Antibiotic prescription prevalence in Iranian outpatients: A focus on defined daily doses and the AWaRe classification system.

BACKGROUND: The inappropriate use and overprescription of antibiotics pose a global health threat, particularly contributing to antimicrobial resistance. This study aims to evaluate antibiotic prescription prevalence in Iranian outpatients using the defined daily doses (DDD) and Access, Watch, and Reserve classification systems. METHODS: This retrospective study analyzed electronic prescriptions for systemic antibiotics in Tehran, Iran, from March 2022 to March 2023. The data were obtained from the Iranian Health Insurance Organization and processed using the Cross-Industry Standard Process. Descriptive statistics and DDD per 1,000 inhabitants per day were calculated. RESULTS: A total of 817,178 antibiotic prescriptions were analyzed, with a sex distribution of 57.43% female and a median age of 48 years. On average, each patient received 1.89 antibiotics per prescription. Over 63% of antibiotics were classified in the "Watch" category, with Azithromycin being the most commonly prescribed (27.56%). The total DDD per 1,000 inhabitants per day was 4.99, with general practitioners accounting for 58.02% of the prescriptions, primarily prescribing Azithromycin. CONCLUSIONS: The study emphasizes the high use of Watch group antibiotics, indicating a need for improved prescribing practices. Education on antibiotic stewardship and stricter guidelines are necessary to combat antimicrobial resistance. Continuous monitoring is crucial to optimize antibiotic use in outpatient settings in Iran.

Overutilization of head computed tomography in cases of mild traumatic brain injury: a systematic review and meta-analysis.

Head computed tomography (CT) is the preferred imaging modality for mild traumatic brain injury (mTBI). The routine use of head CT in low-risk individuals with mild TBI offers no clinical benefit but also causes notable health and financial burden. Despite the availability of related guidelines, studies have reported considerable rate of non-indicated head CT requests. The objectives were to provide an overall estimate for the head CT overutilization rate and to identify the factors contributing to the overuse. A systematic review of PubMed, Scopus, Web of Science, and Embase databases was conducted up to November 2023, following PRISMA and MOOSE guidelines. Two reviewers independently selected eligible articles and extracted data. Quality assessment was performed using a bias risk tool, and a random-effects model was used for data synthesis. Fourteen studies, encompassing 28,612 patients, were included, with 27,809 undergoing head CT scans. Notably, 75% of the included studies exhibited a moderate to high risk of bias. The overutilization rate for pediatric and adult patients was 27% (95% CI: 5-50%) and 32% (95% CI: 21-44%), respectively. An alternative rate, focusing on low-risk pediatric patients, was 54% (95% CI: 20-89%). Overutilization rates showed no significant difference between teaching and non-teaching hospitals. Patients with mTBI from falls or assaults were less likely to receive non-indicated scans. There was no significant association between physician specialty or seniority and overuse, nor between patients' age or sex and the likelihood of receiving a non-indicated scan. Approximately one-third of head CT scans in mTBI cases are avoidable, underscoring the necessity for quality improvement programs to reduce unnecessary imaging and its associated burdens.

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.