TY - JOUR
T1 - A framework for evaluating health system surveillance sensitivity to support public health decision-making for malaria elimination
T2 - a case study from Indonesia
AU - Ahmad, Riris Andono
AU - Nelli, Luca
AU - Surendra, Henry
AU - Arisanti, Risalia Reni
AU - Lesmanawati, Dyah Ayu Shinta
AU - Byrne, Isabel
AU - Dumont, Elin
AU - Drakeley, Chris
AU - Stresman, Gillian
AU - Wu, Lindsey
N1 - Funding Information:
This study was funded by the Bill and Melinda Gates Foundation (Grant OPP1177272) awarded to GS and CD and by the Indonesia Endowment Fund for Education (Lembaga Pengola Dana Pendidikan Indonesia; Grant 20151022084537) awarded to HS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2022, The Author(s).
PY - 2022
Y1 - 2022
N2 - Background: The effectiveness of a surveillance system to detect infections in the population is paramount when confirming elimination. Estimating the sensitivity of a surveillance system requires identifying key steps in the care-seeking cascade, from initial infection to confirmed diagnosis, and quantifying the probability of appropriate action at each stage. Using malaria as an example, a framework was developed to estimate the sensitivity of key components of the malaria surveillance cascade. Methods: Parameters to quantify the sensitivity of the surveillance system were derived from monthly malaria case data over a period of 36 months and semi-quantitative surveys in 46 health facilities on Java Island, Indonesia. Parameters were informed by the collected empirical data and estimated by modelling the flow of an infected individual through the system using a Bayesian framework. A model-driven health system survey was designed to collect empirical data to inform parameter estimates in the surveillance cascade. Results: Heterogeneity across health facilities was observed in the estimated probability of care-seeking (range = 0.01–0.21, mean ± sd = 0.09 ± 0.05) and testing for malaria (range = 0.00–1.00, mean ± sd = 0.16 ± 0.29). Care-seeking was higher at facilities regularly providing antimalarial drugs (Odds Ratio [OR] = 2.98, 95% Credible Intervals [CI]: 1.54–3.16). Predictably, the availability of functioning microscopy equipment was associated with increased odds of being tested for malaria (OR = 7.33, 95% CI = 20.61). Conclusions: The methods for estimating facility-level malaria surveillance sensitivity presented here can help provide a benchmark for what constitutes a strong system. The proposed approach also enables programs to identify components of the health system that can be improved to strengthen surveillance and support public-health decision-making.
AB - Background: The effectiveness of a surveillance system to detect infections in the population is paramount when confirming elimination. Estimating the sensitivity of a surveillance system requires identifying key steps in the care-seeking cascade, from initial infection to confirmed diagnosis, and quantifying the probability of appropriate action at each stage. Using malaria as an example, a framework was developed to estimate the sensitivity of key components of the malaria surveillance cascade. Methods: Parameters to quantify the sensitivity of the surveillance system were derived from monthly malaria case data over a period of 36 months and semi-quantitative surveys in 46 health facilities on Java Island, Indonesia. Parameters were informed by the collected empirical data and estimated by modelling the flow of an infected individual through the system using a Bayesian framework. A model-driven health system survey was designed to collect empirical data to inform parameter estimates in the surveillance cascade. Results: Heterogeneity across health facilities was observed in the estimated probability of care-seeking (range = 0.01–0.21, mean ± sd = 0.09 ± 0.05) and testing for malaria (range = 0.00–1.00, mean ± sd = 0.16 ± 0.29). Care-seeking was higher at facilities regularly providing antimalarial drugs (Odds Ratio [OR] = 2.98, 95% Credible Intervals [CI]: 1.54–3.16). Predictably, the availability of functioning microscopy equipment was associated with increased odds of being tested for malaria (OR = 7.33, 95% CI = 20.61). Conclusions: The methods for estimating facility-level malaria surveillance sensitivity presented here can help provide a benchmark for what constitutes a strong system. The proposed approach also enables programs to identify components of the health system that can be improved to strengthen surveillance and support public-health decision-making.
KW - Care seeking
KW - Decision-making
KW - Freedom from infection
KW - Global health
KW - Malaria elimination
KW - Public health
KW - Surveillance sensitivity
UR - http://www.scopus.com/inward/record.url?scp=85134210796&partnerID=8YFLogxK
U2 - 10.1186/s12879-022-07581-2
DO - 10.1186/s12879-022-07581-2
M3 - Article
C2 - 35840923
AN - SCOPUS:85134210796
SN - 1471-2334
VL - 22
JO - BMC Infectious Diseases
JF - BMC Infectious Diseases
IS - 1
M1 - 619
ER -
