Geospatial analysis of sociodemographic factors associated with the first wave of SARS- CoV-2, 6 districts, Zambia, July 2020

Abstract

Abstract Background The geospatialdistribution of sociodemographic factors can provide a spatial context for understanding the sociodemographic effects of infectious disease transmission, including SARS-CoV-2, a virus that is spread through respiratory secretions. We assessed the relationship between the geospatial distribution of sociodemographic factors and SARS-CoV-2 prevalence in Zambia. Methods We conducted a cross-sectional study of SARS-CoV-2 prevalence in six districts in July 2020, which corresponded to the upwards trend of the first wave in Zambia. In each district, 16 standard enumeration areas (SEAs) were randomly selected, and 20 households from each SEA were sampled. SARS-CoV-2 prevalence was calculated as the number of persons with a positive SARS-CoV-2 polymerase chain reaction test divided by the number tested. We analysed SEA geospatial data for population density, socioeconomic status (SES) (with lower scores indicating reduced vulnerability), literacy, access to water, sanitation, and hygiene factors. Generalized estimating equations (GEEs) were used to measure adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for SARS-CoV-2 prevalence with geospatial factors, adjusting for clustering by district. In addition, we performed multivariable analysis using conditional autoregressive (CAR) models to measure associations between SARS-CoV-2 prevalence and the spatial distribution of sociodemographicfactors. Results Overall, the median SARS-CoV-2 prevalence in the 96 SEAs was 41.7 (interquartile range (IQR), 0.0-96.2) infections per 1000 persons. In the multivariable GEE analysis, increasing SES vulnerability and increasing population density were associated with lower SARS-CoV-2 prevalence (aPR=0.59, 95% confidence interval: CI=0.38-0.92, aPR=0.97, 95% CI=0.95-0.99, respectively). Conversely, urban SEAs were associated with a higher SARS-CoV-2 prevalence (aPR=2.12, 95% CI=1.29-3.49). The findings were similar in the multivariable CAR analysis. Conclusions SARS-CoV-2 prevalence was higher in wealthier, urban EAs, which was counter to our expectations. Because this study was conducted early in the first wave, our findings could be unique to this period. Additional analyses from subsequent waves could confirm whether these findings persist. At the beginning of a COVID-19 wave in Zambia, it is essential that surveillance and response activities focus on urban population centres.