Introduction: The COVID-19 epidemic caused significant disruptions worldwide, particularly in healthcare systems. India's second wave, driven by the Delta variant in 2021, severely affected healthcare capacity, leading to resource shortages and healthcare service disruptions. In this context, understanding the factors influencing SARS-CoV-2 testing is crucial for improving public health responses. This study investigates testing determinants in Uttar Pradesh, India, using Andersen's Behavioral Model of Health Services Use.
Methodology: We chose Lucknow and Sitapur districts in Uttar Pradesh based on the number of SARS-CoV-2 tests conducted per million people during the second wave of the epidemic. We conducted a cross-sectional study and surveyed 675 consenting respondents aged 18 and above from both districts. These respondents reported experiencing at least three COVID-19 symptoms between March and June 2021 (the second wave in the state). The survey was conducted face-to-face using a structured questionnaire on an electronic device. We used multiple correspondence analysis (MCA) to identify underlying factors, which were then utilized in a logistic regression model to assess their impact on SARS-CoV-2 testing.
Results: The testing rate in Lucknow (281, 84.6%) was higher than in Sitapur (117, 34.1%) (P < 0.001). Urban residents had a higher likelihood of being tested (188, 75.8%) than rural residents (210, 49.2%) (P < 0.001). Males (213, 63.0%) were more frequently tested than females (185, 54.9%) (P = 0.032). Postgraduates had the highest testing rate (49, 89.1%) compared to those without formal education (73, 44.8%) (P < 0.001). Individuals in regular jobs were more likely to be tested (171, 67.1%) compared to homemakers (128, 51.2%) and laborers (72, 57.1%) (P = 0.004). Smaller households (<5 members) had higher testing rates (146, 69.9%) than larger ones (252, 54.1%) (P < 0.001). Those living closer to a facility were more frequently tested (90, 64.3%) compared to those farther away (61, 34.1%) (P < 0.001). Additionally, individuals with access to public transport had higher testing rates (294, 62.0%) compared to those without (104, 51.7%) (P = 0.013). Higher-income groups were more likely to be tested (14, 93.3%) than low-income individuals (39, 36.8%) (P < 0.001). Psychological factors such as ease of testing (285, 72.5%) vs. (71, 38.6%) and perceived likelihood of needing testing (312, 90.7%) vs. (78, 25.1%) were strong predictors (both P < 0.001). Logistic regression identified urban residency and education as key determinants (odds ratio [OR] = 2.00, P < 0.001).
Conclusions: This study identifies key sociodemographic, logistical, and psychological factors influencing SARS-CoV-2 testing during the second wave of COVID-19 in Uttar Pradesh, India. Addressing disparities in healthcare infrastructure, improving health literacy, and reducing psychological barriers are essential to enhancing public health responses in future pandemics. Expanding healthcare access in rural areas and targeted public health campaigns could help bridge the gap in testing utilization. Further research is needed to explore these factors longitudinally and in different regional contexts.
Keywords: andersen's behavioral model; covid-19; healthcare access; logistic regression; multiple correspondence analysis (mca); psychological barriers; sars-cov-2; testing; uttar pradesh.
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