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. 2023 Jun 14;45(2):e196-e203.
doi: 10.1093/pubmed/fdac115.

Non-COVID-19 deaths in times of pandemic

Affiliations

Non-COVID-19 deaths in times of pandemic

Adan Silverio-Murillo et al. J Public Health (Oxf). .

Abstract

Background and objective: To investigate the effect of the COVID-19 pandemic on non-COVID-19 deaths in Mexico.

Methods: This study analyzes monthly administrative data on 15 different causes of death in Mexico from 2017 to 2020. The effects of the COVID-19 pandemic on non-COVID-19 deaths are conducted using a difference-in-differences methodology and an event study.

Results: The evidence shows mixed results. There is an increase in six causes of death: diabetes (36.8%), hypertension (25.8%), heart attacks (40.9%), bronchitis- asthma (24.2%), anemia (28.6%) and prostate cancer (21.4%). There is a decrease in two causes of death: traffic accidents (8.8%) and HIV (13.8%). There are null effects for seven causes of death: breast cancer, cerebrovascular disease, malnutrition, alcohol-related liver disease, renal insufficiency, homicides and suicides.

Conclusions: The COVID-19 pandemic affected non-COVID-19 deaths caused by diseases that require intensive healthcare services. Conversely, this pandemic reduced social interactions, which contributed to a decrease on deaths such as traffic accidents.

Keywords: COVID-19; Mexico; health impact assessment; mortality; non-COVID-19 deaths.

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Figures

Fig. 1
Fig. 1
Event study results. SOURCE: INEGI mortality microdata. NOTES: Plotted coefficients are event study dummy variables, βq. Solid lines represent point estimates. Dotted lines display the 95% confidence intervals. Baseline fixed effects are included at the state, month and year. Robust standard errors are clustered at the state level.
Fig. 2
Fig. 2
Event study results. SOURCE: INEGI mortality microdata. NOTES: Plotted coefficients are event study dummy variables, βq. Solid lines represent point estimates. Dotted lines display the 95% confidence intervals. Baseline fixed effects are included at the state, month and year. Robust standard errors are clustered at the state level.

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