Prevalence and factors associated with non-use of health services in the Peruvian population with COVID-19 symptomatology: a secondary analysis of the 2020 National Household Survey

Staying in or out? COVID-19-induced healthcare utilization avoidance and associated socio-demographic factors in rural India

BACKGROUND

Although evidence on healthcare utilization avoidance during COVID-19 pandemic is emerging, such knowledge is limited in rural settings. An effective policy to the COVID-19 shocks and stresses in rural settings require empirical evidence to inform the design of health policies and programmes. To help overcome this evidence gap and also contribute to policy decisions, this study aimed at examining COVID-19-induced healthcare utilization avoidance and associated factors in rural India.

METHODS

This study used the third-round data from the COVID-19-Related Shocks in Rural India survey conducted between 20-24 September, 2020 across six states. The outcome variable considered in this study was COVID-19-induced healthcare utilization avoidance. Multivariable Binary Logistic Regression Model via Multiple Imputation was used to assess the factors influencing COVID-19-induced healthcare utilization avoidance.

RESULTS

Data on 4,682 respondents were used in the study. Of this, the prevalence of COVID-19-induced healthcare utilization avoidance was 15.5% in rural India across the six states. After adjusting for relevant covariates, participants from the Bihar State have significantly higher likelihood of COVID-19-induced healthcare utilization avoidance compared to those from the Andhra Pradesh. Also, participants whose educational level exceeds high school, those who use government hospital/clinic, engage in daily wage labour in agriculture have significantly higher odds of COVID-19-induced healthcare utilization avoidance compared to their counterparts.

CONCLUSION

Our study revealed that state of residence, type of health facility used, primary work activity and educational level were associated with COVID-19-induced healthcare utilization avoidance in rural India. The findings suggest that policy makers and public health authorities need to formulate policies and design interventions that acknowledge socioeconomic and demographic factors that influence healthcare use avoidance.

COVID-19 and drivers of excess death rate in Peru: A longitudinal ecological study

Background

Peru has experienced unprecedented mortality and economic toll due to the COVID-19 (Coronavirus disease 2019) pandemic in 2020. We aimed to assess the association between socioeconomic factors and excess death rate, and to explore the relative contribution of these factors to the differences in excess death rate during January–December 2020.

Methods

Different national secondary data sources were used to describe excess death rates and different determinants, from distal to proximal. A confounding-adjusted multilevel mixed-effects linear regression was used to assess the association between these variables and excess death rates. Their relative contributions to the differences in excess death rate between the periods with the highest and lowest excess death rates were analyzed through regression-based Oaxaca-Blinder decomposition methods.

Findings

The excess death rate showed an increasing trend in all regions, with different slopes. The confounding-adjusted multilevel analysis showed that higher healthcare access was associated with lower excess death rates (difference (95%CI) -0.004 (-0.005, -0.002)), whereas COVID-19 incidence was associated with higher excess death rates (difference (95%CI) 0.052 (0.042, 0.063)). The decomposition analysis showed COVID-19 incidence (41.9%), per capita income (19.4%) and unemployment rate (14.6%) as the main risk factors, while the main protective factors included per capita health expenditure (44.7%), healthcare access (33.2%) and health insurance (12.1%).

Interpretation

Our study suggests that the excess death rate during the COVID-19 pandemic in Peru may have been influenced by other factors besides COVID-19 incidence, from distal to proximal drivers, including socioeconomic determinants, factors outside and within the health sector, and susceptibility factors. Further studies at individual level are needed to corroborate our findings.

Chronic disease relapses: A cross-sectional study of the associated factors and socioeconomic inequalities during the COVID-19 pandemic in Peru

Objectives

To investigate the prevalence, associated factors and socioeconomic inequalities in chronic disease relapses (CDR) during 2020 in Peru.

Methods

A secondary analysis was made of the National Household Survey on Living Conditions and Poverty (ENAHO) 2020. Participants older than 18 years who suffered from a chronic disease and with information about the occurrence of a CDR in the last 4 weeks prior to the survey were included. Adjusted prevalence ratios (aPRs) were estimated to determine the associated factors. Socioeconomic inequality in CDR was estimated using concentration curves (CC) and the Erreygers concentration index (ECI).

Results

Data from 38,662 participants were analyzed; the prevalence of CDR in the last 4 weeks prior to the survey was 16.5% (95% CI: 15.8–17.2). Being female (aPR 1.29; 95% CI: 1.21–1.37), with regards to being male; being 30–39 (aPR 1.22; 95% CI: 1.05–1.42), 40–49 (aPR 1.29; 95% CI: 1.12–1.48), 50–59 (aPR 1.60; 95% CI: 1.41–1.82), and 60 years or older (aPR 1.80; 95% CI: 1.58–2.04), compared to 18–29; reaching up to primary (aPR 1.18; 95% CI: 1.07–1.31), or secondary education (aPR 1.13; 95% CI: 1.02–1.24), in contrast to tertiary education; presenting some physical, psychological or cognitive limitation (aPR 1.33; 95% CI: 1.21–1.46), with respect to experiencing no limitations; and being affiliated to a health insurance (aPR 1.18; CI 95%: 1.09–1.29), opposed to not having health insurance; were associated with a higher probability of CDR. Residing in the natural region of the coastal area (aPR 0.83; 95% CI: 0.74–0.92) was associated with a lower probability of relapse compared to residing in the jungle area. In people with limitations and residents of the jungle areas, the prevalence of CDR was concentrated in those with higher per capita spending.

Conclusions

Approximately 1 in 6 Peruvians with chronic diseases had a relapse within the last 4 weeks prior to the survey of 2020 and certain geographic and sociodemographic factors were found to be associated with CDR. It was also found that a higher concentration of CDR was observed in the population with the highest per capita spending with some limitations, as well as in residents of the jungle, implying the need for appropriate policy interventions that address CDR with a special focus on these populations.