Prostate cancer · 2026
Determinants of the Cost of Illness in Iranian Prostate Cancer Patients.
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Ravangard Ramin, Keshavarz Khosro, Seif Mozhgan, Zeighami Shahryar, Jafari Abdosaleh, Jalali Faride Sadat
Аннотация
BACKGROUND: Prostate cancer (PCa) ranks as the fourth most prevalent form of cancer overall and imposes a substantial financial burden on individuals and communities. Given information on the costs of this disease and the factors influencing them, the present study analyzed factors affecting the costs of medical and nonmedical services for patients with PCa in southern Iran.
METHODS: Factors affecting the cost of PCa were identified through a scoping review of international databases (ISI Web of Science, Scopus, PubMed, ProQuest, Magiran, and SID) and consultation with PCa experts. Subsequently, data on 254 PCa patients were collected between March 2020 and March 2022 using a two-stage stratified random sampling method. Patients were randomly selected from the provincial Cancer Registry Center lists within strata defined by healthcare catchment areas. Multiple regression analysis using SPSS 13.0 was employed to determine the factors influencing costs.
RESULTS: The findings from the multiple regression analysis indicated that premature mortality, grade, and monthly income (all p value < 0.001) were important factors affecting PCa patients' costs.
CONCLUSIONS: In line with the results, implementing low-cost screening systems for early detection and effective treatment of PCa at all income levels, and directing targeted subsidies to the health sector, especially to low-income patients, can help reduce costs for PCa patients.
METHODS: Factors affecting the cost of PCa were identified through a scoping review of international databases (ISI Web of Science, Scopus, PubMed, ProQuest, Magiran, and SID) and consultation with PCa experts. Subsequently, data on 254 PCa patients were collected between March 2020 and March 2022 using a two-stage stratified random sampling method. Patients were randomly selected from the provincial Cancer Registry Center lists within strata defined by healthcare catchment areas. Multiple regression analysis using SPSS 13.0 was employed to determine the factors influencing costs.
RESULTS: The findings from the multiple regression analysis indicated that premature mortality, grade, and monthly income (all p value < 0.001) were important factors affecting PCa patients' costs.
CONCLUSIONS: In line with the results, implementing low-cost screening systems for early detection and effective treatment of PCa at all income levels, and directing targeted subsidies to the health sector, especially to low-income patients, can help reduce costs for PCa patients.