Abstract
Objectives: The adverse effects of the COVID-19 pandemic on tuberculosis (TB) detection have been well documented. Despite shared symptoms, guidance for integrated screening for TBand COVID-19 are limited, and opportunities for health systems strengthening curtailed by lockdowns. We partnered with a high TB burden district in KwaZulu-Natal, South Africa, to co-develop an integrated approach to assessing COVID-19 and TB, delivered using online learning and quality improvement, and evaluated its performance on TB testing and detection.
Methods: We conducted a mixed methods study incorporating a quasi-experimental design and process evaluation in 10 intervention and 18 control clinics. Nurses in all 28 clinics were all provided access to a four-session online course to integrate TB and COVID-19 screening and testing, which was augmented with some webinar and in-person support at the 10 intervention clinics. We estimated the effects of exposure to this additional support using interrupted time series Poisson regression mixed models. Process evaluation data comprised interviews before and after the intervention. Thematic coding was employed to provide explanations for effects of the intervention.
Results: Clinic-level support at intervention clinics was associated with a markedly higher uptake (177 nurses from 10 intervention clinics vs. 19 from 18 control clinics). Lack of familiarity with online learning, and a preference for group learning hindered the transition from face-to-face to online learning. Even so, any exposure to training was initially associated with higher rates of GeneXpert testing (adjusted incidence ratio [IRR] 1.11, 95% confidence interval 1.07–1.15) and higher positive TB diagnosis (IRR 1.38, 1.11–1.71).
Conclusions: These results add to the knowledge base regarding the effectiveness of interventions to strengthen TB case detection during the COVID-19 pandemic. The findings support the feasibility of a shift to online learning approaches in low-resource settings with appropriate support and suggest that even low-intensity interventions are capable of activating nurses to integrate existing disease control priorities during pandemic conditions.
Methods: We conducted a mixed methods study incorporating a quasi-experimental design and process evaluation in 10 intervention and 18 control clinics. Nurses in all 28 clinics were all provided access to a four-session online course to integrate TB and COVID-19 screening and testing, which was augmented with some webinar and in-person support at the 10 intervention clinics. We estimated the effects of exposure to this additional support using interrupted time series Poisson regression mixed models. Process evaluation data comprised interviews before and after the intervention. Thematic coding was employed to provide explanations for effects of the intervention.
Results: Clinic-level support at intervention clinics was associated with a markedly higher uptake (177 nurses from 10 intervention clinics vs. 19 from 18 control clinics). Lack of familiarity with online learning, and a preference for group learning hindered the transition from face-to-face to online learning. Even so, any exposure to training was initially associated with higher rates of GeneXpert testing (adjusted incidence ratio [IRR] 1.11, 95% confidence interval 1.07–1.15) and higher positive TB diagnosis (IRR 1.38, 1.11–1.71).
Conclusions: These results add to the knowledge base regarding the effectiveness of interventions to strengthen TB case detection during the COVID-19 pandemic. The findings support the feasibility of a shift to online learning approaches in low-resource settings with appropriate support and suggest that even low-intensity interventions are capable of activating nurses to integrate existing disease control priorities during pandemic conditions.
| Original language | English |
|---|---|
| Pages (from-to) | 324-334 |
| Number of pages | 11 |
| Journal | Tropical Medicine & International Health |
| Volume | 28 |
| Issue number | 4 |
| Early online date | 8 Feb 2023 |
| DOIs | |
| Publication status | Published - Apr 2023 |
