Abstract

This study investigated the impact of diabetes on work performance of different farming communities from Punjab, Pakistan. This study was based on cross-sectional data. A representative sample of 374 farmers was collected from five selected districts. Three types of respondents were analyzed in the study e.g.,laborer, small and large growers. Poisson and logistic regression techniques were used for the sake of analysis. According to the investigated results for thelabor category, respondents with more age, less qualification, low earning per month (Rupees), and having positive record of family diabetes, would havemore leave per month. In the same way, findings for small farmers revealed that education, family size, family with diabetic records, marital status and availability at farm (hour/day) were significant. In case of third category, study outcome highlighted that age, education, marital status, having positive record offamily diabetes and number of hours spent at farm would be positively correlated with the reduction in working efficiency at farm due to diabetes. It can beconcluded that diabetes have negative influence on the work performance of selected farming groups.

References

1. American Diabetes Association. Economic costs of diabetes in the US in 2012. diabetes care 2013; 36: 1033–1046. Diabetes Care. 2013; 36(6): 1797.

2. Basu S, Yoffe P, Hills N, Lustig RH. The relationship of sugar to population-level diabetes prevalence: an econometric analysis of repeated cross-sectional data. PloS one. 2013; 8(2): e57873.

3. Molloy JC, Barney JB. Who captures the value created with human capital? a market-based view. Academy of Management Perspectives. 2015; 29(3): 309-25.

4. Ford ND, Behrman JR, Hoddinott JF, Maluccio JA, Martorell R, Ramirez-Zea M, et al. Exposure to improved nutrition from conception to age 2 years and adult cardiometabolic disease risk: a modelling study. The Lancet Global Health. 2018; 6(8): e875-e84.

5. Chen L, Magliano DJ, Zimmet PZ. The worldwide epidemiology of type 2 diabetes mellitus—present and future perspectives. Nature Reviews Endocrinology. 2012; 8(4): 228.

6. Gray BJ, Bracken RM, Turner D, Morgan K, Thomas M, Williams SP, et al. Different type 2 diabetes risk assessments predict dissimilar numbers at ‘high risk’: a retrospective analysis of diabetes risk-assessment tools. British Journal of General Practice. 2015; 65(641): e852- e60.

7. Williamson R. Causes of diabetes. The Practitioner. 2009; 253(1718): 37-8.

8. Guariguata L, Whiting D, Weil C, Unwin N. The International Diabetes Federation diabetes atlas methodology for estimating global and national prevalence of diabetes in adults. Diabetes research and clinical practice. 2011; 94(3): 322-32.

9. Holman N, Young B, Gadsby R. Current prevalence of type 1 and type 2 diabetes in adults and children in the UK. Diabetic Medicine. 2015; 32(9): 1119-20.

10. Ogurtsova K, Guariguata L, Linnenkamp U, Fernandes JD, Cho NH, Makaroff LE. Regional differences in diabetes prevalence by age and urban/ rural setting. InDIABETES 2015 Jun 1 (Vol. 64, pp. A440- A440).

11. Belke M, Bolat S. The panel data analysis of female labor participation and economic development relationship in developed and developing countries. Economic Research Guardian. 2016; 6(2): 67-73.

12. Beard HA, Al Ghatrif M, Samper-Ternent R, Gerst K, Markides KS. Trends in diabetes prevalence and diabetes-related complications in older Mexican Americans from 1993–1994 to 2004–2005. Diabetes Care. 2009; 32(12): 2212-7.

13. Razavian N, Blecker S, Schmidt AM, Smith-McLallen A, Nigam S, Sontag D. Population-level prediction of type 2 diabetes from claims data and analysis of risk factors. Big Data. 2015; 3(4): 277-87.

14. Rivera LA, Lebenbaum M, Rosella LC. The influence of socioecono - mic status on future risk for developing type 2 diabetes in the Canadian population between 2011 and 2022: differential associations by sex. International Journal for Equity in Health. 2015; 14(1): 101.

15. Sahoo K, Sethi N. Investigating the impact of agriculture and industrial sector on economic growth of India. OIDA International Journal of Sustainable Development. 2012; 5(5): 11-21.

16. Naik SS. India: key emerging market for hospitality industry. Trade & Commerce; 2012.

17. Khan MA. An explorative view on human resource management with focus on tourism & hospitality industry of India. Advances in Economics and Business Management; 2014.

18. Rosella LC, Mustard CA, Stukel TA, Corey P, Hux J, Roos L, et al. The role of ethnicity in predicting diabetes risk at the population level. Ethnicity & Health. 2012; 17(4): 419-37.

19. Zia-Ul-Haq M, Shahid SA, Ahmad S, Qayum M, Rasool N. Mineral contents and antioxidant potential of selected legumes of Pakistan. Journal of Medicinal Plants Research. 2012; 6(32): 4735-40.

20. Nguyen TT, Nguyen LD, Lippe RS, Grote U. Determinants of farmers’ land use decision-making: comparative evidence from Thailand and Vietnam. World Development. 2017; 89: 199-213.

21. Loveridge R, Mok AL. Theories of labour market segmentation: a critique. Springer Science & Business Media; 2012.

22. Choudriuma A. Estimation of growth models for area, production and productivity of sunflower and groundnut crops in Karnataka: University of Agricultural Sciences GKVK, Bangalore; 2012.

23. Haddad EA, Porsse AA, Pereda PC. Regional economic impacts of climate anomalies in Brazil. Revista Brasileira de Estudos Regionais e Urbanos. 2013; 7(2): 19-33.

24. Odetola T, Etumnu C. Contribution of agriculture to economic growth in Nigeria. The 18th Annual Conference of The African Econometric Society (AES) Accra, Ghana; 2013.

25. Iftikhar S, Mahmood HZ. Human capital development and food security nexus: An empirical appraisal from districts of Punjab Province. Journal of Food and Drug Research. 2017; 1(1).

26. Hussain SA-FS-Z, editor. Effect of agricultural and financial sector reforms on export of cotton lint from Pakistan. International Conference On Applied Economics–ICOAE; 2010.

27. Memon NA. Rice: important cash crop of Pakistan. Pakistan Food Journal. 2013: 21-3.

28. Javed I, Ghafoor A. Determinants of rice export from Pakistan. Proceedings of the sixth international conference on management science and engineering management 2013 (pp. 793-801). Springer: London.

29. Boansi D, Lokonon BOK, Appah J. Co-integration analysis of the determinants of cotton lint exports from Mali. Asian Journal Agricultural Extension Economics and Sociology. 2014; 3(6): 544-61.

30. Arana ODS. La economıa de los hogares: estudios sobre los efectos en la salud del uso de lena y sobre el mercado de trabajo rural en México. 2014.

31. Stabridis O, van Gameren E. Exposure to firewood: consequences for health and labor force participation in Mexico. World Development. 2018; 107: 382-95.

32. Kaiser A, Vollenweider P, Waeber G, Marques-Vidal P. Prevalence, awareness and treatment of type 2 diabetes mellitus in Switzerland: the CoLaus study. Diabetic Medicine. 2012; 29(2): 190-7.

33. Samuel-Hodge CD, Cene CW, Corsino L, Thomas C, Svetkey LP. Family diabetes matters: a view from the other side. Journal of General Internal Medicine. 2013; 28(3): 428-35.

34. Soulimane S, Simon D, Shaw J, Zimmet P, Vol S, Vistisen D, et al. Comparing incident diabetes as defined by fasting plasma glucose or by HbA1c. The AusDiab, Inter99 and DESIR studies. Diabetic Medicine. 2011; 28(11): 1311-8.

35. Larifla L, Maimaitiming S, Velayoudom-Cephise F-L, Ferdinand S, Blanchet-Deverly A, BenAbdallah S, et al. Association of 2238T> C polymorphism of the atrial natriuretic peptide gene with coronary artery disease in Afro-Caribbeans with Type 2 Diabetes. American Journal of Hypertension. 2012; 25(5): 524-7.

36. Wu J. Study applicable for multi-linear regression analysis and logistic regression analysis. Open Electrical & Electronic Engineering Journal. 2014; 8: 782-6.

37. Mallipedhi A, Min T, Prior SL, MacIver C, Luzio SD, Dunseath G, et al. Association between the preoperative fasting and postprandial Cpeptide AUC with resolution of Type 2 Diabetes 6 months following bariatric surgery. Metabolism. 2015; 64 (11): 1556-63.

38. Karagöz A, Onat A, Aydın M, Can G, Şimşek B, Yüksel M. Distinction of hypertriglyceridemic waist phenotype from simple abdominal obesity: interaction with sex hormone-binding globulin levels to confer high coronary risk. Postgraduate Medicine. 2017; 129 (2): 288-95.

39. Mohamed SF, Mwangi M, Mutua MK, Kibachio J, Hussein A, Ndegwa Z, et al. Prevalence and factors associated with pre-diabetes and diabetes mellitus in Kenya: results from a national survey. BMC Public Health. 2018; 18(3): 1215.

40. Gregg EW, Zhuo X, Cheng YJ, Albright AL, Narayan KV, Thompson TJ. Trends in lifetime risk and years of life lost due to diabetes in the USA, 1985–2011: a modelling study. The Lancet Diabetes & Endocrinology. 2014; 2(11): 867-74.

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