Chronic Diseases and Multimorbidity in Iran: A Study Protocol for the Use of Iranian Health Insurance Organization’s Claims Database to Understand Epidemiology, Health Service Utilization, and Patient Costs

Patterns of case fatality and hospitalization duration among nearly 1 million hospitalized COVID-19 patients covered by Iran Health Insurance Organization (IHIO) over two years of pandemic: An analysis of associated factors

BACKGROUND

Different populations and areas of the world experienced diverse COVID-19 hospitalization and mortality rates. Claims data is a systematically recorded source of hospitalized patients' information that could be used to evaluate the disease management course and outcomes. We aimed to investigate the hospitalization and mortality patterns and associated factors in a huge sample of hospitalized patients.

METHODS

In this retrospective registry-based study, we utilized claim data from the Iran Health Insurance Organization (IHIO) consisting of approximately one million hospitalized patients across various hospitals in Iran over a 26-month period. All records in the hospitalization dataset with ICD-10 codes U07.1/U07.2 for clinically/laboratory confirmed COVID-19 were included. In this study, a case referred to one instance of a patient being hospitalized. If a patient experienced multiple hospitalizations within 30 days, those were aggregated into a single case. However, if hospitalizations had longer intervals, they were considered independent cases. The primary outcomes of study were general and intensive care unit (ICU) hospitalization periods and case fatality rate (CFR) at the hospital. Besides, various demographic and hospitalization-associated factors were analyzed to derive the associations with study outcomes using accelerated failure time (AFT) and logistic regression models.

RESULTS

A total number of 1 113 678 admissions with COVID-19 diagnosis were recorded by IHIO during the study period, defined as 917 198 cases, including 51.9% females and 48.1% males. The 61-70 age group had the highest number of cases for both sexes. Among defined cases, CFR was 10.36% (95% CI: 10.29-10.42). The >80 age group had the highest CFR (26.01% [95% CI: 25.75-26.27]). The median of overall hospitalization and ICU days were 4 (IQR: 3-7) and 5 (IQR: 2-8), respectively. Male patients had a significantly higher risk for mortality both generally (odds ratio (OR) = 1.36 [1.34-1.37]) and among ICU admitted patients (1.12 [1.09-1.12]). Among various insurance funds, Foreign Citizens had the highest risk of death both generally (adjusted OR = 2.06 [1.91-2.22]) and in ICU (aOR = 1.71 [1.51-1.92]). Increasing age groups was a risk of longer hospitalization, and the >80 age group had the highest risk for overall hospitalization period (median ratio = 1.52 [1.51-1.54]) and at ICU (median ratio = 1.17 [1.16-1.18]). Considering Tehran as the reference province, Sistan and Balcuchestan (aOR = 1.4 [1.32-1.48]), Alborz (aOR = 1.28 [1.22-1.35]), and Khorasan Razavi (aOR = 1.24 [1.20-1.28]) were the provinces with the highest risk of mortality in hospitalized patients.

CONCLUSION

Hospitalization data unveiled mortality and duration associations with variables, highlighting provincial outcome disparities in Iran. Using enhanced registry systems in conjunction with other studies, empowers policymakers with evidence for optimizing resource allocation and fortifying healthcare system resilience against future health challenges.

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

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.

Prevalence and Characteristics of Persistent Postoperative Pain After Thoracic Surgery: A Systematic Review and Meta-Analysis

BACKGROUND

A systematic review and meta-analysis was conducted to investigate the prevalence and characteristics of persistent (≥3 months) postoperative pain (PPP) after thoracic surgery.

METHODS

For this purpose, Medline, Embase, and CINAHL databases were searched for the prevalence and characteristics of PPP after thoracic surgery from their inception to May 1, 2022. Random-effect meta-analysis was used to estimate pooled prevalence and characteristics.

RESULTS

We included 90 studies with 19,001 patients. At a median follow-up of 12 months, the pooled overall prevalence of PPP after thoracic surgery was 38.1% (95% confidence interval [CI], 34.1-42.3). Among patients with PPP, 40.6% (95% CI, 34.4-47.2) and 10.1% (95% CI, 6.8-14.8) experienced moderate-to-severe (rating scale ≥4/10) and severe (rating scale ≥7/10) PPP, respectively. Overall, 56.5% (95% CI, 44.3-67.9) of patients with PPP required opioid analgesic use, and 33.0% (95% CI, 22.5-44.3) showed a neuropathic component.

CONCLUSIONS

One in 3 thoracic surgery patients developed PPP. There is a need for adequate pain treatment and follow-up in patients undergoing thoracic surgery.

The COVID-19 pandemic's true death toll in Iran after two years: an interrupted time series analysis of weekly all-cause mortality data

INTRODUCTION

This study aimed to investigate overall and age group/region/sex-specific excess all-cause mortality from the inception of the COVID-19 pandemic in Iran until February 2022.

METHODS

Weekly all-cause mortality data were obtained for the period March 2015 until February 2022. We conducted interrupted time series analyses, using a generalized least-square regression model to estimate excess mortality after the COVID-19 pandemic. Using this approach, we estimated the expected post-pandemic death counts based on five years of pre-pandemic data and compared the results with observed mortality during the pandemic.

RESULTS

After the COVID-19 pandemic, we observed an immediate increase (1,934 deaths per week, p = 0.01) in weekly all-cause mortality. An estimated 240,390 excess deaths were observed in two years after the pandemic. Within the same period, 136,166 deaths were officially attributed to COVID-19. The excess mortality was greatest among males compared with females (326 versus 264 per 100k), with an increasing trend by age group. There is a clear increased excess mortality in the central and northwestern provinces.

CONCLUSION

We found that the full mortality burden during the outbreak has been much heavier than what is officially reported, with clear differences by sex, age group, and geographical region.

Polypharmacy Pattern in Iran: A Comprehensive Analysis of a Large Prescription Database

Background

Polypharmacy is a significant patient safety concern.

Objectives

This study aims to estimate the prevalence of polypharmacy, its continuity and associated factors, and common medication classes among a large outpatient population in East Azerbaijan province, Iran.

Methods

A retrospective prescription data analysis was performed. The cohort included all ≥ 20 years old subjects with at least one prescription filled during the main three-month study period (2020 March 1 - 2020 May 31). Polypharmacy was defined as being exposed to more than four different medications during the main study period, and continuous polypharmacy was defined as being exposed to more than four medications during both the main study period and follow-up period (2020 October 1 - 2020 December 31). The frequency and prevalence of polypharmacy, along with predictive factors, were estimated. We performed multivariate logistic regression and estimated odds ratios (ORs) to investigate the risk factors for polypharmacy.

Results

307,820 patients included (mean age 49.8 years, 62.9% female, mean drug use 3.7 (SD = 2.6). Polypharmacy was observed in 28.3% (CI: 28.1 - 28.4), of which 36.6% experienced continuous polypharmacy. The odds of being exposed to polypharmacy increased with being female, increasing age, and exposure to chronic conditions. The groups of medications most utilized by polypharmacy patients were those indicated for gastro-esophageal reflux diseases, beta-blocking agents, antidepressants, blood glucose-lowering drugs, and antithrombotic agents.

Conclusions

Strategies should be formulated to inform healthcare policymakers and providers about the magnitude of the polypharmacy phenomenon, associated factors, and the common medication classes involved.