Productivity Losses Due to Diabetes in Urban Rural China
Abstract
:1. Background
2. Methods
2.1. Study Design and Setting
2.2. Data Sources
2.3. Statistical Analysis
3. Results
3.1. The Prevalence and Mortality of Diabetes in China
3.2. Premature Deaths and Work Years Lost Due to Diabetes
3.3. Cost of Productivity Losses from Premature Death Due to Diabetes
3.4. The Cost of Productivity Losses for Absenteeism, Presenteeism, and Labor Force Dropout Due to Diabetes
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
- Liu, X.; Li, C.; Gong, H.; Cui, Z.; Fan, L.; Yu, W.; Ma, J. An economic evaluation for prevention of diabetes mellitus in a developing country: A modelling study. BMC Public Health 2013, 13, 729. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pedron, S.; Emmert-Fees, K.; Laxy, M.; Schwettmann, L. The impact of diabetes on labour market participation: A systematic review of results and methods. BMC Public Health 2019, 19, 25. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Adepoju, O.E.; Bolin, J.N.; Ohsfeldt, R.L.; Phillips, C.D.; Zhao, H.; Ory, M.G.; Forjuoh, S.N. Can Chronic Disease Management Programs for Patients with Type 2 Diabetes Reduce Productivity-Related Indirect Costs of the Disease? Evidence from a Randomized Controlled Trial. Popul. Health Manag. 2014, 17, 112–120. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Federation ID. IDF Diabetes Atlas, 8th ed.; International Diabetes Federation: Brussels, Belgium, 2017. [Google Scholar]
- Federation ID. IDF Diabetes Atlas, 9th ed.; International Diabetes Federation: Brussels, Belgium, 2019. [Google Scholar]
- Bragg, F.; Holmes, M.V.; Iona, A.; Guo, Y.; Du, H.; Chen, Y. Association Between Diabetes and Cause-Specific Mortality in Rural and Urban Areas of China. JAMA 2017, 317, 280. [Google Scholar] [CrossRef]
- Yang, W.; Lu, J.; Weng, J.; Jia, W.; Ji, L.; Xiao, J.; He, J. Prevalence of diabetes among men and women in China. N. Engl. J. Med. 2010, 362, 1090–1101. [Google Scholar] [CrossRef]
- Xu, Y. Prevalence and Control of Diabetes in Chinese Adults. JAMA 2013, 310, 948. [Google Scholar] [CrossRef]
- Hird, T.R.; Zomer, E.; Owen, A.; Chen, L.; Ademi, Z.; Magliano, D.J.; Lew, D. The impact of diabetes on productivity in China. Diabetologia 2019, 62, 1195–1203. [Google Scholar] [CrossRef] [Green Version]
- Bommer, C.M.; Heesemann, E.M.; Sagalova, V.M.; Manne-Goehler, J.M.; Atun, R.P.; Bärnighausen, T.P.; Vollmer, S. The global economic burden of diabetes in adults aged 20–79 years: A cost-of-illness study. Lancet. Diabetes Endocrinol. 2017, 5, 423–430. [Google Scholar] [CrossRef]
- Bahia, L.R.; Da Rosa, M.Q.M.; Araujo, D.V.; Correia, M.G.; Dos Rosa, R.D.S.; Duncan, B.B.; Toscano, C.M. Economic burden of diabetes in Brazil in 2014. Diabetol. Metab. Syndr. 2019, 11, 1–9. [Google Scholar] [CrossRef]
- Petersen, M. Economic Costs of Diabetes in the US in 2002. Diabetes Care 2003, 3, 917–932. [Google Scholar] [CrossRef] [Green Version]
- American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2007. Diabetes Care 2008, 31, 596–615. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2012. Diabetes Care 2013, 36, 1033–1046. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2017. Diabetes Care 2018, 41, 917–928. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sortsø, C.; Green, A.; Jensen, P.B.; Emneus, M. Societal costs of diabetes mellitus in Denmark. Diabetic Med. 2016, 33, 877–885. [Google Scholar] [CrossRef] [Green Version]
- Magliano, D.J.; Martin, V.J.; Owen, A.J.; Zomer, E.; Liew, D. The Productivity Burden of Diabetes at a Population Level. Diabetes Care 2018, 41, 979–984. [Google Scholar] [CrossRef] [Green Version]
- Sørensen, M.; Arneberg, F.; Line, T.M.; Berg, T.J. Cost of diabetes in Norway 2011. Diabetes Res. Clin. Pr. 2016, 122, 124–132. [Google Scholar] [CrossRef]
- Brooks-Rooney, C.; Griffiths, M.; Chen, G. Estimating the Economic Impact Due to Productivity Losses of Diabetes and Major Depressive Disorder in Singapore: Human Capital Versus Friction cost Approaches. Value Health 2016, 19, A842. [Google Scholar] [CrossRef]
- Weisbrod, B.A. The Valuation of Human Capital. J. Polit Econ. 1961, 69, 425–436. [Google Scholar] [CrossRef]
- Grosse, S.D.; Krueger, K.V.; Mvundura, M. Economic Productivity by Age and Gender. Med. Care 2009, 47, S94–S103. [Google Scholar] [CrossRef]
- Krol, M.; Brouwer, W. How to Estimate Productivity Costs in Economic Evaluations. Pharmacoeconomics 2014, 32, 335–344. [Google Scholar] [CrossRef]
- Statistics PAES. China Population and Employment Statistics Yearbook; China Statistics Press: Beijing, China, 2018. [Google Scholar]
- National Bureau of Statistics of China. Statistical Yearbook of China; China Statistics Press: Beijing, China, 2018. [Google Scholar]
- System CFET. RMB Annual Average Exchange Rate. Available online: https://www.chinamoney.com.cn/chinese/bkccpr/?tab=2 (accessed on 3 May 2019).
- Commission NH. China Health Statistics Yearbook; China Peking Union Medical University: Beijing, China, 2018. [Google Scholar]
- China OOTS. The Five Key Reforms of Medical and Health System 2011 Main Work Arrangements. 2011. Available online: http://www.gov.cn/zhengce/content/2011-02/17/content_6173.htm (accessed on 6 May 2019).
- National Bureau of Statistics of China. Statistical Yearbook of China; China Statistics Press: Beijing, China, 2013. [Google Scholar]
- National Bureau of Statistics of China. Statistical Yearbook of China; China Statistics Press: Beijing, China, 2014. [Google Scholar]
- National Bureau of Statistics of China. Statistical Yearbook of China; China Statistics Press: Beijing, China, 2015. [Google Scholar]
- Zhuo, X.; Zhang, P.; Gregg, E.W.; Barker, L.; Hoerger, T.J.; Pearson-Clarke, T.; Albright, A. A Nationwide Community-Based Lifestyle Program Could Delay Or Prevent Type 2 Diabetes Cases And Save $5.7 Billion In 25 Years. Health Affair. 2012, 31, 50–60. [Google Scholar] [CrossRef] [PubMed]
- Gillies, C.L.; Lambert, P.C. Different strategies for screening and prevention of type 2 diabetes in adults: Cost effectiveness analysis. BMJ 2008, 336, 1180–1185. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Neumann, A.; Schwarz, P.; Lindholm, L. Estimating the cost-effectiveness of lifestyle intervention programmes to prevent diabetes based on an example from Germany: Markov modelling. Cost Eff. Resour. Alloc. 2011, 9, 17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Age Group (Years) | Population a | Diabetes Prevalence b (%) | People with Diabetes | Mortality from Diabetes c (1/100 Thousand) |
---|---|---|---|---|
Male | ||||
20–24 | 29,049,757 | 1.49 | 432,841 | 0.16 |
25–29 | 4,252,913 | 2.84 | 635,528 | 0.49 |
30–34 | 35,580,097 | 4.95 | 530,143 | 0.81 |
35–39 | 33,383,495 | 7.89 | 497,414 | 1.33 |
40–44 | 34,571,602 | 11.51 | 515,117 | 2.75 |
45–49 | 39,391,990 | 15.43 | 586,941 | 4.51 |
50–54 | 34,077,670 | 19.09 | 507,757 | 16.46 |
55–59 | 20,705,097 | 21.98 | 308,506 | 16.01 |
60–64 | 21,843,447 | 23.69 | 325,467 | 36.44 |
65–69 | 15,928,398 | 24.00 | 237,333 | 58.78 |
Total | 307,184,466 | 11.77 | 36,150,668 | 9.75 |
Female | ||||
20–24 | 26,661,408 | 0.90 | 239,953 | 0.07 |
25–29 | 40,408,981 | 1.59 | 642,503 | 0.34 |
30–34 | 34,884,709 | 2.68 | 934,910 | 0.43 |
35–39 | 32,615,291 | 4.29 | 1,399,196 | 0.51 |
40–44 | 33,186,893 | 6.54 | 2,170,423 | 1.42 |
45–49 | 37,519,417 | 9.46 | 3,549,337 | 2.44 |
50–54 | 32,609,223 | 13.01 | 4,242,460 | 8.5 |
55–59 | 20,040,049 | 17.01 | 3,408,812 | 8.09 |
60–64 | 22,262,136 | 21.20 | 4,719,573 | 25.97 |
65–69 | 16,725,728 | 25.28 | 4,228,264 | 51.16 |
Total | 296,913,835 | 8.60 | 25,535,431 | 6.93 |
Total male and female | 604,098,301 | 10.21 | 61,686,098 | 8.37 |
Age Group (Years) | Population a | Diabetes Prevalence b (%) | People with Diabetes | Mortality from Diabetes c (1/100 Thousand) |
---|---|---|---|---|
Male | ||||
20–24 | 17,680,825 | 2.99 | 528,657 | 0.23 |
25–29 | 19,809,466 | 4.17 | 826,055 | 0.55 |
30–34 | 18,711,165 | 5.63 | 1,053,439 | 1.13 |
35–39 | 17,564,320 | 7.34 | 1,289,221 | 1.51 |
40–44 | 19,759,709 | 9.27 | 1,831,725 | 2.92 |
45–49 | 25,811,893 | 11.33 | 2,924,488 | 4.66 |
50–54 | 255,09,709 | 13.42 | 3,423,403 | 13.02 |
55–59 | 16,135,922 | 15.44 | 2,491,386 | 12.79 |
60–64 | 19,714,806 | 17.27 | 3,404,747 | 25.41 |
65–69 | 15,004,854 | 18.82 | 2,823,914 | 44.87 |
Total | 195,702,670 | 10.52 | 20,597,033 | 9.98 |
Female | ||||
20–24 | 15,440,534 | 1.37 | 211,535 | 0.21 |
25–29 | 19,364,078 | 2.19 | 424,073 | 0.44 |
30–34 | 18,824,029 | 3.40 | 640,017 | 0.56 |
35–39 | 16,675,971 | 5.08 | 847,139 | 0.52 |
40–44 | 19,008,495 | 7.26 | 1,380,017 | 1.42 |
45–49 | 25,412,621 | 9.86 | 2,505,684 | 2.8 |
50–54 | 25,424,757 | 12.69 | 3,226,402 | 9.69 |
55–59 | 15,922,330 | 15.49 | 2,466,369 | 10.17 |
60–64 | 19,118,932 | 17.96 | 3,433,760 | 27.15 |
65–69 | 15,307,039 | 19.86 | 3,039,978 | 53.78 |
Total | 190,498,786 | 9.53 | 18,174,975 | 9.87 |
Total male and female | 386,201,456 | 10.04 | 38,772,008 | 9.93 |
Age Group (Years) | Premature Deaths Due to Diabetes | YPLL a (%) | WYLL b (%) | Productivity Losses ($) |
---|---|---|---|---|
Male | ||||
20–24 | 46 | 3138 (0.32) | 3138 (1.51) | 16,907,102 |
25–29 | 209 | 13,076 (1.32) | 13,076 (5.87) | 70,455,597 |
30–34 | 288 | 16,607 (1.67) | 16,607 (6.85) | 89,482,869 |
35–39 | 444 | 23,427 (2.36) | 23,427 (8.64) | 126,230,345 |
40–44 | 951 | 45,643 (4.60) | 45,643 (14.39) | 245,939,085 |
45–49 | 1777 | 76,804 (7.74) | 76,804 (19.20) | 413,847,755 |
50–54 | 5609 | 216,731 (21.86) | 216,731 (36.38) | 1,167,825,945 |
55–59 | 3315 | 113,348 (11.43) | 113,348 (7.17) | 610,758,355 |
60–64 | 7960 | 238,172 (24.02) | 1,283,354,947 | |
65–69 | 9363 | 244,728 (24.68) | 1,318,681,817 | |
Total | 29,962 | 991,672 (69.27) | 508,772 (89.11) | 5,343,483,814 |
Female | ||||
20–24 | 19 | 1093 (0.25) | 1093 (3.63) | 5,891,021 |
25–29 | 137 | 7366 (1.67) | 7366 (21.87) | 39,690,845 |
30–34 | 150 | 7296 (1.66) | 7296 (18.58) | 39,314,072 |
35–39 | 166 | 7266 (1.65) | 7266 (14.71) | 39,152,446 |
40–44 | 471 | 18,296 (4.16) | 18,296 (25.01) | 98,585,863 |
45–49 | 915 | 31,083 (7.07) | 31,083 (16.20) | 167,487,068 |
50–54 | 2772 | 80,768 (18.36) | 435,207,634 | |
55–59 | 1621 | 39,618 (9.01) | 213,478,340 | |
60–64 | 5781 | 114,585 (26.05) | 617,422,102 | |
65–69 | 8557 | 132,546 (30.13) | 714,207,692 | |
Total | 20,591 | 439,918 (30.13) | 72,401 (10.89) | 2,370,437,082 |
Total male and female | 50,552 | 1,431,591 (100) | 581,173 (100) | 7,713,920,897 |
Age Group (Years) | Premature Deaths Due to Diabetes | YPLL a (%) | WYLL b (%) | Productivity Losses ($) |
---|---|---|---|---|
Male | ||||
20–24 | 41 | 2513 (0.44) | 2513 (0.89) | 4,997,584 |
25–29 | 109 | 6224 (1.08) | 6224 (2.21) | 12,376,873 |
30–34 | 211 | 11,099 (1.93) | 11,099 (3.93) | 22,070,892 |
35–39 | 265 | 12,694 (2.20) | 12,694 (4.50) | 25,243,589 |
40–44 | 577 | 25,029 (4.35) | 25,029 (8.87) | 49,772,781 |
45–49 | 1203 | 46,778 (8.12) | 46,778 (16.58) | 93,023,433 |
50–54 | 3321 | 114,813 (19.93) | 114,813 (40.69) | 228,320,655 |
55–59 | 2064 | 63,015 (10.94) | 63,015 (22.23) | 125,313,517 |
60–64 | 5010 | 134,606 (23.37) | 267,682,183 | |
65–69 | 6733 | 159,192 (27.64) | 316,574,550 | |
Total | 19,533 | 575,961 (53.84) | 282,163 (59.81) | 1,145,376,058 |
Female | ||||
20–24 | 32 | 2050 (0.42) | 2050 (1.08) | 4,075,985 |
25–29 | 85 | 4966 (1.01) | 4966 (2.62) | 9,874,940 |
30–34 | 105 | 5624 (1.14) | 5624 (2.97) | 11,184,007 |
35–39 | 87 | 4207 (0.85) | 4207 (2.22) | 8,365,828 |
40–44 | 270 | 11,795 (2.39) | 11,795 (6.22) | 23,455,292 |
45–49 | 712 | 27,777 (5.63) | 27,777 (14.65) | 55,237,348 |
50–54 | 2464 | 84,875 (17.19) | 84,875 (44.76) | 168,785,918 |
55–59 | 1619 | 48,310 (9.79) | 48,310 (25.48) | 96,071,134 |
60–64 | 5191 | 131,550 (26.65) | 261,605,272 | |
65–69 | 8232 | 172,558 (34.95) | 343,154,307 | |
Total | 18,797 | 493,711 (46.16) | 189,603 (40.19) | 981,810,031 |
Total male and female | 38,331 | 1,069,672 (100) | 471,766 (100) | 2,127,186,089 |
Areas | Absenteeism | Presenteeism | Labor Force Dropout | Total |
---|---|---|---|---|
Urban | 22.61 (34.40%) | 1.78 (65.41%) | 207.28 (77.19%) | 231.59 (68.75%) |
Rural | 43.10 (65.60%) | 0.94 (34.59%) | 61.24 (22.80%) | 105.28 (31.25%) |
Total | 65.71 (19.51%) | 2.71 (0.80%) | 268.52 (79.71%) | 336.94 (100%) |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hao, H.; Nicholas, S.; Xu, L.; Leng, A.; Sun, J.; Han, Z. Productivity Losses Due to Diabetes in Urban Rural China. Int. J. Environ. Res. Public Health 2022, 19, 5873. https://doi.org/10.3390/ijerph19105873
Hao H, Nicholas S, Xu L, Leng A, Sun J, Han Z. Productivity Losses Due to Diabetes in Urban Rural China. International Journal of Environmental Research and Public Health. 2022; 19(10):5873. https://doi.org/10.3390/ijerph19105873
Chicago/Turabian StyleHao, Hongying, Stephen Nicholas, Lizheng Xu, Anli Leng, Jingjie Sun, and Zhiyan Han. 2022. "Productivity Losses Due to Diabetes in Urban Rural China" International Journal of Environmental Research and Public Health 19, no. 10: 5873. https://doi.org/10.3390/ijerph19105873