Common patterns of morbidity and multi-morbidity and their impact on health-related quality of life: evidence from a national survey

R E Mujica-Mota, M Roberts, G Abel, M Elliott, G Lyratzopoulos, M Roland, J Campbell, R E Mujica-Mota, M Roberts, G Abel, M Elliott, G Lyratzopoulos, M Roland, J Campbell

Abstract

Background: There is limited evidence about the impact of specific patterns of multi-morbidity on health-related quality of life (HRQoL) from large samples of adult subjects.

Methods: We used data from the English General Practice Patient Survey 2011-2012. We defined multi-morbidity as the presence of two or more of 12 self-reported conditions or another (unspecified) long-term health problem. We investigated differences in HRQoL (EQ-5D scores) associated with combinations of these conditions after adjusting for age, gender, ethnicity, socio-economic deprivation and the presence of a recent illness or injury. Analyses were based on 831,537 responses from patients aged 18 years or older in 8,254 primary care practices in England.

Results: Of respondents, 23 % reported two or more chronic conditions (ranging from 7 % of those under 45 years of age to 51 % of those 65 years or older). Multi-morbidity was more common among women, White individuals and respondents from socio-economically deprived areas. Neurological problems, mental health problems, arthritis and long-term back problem were associated with the greatest HRQoL deficits. The presence of three or more conditions was commonly associated with greater reduction in quality of life than that implied by the sum of the differences associated with the individual conditions. The decline in quality of life associated with an additional condition in people with two and three physical conditions was less for older people than for younger people. Multi-morbidity was associated with a substantially worse HRQoL in diabetes than in other long-term conditions. With the exception of neurological conditions, the presence of a comorbid mental health problem had a more adverse effect on HRQoL than any single comorbid physical condition.

Conclusion: Patients with multi-morbid diabetes, arthritis, neurological, or long-term mental health problems have significantly lower quality of life than other people. People with long-term health conditions require integrated mental and physical healthcare services.

Figures

Fig. 1
Fig. 1
Comorbidity prevalence (%) by medical condition index conditions (rows), comorbidities (columns). The area of each bubble is proportional to the percentage of respondents with the condition identified by the row label who also reported the comorbidity identified in the columns. The range of comorbidity prevalence runs from 47 % for high blood pressure in respondents with kidney/liver disease to 1 % for epilepsy in respondents with high blood pressure. Notes: angina = angina or long-term heart problem, arthritis = arthritis or long-term joint problem, asthma = asthma or long-term chest problem, cancer = cancer in the last five years, deaf = deafness or severe hearing impairment, hbp = high blood pressure, kidney = kidney or liver disease, back = long-term back problem, mental = long-term mental health problem, neuro = long-term neurological problem, other = another long-term problem
Fig. 2
Fig. 2
Quality of life score (EQ-5D × 365) declines with increasing morbidity count. Unadjusted nonparametric regression of EQ-5D scores against number of long-term physical conditions, separately for each of the four subgroups formed by age (under 65 vs. older) and the presence of mental health condition (yes vs. no) categories
Fig. 3
Fig. 3
Three condition combinations with the largest interaction effects (depicted with 95 % CI bars) on full health equivalent days per year (EQ-5D × 365), GPPS 2011–12. Note: Out of 220 triplet combinations, these are 25 whose 95 % CI did not include any of the values in the range [−11, 11]. The full list of combinations and their interaction effects is in Appendix, Table A5. Model 2 with dyads & triads versus dyads only F = 19.3 (286, 823121), p < 0.0001. For a description of the labels, see footnote to Fig. 1
Fig. 4
Fig. 4
Adjusted mean utility scores of 55–64-year-old White woman of average level of material deprivation, by chronic condition. Based on predictions from Model 1, for a White, female, age 55–64 person of mean socioeconomic level, using a linear fixed-effects model adjusting for age, gender, ethnicity, index of multiple deprivation, recent illness or injury, and interactions of age and chronic conditions (see Appendix Tables A4 and A5 for detailed analyses of the impact of combinations of conditions on quality of life). For a description of condition labels, see footnote to Fig. 1

References

    1. Saarni SI, Harkanen T, Sintonen H, Suvisaari J, Koskinen S, Aromaa A, Lonnqvist J. The impact of 29 chronic conditions on health-related quality of life: A general population survey in Finland using 15D and EQ-5D. Quality of Life Research. 2006;15(8):1403–1414. doi: 10.1007/s11136-006-0020-1.
    1. Brettschneider C, Leicht H, Bickel H, Dahlhaus A, Fuchs A, Gensichen J, et al. (MultiCare Study Group). Relative impact of multimorbid chronic conditions on health-related quality of life–results from the MultiCare Cohort Study. PLoS One. 2013;8(6):e66742. doi: 10.1371/journal.pone.0066742.
    1. Whiteford H, Degenhardt L, Rehm J, Baxter A, Farrari A, Erskine H, et al. Global burden of disease attributable to mental and substance misuse disorders: Findings from the Global Burden of Disease Study 2010. Lancet. 2013;382:1575–1586. doi: 10.1016/S0140-6736(13)61611-6.
    1. Alonso J, Ferrer M, Gandek B, Ware JE, Jr, Aaronson NK, Mosconi P, Rasmussen NK, Bullinger M, Fukuhara S, Kaasa S, Leplege A. Health-related quality of life associated with chronic conditions in eight countries: Results from the International Quality of Life Assessment (IQOLA) Project. Quality of Life Research. 2004;13(2):283–298. doi: 10.1023/B:QURE.0000018472.46236.05.
    1. Bhattarai N, Charlton J, Rudisill C, Gulliford MC. Prevalence of depression and utilization of health care in single and multiple morbidity: A population-based cohort study. Psychological Medicine. 2013;43(7):1423–1431. doi: 10.1017/S0033291712002498.
    1. Onubogu UD. Pain and depression in older adults with arthritis. Orthopaedic Nursing. 2014;33(2):102–108. doi: 10.1097/NOR.0000000000000035.
    1. Rast P, Rush J, Piccinin A, Hofer SM. The identification of regions of significance in the effect of multimorbidity on depressive symptoms using longitudinal data: An application of the Johnson-Neyman technique. Gerontology. 2014;60(3):274–281. doi: 10.1159/000358757.
    1. Fortin M, Lapointe L, Hudon C, Vanasse A, Ntetu AL, Maltais D. Multimorbidity and quality of life in primary care: a systematic review. Health and Quality of Life Outcomes. 2004;20(2):51. doi: 10.1186/1477-7525-2-51.
    1. Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research and medical education: a cross-sectional study. The Lancet. 2012;380(9836):37–43. doi: 10.1016/S0140-6736(12)60240-2.
    1. Lawson KD, Mercer SW, Wyke S, Grieve E, Guthrie B, Watt GC, Fenwick EA. Double trouble: The impact of multimorbidity and deprivation on preference-weighted health-related quality of life a cross sectional analysis of the Scottish Health Survey. International Journal for Equity in Health. 2013;12(1):67. doi: 10.1186/1475-9276-12-67.
    1. Agborsangaya CB, Lahtinen M, Cooke T, Johnson JA. Health and Quality of Life Outcomes. 2014;12:74. doi: 10.1186/1477-7525-12-74.
    1. Fried LP, Bandeen-Roche K, Kasper JD, et al. Association of comorbidity with disability in older women: the Women’s Health and Aging Study. Journal of Clinical Epidemiology. 1999;52:27–37. doi: 10.1016/S0895-4356(98)00124-3.
    1. Fultz NH, Ofstedal MB, Herzog AR, Wallace RB. Additive and interactive effects of comorbid physical and mental conditions on functional health. Journal of Aging and Health. 2003;15:465–481. doi: 10.1177/0898264303253502.
    1. Hodek JM, Ruhe AK, Greiner W. Relationship between health-related quality of life and multimorbidity. Gesundheitswesen. 2010;72(8–9):455–465. doi: 10.1055/s-0029-1234121.
    1. Fortin M, Dubois M-F, Hudon C, Soubhi H, Almirall J. Multimorbidity and quality of life: A closer look. Health and Quality of Life Outcomes. 2007;5:52. doi: 10.1186/1477-7525-5-52.
    1. Hunger M, Thorand B, Schunk M, Doring A, Menn P, Peters A, Holle R. Multimorbidity and health-related quality of life in the older population: Results from the German KORA-age study. Health and Quality of Life Outcomes. 2011;9:53. doi: 10.1186/1477-7525-9-53.
    1. McDaid, O., Hanly, M.J., Richardson K., Kee F., Kenny R.A., Savva G.M. et al. (2013). The effect of multiple chronic conditions on self-rated health, disability and quality of life among the older populations of Northern Ireland and the Republic of Ireland: A comparison of two nationally representative cross-sectional surveys. BMJ Open , 3(6), e002571. doi:10.1136/bmjopen-2013-002571.
    1. Sullivan PW, Ghushchyan VH, Bayliss EA. The impact of co-morbidity burden on preference-based health-related quality of life in the United States. Pharmacoeconomics. 2012;30(5):431–442. doi: 10.2165/11586840-000000000-00000.
    1. Agborsangaya CB, Lahtinen M, Cooke T, Johnson JA. Comparing the EQ-5D 3L and 5L: Measurement properties and association with chronic conditions and multimorbidity in the general population. Health and Quality of Life Outcomes. 2014;12:74. doi: 10.1186/1477-7525-12-74.
    1. Campbell J, Smith P, Nissen S, Bower P, Elliott M, Roland M. The GP Patient Survey for use in primary care in the National Health Service in the UK—Development and psychometric characteristics. BMC Family Practice. 2009;10:57. doi: 10.1186/1471-2296-10-57.
    1. GPPS Year 6 Questionnaire. Available from .
    1. Euroqol Group EuroQoL—A new facility for the measurement of health-related quality of life. Health Policy. 1990;16:199–208. doi: 10.1016/0168-8510(90)90421-9.
    1. Dolan P, Gudex C, Kind P, Williams A. Valuing health states: A comparison of methods. Journal of Health Economics. 1996;15(2):209–231. doi: 10.1016/0167-6296(95)00038-0.
    1. Clemens JQ, Elliott MN, Suttorp M, Berry S. Temporal ordering of interstitial cystitis/bladder pain syndrome (IC/BPS) and non-bladder conditions. Urology. 2012;80(6):1231–1232. doi: 10.1016/j.urology.2012.06.061.
    1. Fan J, Gijbels I. Variable bandwidth and local linear regression smoothers. Annals of Statistics. 1992;20(4):2008–2036. doi: 10.1214/aos/1176348900.
    1. Department for Communities and Local Government. London. English Indices of Deprivation 2012 .
    1. Walters SJ, Brazier JE. Comparison of the minimally important difference for two health state utility measures: EQ-5D and SF-6D. Quality of Life Research. 2005;14(6):1523–1532. doi: 10.1007/s11136-004-7713-0.
    1. Kaplan RM. The minimally clinically important difference in generic utility-based measures. COPD. 2005;2:91–97. doi: 10.1081/COPD-200052090.
    1. Bloom DE, Killingsworth MR. Correcting for truncation bias caused by a latent truncation variable. Journal of Econometrics. 1985;27:131–135. doi: 10.1016/0304-4076(85)90048-X.
    1. Heckman JJ. Dummy endogenous variables in a simultaneous equation system. Econometrica. 1978;46:931–959. doi: 10.2307/1909757.
    1. IPSOS MORI Technical Annex for the GP Patient Survey 2011–2012; pp. 47. Annual Report .
    1. White H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica. 1980;48:817–838. doi: 10.2307/1912934.
    1. Mousavi S, Chatterji S, Verdes E, Tandon A, Patel VikramUstun B . Depression, chronic diseases, and decrements in health.: results from the World Health Survey. The Lancet. 2007;370:851–858. doi: 10.1016/S0140-6736(07)61415-9.
    1. Hays R, Reeve BR, Smith AW, Clauser SB. Associations of cancer and other chronic medical conditions with SF-6D preference based scores in Medicare beneficiaries. Quality of Life Research. 2014;23(2):385–391. doi: 10.1007/s11136-013-0503-9.
    1. Sullivan PW, Lawrence WF, Ghushchyan V. A national catalog of preference-based scores for chronic conditions in the United States. Medical Care. 2005;43:736–740. doi: 10.1097/01.mlr.0000172050.67085.4f.
    1. Institute of Medicine. (2012). Living well with chronic illness: a call for public health action. Washington, DC: The National Academies Press. .
    1. Luo N, Wang P, Fu A, Johnson J, Coons SJ. Preference-based SF-6D scores derived from the SF-36 and SF-12 have different discriminative power in a population health survey. Medical Care. 2012;50(7):627–632. doi: 10.1097/MLR.0b013e31824d7471.
    1. Holland R, Smith RD, Harvey I, Swift L, Lenaghan E. Assessing quality of life in the elderly: A direct comparison of the EQ-5D and AQoL. Health Economics. 2004;13:793–805. doi: 10.1002/hec.858.
    1. Brazier J, Ratcliffe J, Salomon JA, Tsuchiya A. Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press; 2007.
    1. Berkanovic E, Telesky C. Mexican-American, black-American and white-American differences in reporting illnesses, disability and physician visits for illnesses. Social Science and Medicine. 1985;20:567–577. doi: 10.1016/0277-9536(85)90395-8.
    1. Johnson, T.P., O’Rourke, D., Chavez, N., Sudman, S. (1996). Cultural variations in the interpretation of health survey questions. In: Warnecke RB (ed), Health Survey Research Methods. National Center for Health Statistics (. pp. 57–62). Hyattsville: MD.
    1. Stewart AL, Napoles-Springer A. Health-related quality-of-life assessments in diverse population groups in the United States. Medical Care. 2000;38(9 Suppl):II102–II124.
    1. Cassano P, Fava M. Depression and public health: And overview. Journal of Psychosomatic Research. 2002;53:849–857. doi: 10.1016/S0022-3999(02)00304-5.
    1. Groves, R. M. (2006). Nonresponse rates and nonresponse bias in household surveys. Public Opinion Quarterly,70(5), 646–675.
    1. Klein DJ, Elliott MN, Haviland AM, Saliba D, Burkhart Q, Edwards C, Zaslavsky AM. Understanding nonresponse to the 2007 Medicare CAHPS survey. The Gerontologist. 2011;51(6):843–855. doi: 10.1093/geront/gnr046.
    1. Paddison C, Elliott M, Parker R, Staestsky L, Lyratzopoulos G, Campbell JL, Roland M. Should measures of patient experience in primary care be adjusted for case mix? Evidence from the English General Practice Patient Survey. BMJ Quality & Safety. 2012;21(8):634–640. doi: 10.1136/bmjqs-2011-000737.
    1. Gonzalez JS, Peyrot M, McCarl LA, Collins EM, Serpa L, Mimaga MJ, Safren SA. Depression and diabetes treatment nonadherence: A meta-analysis. Diabetes Care. 2008;31(12):2398–2403. doi: 10.2337/dc08-1341.
    1. Lustman PJ, Anderson RJ, Freedland KE, De Groot M, Carney RM, Clouse RE. Depression and poor glycemic control: A meta-analytic review of the literature. Diabetes Care. 2000;23(7):934–942. doi: 10.2337/diacare.23.7.934.
    1. De Groot M, Anderson R, Freedland KE, Clouse RE, Lustman PJ. Association of depression and diabetes complications: A meta-analysis. Psychosomatic Medicine. 2001;63(4):619–630. doi: 10.1097/00006842-200107000-00015.
    1. Salisbury C, Johnson L, Purdy S, Valderas JM, Montgomery AA. Epidemiology and impact of multimorbidity in primary care: a retrospective cohort study. British Journal of General Practice. 2011;61(582):e12–e21. doi: 10.3399/bjgp11X548929.

Source: PubMed

3
Subscribe