| چکیده انگلیسی مقاله |
Background: Clinical laboratory tests are vital tools for disease diagnosis and management, yet their prescription patterns are influenced by regional, economic, and infrastructural factors. This study aimed to identify the prescription patterns of clinical laboratory tests and analyze their utilization and economic implications in Iran over the past 8 years to inform health policy.
Methods: Data comprising 3.9 billion tests, 17.8 trillion IRR in costs, and relative frequencies of key tests (TSH, CBC, Glucose, Creatinine, UA, 25-OH Vit D) from 31 provinces were collected. The K-Means algorithm was employed for Regional Patterning prescription patterns, with analysis of variance (ANOVA) to validate differences and principal component analysis (PCA) to identify key variables. Analyses were conducted using Python with the Scikit-learn library. Data were processed in aggregate without individual identifiers.
Findings: Regional Pattern 1 included 7 provinces (Tehran, Khorasan Razavi, Fars, West Azerbaijan, Gilan, Hamadan, Yazd), covering 39% of the population (15,691,287 individuals), with high frequencies of TSH (8-10%) and 25-OH Vit D (5-12%), indicating a focus on thyroid and vitamin D deficiency screening. Regional Pattern 2 encompassed 9 provinces (Isfahan, Alborz, East Azerbaijan, Ardabil, Zanjan, Semnan, Ilam, Bushehr, North Khorasan), covering 19.2% (7,728,040 individuals), emphasizing Glucose (5-6%) and Creatinine (5%) for diabetes and renal conditions. Regional Pattern 3 comprised 10 provinces (Khuzestan, Kerman, Sistan and Baluchestan, Kurdistan, Golestan, Qazvin, Kermanshah, Chaharmahal and Bakhtiari, Kohgiluyeh and Boyer-Ahmad, South Khorasan), covering 29.7% (11,942,624 individuals), with predominant CBC (7-8%), suggesting blood-related disorders. Regional Pattern 4 included 5 provinces (Mazandaran, Qom, Lorestan, Markazi, Hormozgan), covering 12.1% (4,870,662 individuals), exhibiting a balanced, screening-oriented pattern with moderate frequencies (3-5%). Regional Pattern differences were significant (P < 0.05).
Conclusion: Regional variations in test prescriptions highlight disparities in utilization and costs. Regional Pattern 1 incurred high costs, while Regional Pattern 4 demonstrated greater efficiency. Tailored regional policies are essential to optimize prescriptions, reduce costs, and enhance equity in access. Future research should investigate socioeconomic factors, insurance policies, and health infrastructure impacts. |