Impact of the COVID-19 pandemic on the services provided by the Peruvian health system: an analysis of people with chronic diseases

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Abstract

During the pandemic, many individuals with chronic or infectious diseases other than COVID-19 were unable to receive the care they needed due to the high demand for respiratory care. Our study aims to assess the impact of the COVID-19 pandemic on services provided to people with chronic diseases in Peru from 2016 to 2022. We performed a secondary database analysis of data registered by the comprehensive health insurance (SIS), the intangible solidarity health fund (FISSAL), and private healthcare institutions (EPS), using interrupted time series analysis. Our study identified 21,281,128 individual users who received care. The pooled analysis revealed an average decrease of 1,782,446 in the number of users receiving care in the first month of the pandemic compared with the expected values for that month based on pre-pandemic measurements. In addition, during the pandemic months, there was an average increase of 57,911 in the number of new additional single users who received care per month compared with the previous month. According to the time-series analysis of users receiving care per month based on each chronic disease group, the most significant decreases included people with diabetes without complications and chronic lung disease.

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Most cited references 21

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A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

Mary Charlson, Peter Pompei, Kathy Ales … (1987)

The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.

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A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix

Whitney K. Newey, Kenneth West (1987)

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Impact of COVID-19 pandemic on utilisation of healthcare services: a systematic review

Ray Moynihan, Sharon Sanders, Zoe A Michaleff … (2021)

Objectives To determine the extent and nature of changes in utilisation of healthcare services during COVID-19 pandemic. Design Systematic review. Eligibility Eligible studies compared utilisation of services during COVID-19 pandemic to at least one comparable period in prior years. Services included visits, admissions, diagnostics and therapeutics. Studies were excluded if from single centres or studied only patients with COVID-19. Data sources PubMed, Embase, Cochrane COVID-19 Study Register and preprints were searched, without language restrictions, until 10 August, using detailed searches with key concepts including COVID-19, health services and impact. Data analysis Risk of bias was assessed by adapting the Risk of Bias in Non-randomised Studies of Interventions tool, and a Cochrane Effective Practice and Organization of Care tool. Results were analysed using descriptive statistics, graphical figures and narrative synthesis. Outcome measures Primary outcome was change in service utilisation between prepandemic and pandemic periods. Secondary outcome was the change in proportions of users of healthcare services with milder or more severe illness (eg, triage scores). Results 3097 unique references were identified, and 81 studies across 20 countries included, reporting on >11 million services prepandemic and 6.9 million during pandemic. For the primary outcome, there were 143 estimates of changes, with a median 37% reduction in services overall (IQR −51% to −20%), comprising median reductions for visits of 42% (−53% to −32%), admissions 28% (−40% to −17%), diagnostics 31% (−53% to −24%) and for therapeutics 30% (−57% to −19%). Among 35 studies reporting secondary outcomes, there were 60 estimates, with 27 (45%) reporting larger reductions in utilisation among people with a milder spectrum of illness, and 33 (55%) reporting no difference. Conclusions Healthcare utilisation decreased by about a third during the pandemic, with considerable variation, and with greater reductions among people with less severe illness. While addressing unmet need remains a priority, studies of health impacts of reductions may help health systems reduce unnecessary care in the postpandemic recovery. PROSPERO registration number CRD42020203729.

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Author and article information

Contributors

Luciana Bellido-Boza:

ORCID: http://orcid.org/0000-0003-0825-9271

lubellido@gmail.com

Journal

Journal ID (nlm-ta): Sci Rep

Journal ID (iso-abbrev): Sci Rep

Title: Scientific Reports

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2045-2322

Publication date (Electronic): 13 February 2024

Publication date PMC-release: 13 February 2024

Publication date Collection: 2024

Volume: 14

Electronic Location Identifier: 3664

Affiliations

[1 ]Intendencia de Invetigación y Desarrollo, Superintendencia Nacional de Salud, Lima, Peru

[2 ]Instituto Peruano de Orientación Psicológica, ( https://ror.org/046ghm278) Lima, Peru

[3 ]Escuela de Medicina, Universidad César Vallejo, ( https://ror.org/0297axj39) Trujillo, Peru

[4 ]Facultad de Ciencias de la Salud, Universidad Peruana de Ciencias Aplicadas, ( https://ror.org/047xrr705) Lima, Peru

Author information

David Villarreal-Zegarra http://orcid.org/0000-0002-2222-4764

Luciana Bellido-Boza http://orcid.org/0000-0003-0825-9271

Alfonso Erazo http://orcid.org/0009-0004-7099-823X

Max Pariona-Cárdenas http://orcid.org/0009-0005-6609-127X

Paul Valdivia-Miranda http://orcid.org/0009-0004-4090-698X

Article

Publisher ID: 54275

DOI: 10.1038/s41598-024-54275-7

PMC ID: 10864310

PubMed ID: 38351170

SO-VID: 4eb0d1b9-770d-46bc-adfd-691c78a18015

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 13 December 2023

Date accepted : 10 February 2024

Funding

Funded by: Dirección de Investigación de la Universidad Peruana de Ciencias Aplicadas

Award ID: A-093-2024

Award Recipient : Luciana Bellido-Boza

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ScienceOpen disciplines: Uncategorized

Keywords: interrupted time series analysis,chronic diseases,covid-19,peru,health care,public health

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ScienceOpen disciplines: Uncategorized

Keywords: interrupted time series analysis, chronic diseases, covid-19, peru, health care, public health

Impact of the COVID-19 pandemic on the services provided by the Peruvian health system: an analysis of people with chronic diseases

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Novel Coronavirus Disease COVID-19

Most cited references 21

A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix

Impact of COVID-19 pandemic on utilisation of healthcare services: a systematic review

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