Dataset Information

Validating the InterVA-5 cause of death analytical tool: using mortality data from the Comprehensive Health and Epidemiological Surveillance System in Papua New Guinea.

ABSTRACT:

Objective

InterVA-5 is a new version of an analytical tool for cause of death (COD) analysis at the population level. This study validates the InterVA-5 against the medical review method, using mortality data in Papua New Guinea (PNG).

Design and setting

This study used mortality data collected from January 2018 to December 2020 in eight surveillance sites of the Comprehensive Health and Epidemiological Surveillance System (CHESS), established by the PNG Institute of Medical Research in six major provinces.

Methods

The CHESS demographic team conducted verbal autopsy (VA) interviews with close relatives of the deceased, who died in communities within the catchment areas of CHESS, using the WHO 2016 VA instrument. COD of the deceased was assigned by InterVA-5 tool, and independently certified by the medical team. Consistency, difference and agreement between the InterVA-5 model and medical review were assessed. Sensitivity and positive predictive value (PPV) of the InterVA-5 tool were calculated with reference to the medical review method.

Results

Specific COD of 926 deceased people was included in the validation. Agreement between the InterVA-5 tool and medical review was high (kappa test: 0.72; p<0.01). Sensitivity and PPV of the InterVA-5 were 93% and 72% for cardiovascular diseases, 84% and 86% for neoplasms, 65% and 100% for other chronic non-communicable diseases (NCDs), and 78% and 64% for maternal deaths, respectively. For infectious diseases and external CODs, sensitivity and PPV of the InterVA-5 were 94% and 90%, respectively, while the sensitivity and PPV of the medical review method were both 54% for classifying neonatal CODs.

Conclusion

The InterVA-5 tool works well in the PNG context to assign specific CODs of infectious diseases, cardiovascular diseases, neoplasms and injuries. Further improvements with respect to chronic NCDs, maternal deaths and neonatal deaths are needed.

SUBMITTER: Pham BN

PROVIDER: S-EPMC10230342 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Publications

Validating the InterVA-5 cause of death analytical tool: using mortality data from the Comprehensive Health and Epidemiological Surveillance System in Papua New Guinea.

Pham Bang Nguyen BN Abori Norah N Maraga Seri S Jorry Ronny R Jaukae Gasowo S GS Silas Vinson D VD Aga Tess T Okely Tony T Pomat William W

BMJ open 20230522 5

<h4>Objective</h4>InterVA-5 is a new version of an analytical tool for cause of death (COD) analysis at the population level. This study validates the InterVA-5 against the medical review method, using mortality data in Papua New Guinea (PNG).<h4>Design and setting</h4>This study used mortality data collected from January 2018 to December 2020 in eight surveillance sites of the Comprehensive Health and Epidemiological Surveillance System (CHESS), established by the PNG Institute of Medical Resea ...[more]

PMID: 37217264

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Project description:ObjectiveThis study reported the prevalence and sociodemographic distribution of mortalities attributed to injuries in Papua New Guinea (PNG).SettingAs part of a longitudinal study, mortality data were collected from the population who live in eight surveillance sites of the Comprehensive Health and Epidemiological Surveillance System, established in six major provinces in PNG. Verbal autopsy (VA) interviews were conducted by the surveillance team with close relatives of the deceased, using the WHO 2016 VA instrument from January 2018 to December 2020.Participant and interventionMortality data from 926 VA interviews were analysed, using the InterVA-5 diagnostic tool to assign specific cause of death (COD). Distributions of injury-attributed mortality were calculated and multinomial logistic regression analyses were conducted to identify sociodemographic factors and provide ORs, 95% CIs of estimates and p values.ResultInjury-attributed deaths accounted for 13% of the total deaths recorded in the surveillance population, with the highest proportion in Madang (22%), followed by Port Moresby and Central Province (13%). Road traffic accidents were the leading COD, accounting for 43% of the total injury-attributed deaths, followed by assaults (25%) and accidental falls (10%). Young adults (aged 15-24 years) accounted the largest proportion of injury-attributed deaths (34%) and were nearly six times more likely to die from injuries than those aged 75+ years (OR: 5.89 (95% CI: 2.18 to 15.9); p<0.001). Males were twice more likely to die from injuries than females (OR: 2.0 (95% CI: 1.19 to 3.36); p=0.009). Another significant sociodemographic factor associated with the increased injury-attributed mortalities included urban versus rural residence (OR: 2.0 (95% CI: 1.01 to 3.99); p=0.048).ConclusionYoung adults, particularly those who live in urban areas, were at the highest risk of dying from injuries. Public health policies and interventions are needed to reduce premature mortality from injuries in PNG.

Project description:BackgroundPapua New Guinea (PNG) is a diverse country with high mortality and evidence of increased prevalence of non-communicable diseases (NCDs), but there is no reliable cause of death (COD) data because civil registration is insufficient and routine health data comprise only a small proportion of deaths. This study aims to estimate cause-specific mortality fractions (CSMFs) for five broad groups of causes (endemic infections, emerging infections, endemic NCDs, emerging NCDs and injuries), by sex for each of PNG's provinces.MethodsCSMFs are calculated as the average of estimates obtained from: (1) Empirical cause method: Utilising available Verbal Autopsy (VA) data and Discharge Health Information System (DHIS) data, and applying statistical models of community versus facility CODs; and (2) Expected cause patterns method: Utilising existing estimates of mortality levels in each province and statistical models of the relationship between all-cause and cause-specific mortality using Global Burden of Disease (GBD) data.ResultsAn estimated 41% of male and 49% of female deaths in PNG are due to infectious, maternal (female only), neonatal and nutritional causes. Furthermore, 45% of male and 42% of female deaths arise from NCDs. Infectious diseases, maternal, neonatal and nutritional conditions account for more than half the deaths in a number of provinces, including lower socioeconomic status provinces of Gulf and Sandaun, while provinces with higher CSMFs from emerging NCDs (e.g. ischemic heart disease, stroke) tend to be those where socioeconomic status is comparatively high (e.g. National Capital District, Western Highlands Province, Manus Province, New Ireland Province and East New Britain Province). Provinces with the highest estimated proportion of deaths from emerging infectious diseases are readily accessible by road and have the highest rates of sexually transmitted infections (STIs), while provinces with the highest CSMFs from endemic infectious, maternal, neonatal and nutritional causes are geographically isolated, have high malaria and high all-cause mortality.ConclusionsInfectious, maternal, neonatal and nutritional causes continue to be an important COD in PNG, and are likely to be higher than what is estimated by the GBD. Nonetheless, there is evidence of the emergence of NCDs in provinces with higher socioeconomic status. The introduction of routine VA for non-facility deaths should improve COD data quality to support health policy and planning to control both infectious and NCDs.

Project description:IntroductionThe COVID-19 pandemic had an unprecedented impact on the health and well-being of populations worldwide. Few studies have used household data to explore the health risks associated with COVID-19 in low-income and middle-income countries. This study assessed population vulnerability to COVID-19 by examining household socioeconomic factors related to COVID-19 health risks in Papua New Guinea (PNG).MethodsUsing household socioeconomic surveillance data from 2020, encompassing 37 880 residents living within the catchment areas of the Comprehensive Health and Epidemiological Surveillance System, the study assessed COVID-19 health risks based on the socioeconomic demographic characteristics of the surveillance population. Multinomial logistic regression analyses were conducted to determine associated factors and to estimate predictors of COVID-19 health risks.ResultsAmong the surveillance population, more than 9% reported experiencing COVID-19 health risks, including home-based quarantine (9.6%), centre-based quarantine (0.5%), positive COVID-19 test (0.1%), hospitalisation due to COVID-19 (0.3%) and death from COVID-19 (0.3%). People living in semimodern houses (OR 1.47 (95% CI 1.35 to 1.61)) (verse permanent houses), individuals living in houses with 1-2 bedrooms (OR 1.12 (95% CI 1.01 to 1.25)) (verse houses with 4+ bedrooms) and those belonging to the poorest wealth quintile (OR 1.16 (95% CI 1.024 to 1.314)) (verse the richest) were more susceptible to COVID-19 health risks. Protective factors against COVID-19 health risks included urban residence (OR 0.65 (95% CI 0.59 to 0.71)) (verse rurality), aged 0-4 years (OR 0.76 (95% CI 0.64 to 0.91)) (verse aged 55+ years), households with 7-8 members (OR 0.84 (95% CI 0.74 to 0.96)) (verse 10+ members), handwashing with soap (OR 0.3 (95% CI 0.28 to 0.33)) (verse without soap).ConclusionsThe study provides insights into the susceptibility to COVID-19 health risks across socioeconomic groups in PNG. These findings have implications for development of public health policies and interventions that can be extrapolated to similar settings for enhancing preparedness for future public health emergencies.

Dataset Information

Validating the InterVA-5 cause of death analytical tool: using mortality data from the Comprehensive Health and Epidemiological Surveillance System in Papua New Guinea.

Objective

Design and setting

Methods

Results

Conclusion

Publications

Validating the InterVA-5 cause of death analytical tool: using mortality data from the Comprehensive Health and Epidemiological Surveillance System in Papua New Guinea.

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