Introduction
Methods
Design and eligibility criteria
Search strategy
Search syntax | Database |
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(“Indirect cost”[TIAB] OR “Cost of illness”[MH] OR “Illness Cost”[TIAB] OR “Sickness Cost”[TIAB] OR “Burden of Illness”[TIAB] OR “Illness Burden”[TIAB] OR “Cost of Disease”[TIAB] OR “Economic Burden of Disease”[TIAB] OR “Disease Cost”[TIAB] OR “Disease Costs”[TIAB] OR “Cost of Sickness”[TIAB] OR “Sickness Costs”[TIAB] OR “Costs of Disease”[TIAB] OR “Productivity costs”[TIAB] OR “Productivity lost”[TIAB] OR “Productivity loss”[TIAB] OR “Absenteeism cost”[TIAB] OR “Human capital”[TIAB] OR “Economic burden”[TIAB]) AND ("Breast Neoplasms"[MH] OR "Breast Tumors"[TIAB] OR "Breast Tumor"[TIAB] OR "Breast Carcinoma"[TIAB] OR "Breast Cancer"[TIAB] OR "Mammary Cancer"[TIAB] OR "Mammary Cancers"[TIAB] OR "Malignant Neoplasm of Breast"[TIAB] OR "Breast Malignant Neoplasm"[TIAB] OR "Breast Malignant Neoplasms"[TIAB] OR "Malignant Tumor of Breast"[TIAB] OR "Breast Malignant Tumor"[TIAB] OR "Breast Malignant Tumors"[TIAB] OR "Cancer of Breast"[TIAB] OR "Cancer of the Breast"[TIAB] OR "advanced breast cancer"[TIAB] OR "mamma cancer"[TIAB] OR "mammary gland cancer"[TIAB]) | Pubmed |
TS=(“Indirect cost” OR “Cost of illness” OR “Illness Cost” OR “Sickness Cost” OR “Burden of Illness” OR “Illness Burden” OR “Cost of Disease” OR “Economic Burden of Disease” OR “Disease Cost” OR “Disease Costs” OR “Cost of Sickness” OR “Sickness Costs” OR “Costs of Disease” OR “Productivity costs” OR “Productivity lost” OR “Productivity loss” OR “Absenteeism cost” OR “Human capital” OR “Economic burden”) AND TS=("Breast Neoplasms" OR "Breast Tumors" OR "Breast Tumor" OR "Breast Carcinoma" OR "Breast Cancer" OR "Mammary Cancer" OR "Mammary Cancers" OR "Malignant Neoplasm of Breast” OR "Breast Malignant Neoplasm" OR "Breast Malignant Neoplasms" OR "Malignant Tumor of Breast" OR "Breast Malignant Tumor" OR "Breast Malignant Tumors" OR "Cancer of Breast" OR "Cancer of the Breast" OR "advanced breast cancer" OR "mamma cancer” OR "mammary gland cancer") | Web of Science |
TITLE-ABS-KEY (“Indirect cost” OR “Cost of illness” OR “Illness Cost” OR “Sickness Cost” OR “Burden of Illness” OR “Illness Burden” OR “Cost of Disease” OR “Economic Burden of Disease” OR “Disease Cost” OR “Disease Costs” OR “Cost of Sickness” OR “Sickness Costs” OR “Costs of Disease” OR “Productivity costs” OR “Productivity lost” OR “Productivity loss” OR “Absenteeism cost” OR “Human capital” OR “Economic burden”) AND TITLE-ABS-KEY ("Breast Neoplasms" OR "Breast Tumors" OR "Breast Tumor" OR "Breast Carcinoma" OR "Breast Cancer" OR "Mammary Cancer" OR "Mammary Cancers" OR "Malignant Neoplasm of Breast” OR "Breast Malignant Neoplasm" OR "Breast Malignant Neoplasms" OR "Malignant Tumor of Breast" OR "Breast Malignant Tumor" OR "Breast Malignant Tumors" OR "Cancer of Breast" OR "Cancer of the Breast" OR "advanced breast cancer" OR "mamma cancer” OR "mammary gland cancer") | Scopus |
(“Indirect cost”/exp “Indirect cost”:ti,ab,kw OR “Cost of illness”:ti,ab,kw OR “Illness Cost”:ti,ab,kw OR “Sickness Cost”:ti,ab,kw OR “Burden of Illness”:ti,ab,kw OR “Illness Burden”:ti,ab,kw OR “Cost of Disease”:ti,ab,kw OR “Economic Burden of Disease”:ti,ab,kw OR “Disease Cost”:ti,ab,kw OR “Disease Costs”:ti,ab,kw OR “Cost of Sickness”:ti,ab,kw OR “Sickness Costs”:ti,ab,kw OR “Costs of Disease”:ti,ab,kw OR “Productivity costs”:ti,ab,kw OR “Productivity lost”:ti,ab,kw OR “Productivity loss”:ti,ab,kw OR “Absenteeism cost”:ti,ab,kw OR “Human capital”:ti,ab,kw OR “Economic burden”:ti,ab,kw) AND ("Breast Cancer"/exp OR "Breast Cancer":ti,ab,kw OR "Breast Neoplasms":ti,ab,kw OR "Breast Tumors":ti,ab,kw OR "Breast Tumor":ti,ab,kw OR "Breast Carcinoma":ti,ab,kw OR "Mammary Cancer":ti,ab,kw OR "Mammary Cancers":ti,ab,kw OR "Malignant Neoplasm of Breast":ti,ab,kw OR "Breast Malignant Neoplasm":ti,ab,kw OR "Breast Malignant Neoplasms":ti,ab,kw OR "Malignant Tumor of Breast":ti,ab,kw OR "Breast Malignant Tumor":ti,ab,kw OR "Breast Malignant Tumors":ti,ab,kw OR "Cancer of Breast":ti,ab,kw OR "Cancer of the Breast":ti,ab,kw OR "advanced breast cancer":ti,ab,kw OR "mamma cancer":ti,ab,kw OR "mammary gland cancer":ti,ab,kw) | Embase |
Quality assessment
Selection of studies and data extraction
Results
Study selection
Quality assessment
Criteria/author | Scope | General economic | Calculation of costs | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Study objective | Inclusion and exclusion criteria | Disease and diagnostic criteria | Cost-description | Nondiseased comparison group or disease-specific costs | Currency | Reference year | Perspective | Costs incorporated from more than one category | Data source | Valuation of costs | Discounting | |
Lidgren [45] | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ |
Ivanauskiene [40] | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ |
Broekx [38] | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ | ✓ | ✓ | ✓ | ✓ |
Jain [55] | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✓ | ✗ |
Łyszczarz [35] | ✓ | ✗ | ✗ | ✓ | ✗ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ |
Vondeling [52] | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✗ |
ROINE [58] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✗ |
Trogdon [51] | ✓ | ✗ | ✓ | ✓ | ✗ | ✗ | ✓ | ✗ | ✓ | ✓ | ✓ | ✗ |
Max [46] | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✓ | ✓ | ✓ | ✓ |
Meadows [57] | ✓ | ✗ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✗ |
Sorensen [50] | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✗ | ✗ | ✓ | ✓ | ✓ | ✗ |
Heras [60] | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✓ |
Gordon [54] | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✗ |
Wan [31] | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✗ | ✓ | ✓ | ✓ | ✗ |
Binazzi [53] | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ | ✗ | ✗ | ✓ | ✓ | ✗ |
Mahmood [56] | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✗ |
Goyal [61] | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✗ |
Ferrier [32] | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
Daroudi [10] | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✗ |
Yin [59] | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✗ |
Oliva [47] | ✓ | ✗ | ✗ | ✓ | ✗ | ✓ | ✗ | ✗ | ✓ | ✓ | ✓ | ✓ |
Hanly [39] | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Kim [43] | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Pearce [48] | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✓ |
Bradley [33] | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✗ | ✓ | ✓ | ✓ | ✓ | ✓ |
Hanly [36] | ✓ | ✗ | ✓ | ✓ | ✗ | ✗ | ✓ | ✗ | ✓ | ✓ | ✓ | ✓ |
Lee [44] | ✓ | ✗ | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | ✓ | ✓ | ✓ | ✓ |
Khorasani [42] | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✓ | |
Karami-matin [41] | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✓ | ✓ |
Sasser [49] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✓ |
Luengo [34] | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
John [62] | ✓ | ✗ | ✗ | ✓ | ✗ | ✓ | ✓ | ✗ | ✗ | ✓ | ✓ | ✗ |
Barchuk [37] | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✓ | ✓ |
Study characteristics
No. | First author | Study population | Databases | Perspective | Type of study | Discount rate | Region | Income groups | Sample size |
---|---|---|---|---|---|---|---|---|---|
1 | Lidgren (2007) [45] | Female patients with a previous diagnosis of breast cancer | Enrolled | Societal | Prospective | – | Sweden | High income: OECD | 361 |
2 | Ivanauskiene (2010) [40] | A survey of 379 women treated in five major Lithuanian hospitals | Questionnaire | Societal | Prospective | – | Lithuania | High income: nonOECD | 379 |
3 | Broekx (2011) [38] | All women had undergone an initial surgical treatment for breast cancer between 1998 and 2003, allowing us to identify these patients in the Christian Health Insurance Funds databases based on the official billing codes attached to these surgical procedures | Enrolled | Societal | Prospective | 4% | Belgium | High income: OECD | 20,439 |
4 | Jain (2016) [55] | The patients with primary diagnosis as breast cancer, diagnosed in between April 2012 to March 2013, not having any co-morbidities | Interview | Houshold | Prospective | – | India | Lower middle income | 221 |
5 | Łyszczarz (2017) [35] | Population based | Social insurance system and Polish National Cancer Registry | Societal | Retrospective | 0%, 3.5% | Poland | High income: OECD | – |
6 | Vondeling (2018) [52] | Women with breast cancer in netherlands | Dutch National Cancer Registry | – | Retrospective | 2%, 6% | Netherlands | High income: OECD | 320,179 |
7 | Roine (2019) [58] | All patients aged 18 years and over and diagnosed with BC were eligible for the study | questionnair | – | Prospective | – | Finland | High income: OECD | 827 |
8 | Trogdon (2020) [51] | women with no missing responses to questions regarding having ever been diagnosed with cancer, having ever been diagnosed with breast cancer, and age at breast cancer diagnosis | National Health Interview Survey | Social | Retrospective | 3% | United States | High income: OECD | 6935 |
9 | Max (2009) [46] | California women for 2001 using California specific hospitalization and mortality data | California specific hospitalization and mortality data | – | Retrospective | 3% | United States | High income: OECD | 12,934 |
10 | Meadows (2010) [57] | employed women, aged 18 to 64, with BC identified by a validated algorithm between 1999 and 2005, from claims (Market Scan) and attendance databases | Encounters (CC&E) and Health and Productivity Management (HPM) databases from Thomson Reuters | Employer | Reteospective | – | United States | High income: OECD | 880 |
11 | Sorensen (2012) [50] | The incident cohort of MBC patients included both de novo MBC patients and MBC patients who progressed during that year from earlier stages of breast cancer | Medical record | Social | – | 3% | United States | High income: OECD | 49,674 |
12 | Heras (2018) [60] | Patients with newly diagnosed or recurrent mBC diagnosed over 1 year | Physician survey conducted with 10 clinical experts in Spain | Spain | High income: OECD | 2923 | |||
13 | Gordon (2007) [54] | English-speaking women recently diagnosed with unilateral breast cancer, aged 20–75 years and who resided within a 100 km radius of Brisbane (where approximately 70% of the Queensland population resides) | Questionnaire | The perspective of the survivor | Prospective | – | Australia | High income: OECD | 287 |
14 | Wan (2013) [31] | Adult BC patients eligible for employee benefits of sick leave and/or short-term disability were identified with ICD-9 codes | The MarketScan_ Health and Productivity Management database | societal | Retrospective | – | United State | High income: OECD | 326,903 |
15 | Binazzi (2013) [53] | Only subjects over 25 years deceased in 2006 have been selected by cancer site | Italian National Institute of Statistics | – | Retrospective | 1%, 3% | Italy | High income: OECD | 11,476 death |
16 | Mahmood (2018) [56] | Patients were eligible for inclusion if they were (1) female; (2) 18 years of age or older; (3) had been in treatment for 3 months to 2 years since diagnosis; (4) were diagnosed with metastatic breast cancer with any stage; (5) fluent in Urdu, English or regional languages i.e. Punjabi and Saraiki; and (6) able to provide informed consent | Qeustionnair | – | Prospective | – | Pakistan | Lower middle income | 200 |
17 | Goyal (2020) [61] | Patients with MBC was ascertained based on the presence of at least two claims with an ICD-9-CM diagnosis code for secondary malignancy | The IBM MarketScan Commercial Claims and Encounters (CCAE) databases | – | Retrospective | – | United State | High income: OECD | 5563 |
18 | Ferrier (2020) [32] | Female patients with histologically confirmed, previously untreated and primarily operable BC (exclusion of metastatic, locally advanced or inflammatory BC as defined by the AJCC) | Questionnaire | Socetial | Prospective | France | High income: OECD | 168 | |
19 | Daroudi (2015) [10] | Cancer population | National cancer registry reports, hospital records, occupational data, and interviews with expert | Iran | Upper middle income | 39,316 | |||
20 | Yin (2017) [59] | The study sample included employees who had at least two inpatient or outpatient claims with a diagnosis of BC | The MarketScan_ Health and Productivity Management database | Employer | Retrospective | – | United State | High income: OECD | 6409 |
21 | Oliva (2006) [47] | Spanish Registry of Deaths by cause | – | Retrospective | 0%, 3%, 6% | Spain | High income: OECD | 38,025 | |
22 | Hanly (2012) [39] | Population-based sample of 1373 survivors was selected from the National Cancer Registry Ireland. Survivors were between 6 months and 2 years since diagnosis and had been treated at 1 of 17 hospitals across the country (14 mixed public/ private, 3 private) | Questionnaire | Societal (HCA) and an employer’s (FCA) perspective | Prospective | 4% | Irland | High income: OECD | 250 |
23 | KIM (2007) [43] | All cancer population | Health Insurance Review agency & Korean Central Cancer Registry (KCCR) | Social | Retrospective | 3% | Korea | High income: OECD | 36,226 |
24 | Pearce (2016) [48] | Cancer population | Central Statistics Office (CSO) and annual age-specific cancer | – | Retrospective & prospective | 5% | Ireland | High income: OECD | Cancer death 2011–2030 |
25 | Bradley (2008) [33] | All cancer population | National Interim Projections, Berkeley Mortality Database, Current Population Survey (CPS) | – | Retrospective | 3% | United State | High income: OECD | – |
26 | Hanly (2014) [36] | Cancer deaths | WHO mortality database | Social | Retrospective | 3.5% | Europe | – | – |
27 | Lee (2014) [44] | Women with breast cancer | national health insurance claims data | Societal | Retrospective | 3% | Korea | High income: OECD | 42,605 in 2000, 97,507 in 2010 |
28 | Khorasani (2015) [42] | Cancer population | Iranian Ministry of Cooperation Labor and Social Welfare | Social | Retrospective | 3% | Iran | Upper middle income | 3304 |
29 | Karami-matin (2016) [41] | Cancer people | Ministry of Health and Medical Education (MoHME) & Iranian Ministry of Cooperation Labor and Social Welfare | – | Retrospective | 3% | Iran | Upper middle income | 962 in 2006, 1,086 in 2007, 1,122 in 2008, 1,124 in 2009, 1,283 in 2010 |
30 | Sasser (2005) [49] | Female employees, also age 50–64 years (the “comparison group”), during the 3-year period | Medical record | Employer | Retrospective | – | United State | High income: OECD | 555 |
31 | Luengo (2013) [34] | Population based | International and national sources | – | – | European Union | – | – | |
32 | John (2010) [62] | Cancer population | WHO | – | Retrospective | – | Global | – | – |
33 | Barchuk (2019) [37] | Cancer population | Herzen Research Institute of Oncology | Societal burden of cancer in Russia | Retrospective | 0% & 5% | Russia | High income: nonOECD | 2031 |
Items of indirect costs
No. | First author | Reference year for costs | Region | Costing approach | Data gathering | Type of indirect cost | Cost (US dollars) |
---|---|---|---|---|---|---|---|
1 | Mathias Lidgren [45] | 2005 | Sweden | HC | Not specified | Premature death | 165,695.42 |
Missed days’ work | 20,167.36 to 48,407.67 | ||||||
Total | 33,992.96 for women aged lower than 50 years | ||||||
24,724.97 for women aged 50–64 years | |||||||
2 | Rugile Ivanauskiene [40] | 2008 | lithuania | HC | Not specified | Premature death | 38,314,351.03 |
Morbidity | 69,856,613.68 | ||||||
Total | 150,204,061.57 | ||||||
3 | Steven Broekx [38] | 2006 | Belgium | HC | Not specified | Premature death | 33,930.20 per patient |
Morbidity | 12,537.91 per patient | ||||||
Total | 51,325.88 | ||||||
4 | Maneeta Jain [55] | India | HC | Not specified | Missed days’ work | 128,104.58 | |
Unpaid | 297,548.46 | ||||||
Total | 1,337,388.34 | ||||||
Productivity loss | 14,014,584.40 | ||||||
5 | Błażej Łyszczarz [35] | 2010–2014 | Poland | HC | Not specified | Premature death | 103,782,672.49 |
Morbidity | 126,857,360.69 | ||||||
Missed days’ work | 79,153,502.28 | ||||||
Unpaid help | 301,578.48 | ||||||
Total | 434,722,812.46 | ||||||
6 | G. T. Vondeling [52] | 1990–2014 | Netherlands | HC | Not specified | Premature death | 331,729,844.30 |
Morbidity | 354,937,281.96 | ||||||
7 | Eija Roine [58] | 2009–2010 | Finland | HC | Not specified | Mean loss productivity loss in primary treatment | 11,743.73 |
Mean loss productivity loss in metatatic | 9794.59 | ||||||
Sick leave | 11,219.12 | ||||||
Informal care | Primary treatment = 2895.30 | ||||||
Metas = 3944.53 | |||||||
8 | Justin G. Trogdon [51] | 2015 | United State | HC | Not specified | The value of lost work and home productivity days associated with mBC nationally | Younger women = 73,331,141.63 |
Midlife women = 269,245,684.18 | |||||||
Older women = 72,236,646.98 | |||||||
9 | Wendy Max [46] | 2001 | United State | – | Prevalence base | Premature death | 2,157,428,410.62 |
10 | Eric S.Meadows [57] | 2005 | United State | – | Incidence based | Morbidity | 6428.50 |
11 | Sonja V.Sorensen [50] | 2010 | United State | – | Incidence based | Premature death | 321,957,234.86 |
Missed days’ work | 301,934,425.64 | ||||||
Unpaid help | 54,773,302.74 | ||||||
Total | 682,998,152.02 | ||||||
12 | de las.Heras [60] | 2016 | Spain | – | Incidence based | Total | 388.41 |
13 | Louisa Gordon [54] | 2005 | Australia | – | Not specified | Missed days’ work | 2494$ |
Unpaid help | 435.56$ | ||||||
Total | 3732.47 | ||||||
14 | Yin Wan [31] | Not specified | United State | – | Not specified | Morbidity | MBC = 6165.8 |
EBC = 3689.7 | |||||||
Missed days’ work | MBC = 1584 | ||||||
EBC = 1015 | |||||||
15 | Alessandra Binazzi [53] | 2006 | Italy | – | Not specified | Value of work productivity lost | 71,767,637.28$ |
16 | Hafiz Zahid Mahmood [56] | 2015 | Pakistan | – | Not specified | Missed days’ work | 70.49 |
Unpaid help | 21.61 | ||||||
Total | 326.23 | ||||||
17 | Ravi K.Goyal [61] | 2015 | United State | – | Not specified | Total | 11,379.46 |
18 | Clement Ferrier [32] | Not specified | France | HC&FC | Not specified | Missed days’ work | 22,898.12(HC) |
Per patient | |||||||
7571.43(FC) | |||||||
Per patient | |||||||
Total | 25,162.79(HC) | ||||||
Per patient | |||||||
8553.71(FC) | |||||||
Per patient | |||||||
19 | Rajabali Daroudi [10] | 2010 | Iran | HC | Prevalence base | Premature death | 226,544.05 |
Missed days’ work | 6348.27 | ||||||
20 | Wesley Yin [59] | 2013 | United State | HC&FC | Not specified | Missed days’ work | Non-metastatic: 27,238.20 metastatic: 34,564.53 |
21 | Juan Oli va [47] | Not specified | Spain | HC&FC | Not specified | Premature death | 223,328.35 based on HCA and 4,518,628.55 based on FCA |
Permanent disability | 314,671,039.50 based on HCA and 10,771,684.06 based on FCA | ||||||
Total | 570,359,693.86 based on HCA and 22,956,005.13 based on FCA | ||||||
22 | Paul Hanly [39] | 2008 | Ireland | HC&FC | Not specified | Premature death | 108,419.60 based on HCA and 1488.61 based on FCA |
Morbidity | 139,799.76 based on HCA and 8909.85 based on FCA | ||||||
Missed days’ work | 63,566.23 based on HCA and 33.37 based on FCA | ||||||
Total | 248,219.35 based on HCA and 10,398.46 based on FCA | ||||||
23 | KIM S.G [43] | 2002 | Korea | HC | Prevalence base | Premature death | 253,290,344.47 |
Morbidity | 179,708,791.89 | ||||||
24 | Alison Pearce [48] | 2011–2030 | Ireland | HC | Incidence based | Premature death | 52,251,513,523.53 |
Value of lost paid production | 1,772,314,850.60 | ||||||
25 | Cathy J. Bradley [33] | 2010 | United State | HC | Not specified | Premature death | 12,981,917,165.92 |
Unpaid help | 308 billion | ||||||
26 | Paul Hanly [36] | 2008 | HC | Not specified | Premature death | 5,742,690,674.97 | |
27 | Kwang-Sig Lee [44] | 2010 | Korea | HC | Not specified | Premature death | 715,990,885.54 |
Morbidity | 596,659,071.28 | ||||||
28 | Soheila Khorasani [42] | 2012 | Iran | HC | Not specified | Premature death | 171,960,573.41 |
29 | Behzad Karami-matin [41] | 2006–2010 | Iran | HC | Not specified | Premature death | 5698.01 in 2006, 6088.31 in 2007, 5777.21 in 2008, 5900.88 in 2009, 4546.44 in 2010 |
30 | Alicia C.Sasser [49] | 1998–2000 | United State | – | Not specified | Premature death | 6760.01 |
Morbidity | 5338.09 | ||||||
31 | Ramon Luengo [34] | 2009 | European Union | – | Not specified | Unpaid help | 2,612,459,857.54 |
Total | 2,653,279,542.82 | ||||||
32 | Rijo M John [62] | 2008 | Global | – | Not specified | Indirect cost | 88 billions |
33 | Anton Barchuk [37] | 2016 | Russia | HC | Incidence based | Premature death | 22,386 |