Martin Fortin

Prevalence of Multimorbidity Among Adults Seen in Family Practice

PURPOSE There are few valid data that describe the extent of multimorbidity in primary care patients. The purpose of this study was to estimate its prevalence in family practice patients by counting the number of chronic medical conditions and using a measure that considers the severity of these conditions, the Cumulative Illness Rating Scale (CIRS). METHODS The study was carried out in the Saguenay region (Québec, Canada) in 2003. The participation of adult patients from 21 family physicians was solicited during consecutive consultation periods. A research nurse reviewed medical records and extracted the data regarding chronic illnesses. For each chronic condition, a severity rating was determined in accordance with the CIRS scoring guidelines. RESULTS The sample consisted of 320 men and 660 women. Overall, 9 of 10 patients had more than 1 chronic condition. The prevalence of having 2 or more medical conditions in the 18- to 44-year, 45- to 64-year, and 65-year and older age-groups was, respectively, 68%, 95%, and 99% among women and 72%, 89%, and 97% among men. The mean number of conditions and mean CIRS score also increased significantly with age. CONCLUSIONS Whether measured by simply counting the number of conditions or using the CIRS, the prevalence of multimorbidity is quite high and increases significantly with age in both men and women. Patients with multimorbidity seen in family practice represent the rule rather than the exception.

Multimorbidity and quality of life in primary care: a systematic review

BackgroundMany patients with several concurrent medical conditions (multimorbidity) are seen in the primary care setting. A thorough understanding of outcomes associated with multimorbidity would benefit primary care workers of all disciplines. The purpose of this systematic review was to clarify the relationship between the presence of multimorbidity and the quality of life (QOL) or health-related quality of life (HRQOL) of patients seen, or likely to be seen, in the primary care setting.MethodsMedline and Embase electronic databases were screened using the following search terms for the reference period 1990 to 2003: multimorbidity, comorbidity, chronic disease, and their spelling variations, along with quality of life and health-related quality of life. Only descriptive studies relevant to primary care were selected.ResultsOf 753 articles screened, 108 were critically assessed for compliance with study inclusion and exclusion criteria. Thirty of these studies were ultimately selected for this review, including 7 in which the relationship between multimorbidity or comorbidity and QOL or HRQOL was the main outcome measure. Major limitations of these studies include the lack of a uniform definition for multimorbidity or comorbidity and the absence of assessment of disease severity. The use of self-reported diagnoses may also be a weakness. The frequent exclusion of psychiatric diagnoses and presence of potential confounding variables are other limitations. Nonetheless, we did find an inverse relationship between the number of medical conditions and QOL related to physical domains. For social and psychological dimensions of QOL, some studies reveal a similar inverse relationship in patients with 4 or more diagnoses.ConclusionsOur findings confirm the existence of an inverse relationship between multimorbidity or comorbidy and QOL. However, additional studies are needed to clarify this relationship, including the various dimensions of QOL affected. Those studies must employ a clear definition of multimorbidity or comorbidity and valid ways to measure these concepts in a primary care setting. Pursuit of this research will help to better understand the impact of chronic diseases on patients.

A Systematic Review of Prevalence Studies on Multimorbidity: Toward a More Uniform Methodology

PURPOSE We sought to identify and compare studies reporting the prevalence of multimorbidity and to suggest methodologic aspects to be considered in the conduct of such studies. METHODS We searched the literature for English- and French-language articles published between 1980 and September 2010 that described the prevalence of multimorbidity in the general population, in primary care, or both. We assessed quality of included studies with a modified version of the Strengthening the Reporting of Observational Studies in Epidemiology checklist. Results of individual prevalence studies were adjusted so that they could be compared graphically. RESULTS The final sample included 21 articles: 8 described studies conducted in primary care, 12 in the general population, and 1 in both. All articles were of good quality. The largest differences in prevalence of multimorbidity were observed at age 75 in both primary care (with prevalence ranging from 3.5% to 98.5% across studies) and the general population (with prevalence ranging from 13.1% to 71.8% across studies). Apart from differences in geographic settings, we identified differences in recruitment method and sample size (primary care: 980–60,857 patients; general population: 1,099–316,928 individuals), data collection, and the operational definition of multimorbidity used, including the number of diagnoses considered (primary care: 5 to all; general population: 7 to all). This last aspect seemed to be the most important factor in estimating prevalence. CONCLUSIONS Marked variation exists among studies of the prevalence of multimorbidity with respect to both methodology and findings. When undertaking such studies, investigators should carefully consider the specific diagnoses included and their number, as well as the operational definition of multimorbidity.

Relationship Between Multimorbidity and Health-Related Quality of Life of Patients in Primary Care

Previous studies about the association of multimorbidity and the health-related quality of life (HRQOL) in primary-care patients are limited because of their reliance on simple counts of diseases from a limited list of diseases and their failure to assess the severity of disease. We evaluated the association while taking into account the severity of the medical conditions based on the Cumulative Illness Rating Scale (CIRS) score, and controlling for potential confounders (age, sex, household income, education, self-perception of economic status, number of people living in the same dwelling, and perceived social support). We randomly selected 238 patients to construct quintiles of increasing multimorbidity (CIRS). Patients completed the 36-item Medical Outcomes study questionnaire (SF-36) to evaluate their HRQOL. Applying bivariate and multivariate linear regression analyses, we used the CIRS as either a continuous or a categorical (quintiles) variable. Use of the CIRS revealed a stronger association of HRQOL with multimorbidity than using a simple count of chronic conditions. Physical more than mental health deteriorated with increasing multimorbidity. Perceived social support and self-perception of economic status were significantly related to all scales of the SF-36 (p < 0.05). Increased multimorbidity adversely affected HRQOL in primary-care adult patients, even when confounding variables were controlled for.

Managing patients with multimorbidity : systematic review of interventions in primary care and community settings

Objective To determine the effectiveness of interventions designed to improve outcomes in patients with multimorbidity in primary care and community settings. Design Systematic review. Data sources Medline, Embase, CINAHL, CAB Health, Cochrane central register of controlled trials, the database of abstracts of reviews of effectiveness, and the Cochrane EPOC (effective practice and organisation of care) register (searches updated in April 2011). Eligibility criteria Randomised controlled trials, controlled clinical trials, controlled before and after studies, and interrupted time series analyses reporting on interventions to improve outcomes for people with multimorbidity in primary care and community settings. Multimorbidity was defined as two or more chronic conditions in the same individual. Outcomes included any validated measure of physical or mental health and psychosocial status, including quality of life outcomes, wellbeing, and measures of disability or functional status. Also included were measures of patient and provider behaviour, including drug adherence, utilisation of health services, acceptability of services, and costs. Data selection Two reviewers independently assessed studies for eligibility, extracted data, and assessed study quality. As meta-analysis of results was not possible owing to heterogeneity in participants and interventions, a narrative synthesis of the results from the included studies was carried out. Results 10 studies examining a range of complex interventions totalling 3407 patients with multimorbidity were identified. All were randomised controlled trials with a low risk of bias. Two studies described interventions for patients with specific comorbidities. The remaining eight studies focused on multimorbidity, generally in older patients. Consideration of the impact of socioeconomic deprivation was minimal. All studies involved complex interventions with multiple components. In six of the 10 studies the predominant component was a change to the organisation of care delivery, usually through case management or enhanced multidisciplinary team work. In the remaining four studies, intervention components were predominantly patient oriented. Overall the results were mixed, with a trend towards improved prescribing and drug adherence. The results indicated that it is difficult to improve outcomes in this population but that interventions focusing on particular risk factors in comorbid conditions or functional difficulties in multimorbidity may be more effective. No economic analyses were included, although the improvements in prescribing and risk factor management in some studies could provide potentially important cost savings. Conclusions Evidence on the care of patients with multimorbidity is limited, despite the prevalence of multimorbidity and its impact on patients and healthcare systems. Interventions to date have had mixed effects, although are likely to be more effective if targeted at risk factors or specific functional difficulties. A need exists to clearly identify patients with multimorbidity and to develop cost effective and specifically targeted interventions that can improve health outcomes.

Multimorbidity's many challenges

Time to focus on the needs of this vulnerable and growing population

Future of multimorbidity research: how should understanding of multimorbidity inform health system design?

Many people living with chronic conditions have multiple chronic conditions. Multimorbidity is defined here as the co-existence of two or more chronic conditions, where one is not necessarily more central than the others. Multimorbidity affects quality of life, ability to work and employability, disability and mortality. Currently, clinicians have limited guidance or evidence as to how to approach care decisions for such patients. Understanding how to best care and design the health system for patients with multimorbidity may lead to improvements in quality of life, utilization of healthcare, safety, morbidity and mortality. The objective of this paper is to review the implications of multimorbidity for the design of health system and to understand the research needs for this population. The consideration of people with multimorbidity is essential in the design and evaluation of health systems. Fundamentally, people with multimorbidity should receive a patient — and family-centered approach to care throughout the health system, and understanding how to deliver this type of care in effective and efficient ways is an enormous challenge, and opportunity, for clinicians, researchers, and policy makers today.

Randomized Controlled Trials: Do They Have External Validity for Patients With Multiple Comorbidities?

PURPOSE Many randomized controlled trials (RCTs) exclude patients who have multiple comorbidities. The aim of this study was to illustrate the prevalence of comorbidities among patients followed up in primary care who would have met the inclusion criteria of selected RCTs focusing on treatment of a particular condition. We used hypertension as an example of a particular chronic condition. METHODS We used an existing database of 980 patients (660 women) that was representative of a population consulting primary care family doctors and that contained information about all chronic conditions. We randomly selected 5 RCTs that focused on patients with hypertension. The inclusion and exclusion criteria used in each of the 5 RCTs were applied (1 study at a time) to the patients in our database. The patients from our data set who met the inclusion criteria of a given RCT were considered eligible for that RCT. RESULTS Of the patients from our data set who were eligible for each of the RCTs, 89% to 100% had multiple chronic conditions. The mean number of chronic conditions of patients eligible for each RCT ranged from 5.5 ± 3.3 to 11.7 ± 5.3. CONCLUSIONS Results from this study suggest that RCTs targeting a chronic medical condition such as hypertension could find that, in a sample taken from family practice, most eligible patients have comorbid conditions. Whether these patients are sampled or excluded should be reported. Research results intended to be applied in medical practice should take the complex reality of effective treatment of these patients into consideration.

Prevalence estimates of multimorbidity: a comparative study of two sources.

BackgroundPublished prevalence studies on multimorbidity present diverse data collection methods, sources of data, targeted age groups, diagnoses considered and study populations, making the comparability of prevalence estimates questionable. The objective of this study was to compare prevalence estimates of multimorbidity derived from two sources and to examine the impact of the number of diagnoses considered in the measurement of multimorbidity.MethodsPrevalence of multimorbidity was estimated in adults over 25 years of age from two separate Canadian studies: a 2005 survey of 26,000 respondents randomly selected from the general population and a 2003 study of 980 patients from 21 family practices. We estimated the prevalence of multimorbidity based on the co-occurrence of ≥ 2 and ≥ 3 diseases of the seven diseases listed in the general population survey. For primary care patients, we also estimated multimorbidity prevalence using an open list of chronic diseases.ResultsPrevalence estimates were considerably higher for each age group in the primary care sample than in the general population. For primary care patients, the number of chronic diseases considered for estimates resulted in large differences, especially in younger age groups. The prevalence of multimorbidity increased with age in both study populations.ConclusionsThe prevalence of multimorbidity was substantially lower when estimated in a general population than in a family practice-based sample and was higher when the number of conditions considered increased.

Measuring Patients’ Perceptions of Patient-Centered Care: A Systematic Review of Tools for Family Medicine

PURPOSE Patient-centered care is widely acknowledged as a core value in family medicine. In this systematic review, we aimed to identify and compare instruments, subscales, or items assessing patients’ perceptions of patient-centered care in family medicine. METHODS We conducted a systematic literature review using the MEDLINE, Embase, and Cochrane databases covering 1980 through April 2009, with a specific search strategy for each database. The search strategy was supplemented with searching by hand and expert suggestions. We looked for articles meeting all of the following criteria: (1) describing self-administered instruments measuring patient perceptions of patient-centered care; (2) reporting quantitative or psychometric results of development or validation; (3) being relevant to an ambulatory family medicine context. The quality of each article retained was assessed using a modified version of the Standards for Reporting of Diagnostic Accuracy. Instrument’ items were mapped to dimensions of a patient-centered care conceptual framework. RESULTS Of the 3,045 articles identified, 90 were examined in detail, and 26, covering 13 instruments, met our inclusion criteria. Two instruments (5 articles) were dedicated to patient-centered care: the Patient Perception of Patient-Centeredness and the Consultation Care Measure, and 11 instruments (21 articles) included relevant subscales or items. CONCLUSIONS The 2 instruments dedicated to patient-centered care address key dimensions but are visit-based, limiting their applicability for the study of care processes over time, such as chronic illness management. Relevant items from the 11 other instruments provide partial coverage of the concept, but these instruments were not designed to provide a specific assessment of patient-centered care.