Next Article in Journal
Barriers to and Facilitators of the Evaluation of Integrated Community-Wide Overweight Intervention Approaches: A Qualitative Case Study in Two Dutch Municipalities
Next Article in Special Issue
Economies through Application of Nonmedical Primary-Preventative Health: Lessons from the Healthy Country Healthy People Experience of Australia’s Aboriginal People
Previous Article in Journal
Childhood Reports of Food Neglect and Impulse Control Problems and Violence in Adulthood
Previous Article in Special Issue
Age- and Sex-Specific Trends in Lung Cancer Mortality over 62 Years in a Nation with a Low Effort in Cancer Prevention
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 13
Issue 4
10.3390/ijerph13040391
ijerph-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

How Does Sex Influence Multimorbidity? Secondary Analysis of a Large Nationally Representative Dataset

by
Karolina Agur
1,
Gary McLean
1,
Kate Hunt
2,
Bruce Guthrie
3 and
Stewart W. Mercer
1,*
1
General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow, Scotland G12 9LX, UK
2
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, Scotland G2 3QB, UK
3
Quality, Safety and Informatics Research Group, University of Dundee, Dundee, Scotland DD2 4BF, UK
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2016, 13(4), 391; https://doi.org/10.3390/ijerph13040391
Received: 28 January 2016 / Revised: 11 March 2016 / Accepted: 23 March 2016 / Published: 31 March 2016
(This article belongs to the Special Issue Chronic Diseases and Multimorbidity in Primary Care)
Browse Figure
Versions Notes

Abstract

:
Multimorbidity increases with age and is generally more common in women, but little is known about sex effects on the “typology” of multimorbidity. We have characterized multimorbidity in a large nationally representative primary care dataset in terms of sex in ten year age groups from 25 years to 75 years and over, in a cross-sectional analysis of multimorbidity type (physical-only, mental-only, mixed physical and mental; and commonest conditions) for 1,272,685 adults in Scotland. Our results show that women had more multimorbidity overall in every age group, which was most pronounced in the 45–54 years age group (women 26.5% vs. men 19.6%; difference 6.9 (95% CI 6.5 to 7.2). From the age of 45, physical-only multimorbidity was consistently more common in men, and physical-mental multimorbidity more common in women. The biggest difference in physical-mental multimorbidity was found in the 75 years and over group (women 30.9% vs. men 21.2%; difference 9.7 (95% CI 9.1 to 10.2). The commonest condition in women was depression until the age of 55 years, thereafter hypertension. In men, drugs misuse had the highest prevalence in those aged 25–34 years, depression for those aged 35–44 years, and hypertension for 45 years and over. Depression, pain, irritable bowel syndrome and thyroid disorders were more common in women than men across all age groups. We conclude that the higher overall prevalence of multimorbidity in women is mainly due to more mixed physical and mental health problems. The marked difference between the sexes over 75 years especially warrants further investigation.

1. Introduction

Multimorbidity is usually defined as the co-occurrence of two or more long term conditions within one individual, and is an increasing problem with a reported doubling of prevalence over the last 20 years [1]. It is a global phenomenon and has now become the norm rather than exception in many populations [2,3,4,5]. Multimorbidity is associated with increased mortality [6,7,8], lower quality of life [9,10,11], and greater utilization of healthcare including unplanned admissions [12,13,14] and thus higher healthcare costs [6,15,16,17]. Patients with multimorbidity are less satisfied with care provided [18], perhaps due to the fragmentation of care resulting from the single disease-focus that drives much of current health care [19,20,21].
The prevalence of multimorbidity rises rapidly with increasing age [4,5,6,16,22,23,24], although, the absolute number of individuals with multimorbidity is often higher in those under 65 years [4,25,26]. It is also strongly associated with socioeconomic deprivation and occurs 10–15 years earlier in individuals living in the most deprived compared with the least deprived areas [3,4,5]. The effect of sex on multimorbidity has been less well defined [3]. Most previous studies have shown an increased prevalence of multimorbidity among women [1,6,16,26], though not all studies find this [2,27,28]. Although in almost all countries in the world women have a longer life expectancy than men [29], they are more often affected by a number of non-fatal chronic diseases that decrease quality of life and everyday physical ability [23,30].
Multimorbidity is a broad concept, and can be characterised further in a number of ways, such as physical-only, mental-only, and mixed mental and physical [24], as well as by the commonest combinations of individual conditions. The typology of multimorbidity by sex has not been well documented in large primary care populations. Therefore, this study aimed to examine the relationship between sex and different types of multimorbidity across different age groups, using cross-sectional data from a nationally representative large primary care dataset.

2. Methods

We obtained a dataset for 1,751,841 patients of all ages, who were alive on 31 March 2007 and permanently registered with 314 Scottish general practices, from the Primary Care Clinical Informatics Unit at the University of Aberdeen, UK [31]. This accounts for about a third of the Scottish population and is representative of it in terms of age, sex, and socioeconomic deprivation. The NHS National Research Ethics Service had previously approved the use of these anonymised data for research purposes and this analysis did not require independent review [4].
Forty conditions in total were analysed as previously defined [4] using either Quality and Outcomes Framework (QOF) register Read Code sets where available (Read Codes are the clinical coding system used in UK general practice) [32], other Read Code sets used by NHS Scotland for non-QOF conditions, or combinations of Read Codes and prescription data where coding was known to be likely to under-record important conditions. The appendix provides further detail of definitions and the 40 conditions analysed.
Multimorbidity was defined as the presence of two or more disorders in an individual. The choice of conditions was based on recommendations from systematic review for multimorbidity measure [33] as well as diseases in QOF within UK General Practice (GP) contract and important diseases identified by NHS Scotland [32,34]. To specifically examine multimorbidity in terms of physical and mental health disorders, we defined each condition as either a physical or mental health disorder. Of the 40 conditions we classified 32 as physical and 8 as mental disorders (depression, anxiety, drugs misuse, alcohol misuse, schizophrenia/bipolar, dementia, learning disability and anorexia).
In order to characterize the prevalence and type of multimorbidity, we additionally analysed how many people had: (a) 2 or more physical conditions but no mental health conditions (physical-only); (b) 2 or more mental health conditions but no physical conditions (mental-only); or (c) 2 or more conditions including at least 1 physical and 1 mental (mixed physical and mental). We then examined the top 10 most common conditions in those with multimorbidity separately in each age group by sex.
As the current analysis focused on adults with multimorbidity, we excluded adults aged under 25 years in whom multimorbidity is uncommon (prevalence 1.9%) [4]. We divided the remaining patients into 6 age groups (25–34, 35–44, 45–54, 55–64, 65–74 and 75 and over) to reflect different stages in the life course. For each of these age groups, we calculated the number (and percentage) of patients with: no conditions; with one condition; and with two or more conditions. We used graphical display to compare differences by age group in patterns of multimorbidity by sex. We calculated the ten most prevalent conditions by age group and separately for men and women with two or more conditions by dividing the number of patients with each condition by the number of patients in the relevant age groups with two or more conditions. All analysis was conducted using Stata version 13.0 (Stata, College Station, TX, USA).

3. Results

The analysis involved 1,272,685 adults aged 25 years and over, of whom 51% were women and 49% were men. Table 1 shows differences in prevalence of morbidity by age group and sex. For both men and women, the number and percentage of people with multimorbidity was higher in each successive ten-year age group within each sex, however, there were more people with multimorbidity in absolute numbers under the age of 65 (201,311 vs. 194,996). There were more women with multimorbidity than with a single condition or no condition in all age groups above the age of 55, whereas this was only true in men above the age of 65. The prevalence of multimorbidity (two or more conditions) was higher in women than in men in every age group, although the differences were small over the age of 65. The biggest sex difference was found in the 45–54 age group (women 26.5% vs. men 19.6%; difference 6.9 (95% CI 6.5 to 7.2).
Table 2 shows the prevalence of different types of multimorbidity (physical or mental or mixed) across age groups by sex. In general, the differences in prevalence of all types of multimorbidity between men and women were markedly small. However, whilst physical-only morbidity increased with age for both men and women, differences were small up to the age of 64 years but were marginally higher for women. After the age of 65 more men than women had physical-only multimorbidity. The most significant difference was observed in the over 75 age group (women 45.3% vs. men 53.5%; difference −8.3 (95% CI −7.7 to −8.7). Gender differences were greatest for mixed physical and mental multimorbidity (as compared with mental-only and physical-only) at all ages but the magnitude of this difference varied with age. Women’s higher prevalence of mixed physical and mental multimorbidity was small in absolute terms at age 25–34 (2.1% (95% CI 1.9 to 2.2), increasing to around 5% between the ages 45 and 74), and highest in those above the age of 75 (women 30.9% vs. men 21.2%; difference 9.7 (95% CI 9.1 to 10.2).
Figure 1 shows the number of people with the different types of multimorbidity by age group and sex. It illustrates that, overall, women have more multimorbidity across the life course but sex differences are small and diminish even more after 55–64 years. It shows that a large part of this reduction in sex difference is due to a steeper rise in the prevalence of men with physical only multimorbidity compared to women.
Table 3 shows prevalence (%) of the conditions which feature in the top 10 most commonly found in men or women with multimorbidity within each age group. For women with multimorbidity below the age of 55, depression was the condition with the highest prevalence, whereas above the age of 55 hypertension was the most common condition. In contrast for men with multimorbidity, drugs misuse had the highest prevalence for men aged 25–34, depression for men aged 35–44, and hypertension for men aged 45 and over. Women had a consistently higher prevalence of depression, pain, IBS and thyroid disorders across all age groups. For men under 45 with multimorbidity, compared to women in the same age group, prevalence was only notably higher for substance (alcohol and drugs) misuse. Amongst people with multimorbidity, higher prevalence’s of coronary heart disease (CHD) and diabetes were recorded in men than women from the ages of 45 and over.

4. Discussion

This study has examined patterns of multimorbidity by age and sex in 1,272,685 adults in a nationally representative primary care sample in Scotland. Women had more overall multimorbidity than men at all ages, although the sex differences were relatively small. From the age of 45, physical-only multimorbidity was consistently more common in men, and physical-mental multimorbidity more common in women. The biggest sex difference in physical-mental multimorbidity was found in the 75 years and over group. The commonest condition in women was depression until the age of 55 years, thereafter hypertension. Depression, pain, irritable bowel syndrome and thyroid disorders were more common in women than men across all age groups.

4.1. Relationship with Existing Literature

Several studies have reported that women have more multimorbidity than men [4,26,35], as reflected in a recent systematic review [3]. The level of sex difference in multimorbidity varies between studies. One study reported little difference between the sexes on the risk of being mutlimorbid [36]. This study used 20 chronic conditions and the difference in reported results may be explained by the inclusion in our study of additional conditions which are far more prevalent in women such as migraine and thyroid diseases. Moreover, most previous studies have had either much smaller samples sizes [26,27,36,37] or were restricted to elderly [38,39,40] or hospitalised people [41], whereas the current study examined a nationally representative one-third of the Scottish population.
As far as we are aware, our study is the first to show prevalence rates of different types of multimorbidity and particular conditions by sex and across all ages in a large, nationally representative dataset.
In terms of mental health morbidity, our results show that physical-mental multimorbidity is much more common in women than men. Depression was more prevalent in women than men and was the most frequently occurring condition among women below the age of 55. These findings agree with previously published studies in both multimorbid [4,42] and single disease analyses. [43] Whether this reflects “true” levels of depression in patients with at least one other condition, or a difference in the presentation (by patients) or recognition (by patients and doctors) of depression in people with other long-term conditions cannot be determined from these data. Experimental studies support differential recognition by doctors of potential symptoms in women and men [44,45,46], at least for cardiovascular disease and depression. In addition, below the ages of around 60 years, women are more likely to have contact with their general practitioner [47], and there thus may be differences between men and women of prior recording of depression which may alert GPs to be more alert to depression when they see female patients with physical conditions. Recent survey data have shown that 90% of women with depression discussed it with their doctor [48]. Underestimation of depression in men could perhaps also be explained by so called “masculine” ways of responding to negative affect, less help seeking and lack of consideration for some of such symptoms in commonly used diagnostic tools for depression [49]. This may be important particularly in older populations as previous literature has described that depression is related to negative outcomes within chronic diseases such as, e.g., coronary heart disease [50].
We also found higher rates of alcohol misuse for men with multimorbidity, particularly in the younger age groups, which have also been reported elsewhere [51,52]. This is perhaps not surprising given the consistent finding of higher consumption of alcohol in general population samples of men compared with women in many cultures [53]. It is also consistent with the view that men’s mental distress and depressive illness may disproportionately present as substance abuse [54,55].
Although we did not examine the utilization of healthcare in the current study, increased multimorbidity among female patients, especially those with mental health problems, could explain the previous results in literature in relation to consultation rates and emergency inpatient admissions [12,47]. A recent study using a large dataset in England reported that consultation rate among women was higher than men, which was only partially explained by reproduction related visits [47].

4.2. Strengths and Limitations

As far as we are aware, the present study represents the most comprehensive study to date on sex associations with multimorbidity, based as it is on a large nationally representative primary care sample. The study has, however, several limitations. Firstly, it was based on electronic medical records from GP practices and thus relies on the diligence of the practitioners in coding conditions, and their propensity to do this equally diligently by sex for all conditions. We used mixtures of morbidity codes and prescribing to define and measure some conditions, firstly to ensure that coded conditions were still “active” (e.g., asthma code plus recent inhaler prescription) and secondly because some conditions not incentivised in the UK GPs Quality and Outcomes Framework (such as osteoarthritis) are poorly recorded. In addition, the physical conditions we used did not include chronic conditions that are specific to women (in order to compare men and women across conditions). The study is also not able to take account of severity of any of the conditions as these data were not available. Additionally, we did not examine ethnic differences in multimorbidity.

4.3. Implications for Policy and Practice

This study shows that multimorbidity differs between men and women in terms of types of conditions as well as prevalence, raising the potential need for a more tailored gendered approach to patients with multiple chronic conditions.
Lack of appropriate guidelines and integrated health systems creates a gap between patients’ healthcare needs and experiences and its provision [55]. As reflected in previous studies, this gap could be decreased by improved integration and a generalist patient-centred approach [56]. Given the apparently large additional multimorbidity burden in women relating to sex-specific conditions, Gender sensitive services development may be important in some settings, including the implementation of sex related health and socio-economic risk factors reduction policies as suggested by WHO [57,58]
Further research is required to understand the differing burdens and impacts of multimorbidity typologies in men and women.

5. Conclusions

Our study has shown that, overall, women have more multimorbidity across all age groups, which is driven by a higher prevalence of combined physical and mental multimorbidity in women than men, with the biggest difference in this category being found among the oldest (above the age of 75 year). This warrants further study, including the severity, burden and effects of such sex differences in multimorbidity.

Acknowledgments

This work was funded by the Chief Scientist Office of Scottish Government Health Directorate (Applied Research Programme Grant ARPG/07/1), but study design, data analysis, interpretation and publication were the responsibility of the research team who had sole access to the data. The data contained herein were provided by the Primary Care Clinical Informatics Unit (PCCIU) at the University of Aberdeen. The views in this publication are not necessarily the views of the University of Aberdeen, its agents, or employees. Particular thanks go to Katie Wilde and Fiona Chaloner, University of Aberdeen who carried out the initial data extraction and management. Kate Hunt is funded by the Medical Research Council (MC_UU_12017/12).

Author Contributions

Stewart W. Mercer and Bruce Guthrie conceived and designed the original study, and Karolina Agur with input from all the other authors including Kate Hunt conceived and designed this secondary analysis; Gary McLean analysed the data. Karolina Agur wrote the first draft of the paper, and all authors contributed to revisions and the final version.

Conflicts of Interest

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

References

  1. Uijen, A.A.; van de Lisdonk, E.H. Multimorbidity in primary care: Prevalence and trend over the last 20 years. Eur. J. Gen. Pract. 2008, 14 (Suppl. 1), 28–32. [Google Scholar] [CrossRef] [PubMed]
  2. Fortin, M.; Bravo, G.; Hudon, C.; Vanasse, A.; Lapointe, L. Prevalence of multimorbidity among adults seen in family practice. Ann. Fam. Med. 2005, 3, 223–228. [Google Scholar] [CrossRef] [PubMed]
  3. Violan, C.; Foguet-Boreu, Q.; Flores-Mateo, G.; Salisbury, C.; Blom, J.; Freitag, M.; Glynn, L.; Muth, C.; Valderas, J.M. Prevalence, determinants and patterns of multimorbidity in primary care: A systematic review of observational studies. PLoS ONE 2014, 9, e102149. [Google Scholar]
  4. Barnett, K.; Mercer, S.W.; Norbury, M.; Watt, G.; Wyke, S.; Guthrie, B. Epidemiology of multimorbidity and implications for health care, research, and medical education: A cross-sectional study. Lancet 2012, 380, 37–43. [Google Scholar] [CrossRef]
  5. Afshar, S.; Roderick, P.J.; Kowal, P.; Dimitrov, B.D.; Hill, A.G. Multimorbidity and the inequalities of global ageing: A cross-sectional study of 28 countries using the World Health Surveys. BMC Public Health 2015, 15, 776. [Google Scholar] [CrossRef] [PubMed]
  6. Marengoni, A.; Angleman, S.; Melis, R.; Mangialasche, F.; Karp, A.; Garmen, A.; Meinow, B.; Fratiglioni, L. Aging with multimorbidity: A systematic review of the literature. Ageing Res. Rev. 2011, 10, 430–439. [Google Scholar] [CrossRef] [PubMed]
  7. Byles, J.E.; Catherine, D.; Lynne, P.; Rachel, O.; Carla, T. Single index of multimorbidity did not predict multiple outcomes. J. Clin. Epidemiol. 2005, 58, 997–1005. [Google Scholar] [CrossRef] [PubMed]
  8. Tooth, L.; Hockey, R.; Byles, J.; Dobson, A. Weighted multimorbidity indexes predicted mortality, health service use, and health-related quality of life in older women. J. Clin. Epidemiol. 2008, 61, 151–159. [Google Scholar] [CrossRef] [PubMed]
  9. Loza, E.; Jover, J.A.; Rodriguez, L.; Carmona, L.; EPISER study group. Multimorbidity: Prevalence, effect on quality of life and daily functioning, and variation of this effect when one condition is a rheumatic disease. Semin. Arthritis Rheum. 2009, 38, 312–319. [Google Scholar] [CrossRef] [PubMed]
  10. Le Reste, J.Y.; Nabbe, P.; Manceau, B.; Lygidakis, C.; Doerr, C.; Lingner, H.; Czachowski, S.; Munoz, M.; Argyriadou, S.; Claveria, A.; et al. The European General Practice Research Network presents a comprehensive definition of multimorbidity in family medicine and long term care, following a systematic review of relevant literature. J. Am. Med. Dir. Assoc. 2013, 14, 319–325. [Google Scholar] [CrossRef] [PubMed]
  11. Agborsangaya, C.B.; Lau, D.; Lahtinen, M.; Cooke, T.; Johnson, J.A. Health-related quality of life and healthcare utilization in multimorbidity: Results of a cross-sectional survey. Qual. Life Res. 2013, 22, 791–799. [Google Scholar] [CrossRef] [PubMed]
  12. Payne, R.A.; Abel, G.A.; Guthrie, B.; Mercer, S.W. The effect of physical multimorbidity, mental health conditions and socioeconomic deprivation on unplanned admissions to hospital: A retrospective cohort study. CMAJ 2013, 185, E221–E228. [Google Scholar] [CrossRef] [PubMed]
  13. Schneider, K.M.; O’Donnell, B.E.; Dean, D. Prevalence of multiple chronic conditions in the United States’ medicare population. Health Qual. Life Outcomes 2009, 7, 82. [Google Scholar] [CrossRef] [PubMed]
  14. Wolff, J.L.; Starfield, B.; Anderson, G. Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch. Intern. Med. 2002, 162, 2269–2276. [Google Scholar] [CrossRef] [PubMed]
  15. Brilleman, S.L.; Purdy, S.; Salisbury, C.; Windmeijer, F.; Gravelle, H.; Hollinghurst, S. Implications of comorbidity for primary care costs in the UK: A retrospective observational study. Br. J. Gen. Pract. 2013, 63, e274–e282. [Google Scholar] [CrossRef] [PubMed]
  16. Salisbury, C.; Johnson, L.; Purdy, S.; Valderas, J.M.; Montgomery, A.A. Epidemiology and impact of multimorbidity in primary care: A retrospective cohort study. Br. J. Gen. Pract. 2011, 61, e12–e21. [Google Scholar] [CrossRef] [PubMed]
  17. France, E.F.; Wyke, S.; Gunn, J.M.; Mair, F.S.; McLean, G.; Mercer, S.W. Multimorbidity in primary care: A systematic review of prospective cohort studies. Br. J. Gen. Pract. 2012, 62, e297–e307. [Google Scholar] [CrossRef] [PubMed]
  18. Burgers, J.S.; Voerman, G.E.; Grol, R.; Faber, M.J.; Schneider, E.C. Quality and coordination of care for patients with multiple conditions: Results from an international survey of patient experience. Eval. Health Prof. 2010, 33, 343–364. [Google Scholar] [CrossRef] [PubMed]
  19. Boyd, C.M.; Darer, J.; Boult, C.; Fried, L.P.; Boult, L.; Wu, A.W. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: Implications for pay for performance. JAMA 2005, 294, 716–724. [Google Scholar] [CrossRef] [PubMed]
  20. Mangin, D.; Heath, I.; Jamoulle, M. Beyond diagnosis: Rising to the multimorbidity challenge. BMJ 2012, 344, e3526. [Google Scholar] [CrossRef] [PubMed]
  21. Starfield, B.; Shi, L.; Macinko, J. Contribution of primary care to health systems and health. Milbank Q. 2005, 83, 457–502. [Google Scholar] [CrossRef] [PubMed]
  22. Agborsangaya, C.B.; Lau, D.; Lahtinen, M.; Cooke, T.; Johnson, J.A. Multimorbidity prevalence and patterns across socioeconomic determinants: A cross-sectional survey. BMC Public Health 2012, 12, 201. [Google Scholar] [CrossRef] [PubMed]
  23. Schafer, I.; Hansen, H.; Schon, G.; Hofels, S.; Altiner, A.; Dahlhaus, A.; Gensichen, J.; Riedel-Heller, S.; Weyerer, S.; Blank, W.A.; et al. The influence of age, sex and socio-economic status on multimorbidity patterns in primary care. First results from the multicare cohort study. BMC Health Serv. Res. 2012, 12, 89. [Google Scholar] [CrossRef] [PubMed]
  24. McLean, G.; Gunn, J.; Wyke, S.; Guthrie, B.; Watt, G.C.; Blane, D.N.; Mercer, S.W. The influence of socioeconomic deprivation on multimorbidity at different ages: A cross-sectional study. Br. J. Gen. Pract. 2014, 64, e440–e447. [Google Scholar] [CrossRef] [PubMed]
  25. Violan, C.; Foguet-Boreu, Q.; Hermosilla-Pérez, E.; Valderas, J.M.; Bolíbar, B.; Fàbregas-Escurriola, M.; Brugulat-Guiteras, P.; Muñoz-Pérez, M.Á. Comparison of the information provided by electronic health records data and a population health survey to estimate prevalence of selected health conditions and multimorbidity. BMC Public Health 2013, 13, 251. [Google Scholar] [CrossRef] [PubMed][Green Version]
  26. Prados-Torres, A.; Poblador-Plou, B.; Calderón-Larrañaga, A.; Gimeno-Feliu, L.A.; González-Rubio, F.; Poncel-Falcó, A.; Sicras-Mainar, A.; Alcalá-Nalvaiz, J.T. Multimorbidity patterns in primary care: Interactions among chronic diseases using factor analysis. PLoS ONE 2012, 7, e32190. [Google Scholar] [CrossRef] [PubMed]
  27. Britt, H.C.; Harrison, C.M.; Millar, G.M.; Knox, S.A. Prevalence and patterns of multimorbidity in Australia. Med. J. Aust. 2008, 189, 72–77. [Google Scholar] [PubMed]
  28. Rizza, A.; Kaplan, V.; Senn, O.; Rosemann, T.; Bhend, H.; Tandjung, R. Age- and sex-related prevalence of multimorbidity in primary care: The Swiss FIRE project. BMC Fam. Pract. 2012, 13, 113. [Google Scholar] [CrossRef] [PubMed][Green Version]
  29. Barford, A.; Dorling, D.; Davey Smith, G.; Shaw, M. Life expectancy: Women now on top everywhere. BMJ 2006, 332, 808. [Google Scholar] [CrossRef] [PubMed]
  30. Borsch-Supan, A.; Brandt, M.; Hunkler, C.; Kneip, T.; Korbmacher, J.; Malter, F.; Schaan, B.; Stuck, S.; Zuber, S.; SHARE Central Coordination Team. Data Resource Profile: The Survey of Health, Ageing and Retirement in Europe (SHARE). Int. J. Epidemiol. 2013, 42, 992–1001. [Google Scholar] [CrossRef] [PubMed]
  31. Elder, R.; Kirkpatrick, M.; Ramsay, W.; MacLeod, M.; Guthrie, B.; Sutton, M.; Watt, G. Measuring Quality in Primary Medical Services Using Data from SPICE; NHS National Services Scotland: Edinburgh, UK, 2007. [Google Scholar]
  32. QOF Frequently Asked Questions. July 2011. Available online: http://www.nhsemployers.org/~/media/Employers/Documents/Primary%20care%20contracts/QOF/2011-12/UK%20QOF%20-%20Frequently%20asked%20questions%20-%20July%202011.pdf (acessed on 12 December 2105).
  33. Diederichs, C.; Berger, K.; Bartels, D.B. The measurement of multiple chronic diseases—A systematic review on existing multimorbidity indices. J. Gerontol. A Biol. Sci. Med. Sci. 2011, 66, 301–311. [Google Scholar] [CrossRef] [PubMed]
  34. Measuring Long-Term Conditions in Scotland. June 2008. Available online: http://www.isdscotland.org/Health-Topics/Hospital-Care/Diagnoses/2008_08_14_LTC_full_report.pdf (accessed on 12 October 2105).
  35. Van den Akker, M.; Buntinx, F.; Metsemakers, J.F.; Roos, S.; Knottnerus, J.A. Multimorbidity in general practice: Prevalence, incidence, and determinants of co-occurring chronic and recurrent diseases. J. Clin. Epidemiol. 1998, 51, 367–375. [Google Scholar] [CrossRef]
  36. St Sauver, J.L.; Boyd, C.M.; Grossardt, B.R.; Bobo, W.V.; Finney Rutten, L.J.; Roger, V.L.; Ebbert, J.O.; Therneau, T.M.; Yawn, B.P.; Rocca, W.A. Risk of developing multimorbidity across all ages in an historical cohort study: Differences by sex and ethnicity. BMJ Open 2015, 5, e006413. [Google Scholar] [CrossRef] [PubMed]
  37. Garcia-Olmos, L.; Salvador, C.H.; Alberquilla, Á.; Lora, D.; Carmona, M.; García-Sagredo, P.; Pascual, M.; Muñoz, A.; Monteagudo, J.L.; García-López, F. Comorbidity patterns in patients with chronic diseases in general practice. PLoS ONE 2012, 7, e32141. [Google Scholar] [CrossRef] [PubMed]
  38. Lochner, K.A.; Cox, C.S. Prevalence of multiple chronic conditions among Medicare beneficiaries, United States, 2010. Prev. Chronic. Dis. 2013, 10, E61. [Google Scholar] [CrossRef] [PubMed]
  39. Marengoni, A.; Winblad, B.; Karp, A.; Fratiglioni, L. Prevalence of chronic diseases and multimorbidity among the elderly population in Sweden. Am. J. Public Health 2008, 98, 1198–1200. [Google Scholar] [CrossRef] [PubMed]
  40. Van den Bussche, H.; Koller, D.; Kolonko, T.; Hansen, H.; Wegscheider, K.; Glaeske, G.; von Leitner, E.C.; Schäfer, I.; Schön, G. Which chronic diseases and disease combinations are specific to multimorbidity in the elderly? Results of a claims data based cross-sectional study in Germany. BMC Public Health 2011, 11, 101. [Google Scholar] [CrossRef] [PubMed]
  41. Wong, A.; Boshuizen, H.C.; Schellevis, F.G.; Kommer, G.J.; Polder, J.J. Longitudinal administrative data can be used to examine multimorbidity, provided false discoveries are controlled for. J. Clin. Epidemiol. 2011, 64, 1109–1117. [Google Scholar] [CrossRef] [PubMed]
  42. Spangenberg, L.; Forkmann, T.; Brähler, E.; Glaesmer, H. The association of depression and multimorbidity in the elderly: Implications for the assessment of depression. Psychogeriatrics 2011, 11, 227–234. [Google Scholar] [CrossRef] [PubMed]
  43. Faravelli, C.; Alessandra Scarpato, M.; Castellini, G.; Lo Sauro, C. Gender differences in depression and anxiety: The role of age. Psychiatry Res. 2013, 210, 1301–1303. [Google Scholar] [CrossRef] [PubMed]
  44. Adams, A.; Buckingham, C.D.; Lindenmeyer, A.; McKinlay, J.B.; Link, C.; Marceau, L.; Arber, S. The influence of patient and doctor sex on diagnosing coronary heart disease. Sociol. Health Illn. 2008, 30, 1–18. [Google Scholar] [CrossRef] [PubMed]
  45. Arber, S.; McKinlay, J.; Adams, A.; Marceau, L.; Link, C.; O’Donnell, A. Patient characteristics and inequalities in doctors’ diagnostic and management strategies relating to CHD: A video-simulation experiment. Soc. Sci. Med. 2006, 62, 103–115. [Google Scholar] [CrossRef] [PubMed][Green Version]
  46. Arber, S.; McKinlay, J.; Adams, A.; Marceau, L.; Link, C.; O’Donnell, A. Influence of patient characteristics on doctors’ questioning and lifestyle advice for coronary heart disease: A UK/US video experiment. Br. J. Gen. Pract. 2004, 54, 673–678. [Google Scholar] [PubMed]
  47. Wang, Y.; Hunt, K.; Nazareth, I.; Freemantle, N.; Petersen, I. Do men consult less than women? An analysis of routinely collected UK general practice data. BMJ Open 2013, 3, e003320. [Google Scholar] [CrossRef] [PubMed][Green Version]
  48. Lang, K.; Alexander, I.M.; Simon, J.; Sussman, M.; Lin, I.; Menzin, J.; Friedman, M.; Dutwin, D.; Bushmakin, A.G.; Thrift-Perry, M.; et al. The impact of multimorbidity on quality of life among midlife women: Findings from a u.s. Nationally representative survey. J. Womens Health (Larchmt) 2015, 24, 374–383. [Google Scholar] [CrossRef] [PubMed]
  49. Addis, M.E. Gender and depression in men. Clin. Psychol. Sci. Pract. 2008, 15, 153–168. [Google Scholar] [CrossRef]
  50. Ahto, M.; Isoaho, R.; Puolijoki, H.; Vahlberg, T.; Kivelä, S.L. Stronger symptoms of depression predict high coronary heart disease mortality in older men and women. Int. J. Geriatr. Psychiatry 2007, 22, 757–763. [Google Scholar] [CrossRef] [PubMed]
  51. Bijl, R.V.; De Graaf, R.; Ravelli, A.; Smit, F.; Vollebergh, W.A.; Netherlands Mental Health Survey and Incidence Study. Gender and age-specific first incidence of DSM-III-R psychiatric disorders in the general population. Results from the Netherlands Mental Health Survey and Incidence Study (NEMESIS). Soc. Psychiatry Psychiatr. Epidemiol. 2002, 37, 372–379. [Google Scholar] [CrossRef] [PubMed]
  52. Batty, G.D.; Hunt, K.; Emslie, C.; Lewars, H.; Gale, C.R. Alcohol problems and all-cause mortality in men and women: Predictive capacity of a clinical screening tool in a 21-year follow-up of a large, UK-wide, general population-based survey. J. Psychosom. Res. 2009, 66, 317–321. [Google Scholar] [CrossRef] [PubMed]
  53. Wilsnack, R.W.; Vogeltanz, N.D.; Wilsnack, S.C.; Harris, T.R.; Ahlström, S.; Bondy, S.; Csémy, L.; Ferrence, R.; Ferris, J.; Fleming, J.; et al. Gender differences in alcohol consumption and adverse drinking consequences: Cross-cultural patterns. Addiction 2000, 95, 251–265. [Google Scholar] [CrossRef] [PubMed]
  54. Azorin, J.M.; Belzeaux, R.; Fakra, E.; Kaladjian, A.; Hantouche, E.; Lancrenon, S.; Adida, M. Gender differences in a cohort of major depressive patients: Further evidence for the male depression syndrome hypothesis. J. Affect. Disord. 2014, 167, 85–92. [Google Scholar] [CrossRef] [PubMed]
  55. Schuch, J.J.; Roest, A.M.; Nolen, W.A.; Penninx, B.W.; de Jonge, P. Gender differences in major depressive disorder: Results from the Netherlands study of depression and anxiety. J. Affect. Disord. 2014, 156, 156–163. [Google Scholar] [CrossRef] [PubMed]
  56. Plochg, T.; Klazinga, N.S.; Starfield, B. Transforming medical professionalism to fit changing health needs. BMC Med. 2009, 7, 64. [Google Scholar] [CrossRef] [PubMed]
  57. Stange, K.C. The generalist approach. Ann. Fam. Med. 2009, 7, 198–203. [Google Scholar] [CrossRef] [PubMed]
  58. WHO. Gender Disparities in Mental Health. Available online: http://www.who.int/mental_health/media/en/242.pdf (accessed on 12 October 2015).
Ijerph 13 00391 g001 1024
Figure 1. Per cent of people, by type of multimorbidity (physical only, mental only and mixed) and by sex and age.
Figure 1. Per cent of people, by type of multimorbidity (physical only, mental only and mixed) and by sex and age.
Ijerph 13 00391 g001
Table
Table 1. Number of conditions by age group and sex.
Table 1. Number of conditions by age group and sex.
Age GroupNo ConditionsOne ConditionTwo Conditions
Women No. (%)Men No. (%)Difference (95% CI)Women No. (%)Men No. (%)Difference (95% CI)Women No. (%)Men No. (%)Difference (95% CI)
25–3478,941 (70.1)90,806 (77.7)−7.6 (−7.2 to −7.9)22,701 (20.2)18,261 (15.6)4.6 (4.2 to 4.9)10,911 (9.7)7776 (6.7)3.0 (2.8 to 3.2)
35–4480,309 (58.9)99,029 (69.4)−10.5 (−10.1 to −10.8)32,966 (24.1)27,805 (19.5)4.6 (4.3 to 5.0)23,025 (16.9)15,859 (11.1)5.8 (5.5 to 6.0)
45–5459,328 (47.3)72,592 (56.4)−9.1 (−8.7 to −9.5)32,751 (26.1)30,702 (23.9)2.2 (1.9 to 2.5)33,230 (26.5)25,191 (19.6)6.9 (6.5 to 7.2)
55–6435,128 (31.9)40,715 (37.2)−5.3 (−4.8 to −5.7)29,120 (26.4)29,051 (26.6)−0.2 (0.2 to −0.4)45,737 (41.6)39,582 (36.2)5.4 (4.9 to 5.7)
65–7414,868 (18.0)13,794 (18.9)−0.9 (−0.5 to −1.2)18,567 (22.5)16,501 (22.7)−0.2 (0.2 to −0.5)49,009 (59.4)42,541 (58.0)1.4 (0.5 to 1.5)
75 plus6989 (8.3)4864 (9.4)−1.1 (−0.7 to −1.3)12,532 (14.9)8058 (15.5)−0.6 (−0.1 to −1.0)64,491 (76.7)38,955 (75.1)1.6 (1.2 to 2.1)
Analysis based on 40 chronic conditions; 32 physical and 8 mental, percentages in the conditions columns relate distribution within each separate age group by sex.
Table
Table 2. Differences in type of multimorbidity by age group and sex.
Table 2. Differences in type of multimorbidity by age group and sex.
Age GroupPhysical only MultimorbidityMental only MultimorbidityMixed Physical and Mental Multimorbidity
Women No. (%)Men No. (%)Difference (95% CI)Women No. (%)Men No. (%)Difference (95% CI)Women No. (%)Men No. (%)Difference (95% CI)
25–343185 (2.8)1925 (1.6)1.2 (1.0 to 1.3)1847 (1.6)2172 (1.8)−0.2 (−0.1 to −0.3)5879 (5.2)3679 (3.1)2.1 (1.9 to 2.2)
35–447785 (5.7)5455 (3.8)1.9 (1.7 to 3.0)2367 (1.7)2620 (1.8)−0.1 (−0.0 to −0.2)12,873 (9.4)7784 (5.5)3.9 (3.7 to 4.1)
45–5413,999 (11.1)12,643 (9.8)1.3 (1.1 to 1.5)1659 (1.3)1479 (1.1)0.2 (0.0 to 0.3)17,572 (14.0)11,069 (8.6)5.4 (5.1 to 5.7)
55–6425,175 (22.9)24,596 (22.5)0.4 (0.0 to 0.7)891 (0.08)735 (0.07)0.01 (0.00 to 0.06)19,671 (17.9)14,251 (13.0)4.9 (4.5 to 5.1)
65–7431,711 (38.5)30,825 (42.3)−3.8 (−3.3 to −4.3)420 (0.05)245 (0.03)0.02 (0.01 to 0.03)16,878 (20.5)11,471 (15.7)4.8 (4.3 to 5.1)
75 plus38,088 (45.3)27,824 (53.5)−8.3 (−7.7 to −8.7)459 (0.05)127 (0.02)0.03 (0.02 to 0.04)25,944 (30.9)11,004 (21.2)9.7 (9.1 to 10.2)
Analysis based on 40 chronic conditions; 32 physical and 8 mental. Percentages are the % in each age group by sex.
Table
Table 3. Differences by age and sex in the top 10 most prevalent conditions for multimorbid patients.
Table 3. Differences by age and sex in the top 10 most prevalent conditions for multimorbid patients.
Rank Order of ConditionsAge 25–34 (Women)
% (95% CI)
n = 10,911		Age 25–34 (Men)
% (95% CI)
n = 7776		Age 35–44 (Women)
% (95% CI)
n = 23,025		Age 35–44 (Men)
% (95% CI)
n = 15,859		Age 45–54 (Women)
% (95% CI)
n = 33,230		Age 45–54 (Men)
% (95% CI)
n = 25,191
1Depression
52.9 (51.9–53.8)		Drugs Misuse
38.5 (37.4–39.6)		Depression
53.7 (53.1–54.3)		Depression
36.9 (36.1–37.7)		Depression
46.5 (46.0–47.0)		Hypertension
36.0 (35.4–36.6)
2Asthma
27.6 (26.8–28.4)		Depression
36.4 (35.3–37.4)		Pain
28.4 (27.8–29.0)		Pain
23.5 (22.8–24.1)		Pain
32.0 (31.5–32.5)		Depression
28.5 (27.9–28.0)
3Pain
21.4 (20.6–22.1)		Alcohol dependence
22.8 (21.8–23.7)		Asthma
22.6 (22.0–23.1)		Drugs Misuse
22.4 (21.8–23.1)		Hypertension
27.0 (26.5–27.5)		Pain
26.4 (25.9–26.9)
4IBS
19.9 (19.1–20.6)		Asthma
22.1 (21.1–23.0)		IBS
20.2 (19.7–20.7)		Alcohol dependence
21.8 (21.2–22.4)		Thyroid
19.5 (19.1–19.9)		Alcohol dependence
19.0 (18.5–19.5)
5Anxiety
19.5 (18.7–20.2)		Anxiety
20.4 (19.5–21.3)		Anxiety
18.2 (17.7–18.7)		Asthma
19.2 (18.6–19.8)		Asthma
18.7 (18.3–19.1)		Dyspepsia
18.9 (18.4–19.4)
6Drugs Misuse
16.9 (16.1–17.6)		Pain
16.0 (15.1–16.7)		Thyroid
15.7 (15.2–16.1)		Dyspepsia
19.1 (18.4–19.7)		Dyspepsia
18.1 (17.7–18.5)		Diabetes
17.8 (17.3–18.3)
7Thyroid
11.0 (10.3–11.5)		Dyspepsia
12.4 (11.6–13.1)		Dyspepsia
14.6 (14.1–15.1)		Anxiety
17.2 (16.6–19.8)		IBS
17.8 (17.4–18.2)		Asthma
14.4 (14.0–14.8)
8Dyspepsia
9.0 (8.5–9.5)		Schizophren/Bi-polar
8.8 (8.1–9.4)		Hypertension
11.6 (11.2–12.0)		Hypertension
16.6 (16.0–17.2)		Anxiety
15.5 (15.0–15.8)		Inflammatory arthritis
12.6 (12.2–13.0)
9Alcohol dependence
8.6 (8.1–9.1)		Hearing loss
7.9 (7.3–8.5)		Drugs Misuse
9.5 (9.1–9.8)		Diabetes
10.5 (10.0–11.0)		Diabetes
10.7 (10.4–11.0)		Anxiety
11.2 (10.8–11.6)
10Hearing loss
6.2 (3.2–4.0)		IBS
6.7 (6.1–7.3)		Alcohol dependence
7.4 (7.1–7.7)		IBS
8.0 (7.6–8.4)		Inflammatory arthritis
9.5 (9.2–9.8)		CHD
10.9 (10.5–11.3)
Rank order of conditionsAge 55–64 (Women)
% (95% CI)
n = 47,727		Age 55–64 (Men)
% (95% CI)
n = 39,852		Age 65–74 (Women)
% (95% CI)
n = 49,009		Age 65–74 (Men)
% (95% CI)
n = 42,541		Age 75&over (Women)
% (95% CI)
n = 64,491		Age 75& over (Men)
% (CI 95%)
n = 38,955
1Hypertension
46.6 (46.1–47.0)		Hypertension
50.8 (50.3–51.2)		Hypertension
59.3 (58.8–59.7)		Hypertension
57.1 (56.7–57.6)		Hypertension
64.4 (64.0–64.7)		Hypertension
57.9 (57.3–58.4)
2Depression
34.7 (34.2–35.1)		Pain
27.8 (27.4–28.2)		Pain
33.5 (33.0–33.9)		CHD
33.2 (32.8–33.7)		CHD
26.6 (26.3–26.9)		CHD
38.8 (38.2–39.2)
3Pain
34.5 (34.0–34.9)		CHD
22.2 (21.8–22.6)		Depression
23.5 (23.2–23.9)		Pain
25.9 (25.5–26.4)		Pain
26.3 (26.0–26.6)		Diabetes
19.9 (19.5–20.3)
4Thyroid
21.8 (21.4–22.2)		Diabetes
21.8 (21.3–22.3)		Thyroid
22.4 (22.1–22.8)		Diabetes
24.1 (23.7–24.5)		Thyroid
21.5 (21.2–21.8)		Stroke-TIA
19.2 (18.8–19.5)
5Dyspepsia
18.5 (18.1–18.8)		Depression
20.3 (19.9–20.7)		CHD
19.9 (19.6 –20.3)		COPD
15.1 (14.8–15.4)		Depression
20.7 (20.4–21.0)		Pain
19.1 (18.7–19.5)
6Asthma
14.7 (14.3–15.0)		Dyspepsia
15.6 (15.2–16.0)		Diabetes
18.4 (18.1–18.8)		Inflammatory arthritis
14.1 (13.8–14.5)		Chronic Kidney Disease
19.4 (19.1–19.7)		Hearing Loss
18.0 (17.6–18.4)
7Diabetes
14.2 (13.9–14.5)		Alcohol dependence
14.6 (14.3–14.9)		Dyspepsia
18.0 (17.7–18.4)		Dyspepsia
13.5 (13.1–13.8)		Constipation
18.0 (17.7–18.3)		Chronic Kidney Disease
16.8 (16.5–17.1)
8Irritable bowel syndrome
13.3 (13.0–13.6)		Inflammatory arthritis
14.3 (14.0–14.6)		COPD
14.2 (13.9–14.5)		Stroke-TIA
12.9 (12.6–13.2)		Dyspepsia
16.4 (16.1–16.7)		COPD
16.6 (16.2–16.9)
9Anxiety
13.1 (12.8–13.4)		COPD
10.7 (10.4–11.0)		Inflammatory arthritis
13.4 (13.0–13.84)		Depression
12.7 (12.4–13.0)		Anxiety
15.9 (15.6–16.1)		Any Cancer
15.5 (15.1–15.8)
10Inflammatory poly-arthropathy
12.5 (12.2–12.8)		Asthma
10.2 (9.9–10.5)		Anxiety
13.1 (12.8–13.4)		Hearing Loss
12.2 (11.8–12.5)		Diabetes
15.5 (15.2–15.8)		Constipation
15.2 (14.8–15.5)
Abbreviations: Coronary Heart Disease (CHD); Chronic obstructive pulmonary disease (COPD); Irritable bowel syndrome (IBS).

Share and Cite

MDPI and ACS Style

Agur, K.; McLean, G.; Hunt, K.; Guthrie, B.; Mercer, S.W. How Does Sex Influence Multimorbidity? Secondary Analysis of a Large Nationally Representative Dataset. Int. J. Environ. Res. Public Health 2016, 13, 391. https://doi.org/10.3390/ijerph13040391

AMA Style

Agur K, McLean G, Hunt K, Guthrie B, Mercer SW. How Does Sex Influence Multimorbidity? Secondary Analysis of a Large Nationally Representative Dataset. International Journal of Environmental Research and Public Health. 2016; 13(4):391. https://doi.org/10.3390/ijerph13040391

Chicago/Turabian Style

Agur, Karolina, Gary McLean, Kate Hunt, Bruce Guthrie, and Stewart W. Mercer. 2016. "How Does Sex Influence Multimorbidity? Secondary Analysis of a Large Nationally Representative Dataset" International Journal of Environmental Research and Public Health 13, no. 4: 391. https://doi.org/10.3390/ijerph13040391

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop