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Socioeconomic and demographic predictors of high blood pressure, diabetes, asthma and heart disease among adults engaged in various occupations: evidence from India

Published online by Cambridge University Press:  24 October 2019

Sunita Patel
Affiliation:
International Institute for Population Sciences (IIPS), Mumbai, India
Usha Ram*
Affiliation:
Department of Public Health and Mortality Studies, International Institute for Population Sciences (IIPS), Mumbai, India
Faujdar Ram
Affiliation:
International Institute for Population Sciences (IIPS), Mumbai, India Population Council, New Delhi, India
Surendra Kumar Patel
Affiliation:
International Institute for Population Sciences (IIPS), Mumbai, India
*
*Corresponding author. Email: [email protected]

Abstract

In India, non-communicable diseases (NCDs) accounted for nearly 62% of all deaths in 2016. Four NCDs – high blood pressure, diabetes, asthma and heart disease – together accounted for over 34% of these deaths. Using data from two rounds of the India Human Development Surveys (IHDSs), levels and changes in the prevalence rates of the four NCDs (based on diagnosed cases) among adults aged 15–69 years in India between 2004–05 and 2011–12 were examined by socioeconomic and demographic factors and for five broad occupation categories. The socioeconomic and demographic risk factors for each of these NCDs were determined using multiple linear logistic regression analysis of pooled data from two rounds of the IHDS. The results showed that while urban residence, age, female sex and education were associated with higher odds of high blood pressure, diabetes and heart disease, household economic status was associated with higher odds for all four NCDs. Furthermore, increased higher odds of high blood pressure, diabetes and heart disease were found for the legislator/senior official/professional occupation group compared with non-workers. Skilled agricultural/elementary workers had lower odds of high blood pressure, diabetes, asthma and heart disease. Craft/machine-related trade workers had higher odds of high blood pressure and diabetes, and reduced odds of asthma and heart disease. Compared with non-workers, the odds ratios for asthma were lower for all other occupational categories. During the two study decades, the Government of India implemented several programmes designed to improve the health and well-being of its people. However, more focused attention on the adult population is needed, and special attention should be paid to the issue of the occupational health of the working population through the strict implementation of work place safety protocols and the removal of potential health hazards.

Type
Research Article
Copyright
© Cambridge University Press 2019

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References

Abrahamsen, R, Fell, AK, Svendsen, MV, Andersson, E, Toren, K, Henneberger, PKet al. (2017) Association of respiratory symptoms and asthma with occupational exposures: findings from a population-based cross-sectional survey in Telemark, Norway. British Medical Journal Open 7(3), e014018.Google ScholarPubMed
Agardh, E, Allebeck, P, Hallqvist, J, Moradi, T and Sidorchuk, A (2011) Type 2 diabetes incidence and socio-economic position: a systematic review and meta-analysis. International Journal of Epidemiology 40(3), 804818.CrossRefGoogle ScholarPubMed
Agnihotram, RV and Chattopadhyay, A (2004) Respiratory disease burden in rural India: a review from multiple data sources. Internet Journal of Epidemiology 2(2), 16.Google Scholar
Agrawal, S, Pearce, N, Millett, C, Subramanian, SV and Ebrahim, S (2014) Occupations with an increased prevalence of self-reported asthma in Indian adults. Journal of Asthma 51(8), 814824.CrossRefGoogle ScholarPubMed
Ahmed, SM, Hadi, A, Razzaque, A, Ashraf, A, Juvekar, Set al. (2009) Clustering of chronic non-communicable disease risk factors among selected Asian populations: levels and determinants. Global Health Action 2(1), doi: https://doi.org/10.3402/gha.v2i0.1986.CrossRefGoogle ScholarPubMed
Arif, AA, Delclos, GL and Serra, C (2009) Occupational exposures and asthma among nursing professionals. Occupational and Environmental Medicine 66(4), 274278.CrossRefGoogle ScholarPubMed
Babu, GR, Mahapatra, T and Detels, R (2013) Job stress and hypertension in younger software professionals in India. Indian Journal of Occupation & Environmental Medicine 17(3), 101107.CrossRefGoogle ScholarPubMed
Bunker, CH, Ukoli, FA, Nwankwo, MU, Omene, JA, Currier, GW, Holifield-Kennedy, Let al. (1992) Factors associated with hypertension in Nigerian civil servants. Preventive Medicine 21(6), 710722.CrossRefGoogle ScholarPubMed
Cameron, AC and Trivedi, PK (2005) Microeconometrics Methods and Application. Cambridge University Press, New York.CrossRefGoogle Scholar
Chowdhury, MA, Uddin, MJ, Haque, MR and Ibrahimou, B (2016) Hypertension among adults in Bangladesh: evidence from a national cross-sectional survey. BMC Cardiovascular Disorder 16, 22.CrossRefGoogle ScholarPubMed
Clayton, D (1991) The EURODEM collaborative re-analysis of case-control studies of Alzheimer’s disease: some methodological considerations. International Journal of Epidemiology 20 (Supplement 2), S6264.CrossRefGoogle ScholarPubMed
Corsi, DJ and Subramanian, SV (2012) Association between socioeconomic status and self-reported diabetes in India: a cross-sectional multilevel analysis. British Medical Journal Open 2(4), 112.Google ScholarPubMed
Cox, S, Niskar, AS, Narayan, KM and Marcus, M (2007) Prevalence of self-reported diabetes and exposure to organochlorine pesticides among Mexican Americans: Hispanic health and nutrition examination survey, 1982–1984. Environmental Health Perspectives 115(12), 17471752.CrossRefGoogle Scholar
Davis-Lameloise, N, Philpot, B, Janus, ED, Versace, VL, Laatikainen, T, Vartiainen, EAet al. (2013) Occupational differences, cardiovascular risk factors and lifestyle habits in South Eastern rural Australia. BMC Public Health 13, 1090.CrossRefGoogle ScholarPubMed
Delclos, GL, Gimeno, D, Arif, AA, Burau, KD, Carson, A, Lusk, Cet al. (2007) Occupational risk factors and asthma among health care professionals. American Journal of Respiratory and Critical Care Medicine 175(7), 667675.CrossRefGoogle ScholarPubMed
Desai, S, Dubey, A, Joshi, BL, Sen, M, Shariff, A and Vanneman, R (2009) India Human Development Survey: Design and Data Quality. IHDS Technical Paper 1. URL: doi: https://www.ihds.umd.edu/technical-reports.CrossRefGoogle Scholar
Desai, S and Vanneman, R (2018) India Human Development Survey-II (IHDS-II), 2011–12. Inter-university Consortium for Political and Social Research, Ann Arbor, MI. URL: https://doi.org/10.3886/ICPSR36151.v6 (accessed 24th June 2019).Google Scholar
Desai, S, Vanneman, R and National Council of Applied Economic Research (2010) India Human Development Survey (IHDS), 2005. ICPSR22626-v8, New Delhi. Inter-university Consortium for Political and Social Research [distributor], Ann Arbor, MI. URL: http://doi.org/10.3886/ICPSR22626.v8 (accessed 18th March 2016).Google Scholar
Desai, S, Vanneman, R and National Council of Applied Economic (2015) India Human Development Survey-II (IHDS-II), 2011–12. New Delhi. ICPSR36151-v2. Inter-university Consortium for Political and Social Research, Ann Arbor, MI. URL: http://doi.org/10.3886/ICPSR36151.v2 (accessed 18th March 2016).Google Scholar
Friedenreich, CM (1993) Methods for pooled analyses of epidemiologic studies. Epidemiology 4(4), 295302.CrossRefGoogle ScholarPubMed
GBD (2016) Risk factors collaborators. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet 388(10053), 16591724.CrossRefGoogle Scholar
GBD (2017) Causes of death collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet 390(10100), 11511210.CrossRefGoogle Scholar
Government of India (2004) National Classification of Occupations, 2004. Directorate General of Employment, Ministry of Labour and Employment, Government of India. URL: https://labour.gov.in/2004 (accessed 24th August 2017).Google Scholar
Gupta, A, Gupta, R, Sarna, M, Rastogi, S, Gupta, VP and Kothari, K (2003) Prevalence of diabetes, impaired fasting glucose and insulin resistance syndrome in an urban Indian population. Diabetes Research and Clinical Practice 61(1), 6976.CrossRefGoogle Scholar
Gupta, R, Al-Odat, NA and Gupta, VP (1996) Hypertension epidemiology in India: meta-analysis of 50 year prevalence rates and blood pressure trends. Journal of Human Hypertension 10(7), 465472.Google ScholarPubMed
Gupta, R, Deedwania, C, Sharma, K, Gupta, A, Guptha, S, Achari, Vet al. (2012) Association of education, occupational and socio-economic status with cardiovascular risk factor in Asian Indian: a cross sectional study. PLoS One 7(8), 110.CrossRefGoogle Scholar
Hayashi, R, Iso, H, Cui, R and Tamakoshi, A (2016) Occupational physical activity in relation to risk of cardiovascular mortality: the Japan Collaborative Cohort Study for Evaluation for Cancer Risk (JACC Study). Preventive Medicine 89, 286291.CrossRefGoogle Scholar
Hazarika, NC, Biswas, D, Narain, K, Kalita, HC and Mahanta, J (2002) Hypertension and its risk factors in tea garden workers of Assam. National Medical Journal of India 15(2), 6368.Google ScholarPubMed
Heden, SC, Novak, M, Hansson, PO, Lappas, G, Wilhelmsen, L and Rosengren, A (2014) Incidence of Type 2 diabetes among occupational classes in Sweden: a 35-year follow-up cohort study in middle-aged men. Diabetic Medicine 31(6), 674680.CrossRefGoogle Scholar
Hosseinpoor, AR, Bergen, N, Mendis, S, Harper, S, Verdes, E, Kunst, Aet al. (2012) Socioeconomic inequality in the prevalence of noncommunicable diseases in low- and middle-income countries: results from the World Health Survey. BMC Public Health 12, 474.CrossRefGoogle ScholarPubMed
ICMR, PHFI and IHME (2017) India: Health of the Nation’s States – The India State-Level Disease Burden Initiative. Indian Council of Medical Research, Public Health Foundation of India, and Institute for Health and Matrix Evaluations, New Delhi. URL: http://www.healthdata.org/sites/default/files/files/policy_report/2017/India_Health_of_the_Nation%27s_States_Report_2017.pdf (accessed 28th January 2018).Google Scholar
IHME (2016) GBD Compare Data Visualization. Institute for Health Metrics and Evaluation. URL: https://vizhub.healthdata.org/gbd-compare (accessed 11th April 2018).Google Scholar
IHME (2017) GBD Compare Data Visualization. Institute for Health Metrics and Evaluation. URL: https://vizhub.healthdata.org/gbd-compare (accessed 11th April 2018).Google Scholar
Jeebhay, MF and Quirce, S (2007) Occupational asthma in the developing and industrialised world: a review. International Journal of Tuberculosis and Lung Disease 11(2), 122133.Google ScholarPubMed
Juntarawijit, C and Juntarawijit, Y (2018) Association between diabetes and pesticides: a case-control study among Thai farmers. Environmental Health and Preventive Medicine 23(1), 3.CrossRefGoogle ScholarPubMed
Kar, SS, Narayanan, SL, Ramalingam, A, Naik, BN and Sujiv, AG (2015) Burden of occupational health problems and cardiovascular risk factors in a selected industrial population in south India: should we be concerned? Journal of Cardiovascular Disease Research 6(3), 117123.Google Scholar
Kim, JL, Toren, K, Lohman, S, Ekerljung, L, Lotvall, J, Lundback, Bet al. (2013) Respiratory symptoms and respiratory-related absence from work among health care workers in Sweden. Journal of Asthma 50(2), 174179.CrossRefGoogle ScholarPubMed
Kinra, S, Bowen, LJ, Lyngdoh, T, Prabhakaran, D, Reddy, KS, Ramakrishnan, Let al. (2010) Sociodemographic patterning of non-communicable disease risk factors in rural India: a cross sectional study. BMJ Open 341, c4974.Google ScholarPubMed
Kogevinas, M, Anto, JM, Sunyer, J, Tobias, A, Kromhout, H and Burney, P (1999) Occupational asthma in Europe and other industrialised areas: a population-based study. European Community Respiratory Health Survey Study Group. The Lancet 353(9166), 17501754.CrossRefGoogle ScholarPubMed
Kumar, P and Ram, U (2017) Patterns, factors associated and morbidity burden of asthma in India. PLoS One 12(10), e0185938.CrossRefGoogle ScholarPubMed
Lee, DW, Hong, YC, Min, KB, Kim, TS, Kim, MS and Kang, MY (2016) The effect of long working hours on 10-year risk of coronary heart disease and stroke in the Korean population: the Korea National Health and Nutrition Examination Survey (KNHANES), 2007 to 2013. Annals of Occupational and Environmental Medicine 28(64), 110.CrossRefGoogle ScholarPubMed
Lillienberg, L, Andersson, E, Janson, C, Dahlman-Hoglund, A, Forsberg, B, Holm, Met al. (2013) Occupational exposure and new-onset asthma in a population-based study in Northern Europe (RHINE). Annals of Occupational Hygiene 57(4), 482492.Google Scholar
Lim, MS, Park, B, Kong, IG, Sim, S, Kim, SY, Kim, JHet al. (2017) Leisure sedentary time is differentially associated with hypertension, diabetes mellitus, and hyperlipidemia depending on occupation. BMC Public Health 17(278), 19.CrossRefGoogle ScholarPubMed
Liss, GM, Buyantseva, L, Luce, CE, Ribeiro, M, Manno, M and Tarlo, SM (2011) Work-related asthma in health care in Ontario. American Journal of Industrial Medicine 54(4), 278284.CrossRefGoogle ScholarPubMed
Ma, Y, Wang, YJ, Chen, BR, Shi, HJ, Wang, H, Khurwolah, MRet al. (2017) Study on association of working hours and occupational physical activity with the occurrence of coronary heart disease in a Chinese population. PLos One 12(10), e0185598.CrossRefGoogle Scholar
Mirabelli, MC, Zock, JP, Plana, E, Anto, JM, Benke, G, Blanc, PDet al. (2007) Occupational risk factors for asthma among nurses and related healthcare professionals in an international study. Occupational and Environmental Medicine 64(7), 474479.CrossRefGoogle Scholar
Ministry of Health and Family Welfare (2019) Rashtriya Arogya Nidhi (RAN). URL: https://mohfw.gov.in/major-programmes/poor-patients-financial-assistance/rashtriya-arogya-nidhi (accessed 22nd February 2019).Google Scholar
Mohan, V, Mathur, P, Deepa, R, Deepa, M, Shukla, DK, Menon, GRet al. (2008) Urban rural differences in prevalence of self-reported diabetes in India – the WHO–ICMR Indian NCD risk factor surveillance. Diabetes Research and Clinical Practice 80(1), 159168.CrossRefGoogle ScholarPubMed
Murray, CJ, Vos, T, Lozano, R, Naghavi, M, Flaxman, AD, Michaud, Cet al. (2012) Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet 380(9859), 21972223.CrossRefGoogle ScholarPubMed
National Institute of Health and Family Welfare (2014) National Programme for Control and Treatment of Occupational Diseases. URL: http://www.nihfw.org/NationalHealthProgramme/NATIONALPROGRAMMEFORCONTROL.html (accessed 25th January 2018).Google Scholar
Niedhammer, I, Chastang, JF and David, S (2008) Importance of psychosocial work factors on general health outcomes in the national French SUMER survey. Occupational Medicine 58(1), 1524.CrossRefGoogle ScholarPubMed
Norheim, OF, Jha, P, Admasu, K, Godal, T, Hum, RJ, Kruk, MEet al. (2015) Avoiding 40% of the premature deaths in each country, 2010–30: review of national mortality trends to help quantify the UN Sustainable Development Goal for health. The Lancet 385(9964), 239252.CrossRefGoogle ScholarPubMed
Prabhakaran, D, Jeemon, P, Sharma, M, Roth, GA, Johnson, C, Harikrishnan, Set al. (2018) The changing patterns of cardiovascular diseases and their risk factors in the states of India: the Global Burden of Disease Study 1990–2016. The Lancet Global Health 6(12), e1339e1351.CrossRefGoogle Scholar
Registrar General of India (2015) Causes of Death in India, 2004–2006. Centre for Global Health Research, Sample Registration System, New Delhi. URL: http://www.censusindia.gov.in/vital_statistics/consolidated_DATA_2004-6_FINAL.pdf (accessed 8th February 2019)Google Scholar
Salvi, S, Kumar, GA, Dhaliwal, RS, Katherine, P, Agrawal, A, Koul, PAet al. (2018) The burden of chronic respiratory diseases and their heterogeneity across the states of India: the Global Burden of Disease Study 1990–2016. The Lancet Global Health 6(12), e1363e1374.CrossRefGoogle Scholar
Smith, P, Ma, H, Glazier, RH, Gilbert-Ouimet, M and Mustard, C (2018) The relationship between occupational standing and sitting and incident heart disease over a 12-year period in Ontario, Canada. American Journal of Epidemiology 187(1), 2733.CrossRefGoogle Scholar
Stata Corp (2013) Stata Statistical Software: Release 13. StataCorp LP, College Station, TX.Google Scholar
Tenkanen, L, Sjoblom, T, Kalimo, R, Alikoski, T and Harma, M (1997) Shift work, occupation and coronary heart disease over 6 years of follow-up in the Helsinki Heart Study. Scandinavian Journal of Work, Environment & Health 23(4), 257265.CrossRefGoogle ScholarPubMed
Uffelen, JGV, Wong, J, Chau, JY, Ploeg, HPVD, Riphagen, I, Gilson, NDet al. (2010) Occupational sitting and health risks: a systematic review. American Journal of Preventive Medicine 39(4), 379388.CrossRefGoogle ScholarPubMed
Undhad, AM, Bharodiya, PJ and Sonani, RP (2011) Correlates of hypertension among the bank employees of Surat City of Gujarat. National Journal of Community Medicine 2(1), 123125.Google Scholar
United Nations (2015) Transforming Our World: The 2030 Agenda for Sustainable Development. Contract No. Agenda Items 15 and 116. URL: http://www.un.org/en/development/desa/population/migration/generalassembly/docs/globalcompact/A_RES_70_1_E.pdf (accessed 24th November 2018).Google Scholar
United Nations (2019) World Population Prospects 2019: Highlights, Department of Economic and Social Affairs, Population Division (ST/ESA/SER.A/423). URL: https://population.un.org/wpp/Publications/Files/WPP2019_Highlights.pdf.Google Scholar
Virtanen, M, Heikkila, K, Jokela, M, Ferrie, JE, Batty, GD, Vahtera, Jet al. (2012) Long working hours and coronary heart disease: a systematic review and meta-analysis. American Journal of Epidemiology 176(7), 586596.CrossRefGoogle ScholarPubMed
Vizcaya, D, Mirabelli, MC, Anto, JM, Orriols, R, Burgos, F, Arjona, Let al. (2011) A workforce-based study of occupational exposures and asthma symptoms in cleaning workers. Occupational and Environmental Medicine 68(12), 914919.CrossRefGoogle ScholarPubMed
WHO (2003) Social Determinants of Health. The Solid Facts. International Centre for Health and Society. WHO Europe. URL: http://www.euro.who.int/__data/assets/pdf_file/0005/98438/e81384.pdf (accessed 7th February 2019).Google Scholar
WHO (2009) Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks. WHO, Geneva. URL: https://www.who.int/healthinfo/global_burden_disease/GlobalHealthRisks_report_full.pdf (accessed 5th July 2018).Google Scholar
WHO (2013) Draft Action Plan for the Prevention and Control of Non-Communicable Diseases 2013–2020. World Health Assembly, Geneva. URL: http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_9-en.pdf (accessed 30th October 2018).Google Scholar
WHO (2017) Non Communicable Diseases. WHO, Geneva. URL: http://www.who.int/mediacentre/factsheets/fs355/en/ (accessed 11th April 2018).Google Scholar
WHO (2018) Household Air Pollution and Health. WHO, Geneva. URL: https://www.who.int/news-room/fact-sheets/detail/household-air-pollution-and-health (accessed 7th February 2019).Google Scholar
WHO (2019) Risk Factors of Ill Health among Older People. WHO, Geneva. URL: http://www.euro.who.int/en/health-topics/Life-stages/healthy-ageing/data-and-statistics/risk-factors-of-ill-health-among-older-people (accessed 7th February 2019).Google Scholar
Yin, H, Wu, Q, Cui, Y, Hao, Y, Liu, C, Li, Yet al. (2017) Socioeconomic status and prevalence of chronic non-communicable diseases in Chinese women: a structural equation modelling approach. BMJ Open 7(8), e014402.CrossRefGoogle ScholarPubMed
Zachariah, MG, Thankappan, KR, Alex, SC, Sarma, PS and Vasan, RS (2003) Prevalence, correlates, awareness, treatment, and control of hypertension in a middle-aged urban population in Kerala. Indian Heart Journal 55(3), 245251.Google Scholar