The Impact of Health Policies and Sociodemographic Factors on Doubling Time of the COVID-19 Pandemic in Mexico

Abstract

Background. The doubling time is the best indicator of the course of the current COVID-19 pandemic. The aim of the present investigation was to determine the impact of policies and several sociodemographic factors on the COVID-19 doubling time in Mexico. Methods. A retrospective longitudinal study was carried out across March–August, 2020. Policies issued by each of the 32 Mexican states during each week of this period were classified according to the University of Oxford Coronavirus Government Response Tracker (OxCGRT), and the doubling time of COVID-19 cases was calculated. Additionally, variables such as population size and density, poverty and mobility were included. A panel data model was applied to measure the effect of these variables on doubling time. Results. States with larger population sizes issued a larger number of policies. Delay in the issuance of policies was associated with accelerated propagation. The policy index (coefficient 0.60, p < 0.01) and the income per capita (coefficient 3.36, p < 0.01) had a positive effect on doubling time; by contrast, the population density (coefficient −0.012, p < 0.05), the mobility in parks (coefficient −1.10, p < 0.01) and the residential mobility (coefficient −4.14, p < 0.01) had a negative effect. Conclusions. Health policies had an effect on slowing the pandemic’s propagation, but population density and mobility played a fundamental role. Therefore, it is necessary to implement policies that consider these variables.

COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach

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“…[30][31][32] The fast-growing literature on COVID-19 modeling, including its relations with mobility-related interventions, has tackled many issues and advanced in the understanding of the problem. 6,13,18,19,22,23,[33][34][35][36] Regarding mobility data, mobility decrease was observed as correlated with a decrease in COVID-19 incidence in the United States. 18,33 In Mexico, the reduction of mobility was also observed to reduce infection rates.…”

“… 18 , 33 In Mexico, the reduction of mobility was also observed to reduce infection rates. 34 Mobile mobility data were also used in Israel 22 to show the effectiveness of mobility reduction in the decrease of morbidity and mortality. Similarly, mobility restrictions, observed via mobility data, had substantial effects on reducing the spread of COVID-19 in China.…”