Cost analysis of breast cancer: a comparison between private and public hospitals in Iran

Afkar, Abolhasan; Jalilian, Habib; Pourreza, Abolghasem; Mir, Habibeh; Sigaroudi, Abdolhosein Emami; Heydari, Somayeh

doi:10.1186/s12913-021-06136-6

Cited by 10 publications

(7 citation statements)

References 27 publications

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“…Like in other developing countries, the incidence of breast cancer in Iran has increased and imposed a significant financial burden on the families and healthcare system [ 10 , 17 ]. In this regard, Askarzadeh et al (2019) in eastern Iran found that the per capita cost of hospitalization for a breast cancer patient was 243.39 USD in 2018 [ 18 ], and Afkar et al (2021) estimated that the average hospital costs in private and public centers were 10,050 and 3,960 USD, respectively, and the average total indirect cost was 22,350 USD in 2017 [ 17 ]. In their study in 2010, Dorudi et al (2015) estimated the economic burden of breast cancer at 947,375,468 USD [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Economic burden of breast cancer: a case of Southern Iran

Jalali,

Keshavarz,

Seif

et al. 2023

Cost Eff Resour Alloc

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Background Breast cancer is one of the main causes of death from cancer around the world, imposing a significant economic burden on the families and healthcare system. The present study aimed at determining the economic burden of breast cancer in the patients referred to the medical centers in Fars province in southern Iran in 2021. Methods This cross-sectional study is a partial economic evaluation and a cost-of-illness study with a bottom-up and prevalence-based approach, conducted in Fars province in southern Iran in 2021 from the societal perspective. A total of 230 patients were randomly included in the study, and a researcher-made data collection form was used to collect the required data. The data on direct medical costs were collected using the information on patients’ medical and financial records. On the other hand, the data on direct non-medical and indirect costs were obtained using self-reports by the patients or their companions. The Excel 2016 software was used to analyze the collected data. Results The results showed that the annual cost of each breast cancer patient in the studied sample was 11,979.09 USD in 2021. Direct medical costs accounted for the largest share of costs (70.69%, among which the cost of radiotherapy was the highest one. The economic burden of the disease in the country was estimated at 193,090,952 USD. Conclusions In general, due to the high prevalence of breast cancer and the chronicity of this disease, its medical costs can impose a heavy economic burden on society, the health system, the insurance system, and patients. Thus, in order to reduce the costs, the following suggestions can be offered: the use of advanced radiotherapy techniques, increasing the insurance coverage of required services, establishing low-cost accommodation centers near medical centers for the patients and their companions, providing specialized medical services for the patients in towns, using the Internet and virtual space to follow up the treatment of the patients, and carrying out free screening programs and tests for faster diagnosis of the infected patients and susceptible or exposed people.

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Section: Introductionmentioning

confidence: 99%

Economic burden of breast cancer: a case of Southern Iran

Jalali,

Keshavarz,

Seif

et al. 2023

Cost Eff Resour Alloc

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“…The absolute value of the logarithm causes the ratios a b and b a to get the same scores, which is desirable for this study. (7) BC − Scoreg = |logBCg| (8) CRC − Scoreg = |logCRCg|…”

Section: Feature Extractionmentioning

confidence: 99%

“…In addition to the prevalence of cancer, any patient diagnosed with cancer burdens a high cost to the family and society to get proper treatment. Such a treatment varies largely by tumor type and stage (6) or even the care provider facilities regarding the number of costs to be covered (7). According to the Financial Burden of Cancer calculated for the US, on average for all cancer sites, this country approximately spent $208.9 billion to treat patients in 2020.…”

Section: Introductionmentioning

confidence: 99%

Classification of Potential Breast/Colorectal Cancer Cases Using Machine Learning Methods

et al. 2023

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Background: The algorithmic classification of infected and healthy individuals by gene expression has been a topic of interest to researchers in numerous domains, including cancer. Several studies have presented numerous solutions, such as neural networks and support vector machines (SVMs), to classify a diverse range of cancer cases. Such classifications have provided some degrees of accuracy, which highly depend on optimization approaches and suitable kernels. Objectives: This study aimed at proposing a method to classify cancer-prone and healthy cases under breast cancer and colorectal cancer (CRC), using machine learning methods efficiently, increasing the accuracy of the classification process. Methods: This study presented an algorithm to diagnose individuals prone to breast cancer and CRC. The novelty of this algorithm lies in its suitable kernel and the feature extraction approach. By the application of this algorithm, this study first identified the genes closely associated with these types of cancers and, then, tried to find individuals susceptible to the concerned cancers using SVM. The present study highlighted the indirect gene expressions associated with these cancers, which might show health status complications for the patients. To this end, the algorithm consists of SVMs in conjunction with the k-fold method for validation. Results: The results confirmed the superior performance of this approach, compared to the common neural networks. The algorithm’s identification accuracy values were 98.077% and 99.806% for breast cancer and CRC, respectively. The graphic representation of the cause-effect relationships was also provided to help researchers better understand the trend of cancer or other types of diseases. Conclusions: The feature extraction method highly affects the accuracy of the classification. In addition, relying on indirect disease-triggering genes’ expressions highlights a cause-effect relationship between genes and diseases. Such relationships can form Markov models in the clinical domain leading to treatment paths and prediction of patient outcomes.

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“…16 Evidences suggest that place of residence, patient age, stage at diagnosis, use of multiple therapies, type of institutional facility (public vs. private), and extent of insurance coverage are the primary drivers of treatment cost. 15 , 17 , 18 , 19 …”

Section: Introductionmentioning

confidence: 99%

Out-of-pocket payment and financial risk protection for breast cancer treatment: a prospective study from India

Wadasadawala,

Mohanty,

Sen

et al. 2024

The Lancet Regional Health - Southeast Asia

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Cost analysis of breast cancer: a comparison between private and public hospitals in Iran

Cited by 10 publications

References 27 publications

Economic burden of breast cancer: a case of Southern Iran

Economic burden of breast cancer: a case of Southern Iran

Classification of Potential Breast/Colorectal Cancer Cases Using Machine Learning Methods

Out-of-pocket payment and financial risk protection for breast cancer treatment: a prospective study from India

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