Evidence map›Paper›PMID 38164450›Full record

ArticleFrontiers in public health2023

Drug prescription patterns and their association with mortality and hospitalization duration in COVID-19 patients: insights from big data.

Reza Mehrizi, Ali Golestani, Mohammad-Reza Malekpour, Hossein Karami, Mohammad Mahdi Nasehi, Mohammad Effatpanah, Hossein Ranjbaran, Zahra Shahali, Ali Akbari Sari, Rajabali Daroudi

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In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.0field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

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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

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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

6 citing papers in PubMed, 5 citations in OpenAlex.

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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

10 authors at 3 institutions in 1 country.

Reza Mehrizi *National Center for Health Insurance Research, Tehran, Iran.
Ali Golestani *Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Mohammad-Reza MalekpourNon-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Hossein KaramiNational Center for Health Insurance Research, Tehran, Iran.
Mohammad Mahdi NasehiNational Center for Health Insurance Research, Tehran, Iran.
Mohammad EffatpanahNational Center for Health Insurance Research, Tehran, Iran.
Hossein RanjbaranNational Center for Health Insurance Research, Tehran, Iran.
Zahra ShahaliNational Center for Health Insurance Research, Tehran, Iran.
Ali Akbari SariDepartment of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Rajabali DaroudiDepartment of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Tehran University of Medical Sciences · IRMazandaran University of Medical Sciences · IRShahid Beheshti University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Different medication prescription patterns have been associated with varying course of disease and outcomes in COVID-19. Health claims data is a rich source of information on disease treatment and outcomes. We aimed to investigate drug prescription patterns and their association with mortality and hospitalization via insurance data for a relatively long period of the pandemic in Iran. Methods: We retrieved hospitalized patients' data from Iran Health Insurance Organization (IHIO) spanning 26 months (2020-2022) nationwide. Included were patients with ICD-10 codes U07.1/U07.2 for confirmed/suspected COVID-19. A case was defined as a single hospitalization event for an individual patient. Multiple hospitalizations of a patient within a 30-day interval were aggregated into a single case, while hospitalizations with intervals exceeding 30 days were treated as independent cases. The Anatomical Therapeutic Chemical (ATC) was used for medications classification. The two main study outcomes were general and intensive care unit (ICU) hospitalization periods and mortality. Besides, various demographic and clinical associate factors were analyzed to derive the associations with medication prescription patterns and study outcomes using accelerated failure time (AFT) and logistic regression models. Results: During the 26 months of the study period, 1,113,678 admissions with COVID-19 diagnosis at hospitals working in company with IHIO were recorded. 917,198 cases were detected from the database, among which 51.91% were females and 48.09% were males. Among the main groups of medications, antithrombotics (55.84% [95% CI: 55.74-55.94]), corticosteroids (54.14% [54.04-54.24]), and antibiotics (42.22% [42.12-42.32]) were the top used medications among cases with COVID-19. Investigation of the duration of hospitalization based on main medication groups showed antithrombotics (adjusted median ratio = 0.94 [0.94-0.95]) were significantly associated with shorter periods of overall hospitalization. Also, antithrombotics (adjusted odds ratio = 0.74 [95%CI, 0.73-0.76]), corticosteroids (0.97 [0.95-0.99]), antivirals (0.82 [0.80-0.83]), and ACE inhibitor/ARB (0.79 [0.77-0.80]) were significantly associated with lower mortality. Conclusion: Over 2 years of investigation, antithrombotics, corticosteroids, and antibiotics were the top medications for hospitalized patients with COVID-19. Trends in medication prescription varied based on various factors across the country. Medication prescriptions could potentially significantly impact the trends of mortality and hospitalization during epidemics, thereby affecting both health and economic burdens.

Indexed as

COVID-19Adrenal Cortex HormonesAngiotensin-Converting Enzyme InhibitorsAngiotensin Receptor AntagonistsAnti-Bacterial AgentsBig DataCOVID-19 TestingDrug PrescriptionsFemaleFibrinolytic AgentsHospitalizationHumansMaleAdrenal Cortex HormonesAngiotensin-Converting Enzyme InhibitorsAngiotensin Receptor AntagonistsAnti-Bacterial AgentsFibrinolytic Agentsclaims dataCOVID-19drug prescriptionselectronic health recordsmortality

Identifiers

PMID38164450
PMCPMC10758044
OpenAlexW4389925575

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LicenceCC BY
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Registered trials

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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.