, could serve as a cost-effective alternative to Matlab MA to routinely collect and analyze verbal autopsy data in a HDSS to generate essential population level COD data for planning.

Dataset Information

Causes of death in two rural demographic surveillance sites in Bangladesh, 2004-2010: automated coding of verbal autopsies using InterVA-4.

Publications

Causes of death in two rural demographic surveillance sites in Bangladesh, 2004-2010: automated coding of verbal autopsies using InterVA-4.

Similar Datasets

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