Diagnosing Post-Cesarean Surgical Site Infections in Rural Rwanda: Development, Validation, and Field Testing of a Screening Algorithm for Use by Community Health Workers

Abstract

Background: We aimed to develop and validate a screening algorithm to assist community health workers (CHWs) in identifying surgical site infections (SSIs) after cesarean section (c-section) in rural Africa. Methods: Patients were adult women who underwent c-section at a Rwandan rural district hospital between March and October 2017. A CHW administered a nine-item clinical questionnaire 10 ± 3 days post-operatively. Independently, a general practitioner (GP) administered the same questionnaire and assessed SSI presence by physical examination. The GP's SSI diagnosis was used as the gold standard. Using a simplified Classification and Regression Tree analysis, we identified a subset of screening questions with maximum sensitivity for the GP and CHW and evaluated the subset's sensitivity and specificity in a validation dataset. Then, we compared the subset's results when implemented in the community by CHWs with health center-reported SSI. Results: Of the 596 women enrolled, 525 (88.1%) completed the clinical questionnaire. The combination of questions concerning fever, pain, and discolored drainage maximized sensitivity for both the GPs (sensitivity = 96.8%; specificity = 85.6%) and CHWs (sensitivity = 87.1%; specificity = 73.8%). In the validation dataset, this subset had sensitivity of 95.2% and specificity of 83.3% for the GP-administered questions and sensitivity of 76.2% and specificity of 81.4% for the CHW-administered questions. In the community screening, the overall percent agreement between CHW and health center diagnoses was 81.1% (95% confidence interval: 77.2%-84.6%). Conclusions: We identified a subset of questions that had good predictive features for SSI, but its sensitivity was lower when administered by CHWs in a clinical setting, and it performed poorly in the community. Methods to improve diagnostic ability, including training or telemedicine, must be explored.

Keywords: Cesarean section; community health worker; rural sub-Saharan Africa; screening algorithm; surgical site infection.

FIG. 1.

Study timeline for development and validation of surgical site infection detection algorithm in rural Rwanda.

FIG. 2.

Comparing number of questions (Qs) with positive responses by general practitioners (GPs) and community health workers (CHWs) between those with GP-diagnosed surgical site infection and those without.

FIG. 3.

Algorithm based on sensitivity and specificity of general practitioner (A) and algorithm based on sensitivity and specificity of community health workers (B). SSI = surgical site infection.