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Author Archives: Tom Brett

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.

Multimorbidity in two large Australian primary care practices



By Tom Brett

The Annals of Family Medicine recently published our research on multimorbidity among 7,247 patients attending two large Australian primary care practices (1). Our study set out to examine patterns and prevalence of multimorbidity and to estimate disease severity burden using the Cumulative Illness Rating Scale (CIRS).
We adhered strictly to Miller et al’s approach (2,3) in assessing number of body domains affected, the total score, the ratio of total score to number of domains (yielding a severity index), and importantly, the number of domains with maximum scores at levels 3 and 4. Highlighting the number of domains with severity scores of 3 and 4 is important as it helps guard against severity underestimation especially if there is a risk of severity index dilution with increased numbers of level 1 and 2 scores.
Our purposefully collected data, using combination of free-text electronic records, older hard copy files based on histories recorded by primary care physician, hospital discharge and outpatient letters and radiology and pathology reports, was extremely hard work and not for the fainted hearted! We feel our purposefully collected, multisource medical record data, based on 42 conditions across 14 domains and involving patients across the entire age spectrum provides further useful information for those interested in multimorbidity in primary care.
Our current research interest in the area involves patterns and prevalence of multimorbidity and disease severity burden involving disadvantaged and street-based populations.

1.    Brett T, Arnold-Reed DE, Popescu A, et al. Multimorbidity in patients attending 2 Australian primary care practices. Ann Fam Med 2013; 11(6): 535-542.
2.    Miller MD, Towers A. A manual of guidelines for scoring the Cumulative Illness Rating Scale for geriatrics (CIRS-G). Pittsburg, PA: University of Pittsburgh, 1991.
3.    Miller MD, Paradis CF, Houch PR, et al. Rating chronic illness burden in geropsychiatric practice and research: application of the Cumulative Illness Rating Scale. Psychiatry Res. 1992; 41 (3): 237-248.

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.