ABSTRACT
In Indonesia, COVID-19 vaccination hesitancy persists among health care workers (HCWs). Understanding the motives and challenges impacting HCWs' acceptance of the booster vaccination is critical. Efforts are still needed to overcome apprehension about taking a booster dosage. This study aims to analyze the vaccine acceptance among HCWs in Jakarta using an extended, modified model of health behavior theories, namely The Health Belief Model (HBM) and The Theory of Planned Behavior (TPB). A cross-sectional survey from November 2021 to January 2022 was distributed to health care workers in Jakarta. Bivariate analysis followed by multivariate regression was used to assess factors associated with the vaccine intention and collected 1684 responses. The results have shown that the final model combining the constructs and demographic characteristics could explain 50% of the variance of intention to receive a COVID-19 booster vaccination. Moreover, anticipated regret had the most significant standardized coefficient among the constructs (ß = 0.381, p < 0.001). Other significant predictors in the model were attitude (ß = 0.243, p < 0.001), perceived benefits (ß = 0.103, p < 0.001), subjective norms (ß = 0.08, p = 0.005), and perceived susceptibility (ß = 0.051, p = 0.016). The findings can be used to strategize interventions to increase vaccine uptake.
ABSTRACT
OBJECTIVES: Affordable options for COVID-19 epidemiological surveillance are needed. Virus detection by reverse transcription-PCR (RT-PCR) is sensitive but costly, and antigen-based rapid diagnostic tests (RDTs) are cheap but with reduced sensitivity; both detect current infection but not exposure. RDT-IgM/IgG antibodies to SARS-CoV-2 detect exposure but have poor sensitivity for current infection. We investigated if the integration of symptomatic, demographical and diet-related comorbidities data with antibody RDTs improves their potential to assess infection rates in addition to exposure, thereby broadening their utility for surveillance. DESIGN: We conducted a cross-sectional study using data from community surveillance for SARS-CoV-2. Health workers collected nasopharyngeal swabs for RT-PCR and RDT antigen assessments and venous blood for RDT-IgM/IgG from symptomatic and asymptomatic persons. Data on age, gender, contact history, symptoms (ie, fever, cough, runny nose, sore throat, headache, dyspnoea and diarrhoea), diet-related comorbidities (ie, diabetes and hypertension) and chest radiology were collected. SETTING: High-risk communities in Jakarta, Indonesia, in May 2020. PARTICIPANTS: 343 community members' data were included. OUTCOME MEASURES: RDT-IgM/IgG sensitivity, specificity and predictive values and area under receiver operating characteristic curve for RT-PCR positivity using RDT results alone and in combination with other predictors, including symptom components derived from principal component analysis. RESULTS: There were 24 PCR-confirmed infections. RDT-IgM/IgG-positive tests were associated with infection (OR 10.8, 95% CI 4.43 to 26.4, p<0.001) with an area under the curve (AUC) of 0.708% and 50% sensitivity, 91.5% specificity, 30.8% positive predictive value (PPV) and 96.1% negative predictive value (NPV). RDT results combined with age, gender, contact history, symptoms and comorbidities increased the AUC to 0.787 and yielded 62.5% sensitivity, 87.0% specificity, 26.6% PPV and 96.9% NPV. CONCLUSIONS: SARS-CoV-2 RDT-IgM/IgG results integrated with other predictors may be an affordable tool for epidemiological surveillance for population-based COVID-19 exposure and current infection, especially in groups with outbreaks or high transmission.