The impacts of COVID-19 hospitalizations on non-COVID-19 deaths and hospitalizations: A panel data analysis using

Naercio Menezes-Filho1,2, Bruno Kawaoka Komatsu1, Luana Villares1,2

  • 1Ruth Cardoso Chair, Insper, São Paulo, São Paulo, Brazil.

Plos One
|December 14, 2023
PubMed

Insights

The COVID-19 pandemic reduced non-COVID-19 hospitalizations and increased deaths from other causes in Brazil. Vulnerable populations, including African Brazilians, the less-educated, and the elderly, were disproportionately affected.

Area of Science:

  • Public Health
  • Epidemiology
  • Health Services Research

Background:

  • The COVID-19 pandemic strained Brazil's healthcare system, exacerbating existing demands from non-communicable diseases and births.
  • Managing hospital capacity became a significant challenge during the pandemic.

Purpose of the Study:

  • To estimate the impact of the COVID-19 pandemic on non-COVID-19 hospitalizations and deaths in Brazil.
  • To identify the specific causes and demographic groups most affected by these impacts.

Main Methods:

  • Monthly panel data on deaths and hospitalizations by municipality of residence were analyzed.
  • Regression models controlling for municipality and state-month fixed effects were used to assess the relationship between COVID-19 hospitalizations and other health outcomes.
  • Standard errors were clustered at the municipality level.

Main Results:

  • An increase of 100 COVID-19 hospitalizations per 100,000 population was associated with a decrease of 49 non-COVID-19 hospitalizations and four additional deaths from other causes.
  • COVID-19 intensive care unit hospitalizations had a larger impact on mortality.
  • African Brazilians, less-educated individuals, and the elderly experienced the most significant negative impacts.

Conclusions:

  • The pandemic led to reduced hospitalizations for other conditions and increased mortality, particularly affecting vulnerable populations.
  • Hospital overcrowding and patient fear of infection contributed to decreased demand for non-COVID-19 care.
  • Raising awareness about the risks to non-communicable chronic diseases during health emergencies is crucial, utilizing established community outreach programs like Brazil's Family Health Program.

Related Concept Videos

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
371
Pareto Chart00:52

Pareto Chart

A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
6.7K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
195
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
287
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
131
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
424