Evidence map›Paper›PMID 40673071›Full record

ArticleHealth services insights2025

Evaluating Colombia's Hospital Bed Expansion From 2010 to 2022 Using Interrupted Time Series Analysis During the COVID-19 Pandemic.

Paul Rodríguez Lesmes, Dalya Sofía Rua, Catalina Durán

Abstract read
In one paragraph

Article in Health services insights, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Paul Rodríguez LesmesSchool of Economics, Universidad del Rosario, Bogotá, Colombia.ORCID https://orcid.org/0000-0003-1058-3062
Dalya Sofía RuaSchool of Economics, Universidad del Rosario, Bogotá, Colombia.
Catalina DuránSchool of Economics, Universidad del Rosario, Bogotá, Colombia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The COVID-19 pandemic posed unprecedented challenges to healthcare systems worldwide, prompting governments to rapidly expand hospital bed capacity to meet the surge in demand for medical care. This study focuses on evaluating Colombia's healthcare system response by examining the expansion of hospital beds during the health crisis. Methods: In an observational study, we used a national census of all healthcare facilities in Colombia, compiled by the Ministry of Health and Social Protection (MPSP), to analyze changes in hospital bed allocation from 2010 to 2022. Our analysis accounted for the size of each provider, its public or private ownership, and the types of services it was authorized to deliver. We applied interrupted time series models to assess changes at both the hospital and municipal levels over time. Results: Findings reveal a significant reduction in hospital beds in 2020, with adult beds decreasing by 1049 units and pediatric beds by 0.709 units compared to 2019. However, there was an increase in ICU beds for adults across all years, which persisted after the end of the crisis. The expansion mainly targeted small hospitals and the public network of providers. Most of the expansion was restricted to areas of the country that already had a supply of ICU and hospitalization beds. Conclusion: Colombia's healthcare system responded dynamically to the COVID-19 pandemic by reallocating resources and expanding hospital bed capacity, despite the complex command and control configuration of its health system. Yet, it shows that the country needs to modify its financial and organizational structures to ensure better preparedness for future health crises.

Indexed as

COVID-19healthcare systemhospital bedsintensive care units

Identifiers

PMID40673071
PMCPMC12264418

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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.