ArticlePLOS global public health2025
Impact of COVID-19 on unmet needs for healthcare in Peru: an interrupted time series analysis.
Article in PLOS global public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Factors Associated with Unmet Healthcare Needs in Serbia Before and During the COVID-19 Pandemic: Analysis Based on EU-SILC Data.Healthcare (Basel, Switzerland) · 2026Article
- Trends and Factors Associated with the Non-Use of Formal Health Services in Peru, 2015-2024.International journal of environmental research and public health · 2026Article
- Temporal costs of access to formal health care among adult users in Peru: a national analysis of inequalities in travel and waiting times, 2016-2024.Frontiers in health services · 2026Article
- Neonatal mortality inequalities in Peru, 2007-2021: an ecological joinpoint trends analysis.International journal for equity in health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic affected people's health and access to healthcare worldwide. Still, little is known about how distinct phases of the pandemic - namely the lockdown period and the period directly after lockdown - impacted these outcomes, and whether there were differences in these impacts among different segments of the Peruvian population. The aim of this study was to examine the impact of different phases of the COVID-19 response on unmet needs for healthcare across Peru. Using an interrupted time series design, this study analyzed quarterly data from Peru's National Household Survey on Living Conditions and Poverty (ENAHO) from 2018 to 2022. Outcome variables included people reporting having a health problem in the last 4 weeks and, among those reporting a problem, whether they had needed and sought care. These outcomes were stratified by demographic characteristics (i.e., by age group, gender, disability, indigenous status, regions, and rural/urban). Step and trend changes in outcomes during and directly after lockdown periods were compared to the pre-pandemic period. Overall, the prevalence of health problems decreased by 18.4 percentage points (pp) during the lockdown period compared to pre-pandemic levels, although unmet healthcare needs increased by 16.7 pp. Once lockdowns were removed, prevalence of health problems and access to healthcare increased slightly, but remained below pre-pandemic levels. Certain groups, namely people with disabilities, indigenous people and people in rural areas, had persistently worse health and poorer access to care. Persistent disparities in unmet healthcare needs highlight the necessity of targeted interventions to address systemic barriers faced by vulnerable populations and ensure continuity of care during health crises.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.