The International Journal of Person Centered Medicine Vol 2 Issue 4 pp 799-804
ARTICLE
The complexity score: towards a clinically-relevant, clinicianfriendly measure of patient multi-morbidity Ross E.G. Upshur BA (Hons) MA MD MSc CCFP FRCPCa, Li Wang MD MBAb, Rahim Moineddin PhDc, Jason X. Nie BScd and C. Shawn Tracy BSce a Canada Research Chair in Primary Care Research, Primary Care Research Unit, Sunnybrook Health Sciences Centre, Professor, Department of Family & Community Medicine, Dalla Lana School of Public Health, University of Toronto and Institute for Clinical Evaluative Sciences, Toronto, Canada b Research Associate, Primary Care Research Unit, Sunnybrook Health Sciences Centre, Toronto, Canada c Associate Professor, Department of Family & Community Medicine, University of Toronto, Toronto Canada d Research Associate, Primary Care Research Unit, Sunnybrook Health Sciences Centre, Toronto, Canada e Senior Research Associate, Primary Care Research Unit, Sunnybrook Health Sciences Centre, Toronto, Canada
Abstract Rationale and Objectives: The population is rapidly aging and concurrent chronic diseases are increasing rapidly in the developed world. Health systems, clinicians, patients and care givers are challenged by the provision of care in this complex environment. Clinical assessment tools are needed to optimize management and facilitate informed decision-making by patients and care givers. We propose and evaluate a simple tool called the complexity score and evaluate its properties in terms of its distribution in a senior population in an academic family practice and assess its association with health services utilization. Methods: We performed a retrospective chart audit of 2,450 patients aged 65 and older seen in an academic family practice in Ontario, Canada. We calculated the complexity score for each patient and analyzed the scores by age and gender. Using logistic and Poisson regression we evaluated the association of age, gender, marital status and complexity score with hospital admission (surgical and medical), emergency room visits and family practice visits. Results: The complexity score of the practice was high. Overall median complexity score increased with age (65-69: 12, 85+: 16). Age, gender and complexity score were independently and statistically significantly associated with increased hospitalizations, emergency room visits and family physician visits. The odds ratio (OR) for the complexity score was substantially increased for the highest complexity score in comparison to the reference group: OR ER visit: 6.5 (95% CI: 3.798-11.058), OR Hospitalization:12.08 (95% CI: 6.404-22.79). Age alone at most doubled the OR for these associations. Conclusions: Multi-morbidity is common amongst older adults in an academic family practice. The complexity score is a simple to calculate measure that has the capacity to inform clinicians and patients of anticipated health service utilization. Further prospective studies are required to replicate the strong and significant associations described in this study. Further measurement tools that capture patient wellbeing and functional status are required to compliment this measure. Keywords Chronic illness, complexity, complexity score, family medicine, measurement tools, multimorbidity, older patients, personcentered medicine Correspondence address Professor Ross E.G. Upshur, Primary Care Research Unit, Sunnybrook Health Sciences Centre, 2075 Bayview Ave., Room E3-49 Toronto, Ontario, Canada, M4N 3M5. E-mail:
[email protected] Accepted for publication: 18 September 2012
Introduction
challenge in primary care settings where co-ordination and continuity of care take place [3,4]. The second challenge relates to the aging of the population. Though there is debate about the relative impact of aging versus multimorbidity as the most important dimension driving health service utilization, there is good reason to believe that there is a strong association between age and multimorbidity [5]. What constitutes optimal care for older patients with multiple concurrent chronic illnesses in primary care
Two significant challenges face modern healthcare systems in the developed world. Multi-morbidity, defined as the coexistence of multiple chronic diseases or conditions, is now recognized as one of the most pressing challenges facing patients, family caregivers, healthcare providers and health policymakers and planners [1,2]. Multi-morbidity is a manifestation of the emergence of the growing burden of illness associated with chronic diseases. It is a particular 799
Upshur, Wang, Moineddin, Nie and Tracy
Complexity scores and multi-morbidity
has 13 staff physicians, 24 resident physicians and 10 nurses. The FPU provides care to approximately 8,500 patients, with a focus on older patients with complex chronic disease. All patients who were at least 65 years old during 2004 were included in the study (n=2,450). Health service utilizations in the 2-year period of 1 September, 2004 to 31 August, 2006 were abstracted. The study was approved by the Research Ethics Board at Sunnybrook Health Sciences Centre.
contexts is as yet undefined. There are profound limitations with respect to the knowledge base informing care of older patients, particularly those with multi-morbidity [6]. Multimorbidity is associated with high medication usage and uncertainty regarding both the efficacy and safety of medication regimens [7]. Thus, there is a pressing need for a comprehensive research agenda to address multimorbidity including better descriptive epidemiology, the creation and evaluation of models of care and clinically oriented management tools for this patient population. One particular need is for clinically meaningful, relevant and useful tools to aid clinicians in characterizing their practice as well as informing patients about likely prognosis. Clinimetrics, originated by Alvan Feinstein, is the branch of clinical epidemiology that seeks to create and evaluate measurement tools for the assessment, management and prognosis of the clinical course of patients [8]. In addition to desired measurement properties such as validity, reliability and consistency, Feinstein argues that an important trait of measurement is clinical sensibility [8]. Clinical sensibility relates to the attributes of a measurement and connotes the suitability of the measurement to the trait being measured. It is considered to be a key dimension of clinimetric measures, as opposed to reliance on solely psychometric measurement properties, because they capture clinically important dimensions of the patient and assist clinicians to understand the health status of their patients. In this paper, we describe a clinically sensible measurement called the complexity score. The complexity score consists of 2 primary components that are simple to understand and easy to measure in practice from data found in the patient chart. The 2 components are: (1) the number of chronic conditions and (2) the number of medications the patient regularly takes. The sum of these 2 figures is the complexity score. In this study, we characterize the distribution of the complexity scores of older patients in an academic family practice. We further measure the association between the complexity score and selected clinically relevant health services indicators: visits to the family practice, emergency room utilization and medical and surgical admissions.
Variables of interest Demographic information (sex, age and marital status) were collected from the medical record. Six outcome variables were abstracted: Family Physician (FP) visits, Emergency Department (ED) visits, medical hospital admissions, surgical hospital admissions, medication use and chronic health conditions. FP visits included all visits made to the FPU, excluding missed appointments and phone calls. The number of ED visits was established by counting ED reports appearing in the chart. Medical admissions were determined by the number of times a patient was admitted to the hospital for reasons other than surgery or visits to an ED as indicated on discharge summaries in the chart. Surgical admissions included the number of times a patient was admitted to a hospital for surgery. A combined admissions variable was created by summing the number of surgical admissions and the number of medical admissions for each patient. Data on medication use and chronic health conditions were abstracted from the Cumulative Patient Profile (CPP) in the patients’ chart. Any additional medications and conditions not listed in the CPP were identified from patient chart entries for the 2-year study period. Statistical analysis Data were entered into SAS (Version 9.1) for statistical analysis. Patients were grouped by sex and by 5-year age intervals as determined by their age on 1 January, 2005. The complexity score was calculated for each patient by summing the number of chronic conditions and the number of long-term medications. Descriptive statistics (median, first and third quartiles and interquartile range) were calculated in order to assess the distribution of complexity scores. Multivariate logistic regression analysis was used to assess the association between potential predictor variables and hospital and ED utilization rates, after adjustment for covariates. Potential predictors included sex, age group and marital status. Separate regression models were created for hospital admissions and ED visits. Variables were determined to contribute to the models if the significance level for the test was less than 0.05. Goodness-of-fit was assessed by the Hosmer-Lemeslow test. Poisson regression was used to model FP visit rates. The estimated parameters were then used to calculate annualized sex-adjusted rates of FP visits for each age
Methods Study design We conducted a retrospective chart audit for all patients aged 65+ in the Family Practice Unit (FPU) at Sunnybrook Health Sciences Centre in Toronto, Canada. The present study is part of an ongoing mixed-methods research program that aims to describe both the clinical epidemiology and the lived experience of chronic disease among older patients in the primary care setting. Setting and study population The Sunnybrook FPU is an academic primary care practice fully affiliated with the University of Toronto. The clinic 800
The International Journal of Person Centered Medicine
Table 1 Demographic profile of the patient population 65 years and older in the Family Practice Unit at Sunnybrook Health Sciences Centre Age Group ( years ) 65-69 70-74 n (%) n (%)
75-79 n (%)
80-84 n (%)
85+ n (%)
Total n (%)
Sex Female Male Total
245 (57) 186 (43) 431 (100)
294 (58) 217 (42) 511 (100)
338 (57) 258 (43) 596 (100)
317 (61) 204 (39) 521 (100)
274 (70) 117 (30) 391 (100)
1,468 (60) 982 (40) 2,450 (100)
Marital Status Married Not married Widowed Total
303 (71) 80 (19) 46 (11) 429 (101)
349 (69) 79 (16) 81 (16) 509 (101)
354 (60) 78 (13) 158 (27) 590 (100)
264 (51) 58 (11) 195 (38) 517 (100)
129 (33) 38 (10) 221 (57) 388 (100)
1,399 (58) 333 (14) 701 (29) 2,433 (101)
Note: percentages may not sum to 100 due to rounding.
Table 2 Distribution of complexity scores by sex and age group Sex
Age Group
Median Complexity Score
q1
q3
IQR
Females
65-69 70-74 75-79 80-84 85+
12 14 15 16 17
8 9 11 13 12
17 18 21 23 23
9 9 10 10 11
Males
65-69 70-74 75-79 80-84 85+
11 12 15 16 15
7 8 10 11 11
16 17 22 21 23
9 9 12 10 12
Overall
65-69 70-74 75-79 80-84 85+
12 13 15 16 16
8 9 10 12 11
17 17 21 22 23
9 8 11 10 12
Figure 1 Distribution of complexity scores by sex and age group
60
Complexity Score
50 Females
40
Males
30
Overall
20 10 0
65-69 yrs
70-74 yrs
75-79 yrs
Age Group
801
80-84 yrs
85+ yrs
Upshur, Wang, Moineddin, Nie and Tracy
Complexity scores and multi-morbidity
Table 3 Factors associated with ER visits Factor Sex (vs. females) Males Marital Status (vs. married) Not married Widowed Age Group (vs. 65-69) 70-74 75-79 80-84 85+ Complexity Score (vs. 0-5) 6-10 11-15 16-20 21-25 >25
Odds Ratio
95% Confidence Intervals
P Value
1.329
1.091
1.619
0.005
1.274 1.114
0.965 0.887
1.680 1.399
0.087 0.354
0.938 1.479 1.467 1.958
0.676 1.092 1.071 1.399
1.303 2.003 2.008 2.739
0.704 0.012 0.017
