Integrated Disease Surveillance and Response (IDSR) in Malawi: Implementation Gaps and Challenges for Timely Alert

Wu, Tsung-Shu Joseph; Kagoli, Matthew; Kaasbøll, Jens; Bjune, Gunnar

doi:10.1101/363713

Cited by 14 publications

(15 citation statements)

References 32 publications

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“…In overall, we observed that average percentage timeliness tended to be lower compared to average percentage completeness in all the four performance groups. This observation is re ected in other similar studies [12], [32]. Nonetheless, as much as this observation was common among the four performance groups, the outlier performance group speci cally brings to light larger disparities between average completeness and timeliness.…”

Section: Discussionsupporting

confidence: 68%

Evaluating performance of health care facilities at meeting HIV-indicator reporting requirements in Kenya: An application of K-means clustering algorithm

Gesicho

Babić

Were

2020

Preprint

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BackgroundThe ability to report complete, accurate and timely data by HIV care providers and other entities is a key aspect in monitoring trends in HIV prevention, treatment and care, hence contributing to its eradication. In many low-middle-income-countries (LMICs), aggregate HIV data reporting is done through the District Health Information Software 2 (DHIS2). Nevertheless, despite a long-standing requirement to report HIV indicator data to DHIS2 in LMICs, few rigorous evaluations exist to evaluate adequacy of health facility reporting at meeting completeness and timeliness requirements over time. The aim of this study is to conduct a comprehensive assessment of the reporting status for HIV indicators, from the time of DHIS2 implementation, using Kenya as a case study.MethodsA retrospective observational study was conducted to assess reporting performance of health facilities providing any of the HIV services in all 47 counties in Kenya between 2011 to 2018. Using data extracted from DHIS2, K-means clustering algorithm was used to identify homogeneous groups of health facilities based on their performance in meeting timeliness and completeness facility reporting requirements for each of the six programmatic areas. Average silhouette coefficient was used in measuring the quality of the selected clusters.ResultsBased on percentage average facility reporting completeness and timeliness, four homogeneous groups of facilities were identified namely: best performers, average performers, poor performers and outlier performers. Apart from blood safety reports, a distinct pattern was observed in five of the remaining reports, with the proportion of best performing facilities increasing and the proportion of poor performing facilities decreasing over time. However, between 2016 and 2018, the proportion of best performers declined in some of the programmatic areas. Over the study period, no distinct pattern or trend in proportion changes was observed among facilities in the average and outlier groups.ConclusionsThe identified clusters revealed general improvements in reporting performance in the various reporting areas over time, but with noticeable decrease in some areas between 2016 and 2018. This signifies the need for continuous performance monitoring with possible integration of machine learning and visualization approaches into national HIV reporting systems.

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Section: Discussionsupporting

confidence: 68%

Evaluating performance of health care facilities at meeting HIV-indicator reporting requirements in Kenya: An application of K-means clustering algorithm

Gesicho

Babić

Were

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In overall, we observed that average percentage timeliness tended to be lower compared to average percentage completeness in all the four performance groups. This observation is re ected in other similar studies [11], [29]. Nonetheless, as much as this observation was common among the four performance groups, the outlier performance group speci cally brings to light larger disparities between average completeness and timeliness.…”

Section: Discussionsupporting

confidence: 68%

Evaluating performance of health care facilities at meeting HIV-indicator reporting requirements in Kenya: An application of K-means clustering algorithm

Gesicho

Babić

Were

2020

Preprint

View full text Add to dashboard Cite

Introduction: The ability to report complete, accurate and timely data by HIV care providers and other entities is a key aspect in monitoring trends in HIV prevention, treatment and care, hence contributing to its eradication. In many low-middle-income-countries (LMICs), aggregate HIV data reporting is done through the District Health Information Software 2 (DHIS2). Nevertheless, despite a long-standing requirement to report HIV indicator data to DHIS2 in LMICs, few rigorous evaluations exist to evaluate adequacy of health facility reporting at meeting completeness and timeliness requirements over time. In this study, we report on a comprehensive assessment of the reporting status for HIV indicators, from the time of DHIS2 implementation, using Kenya as a case example.Methods: A retrospective observational study was conducted to assess reporting performance of health facilities providing any of the HIV services in all 47 counties in Kenya between 2011 to 2018. Using data extracted from DHIS2, K-means clustering algorithm was used to identify homogeneous groups of health facilities based on their performance in meeting timeliness and completeness reporting requirements for each of the six programmatic areas. Average silhouette coefficient was used in measuring the quality of the selected clusters.Results: Based on percentage average facility reporting completeness and timeliness, four homogeneous groups of facilities were identified namely: best performers, average performers, poor performers and outlier performers. Apart from blood safety reports, a distinct pattern was observed in five of the remaining reports, with the proportion of best performing facilities increasing and the proportion of poor performing facilities decreasing over time. However, between 2016 and 2018, the proportion of best performers declined in some of the programmatic areas. Over the study period, no distinct pattern or trend in proportion changes was observed among facilities in the average and outlier groups.Conclusion: The identified clusters revealed general improvements in reporting performance in the various reporting areas over time, but with noticeable decrease in some areas between 2016 and 2018. This signifies the need for continuous performance monitoring with possible integration of machine learning and visualization approaches into national HIV reporting systems.

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“…For example, in Zambia some of the challenges found were inadequate trained staff, coordination challenges and poor infrastructure (13). Ghana and Malawi found some of the challenges to include lack of technical guidelines, lack of standard case definitions, inadequate supervision/feedback and low prioritization of surveillance activities by health workers (12,14).…”

Section: Discussionmentioning

confidence: 99%

“…Though varied, many challenges are similar across the countries that have evaluated and published their IDSR performance. These challenges include: inadequate financial resources, poor coordination, weak laboratory capacity, poor communication systems, poor supervision, erratic feedback, inadequate training of health workers, lack of IDSR technical guidelines and reporting tools (12)(13)(14)(15)(16).…”

Section: Introductionmentioning

confidence: 99%

Use of technology for public health surveillance reporting: opportunities, challenges and lessons learnt from Kenya

Njeru¹,

Kareko

Kisangau

et al. 2019

Preprint

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Background Infectious diseases remain one of the greatest threats to public health globally. Effective public health surveillance systems are therefore needed to provide timely and accurate information for early detection and response. In 2016, Kenya transitioned its surveillance system from a standalone web-based surveillance system to the more sustainable and integrated District Health Information System 2(DHIS2). As part of Global Health Security Agenda(GHSA) initiatives in Kenya, training on use of the new system was conducted among surveillance officers. We evaluated the surveillance indicators during the transition period in order to assess the impact of this training on surveillance metrics and identify challenges affecting reporting rates.Methods From February to May 2017, we analysed surveillance data for 13 intervention and 13 control counties. An intervention county was defined as one that had received a refresher training on DHIS2 while a control county was one that had not received training. We evaluated the impact of the training by analysing completeness and timeliness of reporting 15 weeks before and 12 weeks after the training. A chi-square test was used to compare the reporting rates between the two groups. A structured questionnaire was administered to the training participants to assess the challenges affecting surveillance reporting. A Likert scale was used to grade the challenges.Results The completeness of reporting increased after the training by 17 percentage points (from 45% to 62%) for the intervention group compared to 3 percentage points (49% to 52%) for the control group. Timeliness of reporting increased by 21 percentage points (from 30% to 51%) for the intervention group compared to 7 percentage points (from 31% to 38%) for the control group. Major challenges identified for the low reporting rates included lack of budget support from government, lack of airtime for reporting, health workers strike, health facilities not sending surveillance data, use of wrong denominator to calculate reporting rates and surveillance officers being given other competing tasks.Conclusions Training plays an important role in improving public health surveillance reporting. However, to improve surveillance reporting rates to the desired national targets, other challenges affecting reporting must be identified and addressed accordingly.

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Integrated Disease Surveillance and Response (IDSR) in Malawi: Implementation Gaps and Challenges for Timely Alert

Cited by 14 publications

References 32 publications

Evaluating performance of health care facilities at meeting HIV-indicator reporting requirements in Kenya: An application of K-means clustering algorithm

Evaluating performance of health care facilities at meeting HIV-indicator reporting requirements in Kenya: An application of K-means clustering algorithm

Evaluating performance of health care facilities at meeting HIV-indicator reporting requirements in Kenya: An application of K-means clustering algorithm

Use of technology for public health surveillance reporting: opportunities, challenges and lessons learnt from Kenya

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