Effect of socio-demographic factors on the association between multimorbidity and healthcare costs: a population-based, retrospective cohort study

Abstract

ObjectivesTo estimate the attributable costs of multimorbidity and assess whether the association between the level of multimorbidity and health system costs varies by socio-demographic factors in young (<65 years) and older (≥65 years) adults living in Ontario, Canada.DesignA population-based, retrospective cohort studySettingThe province of Ontario, CanadaParticipants6 639 089 Ontarians who were diagnosed with at least one of 16 selected medical conditions on 1 April 2009.Main outcome measuresFrom the perspective of the publicly funded healthcare system, total annual healthcare costs were derived from linked provincial health administrative databases using a person-level costing method. We used generalised linear models to examine the association between the level of multimorbidity and healthcare costs and the extent to which socio-demographic variables modified this association.ResultsAttributable total costs of multimorbidity ranged from C$377 to C$2073 for young individuals and C$1026 to C$3831 for older adults. The association between the degree of multimorbidity and healthcare costs was significantly modified by age (p<0.001), sex (p<0.001) and neighbourhood income (p<0.001) in both age groups, and the positive association between healthcare costs and levels of multimorbidity was statistically stronger for older than younger adults. For individuals aged 65 years or younger, the increase in healthcare costs was more gradual in women than in their male counterparts, however, for those aged 65 years or older, the increase in healthcare costs was significantly greater among women than men. Lastly, we also observed that the positive association between the level of multimorbidity and healthcare costs was significantly greater at higher levels of marginalisation.ConclusionSocio-demographic factors are important effect modifiers of the relationship between multimorbidity and healthcare costs and should therefore be considered in any discussion of the implementation of healthcare policies and the organisation of healthcare services aimed at controlling healthcare costs associated with multimorbidity.

HCV-infected individuals have higher prevalence of comorbidity and multimorbidity: a retrospective cohort study

“…To ascertain comorbidities, we used validated algorithms developed at ICES to determine the prevalence of the following chronic conditions: acute myocardial infarction (AMI) [39], asthma [40], congestive heart failure (CHF) [41], chronic obstructive pulmonary disease (COPD) [42], dementia [43], diabetes [44], hypertension [45], HIV [46] and rheumatoid arthritis [47]. As with other multimorbidity studies [48,49], for conditions where a derived ICES cohort did not exist, we adopted a similar approach (i.e. the presence of any one inpatient hospital diagnostic code (DAD data) or two or more outpatient physician billing codes (OHIP data) within a 2 year period using relevant ICD-9 and ICD-10 codes) to define the following chronic conditions: cardiac arrhythmia, osteoarthritis, osteoporosis, renal failure, and stroke, as well as for mental health conditions and substance use disorders.…”

“…the presence of any one inpatient hospital diagnostic code (DAD data) or two or more outpatient physician billing codes (OHIP data) within a 2 year period using relevant ICD-9 and ICD-10 codes) to define the following chronic conditions: cardiac arrhythmia, osteoarthritis, osteoporosis, renal failure, and stroke, as well as for mental health conditions and substance use disorders. (Appendix 1 in Table 3) [48][49][50][51][52][53][54]. We determined the prevalence of any cancer using the OCR.…”

The impact of hospital harm on length of stay, costs of care and length of person-centred episodes of care: a retrospective cohort study

“…30 To obtain the best fit to our data, we use regression models and specified generalized linear models with a log link and γ distribution to estimate the incremental effect of hospital harm on length of index hospital stay, duration of PCE, and costs of PCE (following the approach and results of tests recommended by Manning and Mullahy 31 for cost). 32 Based on this regression, we estimated a prediction of each outcome in patients who did and did not experience hospital harm, and we used this difference to measure incremental outcomes. The difference between groups (those who experienced hospital harm and those who did not) was assessed via 2-tailed t tests.…”

HCV-Infected Individuals have Higher Prevalence of Comorbidity and Multimorbidity: A retrospective cohort study

“…To ascertain comorbidities, we used validated algorithms developed at ICES to determine the prevalence of the following chronic conditions: acute myocardial infarction (AMI) [25], asthma [26], congestive heart failure (CHF) [27], chronic obstructive pulmonary disease (COPD) [28], dementia [29], diabetes [30], hypertension [31], HIV [32] and rheumatoid arthritis [33]. As with other multimorbidity studies [34,35], for conditions where a derived ICES cohort did not exist, we adopted a similar approach (i.e. the presence of any one inpatient hospital diagnostic code (DAD data) or two or more outpatient physician billing codes (OHIP data) within a 2 year period using relevant ICD-9 and ICD-10 codes) to define the following chronic conditions: cardiac arrhythmia, osteoarthritis, osteoporosis, renal failure, and stroke, as well as for mental health conditions and substance use disorders.…”

“…(Appendix 1). [34][35][36][37][38][39][40] We determined the prevalence of any cancer using the OCR. Liver failure and liver transplant were ascertained if one ICD9 or ICD10 code was billed in OHIP or DAD in the previous 10 years.…”