Factors associated with the utilization of diagnostic tools among countries with different income levels during the COVID-19 pandemic - Shuduo Zhou; Xiangning Feng; Yunxuan Hu; Jian Yang; Ying Chen; Jon Bastow; Zhi-Jie Zheng; Ming Xu (BMC, 2023)
Abstract Background Disparities in the utilization of essential medical products are a key factor contributing to inequality in health outcomes. We aimed to analyze the trends and influencing factors in using Coronavirus disease 2019 (COVID-19) diagnostic tools and disparities in countries with different income levels. Methods We conducted a cross-sectional study using open and publicly available data sources. Data were mainly collected from the Foundation for Innovative New Diagnostics, "Our World in Data," and the Global Burden of Disease databases. Negative binomial regression model and generalized linear mixed model were employed to investigate into five sets of factors associated with the usage of diagnostics: severity of COVID-19, socioeconomic status, health status, medical service capacity, and rigidity of response. Dominance analysis was utilized to compare the relative importance of these factors. The Blinder-Oaxaca decomposition was used to decompose the difference in the usage of diagnostics between countries. Results The total COVID-19 testing rate ranged from 5.13 to 22,386.63 per 1000 people from March 2020 to October 2022 and the monthly testing rate declined dramatically from January 2022 to October 2022 (52.37/1000 vs 5.91/1000).. The total testing rate was primarily associated with socioeconomic status (37.84%), with every 1 standard deviation (SD) increase in Gross Domestic Product per capita and the proportion of people aged ≥ 70, the total testing rate increased by 88% and 31%. And so is the medical service capacity (33.66%), with every 1 SD increase in health workforce density, the number increased by 38%. The monthly testing rate was primarily associated with socioeconomic status (34.72%) and medical service capacity (28.67%), and the severity of COVID-19 (21.09%). The average difference in the total testing rates between high-income and low-income countries was 2726.59 per 1000 people, and 2493.43 (91.45%) of the differences could be explained through the five sets of factors. Conclusions Redoubling the efforts, such as local manufacturing, regulatory reliance, and strengthening the community health workforce and laboratory capacity in low- and middle-income countries (LMICs) cannot be more significant for ensuring sustainable and equitable access to diagnostic tools during pandemic.