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Category Archives: Epidemiology and outcomes research

How useful are multimorbidity ‘clusters’ likely to be for important health system outcomes?

By Jonathan Stokes (left), Bruce Guthrie (center), Stewart W. Mercer (right), Nigel Rice, Matt Sutton

“The problem of multimorbidity,” both for individual patients and for health systems, has been well defined. Multimorbidity is now a well-established priority for research and medical practice [1, 2]. But, there has been little success to date in developing effective or cost-effective new models of care for these patients [3].

Multimorbidity is very different from traditional single disease intervention, however. For multimorbidity, there is huge heterogeneity with many possible combinations of conditions potentially requiring different approaches to management. For example, there are over 268 million possible unique combinations if considering 28 individual conditions. Researchers have therefore begun to search for more useful approaches to dealing with multimorbidity, beyond a count of accumulated conditions, but still simplified to a manageable handful of subgroupings, a focus on “clusters”. The hope is this approach might (i) identify target clusters for direct intervention, or, (ii) via further research on aetiological mechanisms, target clusters for preventing disease accumulation.

Two statistical approaches, (1) cluster analysis (grouping diseases), and (2) latent factor analysis (grouping patients), are commonly used to examine clusters in the general population to accomplish this stratification [4]. However, both approaches have inherent methodological and clinical/intervention complexity. To summarise, on a practical level, they risk producing results that are too abstract (e.g. unobservable latent variables) and generally over-simplifying (e.g. to a handful of combinations when there are actually a huge number that matter) compared to what can actually be observed and acted on in clinical practice: symptoms, signs, and conditions. It is also not obvious that highly prevalent clusters in the general population will be the same combinations associated with outcomes that place most pressure on the supply constraints of healthcare systems, such as costs of (potentially preventable) emergency admissions and overall costs of secondary care.

We drew instead on more simple descriptive analysis to assess all observable condition combinations and their (potentially preventable) secondary care costs [5]. We examined the distribution and top 10 unique multimorbidity combinations contributing to total secondary care costs for a cohort of patients, all (over 8 million) patients with an NHS inpatient admission in England in 2017/18. As well as contribution to total system costs, we examined the combinations with particularly high costs for individual patients. Finally, multimorbidity is dynamic and conditions can accumulate over time, so we looked at the sum of costs for all overlapping conditions in the top 10 (e.g., summing costs for all unique combinations containing, at least, diabetes + hypertension), which might offer priority targets for prevention of disease accumulation.

The main limitations were a focus on a single outcome (costs), in a single healthcare setting (secondary care), with potential under-recording of conditions. However, conditions appeared to be well-recorded and we attempted to backfill missing, healthcare costs are an extremely important outcome for policymakers, and secondary care is the highest cost healthcare setting.

Key findings/implications for policy and practice:

• There are no clear discrete disease combinations at which to target interventions, which implies a generalist/multidisciplinary team approach will remain important rather than pathways/guidelines based on a few specific disease clusters.
• Combinations containing the highest cost patients (the current focus of many interventions) were different to those accounting for the highest total costs, implying the need to develop interventions beyond only high-risk patients.
• There might be scope to use clusters to understand and develop preventative interventions, but focusing on addressing well-known disease risk factors (such as obesity, diet, exercise, and deprivation) with public health/primary care interventions might provide the most efficient route to benefit systems financially and benefit many patients with multimorbidities.

These findings also have implications for researchers/research funders, a need to re-examine how much emphasis is placed on research exploring clusters of multimorbidity, and for which specified reasons.

The article can be assessed at the following link: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003514


  1. Academy of Medical Sciences. Multimorbidity: a priority for global health research. 2018.
  2. Whitty CJM, MacEwen C, Goddard A, Alderson D, Marshall M, Calderwood C, et al. Rising to the challenge of multimorbidity. BMJ. 2020;368:l6964. doi: 10.1136/bmj.l6964.
  3. Smith SM, Soubhi H, Fortin M, Hudon C, O’Dowd T. Interventions for improving outcomes in patients with multimorbidity in primary care and community settings. Cochrane Database Syst Rev. 2016;4.
  4. Ng SK, Tawiah R, Sawyer M, Scuffham P. Patterns of multimorbid health conditions: a systematic review of analytical methods and comparison analysis. Int J Epidemiol. 2018;47(5):1687-704. Epub 2018/07/18. doi: 10.1093/ije/dyy134. PubMed PMID: 30016472.
  5. Stokes J, Guthrie B, Mercer SW, Rice N, Sutton M. Multimorbidity combinations, costs of hospital care and potentially preventable emergency admissions in England: A cohort study. PLOS Medicine. 2021;18(1):e1003514. doi: 10.1371/journal.pmed.1003514.

A prognostic model predicted deterioration in health-related quality of life in older patients with multimorbidity and polypharmacy: Findings from the PROPERmed individual participant data meta-analysis.

By Ana I. González (picture on the left), Andreas Meid (picture in the middle), Truc S Dinh, Jeanet W Blom, Marjan van den Akker, Petra JM Elder, Ulrich Thiem, Daniela Küllenberg de Gaudry, Kym Snell, Rafael Perera, Karin MA Swart, Henrik Rudolf, Hans-Joachim Trampish, Joerg J Meerpohl, Benno Flaig, Ghaisom Kom, Walter E Haefeli, Paul P Glasziou, Ferdinand M Gerlach and Christiane Muth (picture on the right*)

Multimorbidity and polypharmacy increase the risk for inappropriate prescriptions and underuse of appropriate medication which may lead to patient deterioration in health-related quality of life (dHRQoL) (1,2). In this heterogeneous general practice population, it would be helpful to identify those patients at high risk of dHRQoL since they may benefit most from complex interventions designed to improve their well-being (3–5).

We aimed to develop and validate a prognostic model to predict dHRQoL at six-month follow-up in older patients with at least one chronic condition and one chronic prescription in general practice (6).

We harmonized individual participant data from five cluster-randomized trials from the Netherlands and Germany. dHRQoL was defined as a decrease in the EQ-5D-3L index score of at least 5% from baseline to 6-month follow-up. Prognostic variables included baseline socio-demographics and lifestyle, morbidity, medication, functional status and well-being related variables. The model was developed using logistic regression with a stratified-intercept to account for between-study heterogeneity in baseline risk. Prognostic variables were selected in complete cases and then refitted in multiply imputed data to obtain the final model equation. Internal validation was performed using bootstrapping within studies to assess reproducibility and internal internal-external cross-validation (IECV) was used to evaluate generalisability.

The complete-case population consisted of 3,582 patients. In 1,046 (29%) patients, health-related quality of life (HRQoL) deteriorated at six-month follow-up. Selected baseline variables contributing significantly to the prediction related to single conditions (i.e. coronary heart disease), prescribed medication (i.e. drugs for acid-related disorders), inappropriate medication (i.e. systemic corticosteroids for maintenance in COPD), medication underuse (e.g. angiotensin converting enzyme inhibitors in heart failure), functional status, and well-being (i.e. HRQoL at baseline and depression), with most prognostic relevance attributable to baseline HRQoL and functional status. Bootstrap internal validation of the final model showed a C-statistic of 0.71 (0.69 to 0.72) and a calibration slope of 0.88 (0.78 to 0.98). With the trials as validation datasets in the IECV loop, the final model provided a pooled C-statistic of 0.68 (0.65 to 0.70) and calibration-in-the-large of 0 (-0.13 to 0.13).

This first IPD-based prognostic model for dHRQoL in older patients with multiple chronic conditions and medication in general practice performed well in discrimination, calibration, and generalisability and might thus help clinicians identify older patients at high risk of dHRQoL.

This work was supported by the German Innovation Funds according to § 92a (2) Volume V of the Social Insurance Code (§ 92a Abs. 2, SGB V – Fünftes Buch Sozialgesetzbuch), grant number: 01VSF16018.The funder had no role in developing the protocol for this review.

The abstract of the article can be accessed at the following link: https://pubmed.ncbi.nlm.nih.gov/33065164/

*Photo: Universität Bielefeld/S. Jonek


  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. Lancet. 2012;380(9836):37–43.
  2. Saqlain M, Ali H, Kamran S, Munir MU, Jahan S, Mazhar F. Potentially inappropriate medications use and its association with health-related quality of life among elderly cardiac patients. Qual Life Res. 2020 May 20;
  3. Romskaug R, Skovlund E, Straand J, Molden E, Kersten H, Pitkala KH, et al. Effect of Clinical Geriatric Assessments and Collaborative Medication Reviews by Geriatrician and Family Physician for Improving Health-Related Quality of Life in Home-Dwelling Older Patients Receiving Polypharmacy. JAMA Intern Med. 2020 Feb 1;180(2):181.
  4. Rankin A, Cadogan CA, Patterson SM, Kerse N, Cardwell CR, Bradley MC, et al. Interventions to improve the appropriate use of polypharmacy for older people. Cochrane Database Syst Rev. 2018 Sep 3;
  5. Smith SM, Wallace E, O’Dowd T, Fortin M. Interventions for improving outcomes in patients with multimorbidity in primary care and community settings. Cochrane Database Syst Rev [Internet]. 2016 Mar 15; Available from: http://doi.wiley.com/10.1002/14651858.CD006560.pub3
  6. González-González AI, Meid AD, Dinh TS, Blom JW, van den Akker M, Elders PJM, et al. A prognostic model predicted deterioration in health-related quality of life in older patients with multimorbidity and polypharmacy. J Clin Epidemiol. 2021;130:1–12.

Role of clinical, functional and social factors in the association between multimorbidity and quality of life: Findings from the Survey of Health, Ageing and Retirement in Europe (SHARE)

By Tatjana T. Makovski (picture on the left), Gwenaëlle Le Coroller, Polina Putrik, Yun Hee Choi, Maurice P. Zeegers, Saverio Stranges, Maria Ruiz Castell, Laetitia Huiart, Marjan van den Akker (picture on the right)

Quality of life (QoL) is often mentioned among the main consequences of multimorbidity and it has been set as one of the core outcomes in multimorbidity research [1].

In exploring the association between multiple conditions and quality of life, researchers most often account for socio-economic factors [2], although it has been recognised a while ago that other aspects such as perceived social support [3] and limitation with activities of daily living [4] play a significant role in this relationship.

With the current study, we intended to underline the relevance of some of these factors, as well as to test other elements for significance. Factors of interest were: symptoms; indicators of treatment burden such as polypharmacy, unmet care needs and frequency of utilisation of care; size of social network, participation in social activities, personal and financial help, loneliness as a proxy for perceived social support [5]; and activities of daily living (ADL) with instrumental activities (IADL).

The study included individuals aged 50+ (n = 67 179) in 18 countries who participated in wave 6 of the population-based Survey of Health, Ageing and Retirement in Europe (SHARE). Wave 6 measured presence of 17+ conditions and applied the Control, Autonomy, Self-Realization and Pleasure (CASP-12v1) QoL questionnaire. We used 3-level random slope linear regression model to test for the association between number of diseases and QoL. The base model adjusted for socio-economic factors only, while factors of interest were subsequently added in the base model, one at the time. A change of ≥15% in the β-coefficient of the number of conditions compared to the β-coefficient in the base model indicated a relevant effect on the association.

Symptoms, loneliness, ADL/IADL and polypharmacy instigated a set change of the coefficient in the models individually. Adding all relevant factors together in the final model attenuated the strength of the association between number of diseases and QoL, as demonstrated with QoL slope of -2.44 [95% CI: -2.72; -2.16] in the base model and much lesser but still statistically significant decline of -0.76 [95%CI: -0.97; -0.55] in the final model.

The study confirmed that other factors beyond socio-economic circumstances explain the relationship between multimorbidity and QoL, and should be considered when further exploring this question. Maybe one of the most interesting contributions of the paper is including elements of the treatment burden as an adjustment factor in the analyses. Treatment burden is gaining increasingly on research interest. It is abundant in patients with multimorbidity and is likely to take its share in the association between multiple conditions and QoL.

The findings may be useful in supporting a comprehensive assessment of patient’s health status and needs during a personalised care planning.

SHARE countries also displayed interesting differences in the findings.

The article can be assessed at the following link:

  1. Smith SM, Wallace E, Salisbury C, Sasseville M, Bayliss E, Fortin M. A Core Outcome Set for Multimorbidity Research (COSmm). Ann Fam Med. 2018;16(2):132-138.
  2. Makovski TT, Schmitz S, Zeegers MP, Stranges S, van den Akker M. Multimorbidity and quality of life: Systematic literature review and meta-analysis. Ageing Res Rev. 2019;53:100903.
  3. Fortin M, Bravo G, Hudon C, et al. Relationship between multimorbidity and health-related quality of life of patients in primary care. Qual Life Res. 2006;15(1):83-91.
  4. Barile JP, Thompson WW, Zack MM, Krahn GL, Horner-Johnson W, Haffer SC. Activities of daily living, chronic medical conditions, and health-related quality of life in older adults. J Ambul Care Manage. 2012;35*(4):292-303.
  5. Bernardon S, Babb KA, Hakim-Larson J, Gragg M. Loneliness, attachment, and the perception and use of social support in university students. Can J Behav Sci. 2011;43(1):40-51.

Middle-aged men with multimorbidity at greatest risk of death

By Bhautesh Jani and Frances Mair

Our study published in BMC Medicine [1], found that multimorbidity is associated with a higher risk of death from cancer, vascular conditions and all causes of death – even after accounting for lifestyle or demographic factors. The effect of multiple long-term conditions (LTCs) on higher mortality risk was largest among men between 37-49 years.

The study used the UK Biobank cohort (approx. half million adults) and found that the type of LTC, as opposed to the number of LTC, may have an important role to play in understanding the relationship between multimorbidity and death.

This is the first study to examine the relationship of multimorbidity with cancer mortality and we have shown a dose-response relationship between number of LTCs and cancer mortality.

Younger participants, especially men, were observed to have a relatively higher risk of mortality with increasing number of LTCs, and that certain combinations of conditions were associated with a particularly higher risk of death. Going forward, further research is needed to study the impact and management of multimorbidity in middle aged adults, as they may be at higher risk of early death.

1. Jani BD, Hanlon P, Nicholl BI, et al. Relationship between multimorbidity, demographic factors and mortality: findings from the UK Biobank cohort. BMC Med 2019;17(1):74. doi: 10.1186/s12916-019-1305-x

Socio-economic inequalities in life expectancy of older adults with and without multimorbidity

By Madhavi Bajekal, on behalf of the UCL Multimorbidity Project Team
New paper by @ucl_dahr and colleagues [1]: https://doi.org/10.1093/ije/dyz052
Using linked electronic health data for a cohort followed up from 2001 to 2010, we have been able to quantify socio-economic differences in the age of onset of multimorbidity (MM) and, with subsequent mortality, how much of remaining life after age 65 are years lived with and without multiple chronic diseases.
A seminal study by Barnett et al [2] reported a 10y gap in the cross-sectional prevalence of MM at younger ages between the most and least deprived populations in Scotland in 2007.  Our study shows that in England the difference in MM incidence was again about 10y between the most deprived (Q5) and the least deprived (Q1) groups in middle-age (Fig 3), with rates converging at successively older ages.  Health- and life expectancy measures are based on incidence rates (of diseases and of subsequent death); we modelled these to estimate and partition remaining life into years with and without MM.
At age 65, men spend 6.9y without MM and 9.9y with MM; and women, 7.6 y and 11.7y respectively. Overall, men and women spend about 60% of their remaining life in old age with two or more chronic diseases, although this proportion varies by up to 5 percentage points above and below between the two ends of the deprivation spectrum (Tables 2 and 3).
We show that the most deprived groups live, on average, fewer years in total than the least deprived groups, but spend almost the same number of years with MM. In terms of healthcare costs this implies that, although average lifetime resource-spend for both groups might be of a similar magnitude, it is shifted to younger ages for the most deprived groups. The evidence adds weight to the argument calling for a shift to the standard age-cost curve used in the national resource allocation formula to younger ages in deprived areas to reflect greater need earlier in the life course [3].
1. Chan MS, van den Hout A, Pujades-Rodriguez M, Jones MM, Matthews FE, Jagger C, Raine R, Bajekal M: Socio-economic inequalities in life expectancy of older adults with and without multimorbidity: a record linkage study of 1.1 million people in England. Int J Epidemiol 2019.
2. 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. Lancet 2012, 380(9836):37–43.
3. Brilleman SL, Gravelle H, Hollinghurst S, Purdy S, Salisbury C, Windmeijer F: Keep it simple? Predicting primary health care costs with clinical morbidity measures. J Health Econ 2014, 35:109-122.

Frailty : Not just a problem for older people

By Peter Hanlon and Frances Mair
It is often said that many of the challenges faced in healthcare are due to ‘ageing populations’. It is clear, however, that health (and the need for health services) is not simply related to how old a person is. There are many other factors more closely linked to an individual’s need for care, many of which are related to age. These include multimorbidity – having two or more long-term health conditions – and frailty. Frailty is closely linked to multimorbidity, but the terms are not interchangeable.
Frailty describes a reduction in the body’s in-built reserves which is generally due to the cumulative effect of a range of individual deficits. People with frailty are therefore more at risk of developing significant illness, sometimes in response to relatively minor events or ‘stressors’. To provide high quality healthcare to people with frailty involves a holistic approach, considering the whole person and their wider context, rather than purely focusing on individual diseases in isolation. Managing frailty also takes considerable resource, as people may require additional support or services, and are more likely to require hospital admission.
Both frailty and multimorbidity are more common with increasing age, and therefore most research and interventions to improve care has focused on elderly people. It is also true, however, that the majority of people with multimorbidity are aged under 65 years. This is particularly true in areas of high socioeconomic deprivation. Despite this, the prevalence and effects of frailty at younger ages and in multimorbidity has not been investigated. Most studies, as well as most health services, that seek to target frailty have tended to exclude people aged less than 65 years, even though many people in this age group are affected by multimorbidity and may benefit from an approach to healthcare that reflects this.
Our recent study [1], published in The Lancet Public Health, seeks to address this research gap. It suggests that frailty affects ‘middle-aged’ as well as older people. We found that frailty, while strongly associated with multimorbidity, identifies middle aged people at increased risk of death, over-and-above known risk factors and number of long-term health conditions.
This study analyses frailty in a younger population than most previous research. We used data from the UK Biobank cohort – a large study of around 500,000 volunteers aged between 37 and 73 years. Participants in the study were considered ‘frail’ if they met three or more of the following criteria: weight loss, slow walking pace, low hand grip strength, low physical activity, and exhaustion. People with one or two of these features were considered ‘pre-frail’.
While frailty does get more common with increasing age, we found that people of all ages had the potential to be ‘frail’ using this definition. While only a small proportion of ‘middle-aged’ people were identified as frail by this definition – 3% overall – frailty was much more common in people with multimorbidity.  Of people with 2 or more long-term conditions, 7% were frail. This increased to 18% among people with 4 or more long-term conditions. Frailty was also closely linked with socioeconomic deprivation and obesity.
Frailty was associated with more than double the risk of death in men of all ages included (37 to 73 years) and in females above the age of 45 years. This was after accounting for deprivation, lifestyle factors such as smoking, obesity and alcohol, and the number of long-term conditions. Frailty, therefore, appears to carry additional risk of premature death in younger people, over-and-above the recognised risk factors such as smoking and multimorbidity. People with ‘pre-frailty’ also had an increased risk of death in all of these age groups.
These findings highlight the challenges faced by primary care teams caring for patients with complex problems and multimorbidity, many of whom may be too young to be eligible for existing services focusing on frailty in the elderly. This is particularly true in areas of high socioeconomic deprivation, where both multimorbidity and frailty among younger people is much more common.
This study shows that frailty may be identifiable at an earlier stage than is traditionally understood. This may, therefore, represent an opportunity to explore ways of intervening earlier. If this is to happen, researchers and healthcare professions will need to broaden their focus on frailty to include a wider age range. Importantly, it also highlights the need for a move away from disease focused to more person centred care that provides a more holistic approach to patient care that is tailored to meet an individual’s specific requirements.
Identifying frailty in those with multimorbidity may have positive implications for care, planning interventions and a patient’s prognosis.  We suggest integration of an assessment of frailty into the routine assessment of people with multimorbidity might help identification of those at greater risk and ensure more accurate targeting of the multidimensional, patient-centred reorganisation of care required to address complex multimorbidity.
There is a pressing need to understand frailty in younger people much more fully. When trying to provide services and care for people with frailty and multimorbidity it will be crucial to consider the needs of younger people (particularly those in areas of high socioeconomic deprivation). Our work demonstrates that frailty, like multimorbidity, is not just a problem that affects older people.
[1] Peter Hanlon, Barbara I Nicholl, Bhautesh Dinesh Jani, Duncan Lee, Ross McQueenie, Frances S Mair. Frailty and pre-frailty in middle-aged and older adults and its association with multimorbidity and mortality: a prospective analysis of 493 737 UK Biobank participants. Lancet Public Health 2018. Published Online June 13, 2018. http://dx.doi.org/10.1016/S2468-2667(18)30091-4.

Multimorbidity in an Australian street health service

By Tom Brett
The Freo Street Doctor service is a free, primary care–based, mobile health clinic that has been operating in Fremantle, Western Australia since 2005. It operates from various locations in and around Fremantle, offering homeless and disadvantaged patients access to an accredited general practice service. It is serviced by a number of general practitioners, nurses, social workers and Aboriginal health workers as well as collaborating with numerous ancillary services to improve the health and circumstances of marginalised patients in this population group.
We report on a total of 4285 patients who attended the service over a 10 year period [1]. We found multimorbidity to be associated with increasing age, male sex and Aboriginality. An important finding from our study is the high Aboriginal attendance, comprising 31.5% of the total cohort (with 50.8% female). This attendance ratio is in sharp contrast with the <2% Aboriginal patients attending mainstream GP clinics Australia-wide.
Our research shows that multimorbidity is increasing over the past decade and presents as chronic physical and mental health problems in these marginalised, street health patients. These patients are at increased risk of ongoing neglect unless provided with a no-cost, multidisciplinary approach capable of delivering health and social services in a non-judgemental, comfortable and secure environment.
The progressive increase in attendance by young, especially Aboriginal, patients over the past decade, and the positive feedback from patients and allied services attending the Freo Street Doctor, make compelling arguments that this accredited, general practice–based service is addressing important health and social needs in an environment where they are clearly needed.
1. Arnold-Reed D, Troeung L,  Brett T, Chan She Ping-Delfos W, Strange C, Geelhoed E, Fischer C,  Preen D. Increasing multimorbidity in an Australian street health service – a 10 year retrospective cohort study. AJGP. 2018; 47 (4): 181-189.

The EpiChron Cohort Study of Chronic Diseases and Multimorbidity

By Alexandra Prados Torres
I would like to share with you the profile of the EpiChron Cohort recently published in the International Journal of Epidemiology, a large-scale population-based study aimed at understanding how multimorbidity and the main chronic conditions appears and evolve in the population, and impact on health services and health outcomes. Created in 2010, it will gather information of the 1.3 M inhabitants of the Spanish region of Aragon until 2020. It has been developed by the EpiChron Research Group on Chronic Diseases from Aragon Health Sciences Institute (IACS) and IIS Aragón. This Cohort aims to study the problems associated to multimorbidity and chronicity (e.g., polypharmacy, low adherence to medical plan, increased risk of mortality, frailty, inappropriate health services use) and to identify risk factors (e.g., clinical, social, demographical) of negative health related outcomes. We also aim to study the evolution of trajectories of multimorbidity patterns over time and their impact on health outcomes with the final goal of developing predictive modeling tools. One key point of the project is to scaling up the knowledge in the area of chronicity and multimorbidity and to foster collaborations with other European and international research groups working in this area to conduct cross-national studies.
Besides the main characteristics of the EpiChron Cohort, this paper describes the data quality control process followed to ensure an adequate level of accuracy, reliability and appropriateness of data for research in multimorbidity.  Moreover, the main findings obtained to date are detailed in the paper.
The publication can be found in the following link: Prados-Torres et al 2018

Paper – Multimorbidity in Brazil

By Bruno P Nunes and Sandro R Rodrigues Batista
In this paper, we evaluate the magnitude of multimorbidity in 60202 Brazilian adults, including the assessment of individual and contextual (state level) variables. The Brazilian national-based study was carried out in 2013. Multimorbidity was evaluated by a list of 22 physical and mental morbidities. Factor analysis and multilevel models were used to analyze the data. Multimorbidity frequency was 22.2% for ≥2 morbidities and 10.2% for ≥3 morbidities. For the whole Brazilian population, at least 41 and 19 million adults had multimorbidity, according ≥2 and ≥3 morbidities, respectively. The occurrence was different among states, being higher in southern Brazil (see below the Supplementary_figure_1). Contextual and individual social inequalities were observed.
To access the manuscript, please click in the following link:
Reference: Nunes BP, Chiavegatto Filho ADP, Pati S, Cruz Teixeira DS, Flores TR, Camargo-Figuera FA, Munhoz TN, Thumé E, Facchini LA, Rodrigues Batista SR. Contextual and individual inequalities of multimorbidity in Brazilian adults: a cross-sectional national-based study BMJ Open 2017;7:e015885. doi: 10.1136/bmjopen-2017-015885

Perceived stress and multimorbidity

By Anders Prior
Multimorbidity and especially mental-physical multimorbidity is an increasing concern worldwide. It is well-known that psychiatric illness impairs the prognosis in persons with chronic physical disease. However, little is known on the impact of non-syndromic mental stress; mental stress is common in the general population, and psychological problems are an increasingly frequent reason for primary care contacts. In two studies, we aimed to determine whether perceived mental stress is associated with potentially preventable hospitalizations and all-cause mortality in persons with various degrees of multimorbidity.
The Danish Civil Registration System allowed us to individually link health survey data with prospectively collected data from Danish health registers creating a unique population-based cohort.  The Danish National Health Survey 2010 provided data on e.g. perceived stress and lifestyle factors on a representative sample of 118,000 Danish citizens aged 25 or older. Danish health registers provided data on hospitalizations, demographic and socioeconomic factors. We developed a new Danish multimorbidity index based on recorded diagnoses and redeemed medicine prescriptions on all Danish citizens identifying 39 mental and physical long-term conditions. We adjusted for and analyzed the modifying effect of multimorbidity on the study outcomes.
In general, we found that high stress perception was associated with multimorbidity, an increased number of potentially preventable hospitalizations and increased all-cause mortality after adjusting for mental-physical multimorbidity, socioeconomic factors and lifestyle where appropriate, and there often seemed to be dose-response relations. In absolute numbers, persons with multimorbidity had a poorer prognosis and psychiatric conditions aggravated this.
This may be the first step to understand the impact of mental stress on physical health, to discuss mental stress in a general practice setting, and to create the foundation for developing potential interventions and practice guidelines for patients with stress in general practice. Hopefully, this may lead to better care and improved life expectancy of people with stress and chronic disease.
Prior A, Fenger-Grøn M, Larsen KK, et al. The association between perceived stress and mortality among people with multimorbidity: A prospective population-based cohort study. Am J Epidemiol. 2016;184(3):199-210.
Prior A, Vestergaard M, Davydow DS, et al. Perceived stress, multimorbidity, and risk for hospitalizations for ambulatory care-sensitive conditions: A population-based cohort study. Med Care. 2017;55(2):131-139.