Data source and population
Our population-based cohort study included all individuals over the age of 65 registered in the Québec Integrated Chronic Disease Surveillance System (QICDSS) on April 1st, 2019, (cohort entry date) and followed them for one year. The QICDSS links provincial health services administrative data since 1996 using a unique patient identifier [13]. The data include demographic, death registry, physician claims, and pharmaceutical claims records obtained from the Provincial health insurance board (Régie de l’assurance maladie du Québec [RAMQ]) as well has hospital discharge abstract records (MED-ECHO) owned by the Quebec Ministry of Health and housed at RAMQ. Demographic data includes place of residence, age, sex and neighbourhood-level social and material deprivation quintiles [14]. Physician claims include diagnoses coded using the International Classification of Diseases, 9th Revision, Quebec adaptation (ICD-9-QC) and the ICD 10th Revision Canadian Coding Standard (ICD-10-CA) since January 1st, 2019. Hospital discharge records include the admission diagnosis, primary diagnosis and up to 29 secondary diagnoses coded using ICD-9-QC system until March 31, 2006, and ICD-10-CA system thereafter. As the province of Quebec has a universal healthcare system, the QICDSS includes medical records for over 99% of the population. In addition, drug insurance is mandatory in Quebec. All individuals aged 65 years and older are eligible for coverage by the public drug plan. However, approximately 10% is not covered due to either their preference to retain their private insurance plan or their medication being provided by the nursing home where they reside.
Multimorbidity measure
We considered three widely used criteria to define multimorbidity: MM2+, MM3+, MM4+ [3]. We also identified three lists of medical conditions commonly used to build the multimorbidity measures. These lists were deemed representative of the high diversity of medical conditions included in multimorbidity measures relying on health administrative data [5] (The lists of diseases and ICD codes for each list are available in Supplemental Digital Content [SDC] 01: Tables A1.1-A1.4). First, the “All-inclusive list”(L60) included all ICD codes corresponding to chronic diseases grouped into 60 diseases by a multidisciplinary team [15]. This list was considered of high quality in a previous systematic review because it met six of the eight quality criteria used to define robust multimorbidity measures methodology [5]. Second, the “Core list” (L20) included a minimal core of 20 diseases identified in a systematic review by Ho and colleagues [3]. This minimum core of diseases includes chronic conditions with the highest disability adjusted life-years (DALYs) or years of life lost (YLLs) from the Global Burden of Disease Project [16]. We added osteoporosis to that list because this chronic condition was reported among the top 20 with the highest impact on DALY in Canada [17]. Third, the “Charlson & Elixhauser list” (L31) included 31 diseases from the Combined comorbidity index, a combination of both Charlson and Elixhauser comorbidity indices [11, 12].
We employed varying LP ranging from 1 to 20 years to estimate multimorbidity prevalence at the cohort entry date (April 1, 2019). We retrospectively retrieved ICD diagnosis codes for each person and medical condition from hospitalization and physician records until April 1st, 1999 (Fig. 1). The choice of a 20-year LP was based on the availability of data in QICDSS, limiting our analysis to this timeframe. We used the algorithm proposed by Klabunde et al. [18] to identify each disease in the administrative databases: we searched both inpatient and outpatient records and identified an individual as having a disease if (1) at least one diagnosis code (primary or secondary) was recorded in the hospitalization records or (2) at least two diagnosis codes were recorded in inpatient or outpatient physician claims within two years and at least 30 days apart.
Illustration of the assessment of multimorbidity prevalence at index date with varied lookback periods (LPs) and 1-year health outcome measurements. For example, for a person aged 66 on April 1st, 2019, the retrospective search in both inpatient and outpatient databases using a LP of 1 year runs from April 1st, 2018 to March 31th, 2019. Using a LP of 20 years, it extends from April 1st 1999 to March 31th, 2019
Outcomes
We investigated the capacity of each multimorbidity measure, computed on April 1st, 2019, to predict six health outcomes that have been associated with multimorbidity and were measurable in the QICDSS during the 1-year follow-up (until March 31th, 2020): all-cause mortality, polypharmacy, hospitalisation and frequent visits to the emergency department (ED), to the general practitioner (GP) and to any specialist physician (SP). We defined polypharmacy as ≥ 10 different medications claimed in the follow-up year. We used the common denomination (each active ingredient or combination has a distinct common denomination code) to identify each medication claimed. Those claims included medications for acute and chronic conditions. We defined frequent ED visits using a commonly used threshold of ≥ 3 visits in the follow-up year [19]. A single visit to the ED was defined as 1 or more ED–related claims on up to 2 consecutive days [20]. Frequent visits to any GP (≥ 7 visits) or any SP (≥ 10 visits) in the follow-up year were defined using the 95th percentile in the annual number of ED visits in the Québec adult population [21, 22].
Statistical analysis
We estimated the prevalence of multimorbidity for each criterion used to define multimorbidity, each list of diseases, and each LP (1 to 20 years) and calculated the relative change in multimorbidity prevalence for each additional year of lookback (Fig. 1).
Then, we used logistic regression models to assess the impact of each criterion used to define multimorbidity on the health outcome. We first built one baseline model for each health outcome where the health outcome was the dependent variable and the covariates (age group, sex, material and social deprivations) were predictors. To estimate the performance of the multimorbidity measures in predicting each health outcome over and beyond that of the baseline covariates and to assess the impact of the LP on the prediction performance, we built 1080 logistic regression models for each combination of criterion used to define multimorbidity (3 criteria), list of diseases (3 lists), LP (20 periods) and health outcomes (6 outcomes). Of note, the analysis of polypharmacy and health services outcomes (hospitalisation, ED, GP, SP visits) included only those alive and covered by the drug plan during the entire one-year follow-up. Performance of each model was assessed using three measures: (1) the discrimination capacity of each model, that is the ability to identify correctly patients having the outcome within 1 year, with the c-statistic (also known as the area under the receiver operating characteristic curve) [23] (A difference in c-statistic superior to [0.010] was considered significant because covariates that contribute such difference may reduce confounding bias in observational studies [24]); (2) the overall performance of the model calculated with the scaled Brier score, which values range from 0 to 1 (higher value indicates better performance); and (3) the level of agreement between observed and predicted probability of the outcome using calibration intercept and slope, for which a value near zero and one indicates a better prediction, respectively [23].
All analyses were performed using SAS 9.4 (SAS Institute, Cary, NC).
Supplementary and sensitivity analyses
Considering the recognized variations in claims history, risk of mortality and healthcare resource utilization associated with age and sex, we conducted stratified analyses to estimate the predictive performance according to these factors. We categorized age groups as 66–79 and ≥ 80 years, and also considered sex as a stratification factor. This approach allowed us to investigate the internal validity by assessing performance heterogeneity between these groups and is preferred to approaches that assess average performances (e.g., via bootstrapping), given the large size of the samples and the low complexity of the models [25].
We also repeated all the analyses using disease specific algorithms to take into account the shorter period of chronicity of some diseases. Because such algorithms are proposed in the literature only for all diseases included in the “Core list”(L20), we used them only for this list. Those algorithms are described in the supplementary material (SDC01: Table A1.3). For the “All-inclusive list”(L60) and the “Charlson & Elixhauser list”(L31), we limited the length of LP to 5 years for all mental health disorders having a remitting or relapsing course in these two lists [26, 27]. For the “Core list” (L20), we also re-ran all analyses by adding one supplementary disease (hypertension) to the list as hypertension is included in a majority of multimorbidity measures [3].
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