Do socio-economic indicators associate with COVID-2019 cases? Findings from a Philippine study

Background: A wide spectrum of indicators has been postulated to associate with Coronavirus Disease 2019 (Covid-2019) cases. Among which were demographic profile, latitude, humidity, temperature, and ozone concentration. Despite obtaining significant results, there is still a dearth of research exploring other substantial determinants of Covid-2019 cases. The Philippine government is currently challenged to address issues pertaining to poverty and substinence. Empirical evidence of these studies suggests how identification of potential indicators could aid in the formulation of targeted strategies to mitigate future health problems. In this study, seven socio-economic indicators were associated with Covid-2019 cases across 17 regions in the Philippines. Methods: This is a retrospective study utilizing readily accessible public data in the analysis. Socio-economic indicators used were poverty incidence, magnitude of poor families, substinence incidence, and magnitude of substinence poor population. In addition, the income, expenditure, and savings recorded per Philippine region were taken for the analysis. A single Philippine region was the sampling unit; hence, a total of 17 regions were assessed. Covid-2019 cases as of April 7, 2020 were considered for the analysis. Descriptive statistics, Kendall rank correlation, and stepwise regression were used to determine if the seven socio-economic indicators were associated with Covid-2019 cases. Findings: Substinence incidence and income were retained for the regression model, which explained 87.2 percent of the variance in the Covid-2019 cases (R2 = .872). The results indicated that for every 1,000 PhP increase in income, there was a decrease of 3.99 Covid-2019 cases in each Philippine region. Meanwhile, for every 1.0 percent increase in substinence incidence, there was an increase of 3.34 Covid-2019 cases in each Philippine region. Interpretation: High income and low substinence incidence are associated with significant reductions in Covid-2019 cases across the 17 regions of the Philippines. This provides additional knowledge to policy makers and health officials in formulating targeted strategies to regions that could potentially record high number of Covid-2019 cases in the future. Early identification of these high-risk regions would warrant prompt preventive measures. Given the seasonal and recurring nature of Covid-2019 with respect to previous outbreaks, it is essential for the Philippine government to formulate directed policies and innovate programs that would decrease substinence and increase income. Concerted multi-region efforts should be made to prepare for possible infection outbreaks in the future. Additional studies could be explored in the future to capture significant changes in the socio-economic indicators.