Neonatal mortality inequalities in Peru, 2007-2021: an ecological joinpoint trends analysis
Jeannette Avila1, Adrián Vásquez-Mejía1, Gabriela Soto-Cabezas2
1National Center for Epidemiology, Prevention and Disease Control, Lima, Peru.
International Journal for Equity in Health
|December 9, 2025
Summary
Peru saw a decrease in neonatal mortality rate (NMR) between 2007 and 2021, but significant pro-rich inequalities persisted. During COVID-19, NMR declined further, and inequalities narrowed, linked to poverty reduction and increased health spending.
Area of Science:
- Public Health
- Epidemiology
- Social Determinants of Health
Background:
- Neonatal disorders are a major global cause of lost healthy life years, with most deaths being preventable.
- Neonatal mortality rate (NMR) is a key indicator for the Sustainable Development Agenda, showing wide variations linked to social conditions.
- This study investigates ecosocial inequalities in Peru's NMR from 2007-2021, their social determinants, and changes in neonatal death profiles.
Purpose of the Study:
- To examine the magnitude and temporal trends of ecosocial inequalities in Peru's neonatal mortality rate (NMR) from 2007 to 2021.
- To analyze the relationship between NMR inequalities and social determinants like poverty, unmet basic needs, and food insecurity.
- To evaluate changes in the epidemiological profile of neonatal deaths in Peru over the study period.
Main Methods:
- An ecological study using data from Peru's 25 regions (2007-2021).
- Analysis of temporal trends in NMR and inequalities using slope index of inequality (SII) and concentration index (CIx).
- Joinpoint regression for trend inflection points and Spearman correlation for associations with contextual variables.
Main Results:
- Peru's NMR decreased from 10.3 to 8.8 deaths per 1,000 live births (2007-2021), with fastest decline around 2010-2014.
- Persistent pro-rich inequalities observed, with higher NMR in regions facing greater unmet basic needs and poverty.
- During COVID-19, inequalities narrowed as NMR fell in poorer regions and rose in richer ones; profile shifted to extreme prematurity and low birth weight.
Conclusions:
- Peru reduced neonatal mortality but persistent pro-rich regional inequalities, concentrated in highland areas, remain a concern.
- The COVID-19 period saw continued NMR decline and narrowed inequalities, coinciding with poverty reduction and increased health expenditure.
- Monitoring health inequalities alongside national averages is crucial for achieving the Sustainable Development Goal of leaving no one behind.
More Related Videos
03:35Determining Gender-Based Differences in Retinal and Choroidal Thickness in Underweight Individuals via Swept-Source Optical Coherence Tomography
Published on: December 1, 2023
580
14:43A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
Published on: January 12, 2018
12.5K
Related Concept Videos
01:29Bias in Epidemiological Studies
1.2K
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:
1.2K
01:25Statistical Methods for Analyzing Epidemiological Data
864
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:
864
01:08Prevalence and Incidence
1.5K
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...
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
1.5K
00:57Population Growth
27.7K
Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
27.7K
01:27Confounding in Epidemiological Studies
554
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...
554
01:21Causality in Epidemiology
1.5K
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...
1.5K