Entete 3

Category Archives: Epidemiology and outcomes research

Multimorbidity in patients enrolled in a community-based methadone maintenance programme



By Tom Brett

The General Practice and Primary Health Care Research Unit at The University of Notre Dame Australia, Fremantle has published a new paper: ‘Multimorbidity in patients enrolled in a community-based methadone maintenance programme delivered through primary care’ (1). The study is a retrospective cohort study using electronic medical record review of patients attending a primary care-based methadone maintenance clinic in Western Australia. The clinic itself is part of a much larger medical centre that offers comprehensive primary health care. Multimorbidity in the methadone cohort was consistently higher across all age groups and contrasted with the comparator group where multimorbidity was positively correlated with age. We found the traditional S-shaped distribution curve of multimorbidity from mainstream practice was replaced by a consistently elevated plateau distribution among the methadone cohort. Our findings suggest challenging implications for the design and delivery of health care services to this population. Diane Arnold-Reed is lead author.

1-Multimorbidity in patients enrolled in a community-based methadone maintenance programme delivered through primary care. Journal of Comorbidity 2014; 4: 46-54.  Doi: 10.15256/joc.2014.4.42

Multimorbidity in a marginalised, street-health Australian population



By Tom Brett

A recent publication from The University of Notre Dame Australia, Fremantle in BMJ Open (1) deals with patterns, prevalence and disease severity of multimorbid chronic conditions among a street-based vulnerable and marginalized population.
Our research brings new information on a disadvantaged cohort of patients who access an innovative, accredited, mobile outreach primary care medical service.
We have again used the Cumulative Illness Rating Scale among the 2587 patients seen over a six year period in the Fremantle area of Western Australia.
Disease patterns and severity were compared with 4583 mainstream patients from a similar geographical area.
A key finding from our research is that this population develops chronic conditions at a much earlier age especially when compared with earlier research worldwide from mainstream practices.
A positive outcome from our research was the willingness of Aboriginal patients to engage with the mobile, outreach primary care medical service. This compares very favourably with the traditional low attendance patterns of Aboriginal patients with mainstream practices.

(1) Brett T, Arnold-Reed DE, Troeung L, Bulsara MK, Williams A, Moorhead RG. Multimorbidity in a marginalised, street-health Australian population: a retrospective cohort study. BMJ Open. 2014 Aug 19;4(8):e005461. doi: 10.1136/bmjopen-2014-005461.

Lifestyle factors and multimorbidity



By Martin Fortin

Many studies have unequivocally shown a close relationship between lifestyle factors and individual chronic diseases. More recently, the association of lifestyle risk factors with multimorbidity has been explored for physical activity, obesity, smoking, alcohol consumption, and nutrition. Some mixed results have been reported. However, the body mass index has been consistently found to be associated with multimorbidity.
In a recent study published in BMC Public Health [1], we analysed the association of accumulating risk factors in the same individual and multimorbidity. We found that accumulating unhealthy lifestyle factors progressively increased the likelihood of multimorbidity. The cross-sectional design of the study did not allow making a causal inference. However, the increase in the likelihood of multimorbidity with the combined effect of unhealthy lifestyle factors may be used to hypothesise that a person-centered approach promoting healthy lifestyles aiming to maximize the number of healthy lifestyles could be an intervention in the fight against multimorbidity.

1. Fortin M, Haggerty J, Almirall J, et al., Lifestyle factors and multimorbidity: a cross sectional study. BMC Public Health 2014;14:686.

Depression Screening and Multimorbidity

By Bhautesh Jani and Frances Mair

Our new paper published in Plos One examines the impact of routine depression screening, using the Hospital Anxiety and Depression Scale (HADS), and its relationship with multimorbidity and chronic disease management. In our study based on more than 125000 patients with chronic disease, the findings highlight the difficulties in implementing depression screening universally in primary care, despite incentivisation. Younger patients and those from deprived socio-economic background were more likely to have a positive result, when screened for depression symptoms. Importantly, depression screening did lead to an increase in the rate of anti-depressant prescribing in patients with chronic disease, which has significant resource implications.

In our study, depression screening was more often undertaken in patients with multimorbidity when compared to those with a single disease. Patients with multimorbidity had a greater chance of having a raised HADS score on depression screening, which resonates with the emerging evidence in this area. The crucial question will be to investigate the effect of depression screening in patients with chronic disease and multimorbidity on clinical outcomes, if any. The next phase of our project aims to address this question.

Comparisons of multi-morbidity in family practice – issues and biases

By Moira Stewart, Martin Fortin, Helena Britt, Christopher Harrison, and Heather Maddocks


A recent study published in Family Practice “Comparisons of multi-morbidity in family practice – issues and biases” [1] compared the methods and results of three separate prevalence studies of multi-morbidity from; i) the Saguenay region of Quebec [2]; ii) a sub-study of the Bettering Evaluation and Care of Health (BEACH) program in Australia [3,4]; and iii) the Deliver Primary Health Care Information (DELPHI) project in South-western Ontario [5,6].

A re-estimate of the prevalence rates using identical age-sex groups found multi-morbidity prevalence to vary by as much as 61%, where reported prevalence was 95% among females aged 45–64 in the Saguenay study, 46% in the BEACH sub-study and 34% in the DELPHI study.

Several aspects of the methods and study designs were identified as differing among the studies, including the sampling of frequent attenders, sampling period, source of data, and both the definition and count of chronic conditions.

The paper offers a guide for authors reporting the methods used in multi-morbidity prevalence research, recommending detailed descriptions of the type of sampling, completeness and accuracy of the source of data, and the definition of chronic conditions.

Further comparisons among multi-morbidity data using agreed upon standards for the definition of chronic conditions and the way to count multi-morbidity are recommended to assess the impact of these methodological variations.

References:

1 Stewart M, Fortin M, Britt H, Harrison C, Maddocks H.  Comparisons of multi-morbidity in family practice – issues and biases.  Family Practice. May 2013.  doi: 10.1093/fampra/cmt012.

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: 223–8.

3 Britt HC, Harrison CM, Miller GC, Knox SA. Prevalence and patterns of multimorbidity in Australia. Med J Aust 2008; 189: 72–7.

4 Knox SA, Harrison CM, Britt HC, Henderson JV. Estimating prevalence of common chronic morbidities in Australia. Med J Aust 2008; 189: 66–70.

5 Stewart M, Thind A, Terry AL, et al. Multimorbidity in primary care: a study using electronic medical record (EMR) data. In: Thirty-Seventh Annual Meeting of North American Primary Care Research Group, Quebec, Canada, 14–18 November, 2009.

6 Stewart M, Thind A, Terry A, Chevendra V, Marshall JN. Implementing and maintaining a researchable database from electronic medical records—a perspective from an academic family medicine department. Healthc Policy 2009; 5: 26–39.

The impact of Multiple Chronic Diseases on ambulatory care use

By Elizabeth Muggah

Our paper, The impact of Multiple Chronic Diseases on ambulatory care use; a population based study in Ontario, Canada, was recently published in BMC Health Services Research. This study is an important addition to what we know about the burden of multimorbidity on the primary care system as we focused specifically on ambulatory health care use and looked at the burden of disease on both the patient and on the health system more broadly.

This research was completed using health administrative data housed at the Institute of Clinical Evaluative Sciences (ICES) in Toronto, Canada. We used well validated methods to search administrative data in one large province of Canada to identify persons who had at least one of nine common chronic diseases (diabetes, congestive heart failure, acute myocardial infarction, stroke, hypertension, asthma, chronic obstructive lung disease, peripheral vascular disease and end stage renal failure).  We then identified the number of outpatient primary care and specialist visits over a 2 year period.

We found that multiple chronic diseases were common among the Ontario population, (in 2009, 26.3% of Ontarians had one chronic disease, 10.3% had two diseases, and 5.6% had three or more diseases). The annual number of primary health care visits per patient increased significantly with each additional chronic disease and patients with two or more diseases made more than twice as many visits each year to primary health care providers compared to specialists. At the extremes of age we saw an increase in the number of primary care visits across all groups while specialist care dropped off. Looking from a health system perspective we found the largest total number of visits were made by those with no or one chronic disease compared to those with multiple diseases.

This study reinforces what we know about the considerable burden of illness felt by persons with multiple chronic diseases and confirms that these patients seek care disproportionately from their primary care providers.  However from a health system perspective those with no or one chronic disease are responsible for the largest number of ambulatory health care visits.  In our view continued investment in primary health care is needed both to care for those with multiple diseases as well as to maintain a focus on preventing the accumulation of chronic diseases with advancing age.  It would be important to explore these trends over time to see if the pattern of health care use we found is changing given the predicted rise in the prevalence of multiple chronic diseases with the aging of our population.

Multimorbidity, polypharmacy, referrals, and adverse drug events

By Amaia Calderón and Alexandra Prados-Torres

A paper entitled “Multimorbidity, polypharmacy, referrals, and adverse drug events: are we doing things well?” was recently published in the British Journal of General Practice. The work was carried out by members of the EpiChron Research Group on Chronic Diseases of the Aragon Health Sciences Institute in Spain, and its objective was to shed light on the interrelations between multimorbidity, polypharmacy, multiple referrals to specialised care, and the occurrence of adverse drug events (ADEs), in the context of a national healthcare system.

Results of this observational study demonstrate that multimorbidity, polypharmacy and multiple referrals are strongly and independently associated to occurrence of ADEs, even after adjusting for potential confounders. As the clinical situation of the patient becomes more complex and requires the intervention of different specialists, the likelihood of a lack of coordination among professionals and potential interactions among prescribed medications could favour the occurrence of undesirable effects, such as ADEs.

As indicated by Starfield et al[1] a decade ago, it is necessary, now more than ever, to design strategies that focus on individual’s health problems in their totality, rather than examining each of the patient’s illnesses individually. This approach is important given the high frequency of multimorbidity in all stages of life, the proved risk of interactions between illnesses and medications or among medications, and the acknowledged impact of not doing so both for the healthcare system and the health of the patient.

This research, financed by the Spanish Institute of Health Carlos III, is framed within a wider project focused on the epidemiology of multimorbidity, utilization patterns and the response of healthcare systems to populations suffering from it.


[1] Starfield B, Lemke KW, Bernhardt T, et al. Comorbidity: implications for the importance of primary care in ‘case’ management. Ann Fam Med 2003; 1(1): 8–14.

The prevalence of multimorbidity

By Martin Fortin

 

Multimorbidity is associated with negative outcomes and increased resource use. Both create a burden on the health-care system.

 Concerned healthcare professionals and decision-makers aware of this information may wonder: 

  • What is the magnitude of this problem in our region?
  • What is the prevalence of multimorbidity in our population?

 Some researchers have attempted to answer these questions with studies involving either nation-wide populations or smaller groups. However, studies in different populations have yielded results with differences in prevalence estimates as important as 95% for a given age. Is this information reliable? Can it be used to determine the allocation of resources to deal with the problem of multimorbidity? Differences of this magnitude are unlikely to reflect real differences between populations and more likely due to methods biases.

 In a systematic review recently published in the Annals of Family Medicine  we identified and compared studies reporting the prevalence of multimorbidity in primary care settings and in the general population. Apart from differences in location, we identified differences in recruitment method and sample size, data collection, and in the operational definition of multimorbidity including the number of conditions and the conditions selected. All of these factors may affect prevalence estimates.

 In this review we discussed differences among studies and possible explanations for variations in the results of prevalence estimates. We also promoted the adoption of a more uniform methodology in this type of research by suggesting methodological aspects to be considered in the conduct of such studies.

 Availability of strong epidemiological data for multimorbidity would benefit both the research and care of this problem.

“Multimorbidity: epidemiology, utilization patterns and the response of the healthcare system”. A project funded by the Spanish Ministry of Science and Innovation (2012-2014)

By Alexandra Prados-Torres, Beatriz Poblador-Plou and Amaia Calderón-Larrañaga (from left to right)

The improvement of living conditions and the scientific-technological advancements have led to an increased prevalence of multimorbidity, especially, but not only, in the elderly population. It is also higher than expected in younger aged individuals [1]. Although multimorbidity has a significant impact on population health and healthcare systems, these are still mainly focused on diseases instead of patients, offering fragmented care (i.e. primary vs. specialized, health vs. social, etc.) and lacking evidence-based guidelines and/or appropriate clinical interventions for managing patients with multimorbidity [2,3].

Multimorbidity has not been sufficiently investigated in terms of its underlying pathophysiological mechanisms, specific diseases interactions, its consequences on health services utilization and outcomes, or even its definition and measurement.

This project, which will be carried out by researchers from the Aragon Health Research Institute (IIS Aragón, Spain) between 2012 and 2014 will focus on:

1- Epidemiology of multimorbidity based on methods that help identify the simultaneous non-random occurrence of health problems (i.e. multimorbidity patterns) in different population groups.

2- Health services utilization patterns among patients with multimorbidity which may reveal an unjustified variability among providers regarding prescription drug costs, scheduled visits and referrals to specialty care as a clear sign of inefficiency.

3- Potential ineffective or unsafe healthcare received by patients with multimorbidity due to preventable hospitalizations, in-hospital complications and readmissions, and polypharmacy-driven adverse drug reactions.

To this end, a retrospective cohort study has been designed including the entire population assigned to any of the 119 primary care centres in the region of Aragon (i.e. over 1,200,000 inhabitants). Thus, a person-based integrated database will be generated containing clinical and administrative information from primary care, specialized care and emergency care. This strategy, which has not been sufficiently exploited in the Spanish context to date, will enable the linking of patients’ health services utilization patterns with their multimorbidity profile.

This project is expected to provide evidence in relation to the causes and consequences of multimorbidity so that this hidden public health problem is recognised and urgently addressed by the various actors of healthcare systems.

The research group led by Dr. Alexandra Prados-Torres (sprados.iacs@aragon.es) is interested in establishing international collaborations and would very much appreciate the feedback of any of the members of the “IRCMo”.

1.- van den Akker M, Buntinx F, Metsemakers JF, Roos S, Knottnerus JA: Multimorbidity in general practice: prevalence, incidence, and determinants of co-occurring chronic and recurrent diseases. J Clin Epidemiol 1998, 51:367-375.
2.- Fortin M, Lapointe L, Hudon C, Vanasse A: Multimorbidity is common to family practice: is it commonly researched? Can Fam Physician 2005, 51:244-245.
3.- Fortin M, Dionne J, Pinho G, Gignac J, Almirall J, Lapointe L: Randomized controlled trials: do they have external validity for patients with multiple comorbidities? Ann Fam Med 2006, 4:104-108.

Summary of Notre Dame multimorbidity TDB

By Tom Brett

Researchers at the General Practice and Primary Health Care Research Unit at The University of Notre Dame Australia, Fremantle have recently completed extracting patient data from the medical records of attendees at two Perth metropolitan general practices. All patients (over 7000 in total) who attended the practices in a six month period in 2008 were included as were patients who were seen for home visits, hostel and nursing home visits in the same period.

We utilised a similar data extraction process to that employed by Fortin et al.1 and used the Cumulative Illness Rating Scale (CIRS) 2-6 to assess both the prevalence of chronic disease presentations and the severity of the disease burden for each patient. We employed ideas from the geriatric version of the CIRS as developed by Miller and Towers (CIRS-G)3  to improve consistency and help with standardisation amongst the data extractors. Three GPs, three practice nurses, a graduate-entry medical student and a senior receptionist were specially trained in the data extraction process.

A combination of electronic medical records and older hard copy files, including hospital discharge and out-patients letters together with radiology and pathology reports, was used to maximise the available data on each patient. Younger attendees were included so as not to discriminate against any age group and because no similar comprehensive information was available in the Australian primary care literature.

We are currently analysing the data and hope to publish some of our findings in 2012. The data extraction process was long and tedious but we are happy with the outcome and appreciate the efforts of the data extractors. We are hoping to expand and develop the study in the future and would welcome input and suggestions from future collaborators.

References

  1. 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.
  2. Linn BS, Linn MW, Gurel L. Cumulative Illness Rating Scale. J Amer Geriatr Soc. 1968; 16: 622-626.
  3. Miller MD, Towers A. A manual of guidelines for scoring the Cumulative Illness Rating Scale for Geriatrics (CIRS-G). Pittsburg, Pa. University of Pittsburg; 1991.
  4. Hudon C, Fortin M, Vanasse A. Cumulative Illness Rating Scale was a reliable and valid index in a family practice context. J Clin Epidemiol. 2005; 58: 603-608.
  5. De Groot V, Beckerman H, Lankhorst GJ, Bouter LM. How to measure co-morbidity: a critical review of available methods. J Clin Epidemiol. 2003; 56: 221-229.
  6. Hudon C, Fortin M, Soubhi H. Abbreviated guidelines for scoring Cumulative Illness Rating Scale (CIRS) in family practice. J Clin Epidemiol. 2007; 60: 212.