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Aging Clinical and Experimental Research

, Volume 31, Issue 2, pp 215–224 | Cite as

Incidence and predictors of multimorbidity among a multiethnic population in Malaysia: a community-based longitudinal study

  • Norlela Mohd Hussin
  • Suzana ShaharEmail author
  • Normah Che Din
  • Devinder Kaur Ajit Singh
  • Ai-Vyrn Chin
  • Rosdinom Razali
  • Mohd Azahadi Omar
Original Article
  • 36 Downloads

Abstract

Background

Multimorbidity in older adults needs to be assessed as it is a risk factor for disability, cognitive decline, and mortality.

Aims

A community-based longitudinal study was performed to determine the incidence and to identify possible predictors of multimorbidity among multiethnic older adults population in Malaysia.

Methods

Comprehensive interview-based questionnaires were administered among 729 participants aged 60 years and above. Data were analyzed from the baseline data of older adults participating in the Towards Useful Aging (TUA) study (2014–2016) who were not affected by multimorbidity (349 without any chronic diseases and 380 with one disease). Multimorbidity was considered present in an individual reporting two or more chronic diseases.

Results

After 1½ years of follow-up, 18.8% of participants who were initially free of any diseases and 40.9% of those with one disease at baseline, developed multimorbidity. The incidence rates were 13.7 per 100 person-years and 34.2 per 100 person-years, respectively. Female gender, smoking, and irregular preparing of food (lifestyle) were predictors for incidence of multimorbidity, especially in those without any disease, while Body Mass Index (BMI) 22–27 kg/m2 and inadequate daily intake of iron were identified as predictors of multimorbidity among participants who already have one disease.

Conclusions

The incidence rates of multimorbidity among Malaysian older adults were between the ranges of 14–34 per 100 person-years at a 1½-year follow-up. Gender, smoking, BMI 22–27 kg/m2, inadequate daily intake of iron and lack of engagement in leisure or lifestyle physical activities were possible predictors in the development of multimorbidity. There is a need to formulate effective preventive management strategies to decelerate multimorbidity among older adults.

Keywords

Multimorbidity Incidence Predictors Older adults 

Notes

Acknowledgements

We are grateful to the Ministry of Higher Education for funding our study via the Longitudinal Research Grant Schema (LRGS/BU/2012/UKM–UKM/K//01). We thanked all the co-researchers and respondents for making this project a success.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Human and animal rights statement

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Participants in this study were recruited after obtaining informed consent.

Supplementary material

40520_2018_1007_MOESM1_ESM.doc (35 kb)
Supplementary material 1 (DOC 35 KB)

References

  1. 1.
    Van den Akker M, Buntinx F, Metsemakers JF et al (1998) Multimorbidity in general practice: prevalence, incidence, and determinants of co-occurring chronic and recurrent diseases. J Clin Epidemiol 51:367–375CrossRefGoogle Scholar
  2. 2.
    Institute of Public Health (2011) The national health and morbidity survey, 1st edn. Ministry of Health, Kuala LumpurGoogle Scholar
  3. 3.
    Gijsen R, Hoeymans N, Schellevis FG et al (2001) Causes and consequences of comorbidity: a review. J Clin Epidemiol 54:661–674CrossRefGoogle Scholar
  4. 4.
    Marengoni A, Angelman S, Melis R et al (2011) Ageing with multimorbidity: a systematic review of the literature. Ageing Res Rev 10:430–439CrossRefGoogle Scholar
  5. 5.
    Daviglus ML, Bell CC, Berrettini W et al (2010) NIH state-of-the-science conference statement: preventing Alzheimer’s diseases and cognitive decline. NIH Consens State Sci Statements 27:1–30Google Scholar
  6. 6.
    Moussavi S, Chatterji S, Verdes E et al (2007) Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet 370:851–858CrossRefGoogle Scholar
  7. 7.
    Melis R, Marengoni A, Angleman S et al (2014) Incidence and predictors of multimorbidity in the elderly: a population-based longitudinal study. PLoS One 9:e103120CrossRefGoogle Scholar
  8. 8.
    Fabbri E, An Y, Zoli M et al (2016) Association between accelerated multimorbidity and age-related cognitive decline in older baltimore longitudinal study of aging participants without dementia. J Am Geriatr Soc 64:965–972CrossRefGoogle Scholar
  9. 9.
    Booth HP, Prevost AT, Gulliford MC (2014) Impact of body mass index on prevalence of multimorbidity in primary care: cohort study. Fam Pract 31:38–43CrossRefGoogle Scholar
  10. 10.
    Almirall J, Fortin M (2013) The coexistence of terms to describe the presence of multiple concurrent diseases. J Comorb 3:4–9CrossRefGoogle Scholar
  11. 11.
    Cooper C, Campion G, Melton LJ III (1992) Hip fractures in the elderly: a world-wide projection. Osteoporos Int 2:285–289CrossRefGoogle Scholar
  12. 12.
    United Nations (2013) The Aging of Asian Population Department for Economic and Social Info and Policy Analysis. New YorkGoogle Scholar
  13. 13.
    Formiga F, Ferrer A, Sanz H et al Octabaix study members (2013) Patterns of comorbidity and multimorbidity in the oldest old: the Octabaix study. Eur J Intern Med 24:40–44CrossRefGoogle Scholar
  14. 14.
    Shahar S, Omar A, Vanoh D et al (2015) Approaches in methodology for population-based longitudinal study on neuroprotective model for healthy longevity (TUA) among Malaysian Older Adults. Aging Clin Exp Res 28:1089–1104CrossRefGoogle Scholar
  15. 15.
    Simpson CF, Boyd CM, Carlson MC et al (2004) Agreement between self-report of disease diagnoses and medical record validation in disabled older women: factors that modify agreement. J Am Geriat Soc 53:123–127CrossRefGoogle Scholar
  16. 16.
    Divya V, Suzana S, Normah CD et al (2017) Predictors of poor cognitive status among older Malaysian adults: baseline findings from the LRGS TUA cohort study. Aging Clin Exp Res 29:173–182Google Scholar
  17. 17.
    Folstein MF, Folstein SE, McHugh PR (1975) Mini-mental state: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198CrossRefGoogle Scholar
  18. 18.
    Wechsler D (1987) WMS-R wechsler memory scale-revised manual. Psychological Corporation Press, New YorkGoogle Scholar
  19. 19.
    Wechsler D (1997) Wechsler adult intelligence scale-III. The Psychological Corporation, San AntonioGoogle Scholar
  20. 20.
    Strauss E, Sherman EM, Spreen O (2006) A compendium of neuropsychological tests: Administration, norms, and commentary. Oxford University Press, New YorkGoogle Scholar
  21. 21.
    Hultsch DF, Hertzog C, Small BJ et al (1999) Use it or lose it: engaged lifestyle as a buffer of cognitive decline in aging? Psychol Aging 2:245–263CrossRefGoogle Scholar
  22. 22.
    Shahar S, Earland J, Abdulrahman S (2000) Validation of a dietary history questionnaire against a 7-D weighed record for estimating nutrient intake among rural elderly Malays. Malays J Nutr 6:33–44Google Scholar
  23. 23.
    Shiekh J, Yesavage J (1986) Geriatric depression scale: recent findings and development of a short version. In: Brink T (ed.) Clinical gerontology: a guide to assessment and intervention. Howarth Press, New YorkGoogle Scholar
  24. 24.
    Sherina M, Rampal L, Aini M et al (2005) The prevalence of depression among elderly in an urban area of Selangor, Malaysia. Int Med J 4:57–63Google Scholar
  25. 25.
    Teh EE, Hasanah CI (2004) Validation of Malay version of geriatric depression scale among elderly inpatients. Penang Hospital and School of Medical Sciences, Universiti Sains Malaysia. Geriatric September 2004. http://priory.com/psych/MalayGDS.html
  26. 26.
    National Coordinating Committee on Food and Nutrition (2005) Recommended nutrient intakes for Malaysia (RNI). A Report of the Technical Working Group on Nutritional Guidelines, 1st edn. Ministry of Health Malaysia, PutrajayaGoogle Scholar
  27. 27.
    Fortin M, Stewart M, Poitras ME et al (2012) A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology. Ann Fam Med 10:142–151CrossRefGoogle Scholar
  28. 28.
    Dhalwani NN, Zaccardi F, Donovan GO et al (2017) Association between lifestyle factors and the incidence of multimorbidity in an older english population. J Gerontol A Biol Sci Med Sci 72:528–534.  https://doi.org/10.1093/gerona/glw146 Google Scholar
  29. 29.
    St. Sauver JL, Boyd CM, Grossardt BR et al (2015) Risk of developing multimorbidity across all ages in an historical cohort study: differences by sex and ethnicity. BMJ Open 5:e006413CrossRefGoogle Scholar
  30. 30.
    Wikstrom K, Lindstrom J, Harald K et al (2015) Clinical and lifestyle-related risk factors for incident multimorbidity: 10-year follow-up of finnish population-based cohorts 1982–2012. Eur J Intern Med 26:211–216CrossRefGoogle Scholar
  31. 31.
    Walker AE (2007) Multiple chronic diseases and quality of life: patterns emerging from a large national sample, Australia. Chronic Illn 3:202–218CrossRefGoogle Scholar
  32. 32.
    Minas M, Koukosias N, Zintzaras E et al (2010) Prevalence of chronic diseases and morbidity in primary health care in central Greece: an epidemiological study. BMC Health Serv Res 10:252CrossRefGoogle Scholar
  33. 33.
    Kaplan MS, Newsom JT, McFarland BH et al (2001) Demographic and psychosocial correlates of physical activity in late life. Am J Prev Med 21:306–312CrossRefGoogle Scholar
  34. 34.
    Cimarras-Otal C, Calderón-Larrañaga A et al (2014) Association between physical activity, multimorbidity, self-rated health and functional limitation in the Spanish population. BMC Public Health 14:1170CrossRefGoogle Scholar
  35. 35.
    Fortin M, Haggerty J, Almirall J et al (2014) Lifestyle factors and multimorbidity: a crosssectional study. BMC Public Health 14:686CrossRefGoogle Scholar
  36. 36.
    Blumental-Perry A (2012) Unfolded protein response in chronic obstructivepulmonary disease: smoking, aging and disease: a SAD trifecta. Curr Mol Med 12:883–898CrossRefGoogle Scholar
  37. 37.
    Mokdad AH, Marks JS, Stroup DF et al (2004) Actual causes of death in the United States, 2000. JAMA 291:1238–1245CrossRefGoogle Scholar
  38. 38.
    Jovic D, Marinkovic J, Vukovic D (2016) Association between body mass index and prevalence of multimorbidity: a crosssectional study. Public Health 139:103–111.  https://doi.org/10.1016/j.puhe.2016.05.014 CrossRefGoogle Scholar
  39. 39.
    Nagel G, Peter R, Braig S et al (2008) The impact of education on risk factors and the occurrence of multimorbidity in the EPIC-Heidelberg cohort. BMC Public Health 8:384CrossRefGoogle Scholar
  40. 40.
    Van den Akker M, Buntinx F, Metsemakers JF et al (2000) Marginal impact of psychosocial factors on multimorbidity: results of an explorative nested case–control study. Soc Sci Med 50:1679–1693CrossRefGoogle Scholar
  41. 41.
    Ng M, Fleming T, Robinson M et al (2014) Global, regional, and national prevalence of overweight and obesity in children and adults during 1980e2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384:766e81CrossRefGoogle Scholar
  42. 42.
    World Health Organization (2016) Obesity and overweight. Fact sheet No. 311. Geneva: World Health Organization. http://www.who.int/mediacentre/factsheets/fs311/en/. Accessed 21 Dec 2017
  43. 43.
    Simkin-Silverman LR, Gleason KA et al (2005) Predictors of weight control advice in primary care practices: patient health and psychosocial characteristics. Prev Med 40:71–82CrossRefGoogle Scholar
  44. 44.
    Nutrition Screening Initiative (1991) Report of nutrition screening I: toward a common view. Washington, DC. Nutrition Screening Initiative. http://www.jblearning.com/samples/0763730629/Frank_Appendix10D.pdf
  45. 45.
    Ruel G, Shi Z, Zhen S et al (2014) Association between nutrition and the evolution of multimorbidity: the importance of fruits and vegetables and whole grain products. Clin Nutr 33:513–520.  https://doi.org/10.1016/j.clnu.2013.07.009 CrossRefGoogle Scholar
  46. 46.
    Sharkey JR (2003) Risk and presence of food insufficiency are associated with low nutrient intakes and multimorbidity among homebound older women who receive home- delivered meals. J Nutr 133:3485–3491CrossRefGoogle Scholar
  47. 47.
    Hamid TA, Krishnaswamy S, Abdullah SS et al (2010) Sociodemographic risk factors and correlates of dementia in older Malaysians. Dement Geriatr Cogn Disord 30:533–539CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Norlela Mohd Hussin
    • 1
  • Suzana Shahar
    • 1
    Email author
  • Normah Che Din
    • 2
  • Devinder Kaur Ajit Singh
    • 3
  • Ai-Vyrn Chin
    • 4
  • Rosdinom Razali
    • 5
  • Mohd Azahadi Omar
    • 6
  1. 1.Dietetic Programme and Research Centre for Healthy Aging and Wellness, Faculty of Health SciencesUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia
  2. 2.Health Psychology Programme and Research Centre for Rehabilitation, Faculty of Health SciencesUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia
  3. 3.Physiotherapy Program Research Centre for Rehabilitation, Faculty of Health SciencesUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia
  4. 4.Ageing and Age-Associated Disorders Research Group, Division of Geriatric Medicine, Faculty of MedicineUniversity of MalayaKuala LumpurMalaysia
  5. 5.Department of Psychiatry, Faculty of MedicineUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia
  6. 6.Institute of Public Health, Ministry of HealthKuala LumpurMalaysia

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