Predictors of Mortality Among Children with Confirmed and Suspected Cases of COVID-19 in East Java, Indonesia

Ferry Efendi1, Joni Haryanto1, Eka Mishbahatul Mar'ah Has1

  • 1Faculty of Nursing, Universitas Airlangga, Surabaya, Indonesia.

Insights

Severe illness and hospitalization with ventilators are key predictors of COVID-19 mortality in children. Prevention programs can reduce hospitalizations and child deaths from the virus.

Area of Science:

  • Pediatrics
  • Infectious Diseases
  • Epidemiology

Background:

  • Coronavirus disease 2019 (COVID-19) presents a significant mortality risk across all age groups, including children.
  • Predictive factors for mortality in pediatric COVID-19 cases require further clarification.

Purpose of the Study:

  • To identify and analyze predictors associated with mortality in children diagnosed with COVID-19.

Main Methods:

  • A secondary data analysis was performed on provincial COVID-19 data from April 2020 to May 2021.
  • The study included 6,441 pediatric patients (under 18 years).
  • Statistical analyses included Chi-square and binary logistic regression.

Main Results:

  • The mortality rate among pediatric COVID-19 cases was 2.7%.
  • Significant predictors of mortality included age, case definition, treatment status, illness severity, and travel history.
  • Severe illness (AOR=46.76) and hospitalization with ventilators (AOR=22.25) were strongly associated with increased mortality risk.

Conclusions:

  • Severe illness is the most potent predictor of mortality in pediatric COVID-19 cases.
  • Implementing disease prevention and health promotion initiatives is crucial for reducing hospitalizations and mortality rates in children.
Abstract

Related Concept Videos

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:  
472
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:
162
Prevalence and Incidence01:08

Prevalence and Incidence

In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
679
Factors Affecting Illness01:18

Factors Affecting Illness

When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
For instance, risk factors are connected to illness,...
4.3K
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
212
Factors Affecting the Risk of Infection01:26

Factors Affecting the Risk of Infection

The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
12.1K