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.

K-means clustering of outpatient prescription claims for health insureds in Iran.

OBJECTIVE: The segmentation of consumers based on their behavior and needs is the most crucial action of the health insurance organization. This study's objective is to cluster Iranian health insureds according to their demographics and data on outpatient prescriptions. SETTING: The population in this study corresponded to the research sample. The Health Insurance Organization's outpatient claims were registered consecutively in 2016, 2017, 2018, and 2019 were clustered. DESIGN: The k-means clustering algorithm was used to cross-sectionally and retrospectively analyze secondary data from outpatient prescription claims for secondary care using Python 3.10. PARTICIPANTS: The current analysis transformed 21 776 350 outpatient prescription claims from health insured into 193 552 insureds. RESULTS: Insureds using IQR were split into three classes: low, middle, and high risk. Based on the silhouette coefficient, the insureds of all classes were divided into three clusters. In all data for a period of four years, the first through third clusters, there were 21 799, 7170, and 19 419 insureds in the low-risk class. Middle-risk class had 48 348,23 321, 25 107 insureds, and 14 037, 28 504, 5847 insured in the high-risk class were included. For the first cluster of low-risk insureds: the total average cost of prescriptions paid by the insurance for the insureds was $211, the average age was 26 years, the average franchise was 88.5US$, the average number of medications and prescriptions were 409 and 62, the total average costs of prescriptions Outpatient was 302.5 US$, the total average number of medications for acute and chronic disease was 178 and 215, respectively. The majority of insureds were men, and those who were part of the householder's family. CONCLUSIONS: By segmenting insurance customers, insurers can set insurance premium rates, controlling the risk of loss, which improves their capacity to compete in the insurance market.

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.

Estimation and predictors of direct hospitalisation expenses and in-hospital mortality for patients who had a stroke in a low-middle income country: evidence from a nationwide cross-sectional study in Iranian hospitals.

OBJECTIVE: Stroke is the second most prevalent cardiovascular disease in Iran. This study investigates the estimation and predictors of hospitalisation expenses and in-hospital mortality for patients who had a stroke in Iranian hospitals. SETTING: Patients who had a stroke in Iran between 2019 and 2020 were identified through the data collected from the Iran Health Insurance Organization and the Ministry of Health and Medical Education. This study is the first to conduct a pervasive, nationwide investigation. DESIGN: This is a cross-sectional, prevalence-based study. Generalised linear models and a multiple logistic regression model were used to determine the predictors of hospitalisation expenses and in-hospital mortality for patients who had a stroke. PARTICIPANTS: A total of 19 150 patients suffering from stroke were studied. RESULTS: Mean hospitalisation expenses per patient who had a stroke in Iran amounted to US$590.91±974.44 (mean±SD). Mean daily hospitalisation expenses per patient who had a stroke were US$55.18±37.89. The in-hospital mortality for patients who had a stroke was 18.80%. Younger people (aged ≤49 years) had significantly higher expenses than older patients. The OR of in-hospital mortality in haemorrhagic stroke was significantly higher by 1.539 times (95% CI, 1.401 to 1.691) compared with ischaemic and unspecified strokes. Compared with patients covered by the rural fund, patients covered by Iranian health insurance had significantly higher costs by 1.14 times (95% CI, 1.186 to 1.097) and 1.319 times (95% CI, 1.099 to 1.582) higher mortality. There were also significant geographical variations in patients who had a stroke's expenses and mortality rates. CONCLUSION: Applying cost-effective stroke prevention strategies among the younger population (≤49 years old) is strongly recommended. Migration to universal health insurance can effectively reduce the inequality gap among all insured patients.

National geographical pattern of COVID-19 hospitalization, case fatalities, and associated factors in patients covered by Iran Health Insurance Organization.

BACKGROUND: Understanding the Spatio-temporal distribution and interpersonal comparisons are important tools in etiological studies. This study was conducted to investigate the temporal and geographical distribution of COVID-19 hospitalized patients in the Iran Health Insurance Organization (IHIO) insured population (the second largest social health insurance organization) and the factors affecting their case fatality rate (CFR). METHODS: In this descriptive-analytical cross-sectional study, the demographic and clinical data of all insured of the IHIO who were hospitalized with COVID-19 in hospitals across the country until March 2021 was extracted from the comprehensive system of handling the inpatient documents of this organization. The Excel 2019 and GeoDA software were used for descriptive reporting and geographical distribution of variables. A multiple logistic regression model was used to estimate the Odds Ratio (OR) of death in patients with COVID-19 using STATA 14 software. RESULTS: During the first 14 months of the COVID-19 outbreak in Iran, 0.72% of the IHIO insured (303,887 individuals) were hospitalized with COVID-19. Hospitalization per 100,000 people varied from 192.51 in East Azerbaijan to 1,277.49 in Yazd province. The overall CFR in hospitalized patients was 14%. Tehran and Kohgiluyeh & BoyerAhmad provinces had the highest and lowest CFR with 19.39% and 5.19%, respectively. The highest odds of death were in those over 80 years old people (OR = 9.65), ICU-admitted (OR = 7.49), Hospitalized in governmental hospitals (OR = 2.08), Being a foreign national (OR = 1.45), hospitalized in November (OR = 1.47) and Residence in provinces such as Sistan & Baluchestan (OR = 1.47) and Razavi Khorasan (OR = 1.66) respectively. Furthermore, the odds of death were lower in females (OR = 0.81) than in males. CONCLUSIONS: A sound understanding of the primary causes of COVID-19 death and severity in different groups can be the basis for developing programs focused on more vulnerable groups in order to manage the crisis more effectively and benefit from resources more efficiently.

Mapping Drug Prescription, Polypharmacy, and Pharmaceutical Spending in Older Adults in Iran: A Multilevel Analysis Based on Claims Data.

Background: To date, comprehensive data on drug utilization in Iranian people are lacking. The purpose of this study was to graphically describe drug prescription, polypharmacy, and pharmaceutical spending in > 3 million Iranian elderly people. Methods: In this multilevel cross-sectional study, using administrative claims data provided by the Iran Health Insurance Organization (IHIO), we assessed drug claims and drug costs in 2018 in >3 million individuals living in Iran and who have been insured with health insurance (Bimeh Salamat). In particular, we analyzed the prevalence of polypharmacy and pharmaceutical spending use according to the annual Report of Iranian Health Insurance Organization. Multilevel ordinal logistic and multilevel beta regression models were used to analyze the data. Significance level was set as P ≤ .05 and CI at 95%. Results: Nationally, the mean number of drug prescriptions per patient was 1.46 (SD, 0.81). The mean number of prescribed drugs per patient was 4.32 (SD, = 3.04). The drug cost for each elderly patient was $6.86 (interquartile range (IQR), 12.26), with $4.96 and $1.76 for the insurance and the insured shares, respectively. For elderly women, the odds of polypharmacy (excessive and nonexcessive vs no polypharmacy) were 1.164 (95% CI, 1.142 to 1.186) times that of elderly men. In addition, in the spring season, the odds of polypharmacy were 1.274 (95% CI, 1.241 to 1.309) times that of the winter. Similarly, polypharmacy was strongly higher among patients who had noncommunicable diseases (OR, 2.174; 95% CI, 2.069 to 2.275 (P < 0.001)). Conclusion: The high prevalence of hyper prescription in Iran elderly people may indicate a need for interventions aiming at deprescribing drugs with an unfavorable benefit-risk profile. The best practice guidelines should be developed for improved medical practice in the prescription of medications for such a vulnerable population.