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. 2023 Oct 25;23(1):2086.
doi: 10.1186/s12889-023-16982-0.

Household hardships and responses to COVID-19 pandemic-related shocks in Eastern Ethiopia

Jonathan A Muir  1 , Merga Dheresa  2 , Zachary J Madewell  3 , Tamirat Getachew  2 , Gamachis Daraje  2 , Gezahegn Mengesha  2 , Cynthia G Whitney  4 , Nega Assefa  2 , Solveig A Cunningham  4
Affiliations

Household hardships and responses to COVID-19 pandemic-related shocks in Eastern Ethiopia

Jonathan A Muir et al. BMC Public Health. .

Abstract

Background: COVID-19 resulted in enormous disruption to life around the world. To quell disease spread, governments implemented lockdowns that likely created hardships for households. To improve knowledge of consequences, we examine how the pandemic period was associated with household hardships and assess factors associated with these hardships.

Methods: We conducted a cross-sectional study using quasi-Poisson regression to examine factors associated with household hardships. Data were collected between August and September of 2021 from a random sample of 880 households living within a Health and Demographic Surveillance System (HDSS) located in the Harari Region and the District of Kersa, both in Eastern Ethiopia.

Results: Having a head of household with no education, residing in a rural area, larger household size, lower income and/or wealth, and community responses to COVID-19, including lockdowns and travel restrictions, were independently associated with experiencing household hardships.

Conclusions: Our results identify characteristics of groups at-risk for household hardships during the pandemic; these findings may inform efforts to mitigate the consequences of COVID-19 and future disease outbreaks.

Keywords: Africa; Resilience; SARS-CoV-2; Vulnerability.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The Harar and Kersa Health and Demographic Surveillance System (HDSS) in Eastern Ethiopia. The smaller map panels on the right identify the location of the HDSS catchment areas within Eastern Ethiopia. Maps were created by the research team using shapefiles from the UN Humanitarian Data Exchange hosted by DataHub [37]
Fig. 2
Fig. 2
Percentage (95% confidence interval) of Households that Reported a Given Hardship. Represents the distribution of hardships used in the additive index Household Hardships (n = 880)
Fig. 3
Fig. 3
Number of Hardships Experienced during the COVID-19 Pandemic (n = 880). a presents a heat map of the number of hardships reported by the 880 sampled households. b presents the distribution of the additive index Household Hardships
Fig. 4
Fig. 4
Adjusted Association with Household Hardships presented as Adjusted Incidence Rate Ratios (AIRR). Household Hardships was generated as an additive index of the number of hardships a household reported experiencing since the onset of the COVID-19 pandemic. The forest plot presents AIRRs with 95% confidence intervals from a multivariate quasi-Poisson regression model. The AIRRs were adjusted for the other variables included in the model. Education and Occupation had 9 missing values; Income had one outlier set to missing. (n=870)
Fig. 5
Fig. 5
Adjusted Associations with Household Disruptions presented as Adjusted Incidence Rate Ratios (AIRR). Household Disruptions was generated as a PCA-based index of hardships a household reported experiencing since the onset of the COVID-19 pandemic. The forest plot presents AIRRs with 95% confidence intervals from a multivariate quasi-Poisson regression model. The AIRRs were adjusted for the other variables included in the model. Education and Occupation had 9 missing values; Income had one outlier set to missing. (n=870)
Fig. 6
Fig. 6
Evaluating Effect Modification between Urbanicity and Other Factors Associated with Household Hardships. The figure presents a facet grid of the predicted number of hardships households experienced from multiple analytic models. The top facet presents the predicted number of hardships experienced for households that resided in an urban (represented in green) vs. rural (represented in yellow) area, holding all other variables at their mean, to establish the baseline hardship count for households living in either of the two communities. The counts for this baseline were estimated using the results from the model presented in Fig. 5. The remaining facets present the predicted number of hardships that households experienced based on estimates from models that included interaction terms between urbanicity, and other variables associated with Household Hardships while holding all adjusting variables at their mean
Fig. 7
Fig. 7
Percentage of Households that Utilized a Given Response to Hardships during the COVID-19 Pandemic (n = 766). Based on households that reported experiencing at least one hardship during the pandemic
See this image and copyright information in PMC

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