Dataset Information

Exploring challenges and recommendations for verbal autopsy implementation in low-/middle-income countries: a cross-sectional study of Iringa Region-Tanzania.

ABSTRACT:

Background

Verbal autopsy (VA) plays a vital role in providing cause-of-death information in places where such information is not available. Many low-/middle-income countries (LMICs) including Tanzania are still struggling to yield quality and adequate cause-of-death data for Civil Registration and Vital Statistics (CRVS).

Objective

To highlight challenges and recommendations for VA implementation to support LMICs yield quality and adequate mortality statistics for informed decisions on healthcare interventions.

Design

Cross-sectional study.

Study setting

Iringa region in Tanzania.

Participants

41 people including 33 community health workers, 1 VA national coordinator, 5 national task force members, 1 VA regional coordinator and 1 member of the VA data management team.

Results

The perceived challenges of key informants include a weak death notification system, lengthy VA questionnaire, poor data quality and inconsistent responses, lack of clarity in the inclusion criteria, poor commitment to roles and responsibilities, poor coordination, poor financial mechanism and no or delayed feedback to VA implementers. Based on these findings, we recommend the following strategies for effective adaptation and use of VAs: (1) reinforce or implement legislative procedures towards the legal requirement for death notification. (2) Engage key stakeholders in the overall implementation of VAs. (3) Build capacity for data collection, monitoring, processing and use of VA data. (4) Improve the VA questionnaire and quality control mechanism for optimal use in data collection. (5) Create sustainable financing mechanisms and institutionalisation of VA implementation. (6) Integrating VA Implementation in CRVS.

Conclusion

Effective VA implementation demands through planning, stakeholder engagement, upskilling of local experts and fair compensation for interviewers. Such coordinated endeavours will overcome systemic, technical and behavioural challenges hindering VA's successful implementation.

SUBMITTER: Tunga M

PROVIDER: S-EPMC10729186 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Publications

Exploring challenges and recommendations for verbal autopsy implementation in low-/middle-income countries: a cross-sectional study of Iringa Region-Tanzania.

Tunga Mahadia M Lungo Juma Hemed JH Chambua James J Kateule Ruthbetha R Lyatuu Isaac I

BMJ open 20231212 12

<h4>Background</h4>Verbal autopsy (VA) plays a vital role in providing cause-of-death information in places where such information is not available. Many low-/middle-income countries (LMICs) including Tanzania are still struggling to yield quality and adequate cause-of-death data for Civil Registration and Vital Statistics (CRVS).<h4>Objective</h4>To highlight challenges and recommendations for VA implementation to support LMICs yield quality and adequate mortality statistics for informed decisi ...[more]

PMID: 38086579

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Project description:BackgroundPhysician-coded verbal autopsy (PCVA) is the most widely used method to determine causes of death (CODs) in countries where medical certification of death is uncommon. Computer-coded verbal autopsy (CCVA) methods have been proposed as a faster and cheaper alternative to PCVA, though they have not been widely compared to PCVA or to each other.MethodsWe compared the performance of open-source random forest, open-source tariff method, InterVA-4, and the King-Lu method to PCVA on five datasets comprising over 24,000 verbal autopsies from low- and middle-income countries. Metrics to assess performance were positive predictive value and partial chance-corrected concordance at the individual level, and cause-specific mortality fraction accuracy and cause-specific mortality fraction error at the population level.ResultsThe positive predictive value for the most probable COD predicted by the four CCVA methods averaged about 43% to 44% across the datasets. The average positive predictive value improved for the top three most probable CODs, with greater improvements for open-source random forest (69%) and open-source tariff method (68%) than for InterVA-4 (62%). The average partial chance-corrected concordance for the most probable COD predicted by the open-source random forest, open-source tariff method and InterVA-4 were 41%, 40% and 41%, respectively, with better results for the top three most probable CODs. Performance generally improved with larger datasets. At the population level, the King-Lu method had the highest average cause-specific mortality fraction accuracy across all five datasets (91%), followed by InterVA-4 (72% across three datasets), open-source random forest (71%) and open-source tariff method (54%).ConclusionsOn an individual level, no single method was able to replicate the physician assignment of COD more than about half the time. At the population level, the King-Lu method was the best method to estimate cause-specific mortality fractions, though it does not assign individual CODs. Future testing should focus on combining different computer-coded verbal autopsy tools, paired with PCVA strengths. This includes using open-source tools applied to larger and varied datasets (especially those including a random sample of deaths drawn from the population), so as to establish the performance for age- and sex-specific CODs.

Project description:Background: Child and adolescent mental health (CAMH) policy is essential for the rational development of mental health systems for children and adolescents. However, there is a universal lack of CAMH policy, especially in low- and middle-income countries (LMICs). Therefore, this review aims to identify challenges and lessons for LMICs to develop and implement CAMH policy. Methods: PubMed (1781-), MEDLINE (1950-), EMBASE (1966-), and PsycINFO (1895-) were searched from inception to December 31, 2018, for publications on CAMH policy development and/or implementation. Abstracts and main texts of articles were double screened, and extracted data were analyzed through thematic synthesis. Results: A total of 31 publications were included through the systematic review. Six major challenges were identified for CAMH policy in LMICs: (i) poor public awareness and low political willingness; (ii) stigma against mental disorders; (iii) biased culture values toward children, adolescents and CAMH, from developmental nihilism to medicalization; (iv) the lack of CAMH data and evidence, from service statistics to program evaluation; (v) the shortage of CAMH resources, including human resources, service facilities, and funding; and (vi) unintended consequence of international support, including reducing local responsibilities, planning fragmentation, and unsustainability. Six lessons to overcome challenges were summarized: (i) rethinking the concept of CAMH, (ii) encouraging a stand-alone CAMH policy and budget, (iii) involving stakeholders, (iv) reinforcing the role of research and researchers in policy process, (v) innovating the usage of human and service resources, and (vi) maximizing the positive influence of international organizations and non-governmental organizations. Conclusion: Many LMICs are still facing various challenges for their CAMH policy development and implementation. To overcome the challenges, great and long-term efforts are needed, which include great determination of from domestic and global agents, multidisciplinary innovations, and collaboration and coordination from different sectors.

Dataset Information

Exploring challenges and recommendations for verbal autopsy implementation in low-/middle-income countries: a cross-sectional study of Iringa Region-Tanzania.

Background

Objective

Design

Study setting

Participants

Results

Conclusion

Publications

Exploring challenges and recommendations for verbal autopsy implementation in low-/middle-income countries: a cross-sectional study of Iringa Region-Tanzania.

Similar Datasets

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