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European Journal of Epidemiology

, Volume 30, Issue 5, pp 357–395 | Cite as

The global impact of non-communicable diseases on macro-economic productivity: a systematic review

  • Layal Chaker
  • Abby Falla
  • Sven J. van der Lee
  • Taulant Muka
  • David Imo
  • Loes Jaspers
  • Veronica Colpani
  • Shanthi Mendis
  • Rajiv Chowdhury
  • Wichor M. Bramer
  • Raha Pazoki
  • Oscar H. Franco
Open Access
Review

Abstract

Non-communicable diseases (NCDs) have large economic impact at multiple levels. To systematically review the literature investigating the economic impact of NCDs [including coronary heart disease (CHD), stroke, type 2 diabetes mellitus (DM), cancer (lung, colon, cervical and breast), chronic obstructive pulmonary disease (COPD) and chronic kidney disease (CKD)] on macro-economic productivity. Systematic search, up to November 6th 2014, of medical databases (Medline, Embase and Google Scholar) without language restrictions. To identify additional publications, we searched the reference lists of retrieved studies and contacted authors in the field. Randomized controlled trials, cohort, case–control, cross-sectional, ecological studies and modelling studies carried out in adults (>18 years old) were included. Two independent reviewers performed all abstract and full text selection. Disagreements were resolved through consensus or consulting a third reviewer. Two independent reviewers extracted data using a predesigned data collection form. Main outcome measure was the impact of the selected NCDs on productivity, measured in DALYs, productivity costs, and labor market participation, including unemployment, return to work and sick leave. From 4542 references, 126 studies met the inclusion criteria, many of which focused on the impact of more than one NCD on productivity. Breast cancer was the most common (n = 45), followed by stroke (n = 31), COPD (n = 24), colon cancer (n = 24), DM (n = 22), lung cancer (n = 16), CVD (n = 15), cervical cancer (n = 7) and CKD (n = 2). Four studies were from the WHO African Region, 52 from the European Region, 53 from the Region of the Americas and 16 from the Western Pacific Region, one from the Eastern Mediterranean Region and none from South East Asia. We found large regional differences in DALYs attributable to NCDs but especially for cervical and lung cancer. Productivity losses in the USA ranged from 88 million US dollars (USD) for COPD to 20.9 billion USD for colon cancer. CHD costs the Australian economy 13.2 billion USD per year. People with DM, COPD and survivors of breast and especially lung cancer are at a higher risk of reduced labor market participation. Overall NCDs generate a large impact on macro-economic productivity in most WHO regions irrespective of continent and income. The absolute global impact in terms of dollars and DALYs remains an elusive challenge due to the wide heterogeneity in the included studies as well as limited information from low- and middle-income countries.

Keywords

Noncommunicable diseases Productivity Return to work absenteeism Systematic review 

Introduction

Non-communicable diseases (NCDs), such as coronary heart disease (CHD), stroke, chronic obstructive pulmonary disease (COPD), cancer, type 2 diabetes and chronic kidney disease (CKD) currently constitute the number one cause of morbidity and mortality worldwide, claiming 36 million lives each year (accounting for 63 % of all adult deaths) [1]. Infectious disease prevention and control, economic growth, improvements in medical and scientific knowledge, and health and social systems development have all contributed to increased life expectancy, improved quality of life and increased likelihood of living to age 60 years and beyond. While these are notable achievements, together with lifestyle-related shifts, these epidemiological and socio-demographic changes also mean that the burden of NCDs will grow [2].

Productivity is a measure of the efficiency of a person, business or country in converting inputs into useful outputs. The productive age span of a person is from adulthood to retirement and ranges from 18 years to around 65 years of age depending on, amongst other things, profession and country. The measurement of productivity greatly relies on the output and the economic or social system context. The focus in this report is macro-economic productivity loss in the productive age range due to NCDs. Key macro-economic measures related to the labor market include: (un-) employment, (loss in) hours worked (including full or part-time work status change), presenteeism (defined as impaired performance while at work), absenteeism, disability adjusted life years (DALYs) and productivity costs/losses. Key macro-economic outcomes are reduction in the able workforce, NCD-related health and welfare expenditure and loss of income earned by the productive workforce. While both the burden of NCDs and the socio-economic contexts vary greatly, the impact of the former on macro-economic outcomes across the global regions remains unclear.

We aimed to systematically identify and summarize the literature investigating the impact of six NCDs (CHD, stroke, COPD cancer, type 2 diabetes and CKD) on macro-economic productivity and to determine directions for future research.

Methods

Search strategy and inclusion criteria

We systematically searched the electronic medical databases (Medline, Embase and Google Scholar) up to November 6th, 2014 (date of last search) to identify relevant articles evaluating the macro-economic consequences of the six selected NCDs, specifically the impact on economic productivity of working age citizens. The complete search strategy is available in “Appendix 1”. We defined the major NCDs of interest as CHD, stroke, chronic obstructive lung disease (COPD), type 2 diabetes mellitus (DM), cancer (lung, colon, breast and cervical) and chronic kidney disease (CKD). The step-wise inclusion and exclusion procedure is outlined in Fig. 1. Eligible study design included randomized controlled trials (RCTs), cohort, case–control, cross-sectional, systematic reviews, meta-analysis, ecological studies and modeling studies. We included studies that estimated the impact of at least one of the NCDs defined above on at least one of the following measures of macro-economic productivity: DALYs, economic costs related to reduced work productivity, absenteeism, presenteeism, (un) employment, (non-) return to work (RTW) after sickness absence and medical/sick leave. DALY is also considered as essentially it is an economic measure of human productive capacity for the affected individual and when taken together (e.g. all those in a company, society etc.) forms an economic measure also on the group level. Only studies involving adults (>18 years old) were included, without any restriction on language or date.
Fig. 1

Flowchart of studies for the global impact of non-communicable diseases on macro-economic productivity

Study selection

Two independent reviewers screened the titles and abstracts of the initially identified studies to determine if they satisfied the selection criteria. Any disagreements were resolved through discussion and consensus, or by consultation with a third reviewer. In order to ensure that all retrieved full texts (of the selected abstracts) satisfied the inclusion criteria appropriately, they were further assessed by two independent reviewers. We further screened the reference lists of all retrieved studies to retrieve relevant articles. Systematic reviews were not included in the data extraction but a supplementary scan of their reference lists was performed to identify any additional studies.

Data extraction

A data collection form (DCF) was prepared to extract the relevant information from the included full texts, including study design, World Health Organization (WHO) region, participants, NCD-related exposure and macro-economic outcome characteristics. When evaluating economic costs, US dollars (USD) was used as outcome measure. If a study reported costs in another currency, the corresponding exchange rate to USD as reported by the study itself was used. However, if an exchange rate was not provided, we calculated USD applying the conversion rate for the indicated study time-period.

Quality evaluation

To evaluate the quality of the included non-randomized studies, we applied the Newcastle–Ottawa Scale (NOS) [3]. The NOS scale assesses the quality of articles in three domains: selection, comparability and exposure. ‘Selection’ assesses four items and a maximum of one star can be awarded for each item. ‘Comparability’ awards a maximum of two stars to the one item within the category. Finally, ‘exposure’ includes four items for which one star can be awarded. A quality score is made for each study by summing the number of stars awarded, and thus the NOS scale can have maximum of nine stars. We used this scale to assess the quality of case–control and cohort studies. For cross-sectional and descriptive studies, we used an adapted version of NOS scale (“Appendix 2”).

Statistical methods

We aimed to pool the results using a random effects model. If pooled, results would be expressed as pooled relative risks with 95 % confidence intervals. Pooling possibility was conditional on the level of heterogeneity between studies.

Results

General characteristics of the included studies

From 4542 references initially identified, a total of 126 unique studies met the inclusion criteria (Fig. 1; Table 1). All eligible studies were published between 1984 and 2014. Of the 126 studies identified, 52 were from the WHO European Region, 53 from the Region of the Americas (of which all but two were from Canada or the United States of America [USA]), 16 from the Western Pacific Region, four were from the WHO African Region and one from the Eastern Mediterranean Region. We found no studies from South East Asia. The majority of the identified studies were observational in design, analyzed prospectively as well as cross-sectional. Two studies reported cross-sectional data from an RCT and six were modeling studies. National or hospital-based disease registries were often used to select patients, which were in some cases linked to national socio-economic databases to extract corresponding employment data. The control group, if used, was often a sample from the general population and sometimes sought within the same environment of the patients (e.g. same company). Many studies focused on the impact of more than one NCD on productivity. Most studies used one measure of productivity. Of all the published studies including cancers, cervical cancer was included in seven studies, breast cancer in 45, colon cancer in 24 and lung cancer in 16. Stroke was included in a total of 31 studies, COPD in 24, DM in 22 and CHD was included in 15 studies. Relevant data on CKD was included in only two of the studies and two of the studies mention NCDs in general.
Table 1

General characteristics of the included studies

Source

Period of surveillance

Location

WHO region

Study design

Number in analysis

Gender

Ethnicity

Reported NCDs

Adepoju et al. [71]

2007–2012

USA

RA

Retrospective

376

Both

Hispanic, non-Hispanic black, non-Hispanic white

DM

Ahn et al. [31]

1993–2002

South Korea

WPR

Cross-sectional

1594

Female

NR

Breast cancer

Alavinia and Burdorf [69]

2004

10 EU countries

ER

Cross-sectional

11,462

Both

NR

CVD, stroke, DM

Alexopoulos and Burdorf [54]

1993–1995

The Netherlands

ER

Prospective cohort

326

Male

NR

COPD

Anesetti-Rothermel and Sambamoorthi [10]

2007

USA

RA

Cross-sectional

12,860

Both

White, Latino, African American, other

COPD, CVD, stroke, DM

Angeleri et al. [80]

NR

Italy

ER

Prospective study

180

Both

NR

Stroke

Arrossi et al. [23]

2002–2004

Argentina

RA

Cross-sectional

120

Female

NR

Cervical cancer

Bains et al. [44]

2008–2009

UK

ER

Prospective cohort

50

Female

NR

Colon cancer

Balak et al. [34]

2001–2007

The Netherlands

ER

Retrospective cohort

72

Female

NR

Breast cancer

Bastida and Pagan [81]

1994–1999

USA

RA

Population based

1021

Both

Mexican Americans

DM

Black-Schaffer and Osberg [82]

1984–1986

USA

RA

Prospective study

79

Both

NR

Stroke

Bogousslavsky and Regli [83]

NR

Switzerland

ER

Prospective study

41

Both

NR

Stroke

Boles et al. [84]

2001

USA

RA

Cross-sectional

2264

Both

NR

DM

Bouknight et al. [37]

2001–2002

USA

RA

Prospective study

416

Female

White, black

Breast Cancer

Bradley and Bednarek [85]

1999

USA

RA

Cross-sectional

184

Both

Caucasian, African-American, Hispanic, other

Breast cancer, colon cancer, lung cancer

Bradley et al. [86]

1992

USA

RA

Retrospective study

5974

Female

Caucasian, African-American, Hispanic, other

Breast cancer

Bradley et al. [87]

1992

USA

RA

Cross-sectional

5728

Female

Caucasian, African-American, Hispanic, other.

Breast cancer

Bradley et al. [88]

2001–2002

USA

RA

Prospective study

817

Female

Non-Hispanic White, Non-Hispanic African American, other

Breast cancer

Bradley et al. [89]

2001–2002

USA

RA

Prospective study

239

Female

Non-Hispanic White, Non-Hispanic African American, other

Breast cancer

Bradley and Dahman [33]

2007–2011

USA

RA

Cross-sectional

828

Both

Non-Hispanic white, non-Hispanic black, other

Breast cancer

Bradley et al. [40]

2005

USA

RA

Modelling study

NR

Both

NR

Colon cancer

Bradshaw et al. [66]

2000–2000

South Africa

AR

Modelling

NR

Both

NR

DM

Broekx et al. [90]

1997–2004

Belgium

ER

Cost–of–Illness analysis

20,439

Female

NR

Breast cancer

Burton et al. [91]

2002

USA

RA

Survey

16,651

Both

NR

DM

Carlsen et al. [45]

2001–2009

Denmark

ER

Epidemiological

4343

Both

NR

Colon cancer

Carlsen et al. [29]

2001–2011

Denmark

ER

Cross-sectional and propective

14,750

Female

NR

Breast cancer

Catalá-López et al. [13]

2008

Spain

ER

Cross-sectional

37,563,454

Both

NR

Stroke

Choi et al. [42]

2001–2003

South Korea

WPR

Prospective cohort

305

Male

NR

Colon cancer

Collins et al. [92]

2002

USA

RA

Survey

7797

Both

NR

DM

Costilla et al. [22]

2006

New Zealand

WPR

Modelling

NR

Both

Maori and non-Maori

Breast cancer, colon cancer, lung cancer, cervical cancer

Dacosta DiBonaventura et al. [53]

2009

USA

RA

Cross-sectional

20,024

Both

Non-Hispanic White, Non-Hispanic Black/African-American, Hispanic, other

COPD

Dall et al. [68]

2007–2007

USA

RA

Modelling

NR

NR

NR

DM

Darkow et al. [63]

2001–2004

USA

RA

Case–control

4045

Both

NR

COPD

De Backer et al. [93]

1994–1998

Belgium

ER

Prospective cohort

15,740

Both

NR

DM

Eaker et al. [94]

1993–2003

Sweden

ER

Cross-sectional

28,566

Female

NR

Breast Cancer

Earle et al. [46]

2003–2005

USA

RA

Prospective cohort

2422

Both

Non-Hispanic white, African American, Hispanics, Asian, mixed race

Lung cancer, colon cancer

Ekwueme et al. [26]

1970–2008

USA

RA

Retrospective cohort

53,368

Female

White and Black

Breast cancer

Etyang et al. [6]

2007–2012

Kenya

AR

Prospective surveillance

18,712

Both

NR

CVD, Stroke, DM

Fantoni et al. [38]

2004–2005

France

ER

Cross-sectional

379

Female

NR

Breast cancer

Fernandez de Larrea-Baz et al. [95]

2000

Spain

ER

Ecological

40,376,294

Both

NR

Breast cancer, colon cancer, lung cancer

Ferro and Crespo [96]

1985–1992

Portugal

ER

Prospective cohort

215

Both

NR

Stroke

Fu et al. [97]

2004–2006

USA

RA

Survey

46,617

Both

White, black, Asian, other

DM

Gabriele and Renate [18]

2001–2004

Germany

ER

Prospective cohort

70

Both

NR

Stroke

Genova-Maleras et al. [4]

2008

Spain

ER

Modelling

NR

Both

NR

CVD, stroke, COPD, lung cancer, colon cancer, breast cancer, DM

Gordon et al. [47]

2003–2004

Australia

WPR

Prospective cohort

975

Both

NR

Colon cancer

Hackett et al. [19]

2008–2010

Australia

WPR

Prospective cohort

441

Both

NR

Stroke

Halpern et al. [98]

2000

USA

RA

Economical evaluation

447

Both

NR

COPD

Hansen et al. [99]

NR

USA

RA

Cross-sectional

203

Female

White and non-white

Breast cancer

Hauglann et al. [30]

1992–1996

Norway

ER

National registry cohort

3096

Female

NR

Breast cancer

Hauglann et al. [49]

1992–1996

Norway

ER

Case–control

1480

Both

NR

Colon cancer

Helanterä et al. [65]

2007

Finland

ER

Cross-sectional

2637

Both

NR

CKD

Herquelot et al. [100]

1989–2007

France

ER

Prospective cohort

20,625

Both

NR

DM

Holden et al. [52]

2004–2006

Australia

WPR

Cross-sectional

78,430

Both

NR

CVD, COPD, DM

Hoyer et al. [101]

2007–2008

Sweden

ER

Prospective cohort

651

Female

NR

Breast cancer

Jansson et al. [59]

1999

Sweden

ER

Economic evaluation

212

Both

NR

COPD

Kabadi et al. [17]

2005–2006

Tanzania

AR

Prospective surveillance study

16

Both

NR

Stroke

Kang et al. [16]

2008

South Korea

WPR

Economic Evaluation

 

Both

NR

Stroke

Kappelle et al. [102]

1977–1992

USA

RA

Prospective study

296

Both

White, other

Stroke

Katzenellenbogen et al. [14]

1997–2002

Western Australia

WPR

Modelling, ecologocial

68,661

Both

Indigenous; non-indigenous

Stroke

Kessler et al. [70]

1995–1996

USA

RA

Survey

2074

Both

NR

DM

Klarenbach et al. [64]

1988–1994

USA

RA

Cross-sectional

5558

Both

White, black, other

CVD, COPD, DM, CKD

Kotila et al. [103]

1978–1980

Finland

ER

Prospective

255

Both

NR

Stroke

Kremer et al. [55]

2000–2001

Australia

ER

Cross-sectional

826

Both

NR

COPD

Kruse et al. [104]

1980–2003

Denmark

ER

Cohort

2212

Both

NR

CHD

Lauzier et al. [35]

2003

Canada

RA

Prospective cohort

962

Female

NR

Breast cancer

Lavigne et al. [67]

1999–1999

USA

RA

Cross-sectional

472

Both

NR

DM

Leigh et al. [105]

1996

USA

RA

Ecological study

2,395,650

Both

NR

COPD

Leng [106]

2004–2005

Singapore

WPR

Retrospective cohort

29

NR

NR

Stroke

Lenneman et al. [107]

2005–2009

USA

RA

Survey

577,186

Both

White, black, Hispanic, Asian, other

DM

Lindgren et al. [108]

1994

Sweden

ER

Cross-sectional

393

Both

NR

Stroke

Lokke et al. [62]

1998–2010

Denmark

ER

Case–control

262,622

Both

NR

COPD

Lokke et al. [61]

1998–2010

Denmark

ER

Case–control

1,269,162

Both

NR

COPD

Lopez–Bastida et al. [15]

2004

Canary Islands, Spain

ER

Cross-sectional

448

Both

NR

Stroke

Mahmoudlou [39]

2008

Iran

EMR

Cross-sectional

72,992,154

Both

NR

Colon cancer

Maunsell et al. [32]

1999–2000

Canada

RA

Cross-sectional

57,307

Female

NR

Breast cancer

Mayfield et al. [109]

1987

USA

RA

Survey

35,000

Both

(non)African American, (non) Hispanic

DM

McBurney et al. [110]

1999–2000

USA

RA

Cross-sectional survey

89

Both

Caucasian or minority/unknown

CVD

Molina et al. [111]

2004–2005

Spain

ER

Cross-sectional

347

Both

NR

Breast cancer, colorectal cancer, lung cancer

Molina Villaverde et al. [112]

NR

Spain

ER

Cohort

96

Female

NR

Breast Cancer

Moran et al. [5]

2000–2029

China

WPR

Ecological and modelling

1,270,000,000

Both

NR

CVD

Nair et al. [113]

2000–2007

USA

RA

Economic evaluation

853,496

Both

NR

COPD

Neau et al. [114]

1990–1994

France

ER

Retrospective

67

Both

NR

Stroke

Niemi et al. [115]

1978–1980

Finland

ER

Retrospective case-series

46

Both

NR

Stroke

Nishimura and Zaher [58]

1990–2002

Japan

WPR

Modelling study

1,848,000

Both

NR

COPD

Noeres et al. [28]

2002–2010

Germany

ER

Prospective cohort

874

Female

NR

Breast cancer

Nowak et al. [60]

2001

Germany

ER

Cross-sectional

814

Both

NR

COPD

O’Brien et al. [116]

NR

USA

RA

Cross-sectional

98

Both

Caucasian and African American

Stroke

Ohguri et al. [117]

2000–2005

Japan

WPR

Cross-sectional

43

Both

NR

Lung cancer, colon cancer

Orbon et al. [56]

1998–2000

The Netherlands

ER

Cross-sectional

2010

Both

NR

COPD

Osler et al. [12]

2001–2009

Denmark

ER

Cohort

21,926

Both

NR

CVD

Park et al. [48]

2001–2006

South Korea

WPR

Cross-sectional

2538

Both

NR

Lung cancer, colon cancer, breast cancer, cervical cancer

Park et al. [118]

2001–2006

South Korea

WPR

Prospective study

1602

Both

NR

Lung cancer, colon cancer, breast cancer, cervical cancer

Peters et al. [119]

NR

Nigeria

AR

Cross-sectional

110

Both

NR

Stroke

Peuckmann et al. [120]

1989–1999

Denmark

ER

Cross-sectional

1316

Female

NR

Breast cancer

Quinn et al. [20]

1998–2008

UK

ER

Prospective Cohort

214

Both

NR

Stroke

Robinson et al. [121]

1985–1989

UK

ER

Cross-sectional

2104

Both

Caucasian, West-Indian, Asian

DM

Roelen et al. [122]

2001–2005

The Netherlands

ER

Ecological

259

Female

NR

Breast cancer

Roelen et al. [50]

2004–2006

The Netherlands

ER

Retrospective cohort

300,024

Both

NR

Lung cancer, breast cancer

Saeki and Toyonaga [123]

2006–2007

Japan

WPR

Prospective cohort

325

Both

NR

Stroke

Sasser et al. [8]

1998–2000

USA

RA

Economic evaluation

38,012

Female

NR

Breast cancer, CVD

Satariano et al. [27]

1984–1985 1987–1988

USA

RA

Cross-sectional

1011

Female

White, black

Breast cancer

Short et al. [124]

1997–1999

USA

RA

Cross-sectional

1433

Both

White, non-white, undetermined

Breast cancer

Short et al. [11]

2002

USA

RA

Cross-sectional

6635

Both

NR

CVD, stroke, COPD, DM

Sin et al. [125]

1988–1994

USA

RA

Cross-sectional

12,436

Both

White, Black, other

COPD

Sjovall et al. [36]

2004–2005

Sweden

ER

Ecological study

14,984

Both

NR

Breast cancer, colon cancer, lung cancer

Spelten et al. [126]

NR

The Netherlands

ER

Prospective cohort

235

Female

NR

Breast cancer

Stewart et al. [127]

NR

Canada

RA

Cross-sectional

378

Female

NR

Breast cancer

Strassels et al. [128]

1987–1988

USA

RA

Cross-sectional

238

Both

African American, White, other

COPD

Syse et al. [51]

1953–2001

Norway

ER

Cross-sectional population based

1,116,300

Both

NR

Breast cancer, lung cancer, colorectal cancer

Taskila-Brandt et al. [24]

1987–1988 1992–1993

Finland

ER

Cross-sectional population based

5098

Both

NR

Cervical cancer, breast cancer, colon cancer lung cancer

Taskila et al. [129]

1997–2001

Finland

ER

Cross-sectional

394

Female

NR

Breast cancer

Teasell et al. [130]

1986–1996

Canada

RA

Retrospective cohort

563

Both

NR

Stroke

Tevaarwerk et al. [43]

2006–2008

USA and Peru

RA

Cross-sectional

530

Both

Non-Hispanic whites and whites

Breast cancer, lung cancer, colon cancer

Timperi et al. [131]

2006–2011

USA

RA

Prospective cohort

2013

Female

Whites, Blacks, Hispanic, Asian, other

Breast Cancer

Torp et al. [25]

1999–2004

Norway

ER

Prospective Registry

9646

Both

NR

Cervical cancer, breast cancer, colon cancer, lung cancer

Traebert et al. [21]

2008

Brazil

RA

Modelling, ecological

NR

Both

NR

Cervical cancer, breast cancer, colon cancer, lung cancer

van Boven et al. [57]

2009

The Netherlands

ER

Economic evaluation

45,137

Both

NR

COPD

Van der Wouden et al. [132]

1978–1980

The Netherlands

ER

Cross-sectional

313

Female

NR

Breast cancer

Vestling et al. [133]

NR

Sweden

ER

Retrospective study

120

Both

NR

Stroke

Wang et al. [134]

NR

USA

RA

Cross-sectional

199

Both

NR

CVD, COPD, diabetes

Ward et al. [135]

1993–1994

USA

RA

Cross-sectional

2529

Both

Mixed ethnicities

COPD

Wozniak et al. [136]

NR

USA

RA

Retrospective study

203

Both

Whites, blacks and other

Stroke

Yaldo et al. [41]

2006–2009

USA

RA

Case–control

330

Both

NR

Colon Cancer

Yabroff et al. [137]

2000

USA

RA

Cross-sectional

496

Both

Hispanic, non-Hispanic white, non-Hispanic black, other

Breast cancer, colon cancer

Zhao and Winget [7]

2003–2006

USA

RA

Retrospective cohort

10,487

Both

NR

CVD (CHD)

Zheng et al. [9]

2004

Australia

WPR

Economic evaluation

NR

Both

NR

CVD (CHD)

AR African Region, COPD chronic obstructive pulmonary disease, CKD chronic kidney disease, CVD cardiovascular disease, DM diabetes mellitus, EMR Eastern Mediterranean Region, ER European Region, NCD no-communicable diseases, NR not reported, RA Region of the Americas, USA United States of America, WHO World Health Organization, WPR Western Pacific Region

Measures of productivity

Measures of productivity impact in the available studies included DALYs, absenteeism, presenteeism, labor market (non-) participation, RTW, change in hours worked and medical/sickness leave. Most studies focused on the direct impact on the patient but a minority also examined the impact on caregivers/spouses. Outcomes were quantified using risks, proportions, odds, dollars, years and days. In some studies, time-to-event data was analyzed using Cox proportional-hazards regression. Adjusting for education, age and employment status was most frequently applied, although the measurement of education and employment was not consistently defined, measured or validated. A small minority of studies reported differences in impact according to ethnicity. Pooling of outcomes was not possible due to substantial heterogeneity across and within NCD groups (I 2 > 70 %).

Impact of cardiovascular disease on productivity

Of all DALYs on a population level in Spain (Table 2a), 4.2 % were attributable to CHD [4] with an estimated age-standardized rate of 4.7 per 1000 persons per year. In China, DALYs attributable to CHD were estimated to be 8,042,000 for the year 2000 and predicted to more than double in 2030, rising up to 16,356,000 [5]. In the same study, the estimated DALY in 2000 was 16.1 per 1000 persons and predicted to be 20.4 in 2030 (estimate not accounted for age). A study from Kenya estimated the DALY to be 68 per 100,000 person-years of observation [6]. CHD-related productivity loss in the USA was estimated to be 8539 USD per person per year (PP/PY), at 10175 USD PP/PY [7] for absenteeism and 2698 USD PP/PY for indirect work-related loss [8]. Total absenteeism-related costs in Australia were estimated at 5.69 billion USD, mortality-related costs at 23 million USD and costs related to lower employment at 7.5 billion USD [9]. An estimated 4.7 working days PP/PY were lost in the USA owing to CHD [10]. Also in the USA, the odds of experiencing limited amount of paid work due to illness were significantly higher for those with CHD compared to the control group, with an odds ratio (OR) of 2.91 for women (95 % CI 2.34–3.61) and 2.34 for men (95 % CI 1.84–2.98) [11]. In Denmark workforce participation increased with increasing time from 37 % after 30 days to 65 % after 5 years of diagnosis [12]. In a study conducted in 10 European Union (EU) countries, no difference was found for the risk of non-participation in the labor force between those with and without self-reported CHD with an OR of 0.96 (95 % CI 0.66–1.40).
Table 2

Results of the included studies investigating the impact of CVD on productivity

Study

Type of outcome

Outcome specified as

Assessment type

Point estimate

SD for mean

95 % CI

Quality score

a

Alavinia and Burdorf [69]

Unemployment

Non-participation in the labor force

OR

 

NR

0.66–1.40

4

Anesetti-Rothermel and Sambamoorthi [10]

Sick leave

Work days in last year lost due to illness

Mean

4.700

7.89 (SE)

NR

6

Etyang et al. [6]

DALYs

Rate per 100,000 person year of observation

Rate

68

NR

NR

5

Genova-Maleras et al. [4]

DALYs

Rate per 1000 age standardised

Rate

4.7

NR

NR

NA

Percentage of all causes of mortality

Percent

4.2

NR

NR

 

Holden et al. [52]

Productivity Loss

Absenteeism (no. days or part days missed from work in last 4 weeks)

IRR

1.17

NR

1.03–1.32

3

Presenteeism (self-rated score of overall performance over last 4 weeks)

IRR

1.65

NR

1.22–2.21

 

Klarenbach et al. [64]

Unemployment

Non-participation in labor force

OR

1.27

NR

0.45–3.53

6

Kruse et al. [104]

Labor market participation

Labor market withdrawal a year after the disease debut (controls 7 %)

Percent

21

NR

NR

6

Risk of labor market withdrawal

HR

1.32

NR

1.11–1.57

 

McBurney et al. [110]

Return to work

Return to work at a mean of 7.5 months

Percent

76.4

NR

NR

4

Presenteeism

Perceived work performance

Mean

3.6

0.52

NR

 

Moran et al. [5]

DALYs

Observed period 2000

Count

80,420,00

NR

NR

NA

Observed period 2000

Rate

16.1

NR

NR

 

Predicted 2010

Count

107,300,00

NR

NR

 

Predicted 2010

Rate

16.5

NR

NR

 

Predicted 2020

Count

134,220,00

NR

NR

 

Predicted 2020

Rate

18.2

NR

NR

 

Predicted 2030

Count

16356000

NR

NR

 

Predicted 2030

Rate

20.4

NR

NR

 

Osler et al. [12]

Labor market participation

Workforce participation 30 days after diagnosis (among patients who were part of the workforce at time of diagnosis)

Percent

37.2

NR

NR

5

Workforce participation 1 year after diagnosis (among patients who were part of the workforce at time of diagnosis)

Percent

40.1

NR

NR

 

Workforce participation 2 years after diagnosis (among patients who were part of the workforce at time of diagnosis)

Percent

45.0

NR

NR

 

Workforce participation 5 years after diagnosis (among patients who were part of the workforce at time of diagnosis)

Percent

65.2

NR

NR

 

Sasser et al. [8]

Productivity loss costs

Attributable annual indirect work-loss costs per patient

USD

2698

NR

NR

8

Short et al. [124]

Unemployment

Limited amount of paid work possible due to illness female

OR

2.91

NR

2.34–3.61

5

Limited amount of paid work possible due to illness male

OR

2.34

 

1.84–2.98

 

Wang et al. [134]

Absenteeism

Annual excess in days

Mean

8.8

7.0 (SE)

NR

4

Presenteeism

Annual excess in days

Mean

8.9

11.8 (SE)

NR

 

Absenteeism and presenteeism combined

Annual excess in days

Mean

16.3

12.7 (SE)

NR

 

Zhao and Winget [7]

Productivity loss costs

Short term 1 year productivity costs/per person

USD

8539

NR

NR

6

Absenteeism 1 year productivity costs/per person

USD

10175

NR

NR

 

Zheng et al. [9]

Productivity loss costs

Absenteeism related total

USD

568,500,000

NR

NR

NA

Mortality related

USD

235,650,00

NR

NR

 

Due to lower employment

USD

750,000,000

NR

NR

 

b

Alavinia and Burdorf [69]

Unemployment

Non participation in the labour force

OR

1.110

NR

0.530–2.320

4

Anesetti-Rothermel and Sambamoorthi [10]

Sick leave

Work days in last year lost due to illness

Mean

17.960

5.83 (SE)

6

Angeleri et al. [80]

Return to work

Return to work 12–196 months (mean 37.5) in hemiplegic patients

Percent

20.64

NR

NR

6

Black-Schaffer and Osberg [82]

Return to work

Return to work at 6–25 months post-rehabilitation

Percent

49

NR

NR

3

Time return to work in months from rehabilitation

Mean

3.1

2.12

NR

 

Return to prior job at 6–25 months post-rehabilitation

Percent

43

NR

NR

 

Bogousslavsky and Regli [83]

Return to work

Return to work 6–96 months (mean 46)

Count

19

NR

NR

3

Catalá-López et al. [13]

DALYs

Total

Count

418,052

NR

NR

4

Male

Count

220,005

NR

NR

 

Female

Count

198,046

NR

NR

 

Etyang et al. [6]

DALYs

Rate per 100,000 person year of observation

Rate

166

NR

NR

5

Ferro and Crespo [96]

Unemployment

Inactive at end of follow-up (mean 33.4 months, range 1–228 months)

Percent

27

NR

NR

4

Gabriele and Renate [18]

Return to Work

Return to work after 1 year of those employed

Percent

26.7

NR

NR

4

Genova-Maleras et al. [4]

DALYs

Rate per 1000 age standardised

Rate

3.8

NR

NR

NA

Percentage of all causes of mortality

Percent

3.5

NR

NR

 

Hackett et al. [19]

Return to work

Return to work 1 year after event

Percent

75

NR

NR

2

Kabadi et al. [17]

Return to work

Average months off work in 6 month follow up period

Mean

6

NR

NR

4

Costs

Mean productivity losses due to stroke

USD

213

NR

NR

 

Kang et al. [16]

Productivity loss costs

Male, total modelled costs per severe stroke per year

USD

537,724

NR

NR

NA

Female, total modelled costs per severe stroke per year

USD

171,157

NR

NR

 

Kappelle et al. [102]

Unemployment

Unemployment at 0.02–16 years after event (mean 6 years)

Percent

58

NR

NR

5

Katzenellenbogen et al. [14]

DALYs

Male

Count

26,315

NR

NR

NA

Female

Count

30,918

NR

NR

 

Male, rate per 10,000 people, age standardized—indigenous

Rate

2027

NR

1909–2145

 

Female, rate per 10,000 people, age standardized—indigenous

Rate

1598

NR

1499–1697

 

Male, rate per 10,000 people, age standardized—non-indigenous

Rate

640

NR

633–648

 

Female, Rate per 10,000 people, age standardized—non-indigenous

Rate

573

NR

567–580

 

Klarenbach et al. [64]

Unemployment

Non-participation in labour force

OR

2.21

NR

(0.7–7)

6

Kotila et al. [103]

Return to work

Return to work after 12 months

Percent

59

NR

NR

4

Leng [106]

Return to work

Return to work in 1 year

Percent

55.0

NR

NR

NA

Lindgren et al. [108]

Productivity loss costs

Indirect costs during one ear

USD

17,844

NR

12,275–23,864

4

Lopez-Bastida et al. [15]

Productivity loss costs

Indirect per person, 1 year after stroke

USD

2696

6462

NR

5

Indirect per person, 2 year after stroke

USD

1393

4754

NR

 

Indirect per person, 3 year after stroke

USD

1362

4931

NR

 

Caregivers cost per person per year, 1 year after stroke

USD

14,732

14,616

NR

 

Caregivers cost per person per year, 2 year after stroke

USD

15,621

14,693

NR

 

Caregivers cost per person per year, 3 year after stroke

USD

13,759

15,470

NR

 

Neau et al. [114]

Return to work

Return to work in same position as prior to stroke

Percent

54

NR

NR

3

Return to work after 0–40 month (mean 7.8)

Percent

73

NR

NR

6

Niemi et al. [115]

Return to work

Return to work after 4 years

Percent

54

NR

NR

 

O’Brien et al. [116]

Return to work

Return after 6–18 months

Percent

56.0

NR

NR

1

Peters et al. [119]

Return to work

Return to work after 3–104 months (mean 19.5)

Percent

55

NR

NR

3

Quinn et al. [20]

Return to Work

unemployment at 1 year follow up

Percent

47

NR

NR

3

Roelen et al. [122]

Return to Work

Return to work after 3–104 months (mean 19.5)

Percent

55.0

NR

NR

6

Saeki and Toyonaga [123]

Return to Work

Return to work at 18 months

Percent

55.0

NR

NR

6

Short et al. [124]

Unemployment

Limited amount of paid work possible due to illness female

OR

2.26

NR

1.56–2.26

5

Limited amount of paid work possible due to illness male

OR

3.86

NR

2.55–3.60

 

Teasell et al. [130]

Return to work

Return to work at 3 months

Percent

20

NR

NR

3

Return to work full-time at 3 months

Percent

6

NR

NR

 

Vestling et al. [133]

Return to work

Return to work mean of 2.7 years

Percent

41

NR

NR

3

Time to return to work in months

Mean

11.9

9

NR

 

Return to work with reduced work hours

Percent

21

NR

NR

 

Wozniak et al. [136]

Return to work

Return to work after 1 year

Percent

53

NR

NR

6

Return to work after 2 year

Percent

44

NR

NR

 

c

Arrossi et al. [23]

Return to work

Reduced in hours worked (patients)

Percent

45

NR

NR

4

Change of work (pat.)

Percent

5

NR

NR

 

Starting paid work (pat.)

Percent

14

NR

NR

 

Increased in hours worked (pat.)

Percent

11

NR

NR

 

Odds of work interruption (pat.)

OR

4

NR

NR

 

Odds of reduction in hours worked (pat.)

OR

1

NR

NR

 

Odds of starting paid work (pat.)

OR

2

NR

NR

 

Odds of increase in hours worked (pat.)

OR

1

NR

NR

 

Work interruption (caregivers)

Percent

3

NR

NR

 

Reduction in hours worked (caregivers)

Percent

61

NR

NR

 

Change of work (caregivers)

Percent

2

NR

NR

 

Starting paid work (caregivers)

Percent

5

NR

NR

 

Increased in hours worked (caregivers)

Percent

24

NR

NR

 

Work interruption (patients)

Percent

28

NR

NR

 

Costilla et al. [22]

DALYs

Female

Count

1016

NR

NR

NA

Percentage of all cancers, female

Percent

1.6

NR

NR

 

Rate per 10,000 people (age standardized)

Rate

84

NR

NR

 

Park et al. [48]

Labour market participation

Time until job loss between patients and controls Cox PH

HR

1.32

NR

0.95–1.82

7

Park et al. [118]

Labour market participation

Time until job loss between patients and controls Cox PH

HR

1.68

NR

1.40–2.01

5

Time until re-employment between patients and controls Cox PH

HR

0.67

NR

0.46–0.97

 

Taskila-Brandt et al. [24]

Labor market participation

Employment status cancer survivors 2–3 years post-diagnosis compared to general population (58 vs. 75 %)

RR

0.77

NR

0.67–0.90

6

Traebert et al. [21]

Labor market participation

Employment in 5 years from diagnosis

OR

0.92

NR

0.63–1.34

9

Traebert et al. [21]

DALY

Rate per 10,000 people (age standardized)

Rate

118.7

NR

NR

NA

Percentage of all cancers (in females)

Percent

13.4

NR

NR

 

Total

Count

2516.1

NR

NR

 

d

Ahn et al. [31]

Labour market drop-out

Not working current for cancer survivors versus the general population (adjusted)

OR

1.680

1.350

2.100

3

OR of not working for cancer survivors of currently not working compared with their employment status at the time of diagnosis

OR

1.630

1.510

1.760

 

Unemployment

Adjusted OR for not working at the time of diagnosis versus the general population

OR

1.210

0.960

1.530

 

Balak et al. [34]

Sick leave

Months to fully return to work

Mean

11.4

NR

NR

3

Months to return to partial work

Mean

9.5

NR

NR

 

Bouknight et al. [37]

Return to work

Return to work in 12 months after diagnosis

Percent

82

NR

NR

5

Return to work in 18 months after diagnosis

Percent

83

NR

NR

 

Bradley and Bednarek [85]

Unemployment

Unemployed 5–7 years after diagnosis for cancer survivors

Percent

54.8

NR

NR

5

Unemployed 5–7 years after diagnosis for cancer survivors

Percent

45.4

NR

NR

 

Bradley et al. [86]

Labor market participation

Probability of working of breast cancer patients compared to controls at mean of 7 years

Percent

−7

4

NR

8

Bradley et al. [87]

Labor market participation

Probability of working of breast cancer patients compared to controls at mean of 7.15 years

Percent

−10

4

NR

5

Bradley et al. [89]

Employment

Probability of being employed for patients compared to controls at 6 months

Percent

−25

NR

NR

7

Reduced weekly hours of work for patients compared to controls after 6 months

Percent

−18

NR

NR

 

Bradley et al. [40]

Absenteeism

Days absent from work evaluated at 6 months after diagnosis

Mean

44.5

55.2

NR

7

Bradley and Dahman [33]

Labor market participation

Probability of stopping work at 2 months post diagnosis (husbands of female patients)

OR

2.642

NR

0.848–8.225

5

Labor market participation

Probability of stopping work at 9 months post diagnosis (husbands of female patients)

OR

0.843

NR

0.342–2.198

 

Productivity

Odds of decrease in weekly hours at 2 months post diagnosis (husbands of female patients)

OR

1.449

 

0.957–2.192

 

Productivity

Odds of decrease in weekly hours at 9 months post diagnosis (husbands of female patients)

OR

1.057

 

0.69–1.62

 

Productivity

Change in weekly hours at 2 months post diagnosis (husbands of female patients) (hours)

Count

−0.007

(0.885) SE

NR

 

Productivity

Change in weekly hours at 9 months post diagnosis (husbands of female patients) (hours)

Count

1.814

(1.261) SE

NR

 

Broekx et al. [90]

Productivity

Indirect costs work per patient per year (attributable)

USD

5248

NR

NR

3

Indirect costs housekeeping per patient per year (attributable)

USD

2034

NR

NR

 

Indirect costs mortality per patient per year (attributable)

USD

14,203

NR

NR

 

Sick leave days per year

USD

47.2

NR

NR

 

Total indirect costs per patient per year (attributable)

USD

21,485

NR

NR

 

Carlsen et al. [45]

Unemployment

% of working women 2 years after treatment

Percent

72

NR

NR

5

Costilla et al. [22]

DALYs

DALYs % of all cancers

Percent

27.2

NR

NR

NA

Rate per 10,000 people (age standardized)

Rate

1065

NR

NR

 

DALYs

Count

17,840

NR

NR

 

Eaker et al. [94]

Sick leave

Percentage difference of sickness absence comparing patients 5 years after diagnosis with women without breast cancer

Percent

10.100

NR

NR

7

Percentage difference of sickness absence comparing patients 3 years after diagnosis with women without breast cancer

Percent

11.100

NR

NR

 

Ekwueme et al. [26]

Productivity loss

Mortality-related total lifetime productivity loss (whites)

USD

3,920,400,000

NR

NR

4

Mortality-related total lifetime productivity loss (blacks)

USD

1323200000

NR

NR

 

Mortality-related total lifetime productivity loss/per death (all)

USD

1,100,000

NR

NR

 

Mortality-related total lifetime productivity loss/per death (whites)

USD

1,090,000

NR

NR

 

Mortality-related total lifetime productivity loss/per death (blacks)

USD

1,110,000

NR

NR

 

Mortality-related total lifetime productivity loss (all)

USD

5,488,600,000

NR

NR

 

Fantoni et al. [38]

Return to work

Return to work 12 months after starting treatment

Percent

54.3

NR

NR

5

Return to work after 3 years after starting treatment

Percent

82.1

NR

NR

 

Sick leave

Duration of sick leave 36 months after starting treatment in months

Mean

1.8

NR

9.2–12.1

 

Fernandez de Larrea-Baz N et al. [95]

DALYs

Rate per 10,000 people, age standardized, male

Rate

2

NR

NR

4

Rate per 10,000 people, age standardized, total

Count

77,382

NR

NR

 

Rate per 10,000 people, age standardized, female

Rate

374

NR

NR

 

Genova-Maleras et al. [4]

DALYs

Rate per 1,000 people, age standardized

Rate

1.6

NR

NR

NA

Percentage of all causes of mortality

Percent

1.4

NR

NR

 

Hansen et al. [99]

Presenteeism

Average score difference on work limitation scale between cases and non-cancer controls

Mean

2.9

NR

NR

5

Hauglann et al. [30]

Unemployment

Unemployment at 9 years in females

Percent

18

NR

NR

9

Hoyer et al. [101]

Unemployment

Unemployment at follow up

Percent

26

NR

NR

4

Lauzier et al. [35]

Sick leave

Percent taking sick leave for 1 week or more

Percent

90.7

NR

NR

6

Weeks of absence due to breast cancer

Count

32.3

NR

NR

 

Maunsell et al. [32]

Unemployment

Unemployment among disease free survivors

Risk ratio

1.35

NR

1.08–1.7

7

Unemployment

Unemployment among survivors with new breast cancer event

Risk ratio

2.24

NR

1.57–3.18

 

Unemployment

Unemployment among all survivors (3 years after diagnosis)

Risk ratios

1.46

NR

1.18–1.81

 

Productivity loss

Survivors reporting part-time working compared to controls (3 years after diagnosis)

Percent

4

NR

NR

 

Productivity loss

Change in working hours among survivors–change over time compared to controls (3 years after diagnosis)

Mean

−2.6

NR

NR

 

Molina et al. [111]

Return to work

Return to work at mean time since diagnosis(32.5 months)

Percent

56

NR

NR

5

Molina Villaverde et al. [112]

Return to work

Return to work by end of treatment

Percent

56

NR

NR

NA

Noeres et al. [28]

Unemployment

6 years after diagnosis

Percent

43.2

NR

NR

5

1 year after diagnosis

Percent

49.8

NR

NR

 

Park et al. [48]

Labour market participation

Time until job loss (months)

Mean

36

NR

 

7

Time until 25 % of patients were re-employment (months)

Mean

30

NR

  

Park et al. [118]

Labour market participation

Cox proportional analysis comparing time until job loss between patients and controls

HR

1.83

NR

1.60–2.10

5

Cox proportional analysis comparing time until re-employment between patients and controls

HR

0.61

NR

0.46–0.82

 

Peuckmann et al. [120]

Labor market participation

Age-standardized prevalence of employment at 5–15 years post primary surgery

Percent

49

NR

NR

4

Age standardized risk ratio (SRR) of employment at 5–15 years post primary surgery

SRR

1.02

NR

0.95–1.10

 

Age-standardized prevalence of sick leave at 5–15 years post primary surgery

Percent

12

NR

NR

 

Age standardized risk ratio (SRR) of sick leave at 5–15 years post primary surgery

SRR

1.28

NR

0.88–1.85

 

Roelen et al. [50]

Return to work

Time to return to full-time work (days)

Count

349.0

NR

329–369

6

Time to return to part-time work (days)

Count

271.0

NR

246–296

 

Roelen et al. [112]

Return to work

Return to work at 2 years

Percent

89.4

NR

NR

4

Sick leave

Days of absence due to breast cancer

Count

349

NR

NR

 

Sasser et al. [8]

Productivity loss costs

Attributable annual indirect work-loss costs per female patient

USD

5944.0

NR

NR

8

Satariano et al. [27]

Return to work

3 months after diagnosis (white women)

Percent

74.2

NR

NR

3

Return to work

3 months after diagnosis (black women)

Percent

59.6

NR

NR

 

Sick leave

3 months after diagnosis (white women)

Percent

25.8

NR

NR

 

Sick leave

3 months after diagnosis (black women)

Percent

40.4

NR

NR

 

Short et al. [124]

Unemployment

The chances of quitting work/unemployment 1–5 years after diagnosis

OR

0.44

NR

0.20–0.95

5

Sjovall et al. [36]

Sick leave

Days sick leave taken before return to work

Count

90

NR

NR

5

Spelten et al. [126]

Return to work

Time to return to work after diagnosis analyzed using Cox PH

HR

0.45

NR

0.24–0.86

4

Stewart et al. [127]

Unemployment

Unemployment assessed at least at 2 years after diagnosis, mean of 9 years

Percent

41

NR

NR

3

Syse et al. [51]

Labor market participation

Employment probability in the year 2001 of cancer survivors compared to general population

OR

0.74

NR

0.65–0.84

6

Taskila-Brandt et al. [24]

Labor market participation

Employment status of cancer survivors 2–3 years post-diagnosis compared to general population (61 vs. 65 %)

RR

0.95

NR

0.92–0.98

6

Taskila et al. [129]

Work ability

Current work ability assessed between 0 and 10 by questionnaire (reference group 8.37)

Mean

8.23

NR

NR

8

Tevaarwerk et al. [43]

Unemployment

Unemployment

Percent

19.4

NR

NR

6

Timperi et al. [131]

Unemployment

6 months post diagnosis

Percent

52.0

NR

NR

4

Torp et al. [25]

Labor market participation

Employment 5 years from diagnosis

OR

0.74

NR

0.63–0.87

9

Traebert et al. [21]

DALYs

Percentage of all cancers, female

Percent

21.9

NR

NR

NA

Rate per 10,000 people, age standardized, male

Rate

3.2

NR

NR

 

Percentage of all cancers, male

Percent

0.3

NR

NR

 

Total

Count

6032.3

NR

NR

 

Rate per 10,000 people, age standardized, female

Rate

195

NR

NR

 

Van der Wouden et al. [132]

Labor market participation

Changes in employment status at least 5 years cancer free

Percent

−7

NR

NR

3

Maintained employment status after diagnosis

Percent

16

NR

NR

 

Yabroff et al. [137]

Labor market participation

Job in past 12 months, compared to control group (45.9 % with a p value <0.001 for difference)

Percent

36.9

NR

31.0–42.8

6

Sick leave

Days lost from wok due to health problems in past 12 months compared to control group (5.7 % with a p value <0.001 for difference)

Mean

21.0

NR

28.4–58.3

 

Presenteeism

Limited in work due to health issues compared to control group (17.6 % with a p value of <0.001 for difference)

Percent

22.5

NR

17.4–27.6

 

e

Bains et al. [44]

Unemployment

6 months after surgery

Percent

61

NR

NR

2

Bradley et al. [40]

Productivity loss

Annual productivity losses total 2020 modelled (millions)

USD

21,780

NR

NR

NA

Annual productivity losses total 2005 (millions)

USD

20,920

NR

NR

 

Bradley and Bednarek [85]

Unemployment

Unemployed 5–7 years after diagnosis cancer survivors

Percent

54.8

NR

NR

5

Unemployed 5–7 years after diagnosis spouse of cancer survivors

Percent

53

NR

NR

 

Carlsen et al. [29]

Return to Work

Return to work after 1 year after diagnosis

Percent

69

NR

NR

8

Choi et al. [42]

Unemployment

Lost job at 24 months in males

Percent

46

NR

NR

7

Costilla et al. [22]

DALYs

Female

Count

8431

NR

NR

NA

% of all cancers (Female)

Percent

12.9

NR

NR

 

Rate per 10,000 people (age standardised, Female)

Rate

333

NR

NR

 

Male

Count

8316

NR

NR

 

% of all cancers (Male)

Percent

13.5

NR

NR

 

Rate per 10,000 people (age standardised, Male)

Rate

414

NR

NR

 

Earle et al. [46]

Unemployment

Unemployment at 15 months

Percent

65

NR

NR

4

Fernandez de Larrea-Baz N et al. [95]

DALYs

Rate per 10,000 people, age standardized, female

Rate

212

NR

NR

4

Rate per 10,000 people, age standardized, male

Rate

284

NR

NR

 

Rate per 10,000 people, age standardized, total

Count

99,833

NR

NR

 

Genova-Maleras et al. [4]

DALYs

Rate per 1000 people, age standardized

Rate

2.3

NR

NR

NA

Percentage of all causes of mortality

Percent

2.1

NR

NR

 

Gordon et al. [47]

Return to work

Working 1 year after diagnosis (%)

Percent

65

NR

NR

5

Hauglann et al. [49]

Return to work

% of employed that were on sick-leave at some point after 1 year of diagnosis

Percent

85

  

9

Sickness absence for CRC localized, the OR is for 3 years after diagnosis

Odds Ratio

2.61

1.36

4.95

 

Sickness absence for CRC regional, the OR is for 3 years after diagnosis

Odds Ratio

1.09

0.56

2.11

 

Sickness absence for CRC distant, the OR is for 3 years after diagnosis

Odds Ratio

2.30

0.57

0.927

 

Mahmoudlou [39]

DALYs

Total burden of colorectal cancer according to DALY in Iran in 2008

Count

52,534

NR

NR

8

DALYs for men in 2008

Count

29,928

NR

NR

 

DALYs for women in 2008

Count

22,606

NR

NR

 

Molina et al. [111]

Return to work

Return to work at mean time since diagnosis(32.5 months)

Percent

55

NR

NR

5

Ohguri et al. [117]

Sick leave

Attendance rate after return to work of employees with disease compared to controls (p value 0.67)

Percent

86

NR

NR

4

Park et al. [48]

Return to work

Time until re-employment (patients after job loss) Cox PH analysis

HR

0.96

NR

0.7–1.32

7

Unemployment

Cox PH analysis time until job loss

HR

1.04

NR

0.91–1.2

 

Park et al. [118]

Labour market participation

Cox PH analysis comparing time until job loss between patients and controls

HR

1.69

NR

1.50–1.90

5

 

Cox PH analysis comparing time until re-employment between patients and controls

HR

0.57

NR

0.43–0.75

 

Sjovall et al. [36]

Sick leave

Days sick leave

Count

115

NR

NR

5

Syse et al. [51]

Employment

Employment probability in year 2001 of cancer survivors compared to general population–men

OR

0.67

NR

0.58–0.78

6

Employment probability in year 2001 of cancer survivors compared to general population–women

OR

0.74

NR

0.65–0.84

 

Taskila-Brandt et al. [24]

Labor market participation

Employment status of cancer survivors 2–3 years post-diagnosis compared to general population (53 vs. 59 %)

RR

0.90

NR

0.81–0.99

6

Tevaarwerk et al. [43]

Unemployment

Unemployment

Percent

24.1

NR

NR

6

Torp et al. [25]

Labour market participation

Employment in 5 years from diagnosis (females)

OR

0.84

NR

0.53–1.35

9

Employment in 5 years from diagnosis (male)

OR

0.7

NR

0.43–1.15

 

Traebert et al. [21]

DALYs

Rate per 10,000 people, age standardized, female

Rate

82.6

NR

NR

NA

Percentage of all cancers, female

Percent

9.3

NR

NR

 

Rate per 10,000 people, age standardized, male

Rate

73.1

NR

NR

 

Percentage of all cancers, male

Percent

7.5

NR

NR

 

Total

Count

4867.2

NR

NR

 

Yabroff et al. [137]

Labor market participation

Job in past 12 months, compared to control group (45.9 % with a p value <0.001 for difference)

Percent

22.4

NR

15.6–29.3

6

Sick leave

Days lost from wok due to health problems in past 12 months compared to control group (5.7 % with a p value <0.001 for difference)

Mean

10.0

NR

3.4–16.7

 

Presenteeism

Limited in work due to health issues compared to control group (17.6 % with a p value of <0.001 for difference)

Percent

32.4

NR

24.2–40.6

 

Yaldo et al. [41]

Absenteeism

Mean higher absenteeism costs after 1 year of diagnosis compared to controls

USD

4245

NR

NR

7

f

Bradley and Bednarek [85]

Unemployment

Unemployed 5–7 years after diagnosis cancer survivor

Percent

62.2

NR

NR

5

Unemployed 5–7 years after diagnosis spouse of cancer survivor

 

51.3

NR

NR

 

Costilla et al. [22]

DALYs

Female

Count

9334

NR

NR

NA

% of all cancers (female)

Percent

14.3

NR

NR

 

Rate per 10,000 people (age standardised, female)

Rate

849

NR

NR

 

Male

Count

9806

NR

NR

 

% of all cancers (male)

Percent

15.9

NR

NR

 

Rate per 10,000 people (age standardised, male)

Rate

775

NR

NR

 

Earle et al. [46]

Unemployment

Unemployment at 15 months

Percent

79

NR

NR

4

Fernandez de Larrea-Baz N et al. [95]

DALYs

Rate per 10,000 people (age standardised, female)

Rate

98

NR

NR

4

Rate per 10,000 people (age standardised, male)

Rate

736

NR

NR

 

Rate per 10,000 people (age standardised, all)

Count

165,611

NR

NR

 

Genova-Maleras et al. [4]

DALYs

Percentage of all causes of mortality

Percent

3.4

NR

NR

NA

Rate per 1000 people, age standardized

Rate

3.8

NR

NR

 

Molina et al. [111]

Return to work

Return to work at mean time since diagnosis(32.5 months)

Percent

15

NR

NR

5

Ohguri et al. [117]

Sick leave

Attendance rate after return to work of employees with disease compared to controls (p value 0.59)

Percent

75

NR

NR

4

Park et al. [48]

Labour market participation

Time until job loss

Cox PH

1.31

NR

1.12–1.53

7

Time until re-employment (patients after job loss)

Cox PH

0.79

NR

0.55–1.16

 

Park et al. [118]

Labour market participation

Cox proportional analysis comparing time until job loss between patients and controls

HR

2.22

NR

1.93–2.65

5

Cox proportional analysis comparing time until re-employment between patients and controls

HR

0.45

NR

0.32–0.64

 

Roelen et al. [122]

Return to work

Time to return to full-time work (days)

Count

484.0

NR

307–447

6

Time to return to part-time work (days)

Count

377.0

NR

351–617

 

Syse et al. [51]

Employment

Employment probability in year 2001 of cancer survivors compared to general population–men

OR

0.37

NR

0.31–0.45

6

Employment probability in year 2001 of cancer survivors compared to general population–women

OR

0.58

NR

0.48–0.71

 

Sjovall et al. [36]

Sick leave

Days

Count

275

NR

NR

5

Taskila-Brandt et al. [24]

Labor market participation

Employment status of cancer survivors 2–3 years post-diagnosis compared to general population (19 vs. 43 %)

RR

0.45

NR

0.34–0.59

6

Tevaarwerk et al. [43]

Unemployment

Unemployment

Percent

33

NR

 

6

Torp et al. [25]

Unemployment

Employment in 5 years from diagnosis (male)

OR

0.39

NR

0.18–0.83

9

Employment in 5 years from diagnosis (female)

OR

0.39

NR

0.19–0.81

 

Traebert et al. [21]

DALYs

Rate per 10,000 people, age standardized, female

Rate

87.6

NR

NR

NA

Percentage of all cancers, female

Percent

9.8

NR

NR

 

Rate per 10,000 people, age standardized, male

Rate

239.9

NR

NR

 

Percentage of all cancers, male

Percent

24.5

NR

NR

 

Total

Count

10,832.2

NR

NR

 

g

Alexopoulos and Burdorf [54]

Sick leave

Days of sick leave during 2 year follow up attributable to COPD

Mean

8.53

NR

NR

2

Anesetti-Rothermel and Sambamoorthi [10]

Sick Leave

Work days in last year lost due to illness

Mean

8.600

0.76 (SE)

NR

6

Dacosta DiBonaventura et al. [53]

Productivity loss

Percentage reporting absenteeism (difference between cases of COPD and controls)

Percent

4.190

NR

NR

7

Absenteeism hours (over last 7 days) (difference between COPD cases and controls)

Mean

1.250

NR

NR

 

Percentage reporting presenteeism (difference between cases of COPD and controls)

Percent

16.550

NR

NR

 

Estimated number of hours of presenteeism in last 7 days (difference between COPD cases and controls)

Mean

4.780

NR

NR

 

Percentage of those reporting work impairment (difference between cases of COPDand controls)

Percent

17.280

NR

NR

 

Percentage reporting absenteeism (difference between cases of COPD and controls)

Percent

2.330

NR

NR

 

Absenteeism hours (over last 7 days) (difference between cases of COPD and controls)

Mean

0.330

NR

NR

 

Percentage reporting presenteeism (difference between cases of COPD and controls)

Percent

10.230

NR

NR

 

Estimated number of hours of presenteeism in last 7 days (difference between cases of COPD and controls)

Mean

2.070

NR

NR

 

Percentage of those reporting work impairment (difference between cases of COPD and controls)

Percent

11.530

NR

NR

 

Darkow et al. [63]

Productivity loss

Indirect per person per year

USD

9815

NR

8384–11246

6

Genova-Maleras et al. [4]

DALYs

Rate per 1000 age standardised

Rate

2.6

NR

NR

2

Percentage of all causes of mortality

Percent

2.3

NR

NR

 

Halpern et al. [98]

Productivity loss

Costs due to work loss up from 45 years up to age of retirement per patient per day

USD

100.55

NR

NR

6

Days lost per patient of working age per year

Mean

18.7

NR

NR

 

Days lost per caregiver of working age per year

Mean

1.7

NR

NR

 

Unemployment

Unemployment due to condition

Percent

34

NR

NR

 

Holden et al. [52]

Productivity loss

Absenteeism (no. of full/part days missed from work in last 4 weeks)

IRR

1.57

NR

1.33–1.86

3

Presenteeism (self-rated score of overall performance in last 4 weeks)

IRR

1.22

NR

1.04–1.43

 

Jansson et al. [59]

Productivity loss

Indirect per person per year

USD

749

NR

NR

6

Kremer et al. [55]

Unemployment

Percentage of who stopped work (among people in work) because of the onset of COPD

Percent

39

NR

NR

5

Leigh et al. [105]

Productivity loss

Total indirect costs in 1996 in billions of dollars

USD

21,400

NR

NR

3

Lokke et al. [62]

Unemployment

% receiving income from employment

Percent

16.7

NR

NR

7

Productivity loss

Indirect costs per patient before the diagnosis

USD

4266

NR

NR

 

indirect costs per patient after diagnosis

USD

2816

NR

NR

 

Lokke et al. [61]

Productivity loss

Indirect costs per patient before the diagnosis

USD

5912

NR

NR

9

indirect costs per patient after diagnosis

USD

3819

NR

NR

 

Unemployment

% of spouses receiving income from employment

Percent

36.9

NR

NR

 

Nair et al. [113]

Productivity loss

Short term 1 year productivity costs/per person

USD

527

NR

NR

9

Absenteeism 1 year productivity costs/per person

USD

55

NR

NR

 

Total costs

USD

 

NR

NR

 

Nishimura and Zaher [58]

Productivity loss

Modelled total annual costs per year in country (millions)

USD

1471

NR

NR

2

Modelled indirect per patient

USD

262

NR

NR

 

Days modelled per person

Count

8.1

NR

NR

 

Nowak et al. [60]

Productivity loss

early retirement (per patient/year) (all COPD stages)

USD

566

NR

NR

3

early retirement (per patient/year) (light COPD)

USD

489

NR

NR

 

early retirement (per patient/year) (medium COPD)

USD

567

NR

NR

 

early retirement (per patient/year) (severe COPD)

USD

1064

NR

NR

 

disability (per patient/year) (all COPD stages)

USD

398

NR

NR

 

disability (per patient/year) (light COPD)

USD

459

NR

NR

 

disability (per patient/year) (medium COPD)

USD

249

NR

NR

 

disability (per patient/year) (severe COPD)

USD

340

NR

NR

 

Orbon et al. [56]

Unemployment

Unemployment

Percent

53.8

NR

NR

4

Sin et al. [125]

Employment

Adjusted probability of being in work force for those with self-reported COPD compared to those without self-reported COPD

Percent

−3.9

NR

−1.3 to −6.4

4

Productivity loss

Total loss productivity cost in 1994 in billions

USD

9.9

NR

NR

 

Short et al. [124]

Unemployment

Limited amount of paid work possible due to illness (female)

OR

2.63

NR

2.03–3.42

5

Limited amount of paid work possible due to illness (male)

OR

4.89

NR

3.46–6.9

 

Strassels et al. [128]

Productivity loss

Number of lost work days COPD related

Mean

1.0

NR

<0.1–2.0

5

Number of restricted activity days COPD related

Mean

15.9

NR

10.3–21.5

 

van Boven et al. [57]

Productivity loss

Costs total per patient a year (2009)

USD

938

NR

NR

6

Costs in total (2009)

USD

88,340,000

NR

NR

 

Absenteeism

Days total per patient (2009)

Count

10.7

NR

NR

 

Days total (2009)

Count

482,966

NR

NR

 

Wang et al. [134]

Absenteeism

Annual excess in days

Mean

19.4

8.9 (SE)

NR

4

Presenteeism

Annual excess in Days

Mean

27.5

15.6 (SE)

NR

 

Absenteeism & Presenteeism combined

Annual excess in days

Mean

42.9

17.0 (SE)

NR

 

Ward et al. [135]

Unemployment

Inability to work attributable to COPD

Percent

10.6

NR

NR

6

Productivity loss

Number work loss days per year

Mean

1.4

NR

NR

 

h

Helantera et al. [65]

Unemployment

Unemployed in 2007 for patients with dialysis or after kidney transplant

Percent

35

NR

NR

6

Klarenbach et al. [64]

Unemployment

Non-participation in labour force

OR

7.94

NR

1.6–39.43

6

i

Adepoju et al. [71]

Absenteeism

Absenteeism Days total

Count

11,664

NR

NR

9

Absenteeism Costs total

USD

85,314

NR

NR

 

Proportion of total productivity losses attributable to absenteeism

Percent

4

NR

NR

 

Days of reduced time at work as a sum of Inpatient and ambulatory visits

Count

7864

NR

NR

 

Costs of reduced time at work as sum of Inpatient and ambulatory visits

USD

866,744

NR

NR

 

Proportion of total productivity losses attributable to reduced time at work

Percent

3

NR

NR

 

Presenteeism

Presenteeism days total

Count

7864

NR

NR

 

Presenteeism Costs total

USD

866,744

NR

NR

 

Proportion of total productivity losses attributable to presenteeism

Percent

44

NR

NR

 

Productivity loss

Costs of premature mortality costs as a product of YLL and income

USD

953,373

NR

NR

 

Proportion of total productivity losses attributable premature mortality

Percent

49

NR

NR

 

Total productivity related loss

Count

20,064

NR

NR

 

Total productivity related costs loss

USD

1,962,314

NR

NR

 

Alavinia and Burdorf [69]

Unemployment

Non participation in the labor force

OR

1.380

NR

0.990–1.930

4

Anesetti-Rothermel and Sambamoorthi [10]

Sick leave

Work days in last year lost due to illness

Mean

7.250

1.18 (SE)

NR

6

Bastida and Pagan [81]

Productivity loss

Unemployment due to diabetes

In females

Maximum likelihood

−0.073

0.198

NR

NA

Unemployment due to diabetes

In males

Maximum likelihood

−1.047

0.447

NR

 

Boles et al. [84]

Productivity loss

Lost earnings per diabetic person/week

USD

67

NR

NR

4

Absenteeism

Absenteeism

OR

2.285

NR

1.167–4.474

 

Absenteeism

Least squares regression coefficient

3.254

7.286

NR

 

Presenteeism

Presenteeism

OR

1.271

NR

0.724–2.230

 

Presenteeism

Least squares regression coefficient

4.308

4.369

NR

 

Bradshaw et al. [66]

DALYs

Total

Count

162,877

NR

NR

3

Male

Count

102,454

NR

NR

 

Female

Count

101,690

NR

NR

 

Burton et al. [91]

Presenteeism

Time management (work the required no. of hours; start work on time)

OR

1.401

NR

1.14–1.73

5

Physical work activities (e.g. repeat the same hand motions; use work equipment)

OR

1.415

NR

1.15–1.75

 

Mental/interpersonal activities (concentration; teamwork)

OR

1.233

NR

1.02–1.50

 

Overall output (complete required amount of work; worked to capability)

OR

1.158

NR

0.95–1.42

 

Collins et al. [92]

Productivity loss

Impairment score (WIS)

Count

17.8

NR

15.9, 19.6

7

Absent hours per patient/month

Count

1.3

NR

0.6, 1.9

 

Work Impairment

Linear regression coefficient

−2.4

NR

NR

 

Absence

Logistic regression coefficient

1.2 (not significant)

NR

NR

 

Dall et al. [68]

Productivity loss

Absenteeism

USD

2470

NR

NR

1

Presenteeism

USD

18,715

NR

NR

 

Inability to work due to diabetes

USD

7276

NR

NR

 

De Backer et al. [93]

Sick leave

Univariate analysis of high 1 year incidence rate of sick leave in diabetes compared to controls (25.3 %) in men (p value <0.001)

Percent

36.9

NR

NR

8

Univariate analysis of long absences (defined as more than 7 days) in diabetes compared to controls (19.3 %) in men, (p value 0.002)

Percent

25.3

NR

NR

 

Univariate analysis for repetitive absences in diabetes compared to controls (14.5 %) in men (p value <0.001)

Percent

21.2

NR

NR

 

Adjusted analysis of high 1 year incidence rate of sick leave in diabetes compared to controls in men

OR

1.51

NR

1.22–1.88

 

Adjusted analysis of long absences in diabetes compared to controls in men

OR

1.11

NR

0.87–1.41

 

Adjusted analysis for repetitive absences in diabetes compared to controls in men

OR

1.54

NR

1.20–1.98

 

Univariate analysis of high 1 year incidence rate of sick leave in diabetes compared to controls (25.1 %) in women (p value <0.04)

Percent

33.9

NR

NR

 

Univariate analysis of long absences (defined as more than 7 days) in diabetes compared to controls (25.2 %) in women, (p value 0.04)

Percent

33.9

NR

NR

 

Univariate analysis for repetitive absences in diabetes compared to controls (24.0 %) in women (p value 0.002)

Percent

36.7

NR

NR

 

Adjusted analysis of high 1 year incidence rate of sick leave in diabetes compared to controls in women

OR

1.38

NR

0.89–2.14

 

Adjusted analysis of long absences in diabetes compared to controls in women

OR

1.45

NR

0.94–2.23

 

Adjusted analysis for repetitive absences in diabetes compared to controls in men

OR

1.71

NR

1.12–2.62

 

Etyang et al. [6]

DALYs

Rate per 100,000 PY of observation

Rate

364

NR

NR

5

Fu et al. [97]

Productivity loss

Work loss days due to diabetes/year

Count

6.7

NR

NR

8

Bed days due to diabetes/year

Count

13

NR

NR

 

Genova-Maleras et al. [4]

DALYs

Rate per 1000 age standardised

Rate

2.2

NR

NR

2

Percentage of all causes of mortality

Percent

1.9

NR

NR

 

Herquelot et al. [100]

Presenteeism

Work disability due to diabetes

Incidence rate per 1000 person-years

7.9

NR

NR

7

Work disability due to diabetes

HR

1.7

NR

1.0–2.9

 

Holden et al. [52]

Productivity loss

Absenteeism, number of full/part days missed from work in last 4 weeks

IRR

1.17

NR

1.09–1.26

3

Presenteeism, self-rated score of overall performance over last 4 weeks

IRR

0.89

NR

0.83–0.96

 

Lenneman et al. [107]

Productivity loss

Productivity impairment

Unstandardized linear regression coefficient

1.816

NR

0.717–2.820

4

Klarenbach et al. [64]

Unemployment

Non-participation in labour force

OR

2.17

NR

1.2–3.93

6

Kessler et al. [70]

Productivity loss

Impairment days

Count

3.6

0.8

NR

2

Any work impairment

OR

1.1

NR

0.6–1.9

 

Impairment days

Unstandardized linear regression coefficient

−0.3

0.5

NR

 

Lavigne et al. [67]

Productivity loss

Work while feeling unwell

Percent

0.54

NR

NR

4

Variance explained work efficiency losses

Percent

13

NR

NR

 

Hours of work lost due to diabetes, per month per person

Tobit regression coefficients

−1

NR

−13.92 to −12.18

 

Hours of absence from work due to diabetes, per month per person

Tobit regression coefficients

1

NR

−1.09 to −3.45

 

Hours of total productivity time lost per month per person due to diabetes

Tobit regression coefficients

8

NR

1.42–15.03

 

Cost of productivity time lost due to diabetes

Tobit regression coefficients

94

NR

−456.8 to −645.2

 

Mayfield et al. [109]

Productivity loss

Work disability due to diabetes

Probit model estimates

1.46

0.228

NR

8

Work disability due to diabetes

Percent

25.6

NR

NR

 

Work loss days due to diabetes

Linear regression

0.67

0.318

NR

 

Work loss days due to diabetes per year

Count

5.65

NR

NR

 

Lost earnings per diabetic person/year

USD

3099

NR

NR

 

Robinson et al. [121]

Unemployment

Rate of unemployed in those economically active for males (controls 7.8 %)

Percent

21.9

NR

NR

7

Rate of unemployed in those economically active for females (controls 5.1 %)

Percent

11.5

NR

NR

 

Rate of unemployed in those economically active for females (controls 7.0 % with a p value of <0.001 for difference)

Percent

18

   

Short et al. [11]

Unemployment

Limited amount of paid work possible due to illness Female

OR

1.54

NR

1.23–1.92

5

Limited amount of paid work possible due to illness Male

OR

2.02

NR

1.57–2.6

 

Wang et al. [134]

Absenteeism

Annual excess in days

Mean

6.4

6.0 (SE)

NR

4

Presenteeism

Annual excess in days

Mean

7.3

10.3 (SE)

NR

 

Absenteeism and Presenteeism combined

Annual excess in days

Mean

16.0

11.0 (SE)

NR

 

j

Torp et al. [25]

Unemployment

Unemployment at follow up

Percent

25.6

NR

NR

9

Earle et al. [46]

Unemployment

Unemployment at 15 months

Percent

69

NR

NR

4

Cox PH Cox proportional hazard regression, DALY’s disability adjusted life years, IRR incidence risk ratio, NCD no-communicable diseases, NA not applicable, NR not reported, OR odds ratio, RR relative risk, SD standard deviation, USD United States of America dollars

Impact of stroke on productivity

Stroke accounted for 3.5 % of all DALYs reported in Spain (Table 2b) with a rate of 3.8 per 1000 people [4]. Another study from Spain reports a total count of DALYs of 418,052 with a higher number of male than for female (220,005 vs. 198,046) [13]. A study from Kenya reports a rate of 166 DALYs per 100,000 person-years observed [6]. In Western Australia, the average annual stroke-attributable DALY count is an estimated 26,315 for men and 30,918 for women [14]. In Spain, costs after diagnosis increased over time for caregivers but declined for patients (14,732 USD in caregivers compared to 2696 USD among patients after 1 year and 15,621 USD to 1362 USD after 2 years) [15]. Modeled productivity losses in South Korea were higher for a severe stroke among men (537,724 USD) than women (171,157 USD) [16]. A prospective surveillance study from Tanzania report a mean costs of productivity loss to be 213 USD [17]. Inconclusive evidence of the impact of stroke on RTW was reported. Estimates ranged from 26.7 to 75 % in studies reporting RTW in stroke patients after 1 year of the event [18, 19]. In Nigeria, 55 % returned to work at a mean of 19.5 months after stroke. A report from the United Kingdom (UK) found that 47 % were unemployed 1 year after stroke [20]. Increased odds to report limited ability for paid work were found among men (3.86) and women (2.26) after stroke [11].

Impact of cervical cancer on productivity

There are strong regional differences in the percentage of DALYs attributable to cervical cancer (Table 2c) among women, from 1.6 % (absolute DALYs, 1061 per year) in New Zealand to 13.4 % (2516 per year) in Brazil [21, 22]. Cervical cancer patients in Argentina reported negative outcomes after 1 year; 45 % of patients reported reduced labor market participation, 28 % experienced work interruption and 5 % changed work [23]. Compared to the general population, the relative risk (RR) for cervical cancer survivors in labor force participation was 0.77 (95 % CI 0.67–0.90), 2–3 years after diagnosis in Finland [24]. In Norway however, no differences were found 5 years from diagnosis with an OR of 0.92 (0.63–1.34) [25].

Impact of breast cancer on productivity

Of all the DALYs attributable to cancers among women, 27.3 % (17,840 per year) in New Zealand (Table 2d) and 13.4 % (6280 per year) in Brazil are attributable to breast cancer [21, 22]. Total mortality-related lifetime productivity loss costs in the USA were estimated to be 5.5 billion USD [26]. This was differentially distributed between the two ethnic groups reported, with 71 % (or 3.9 billion USD) of the costs attributable to white women and 24 % (or 1.3 billion) attributable to black women. Differential RTW and sick absence rates are also observed comparing black and white women in the USA; the percentage of white women returning to work three months after diagnosis was 74.2 % compared to 59.6 % of black women; the proportion reporting sick leave was 25.8 % of white women compared to 40.4 % of black women [27]. 1 year after primary surgery in Germany, nearly three times as many cancer survivors had left their job as compared to women in the control group. [28] Various studies suggest higher unemployment among breast cancer survivors, reported by around half after 1 year, 72 % after 2 years [29], 43 % after 6 years and 18 % after 9 years [27, 28, 30, 31, 32]. In contrast, in a study assessing unemployment among the spouses of breast cancer patients, no differences were found [33]. Differences between countries in average time to RTW were also found, from 11.4 months in the Netherlands [34] and 7.4 months in Canada [35] to only 3 months in Sweden [36]. Percentage of RTW after 1 year ranged from 54.3 % in a cross-sectional study from France to 82 % in a prospective study from the USA [37, 38].

Impact of cancer on productivity

In New Zealand, of all the DALYs attributable to cancers, 12.9 % (8431 per year) among women and 13.5 % (8316 per year) among men are attributable to colon cancer (Table 2e) [22]. In Brazil, these proportions are 9.3 % among women and 7.5 % among men [21]. In Spain, 2.1 % of DALY’s overall are attributable to colon cancer [4]. In Iran the total burden of colorectal cancer in 2008 was 52,534 DALYs and higher for men than for women [39]. In the USA, annual productivity losses were calculated to be 20.9 billion USD [40], while costs due to absenteeism after 1 year of diagnosis was 4245 USD per patient compared to the general population [41]. Although the DALY and dollar costs of colon cancer are undoubtedly large, the evidence for micro-level labor market indicators including risk and proportions of RTW, sickness absence and employment following diagnosis and treatment is however inconclusive [25, 42, 43, 44, 45, 46, 47, 48, 49]. In New Zealand, of all cancer-attributable DALYs, 14.4 % (9334 per year) among women and 15.9 % (9806 per year) among men are attributable to lung cancer (Table 2f) [22]. In Brazil, lung cancer results in an estimated 10,832 DALYs per year, 9.8 % of all cancer-related DALYs among women and 24.5 % among men [21]. In Spain, 3.4 % of all DALYs are attributable to lung cancer [4]. Most of the first year of disease (275 days) is spent in sickness absence in Sweden [36] and between 33 and 79 % of lung cancer patients in the USA were unemployed 15 months after diagnosis [43, 46]. Average time to re-enter the labor market was 484 days for full-time work and 377 for part-time work in the Netherlands [50]. The odds of re-entry into the labor market were significantly lower for lung cancer than the general population [24, 25, 51].

Impact of COPD on productivity

COPD patients have a higher chance of working fewer hours, of absenteeism and of poorer work performance (presenteeism) (Table 2g). [11, 52, 53]. A COPD patient loses around 8.5 workdays per year due to disease [10, 54]. Between 39 and 50 % of people stopped working due to the onset of COPD in the Netherlands [55, 56]. COPD-related productivity losses cost the US economy around 88 million USD or around 482,966 working days per year [57]. Modeled annual costs of COPD, estimated at 1.47 billion USD [58], are higher in Japan than the USA. The productivity loss costs PP/PY were somewhat comparable between Germany, Sweden and the Netherlands (566, 749 and938 USD respectively) [57, 59, 60], but differed four-fold to estimated costs in Denmark (2816–3819 USD) [61, 62] and more than tenfold to what was estimated (9815 USD) in the USA [63]. In the USA, 8.5 work days are lost PP/PY on average [10], while COPD patients take an estimated 8.6 days of sickness absence in the Netherlands during a 2 year follow-up period [54]. Also in the Netherlands, 39 % of COPD patients left the labor force due to disease onset [55].

Impact of chronic kidney disease on productivity

Only two studies (Table 2 h) examined the impact of CKD on productivity. One found that renal dysfunction was independently associated with labor force non-participation, with an odds ratio of 7.94 (95 % confidence interval, 1.60–39.43) [64]. The second study, evaluating labor market participation in CKD patients specifically after dialysis or transplantation, found that 35 % of these CKD patients were unemployed [65].

Impact of diabetes mellitus on productivity

In Spain, nearly 2 % of all mortality-related DALYs are attributable to DM [4]. In South Africa, 162,877 DALYs annually are attributable to DM (Table 2i) [4, 66]. A study from Kenya reports a rate of 364 DALYs per 100,000 observed person-years [6]. An estimated 7.2 days are lost PP/PY due to DM in the USA [10] and DM patients have an increased risk of absenteeism, presenteeism and inability to work [4, 10, 11, 52, 64, 67, 68, 69]. Productivity days lost per year due to diabetes ranged from 3.6 to 7.3 [10, 70]. In the USA, proportion of productivity loss was large due to premature mortality (49 %) and presenteeism (44 %) compared to absenteeisim (4 %) and total productivity related costs were estimated to be 1,962,314 USD [71]. The odds of non-participation of the labor force for diabetes patients compared to the general population were slightly higher with borderline significance in the EU, an OR of 1.38 (95 % CI 0.99–1.93) [69].

Discussion

This systematic review identified 126 studies investigating the impact of NCDs on productivity. Most studies (96 %) were from the Western world (North America, Europe or Asia Pacific), with limited evidence available from Brazil, South Africa, Kenya, Tanzania, Iran, Japan, South Korea and Argentina. Macro-economic productivity losses were measured in percentage and absolute numbers of DALYs and annual productivity loss costs (in USD). Studies also estimated productivity losses using labor market indicators including unemployment, RTW, absenteeism, presenteeism, sickness absence and loss in working hours. There is a clear scarcity in literature concerning the effect of CKD on productivity, with only two studies both reporting a substantial impact on productivity [64, 65].

Diversity in the macroeconomic measures and outcomes

There were considerable global differences in the NCD-attributable DALY burden, especially the differential impact of each NCD comparing high-income countries (HIC) and low- and middle-income countries (LMIC). Lung and colon cancer account for nearly 30 % of all cancer-attributable DALYs in men in New Zealand whereas in Brazil, lung cancer alone accounts for nearly 25 %. Among women in HIC, breast cancer seems to impose a large productivity burden whereas cervical cancer impacts more dramatically in LMIC [4, 21, 22]. Although DALYs are a reliable measure and capture both years of life lost and years spent in ill-health, we found inconsistent application in the identified studies; some estimated proportions within specific disease groups or of the overall DALY burden in a country; others estimated absolute DALY numbers.

Diversity in the macro-economic impact of the cardiopulmonary diseases

Absolute costs (measured in USD) were estimated for COPD, CHD, and stroke events [7, 9, 15, 57, 58, 71]. These studies mainly came from HIC, although two studies, one from Kenya and one from Tanzania, were also retrieved. In Australia, absenteeism and lower employment due to CHD cost 13.2 billion USD annually, as well as an additional 23 million USD in mortality-related costs [9]. Evidence suggests that COPD costs around 88 million USD or nearly 500,000 working days per year in the US compared to 1.47 billion (modeled) in Japan. While annual COPD-related productivity costs were comparable in Germany, Sweden and the Netherlands (between 566 and 938 USD), costs differed fourfold (2816–3819 USD) in Denmark, tenfold (9815 USD) in the USA [57, 59, 60, 61, 62, 63]. In the USA, nearly half of the annual 1.96 m USD productivity losses due to DM are attributable to mortality, with 44 % attributable to presenteeism and just 4 % to absenteeism In South Korea, modeled productivity losses for a stroke were 68 % higher among men compared to women [16]. Around half of all stroke survivors in unemployed after 1 year [20]. In Tanzania, productivity losses after 6 months following stroke were 213 USD on average although these losses were most acutely experienced by those in higher skill roles [17]. Interestingly, indirect productivity losses were higher among caregivers than stroke patients themselves and costs increased for caregivers but declined for patients after 1 and 2 years following a stroke in Spain. COPD patients experience reduced working hours, unemployment, absenteeism and presenteeism [10, 11, 52, 53, 54, 55, 56]. DM patients also have an increased risk of reduced labor market participation [10, 11, 52, 64]. By contrast, other than for absenteeism [10] the evidence for the risk of reduced labor market participation due to CVD is inconclusive. In Kenya, 68/100,000 person year observed are attributable to CVD compared to 166/100,000 for stroke and 364/100,000 for DM [6]. Although evidence is limited, the higher productivity impact associated with diseases with a large morbidity was perhaps to be expected; chronic diseases such as COPD and DM affect people during their productive years and cannot really be ‘cured’, only managed. The extent to which employers or societies support and enable NCD populations to remain members of the productive workforce will also differentially distribute the impact. The extent to which secondary or tertiary prevention is possible will also affect productivity estimates, specifically so for labor market indicators such as RTW, change in work status or unemployment.

Diversity in the macroeconomic impact of cancer

Lung cancer survival is associated with reduced labor market participation through sickness absence, extended RTW [36, 50] and unemployment [25, 43, 46]. Total mortality-related lifetime productivity loss due to breast cancer were an estimated 5.5 billion USD in the USA [26] and annual productivity losses due to colon cancer costs the US economy 20.9 billion USD [40].We found inconclusive evidence of risk of reduced labor market participation (RTW, sickness absence and unemployment) following colon cancer diagnosis and treatment [25, 42, 43, 44, 45, 46, 48]. The evidence for breast cancer-related labor market drop-out shows higher unemployment among survivors 1, 2, 6 and 9 years after diagnosis [29, 30, 31, 32]. Evidence from the USA also suggests ethnicity-patterned differences in sick leave and unemployment [27]. Along with possible socio-economic differences associated with these outcomes [72], pathophysiological differences may also play a role. African-American women have lower incidence of breast cancer but higher mortality and are also diagnosed in later stages and with more aggressive types of tumors [73]. However, we are cautious in over interpretation of this finding as few studies included ethnicity. Geographic differences in average months to RTW were observed from 11.4 in the Netherlands [34] to 7.4 in Canada [35] to just three months in Sweden [36].

Although evidence is limited, the higher productivity impact associated with diseases with a large morbidity was perhaps to be expected; chronic diseases such as COPD and DM affect people during their productive years and cannot really be ‘cured’, only managed. It is surprising that half of all productivity losses in the USA attributable to DM are due to mortality rather than absenteeism and presenteeism. The extent to which employers or societies support and enable NCD populations to remain members of the productive workforce will also differentially distribute the impact both within societies but also comparing more affluent to less affluent countries. The extent to which secondary or tertiary prevention is possible will also affect productivity estimates, specifically so for labor market indicators such as RTW, change in work status or unemployment.

Comparison with the previous work

Findings of this systematic review generally concur with and further extend the previous reviews. This study is a comprehensive systematic review tackling work-related burden of six major NCDs using a global perspective and without language limitation. Two reviewers included and assessed the studies and references of the included studies were tracked for any missing evidence. These approaches ensured that we included most of the relevant articles in our review. Similar to previous reviews, we found that, due to a great amount of variation in the studies included, comparability and pooling the studies were not possible. Most of the previous reviews were performed non-systematically and previous systematic reviews have included studies only in English. Previous studies were mainly focused on the impact of cancers [74, 75, 76, 77, 78] on work-related outcomes (mainly RTW) and often included a mix of cancers without specifying the type of cancer. Van Muijen and colleagues [78] reviewed only cohort studies of cancer-related work outcomes and were focused on English language. Steiner and colleagues [76] reviewed English publications published up until 2003, Breton and colleagues were focused only on diabetes and Krisch and colleagues focused on COPD in Germany [79].

Strengths and limitations of the current work

In this systematic review we evaluated the literature concerning the impact on productivity of six top NCDs. These six were selected based on their dominance in the global burden of disease and together make a huge contribution to mortality and morbidity worldwide. Several important issues are out of scope for this work but do merit future research. First, we did not look into the underlying mechanisms of what forces people with NCDs in and out of the labor force, specifically in terms of co-morbidities (certain NCDs cluster in the same populations) and financial/social means available at an individual and collective level. How these mechanisms interact will also be different according to the level of economic and social development. For example, children in LMIC are more likely to be forced into the labor market due to the onset of NCDs in parents compared to children in HIC and the productive output of this child cannot replace the loss due to drop out by the parents. These related topics should be addressed separately to better understand how to modify and target these outcomes more specifically. Second, we observed wide heterogeneity in all domains within the studies selected, including study design, methods and sources used to measure productivity, adjustment for confounders and analyses. Third, no identified studies quantified the differential productivity impact by national economic development and labor market structure across countries. How these inter-country macro-economic differences might mitigate or magnify productivity losses associated with NCDs is worth further exploration. Fourth, we identified a crucial gap of relevant information from LMICs—limiting the relevance of our review most acutely in these settings. This lack of evidence could reflect differences in disease burden, in research capacity, in welfare systems and in epidemiological surveillance. The burden of NCDs is growing rapidly in LMIC; countries that often lack capacity in these key areas of support, prevention and knowledge generation. Further evaluation, therefore, of the macro-economic impact in the LMIC countries is urgently needed. Also, many NCDs affect people cumulatively over time; people may suffer DM, may experience absenteeism/presenteeism as a result, may reduce work as DM worsens and may finally drop out of the workforce due a stroke or CHD, which is related to the DM. Given NCDs are shifting more and more into chronic conditions, as our understanding of treatment and natural history improve, it would be of great interest to investigate the effects over the life course rather than using short time horizons such as a year. This is no mean feat, but could be crucial for developing a better understanding of the economic impact of NCDs on a regional, national and international level. Also out of scope for this review but of interest for future work are the productivity-related impact of behavioural risk factors that contribute to the development of NCDs.

Conclusions

In summary, available studies indicate that the six main NCDs generate a large impact on macro-economic productivity in the WHO regions. However, this evidence is heterogeneous, of varying quality and not evenly geographically distributed. Data from LMI countries in economic and epidemiological transition are virtually absent. Further work to reliably quantify the absolute global impact of NCDs on macro-economic productivity and DALYs is urgently required.

Notes

Acknowledgments

Completion of this manuscript was supported by a grant from the WHO. O. H. Franco and L. Jaspers work in ErasmusAGE, a center for aging research across the life course funded by Nestlé Nutrition (Nestec Ltd.); Metagenics Inc.; and AXA. Nestlé Nutrition (Nestec Ltd.); Metagenics Inc.; and AXA had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review or approval of the manuscript. Dr. Shanthi Mendis from the WHO and co-author on this manuscript participated in the interpretation and preparation of this manuscript. The manuscript was approved by the WHO for submission.

Conflict of interest

With regard to potential conflicts of interest, there is nothing to disclose. Drs. Chaker, van der Lee, Falla and Franco had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Supplementary material

10654_2015_26_MOESM1_ESM.doc (94 kb)
Supplementary material 1 (DOC 94 kb)

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© The Author(s) 2015

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Layal Chaker
    • 1
    • 2
  • Abby Falla
    • 3
    • 4
  • Sven J. van der Lee
    • 1
  • Taulant Muka
    • 1
  • David Imo
    • 1
  • Loes Jaspers
    • 1
  • Veronica Colpani
    • 1
  • Shanthi Mendis
    • 5
  • Rajiv Chowdhury
    • 6
  • Wichor M. Bramer
    • 7
  • Raha Pazoki
    • 1
  • Oscar H. Franco
    • 1
  1. 1.Department of EpidemiologyErasmus MC, University Medical Center RotterdamRotterdamThe Netherlands
  2. 2.Department of EndocrinologyErasmus MCRotterdamThe Netherlands
  3. 3.Department of Public HealthErasmus MCRotterdamThe Netherlands
  4. 4.Division of Infectious Disease ControlMunicipal Public Health Service (GGD) Rotterdam-RijnmondRotterdamThe Netherlands
  5. 5.Chronic Diseases Prevention and Management, Department of Chronic Diseases and Health PromotionWorld Health OrganizationGenevaSwitzerland
  6. 6.Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
  7. 7.Medical LibraryErasmus MCRotterdamThe Netherlands

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