Improving Personal Characterization of Meaningful Activity in Adults with Chronic Conditions Living in a Low-Income Housing Community

Abstract Background Early identification of patients with chronic conditions and complex health needs in emergency departments (ED) would enable the provision of services better suited to their needs, such as case management. A case-finding tool would ultimately support ED teams to this end and could reduce the cost of services due to avoidable ED visits and hospitalizations. The aim of this study was to develop and validate a short self-administered case-finding tool in EDs to identify patients with chronic conditions and complex health needs in an adult population. Methods This prospective development and initial validation study of a case-finding tool was conducted in four EDs in the province of Quebec (Canada). Adult patients with chronic conditions were approached at their third or more visit to the ED within 12 months to complete a self-administered questionnaire, which included socio-demographics, a comorbidity index, the reference standard INTERMED self-assessment, and 12 questions to develop the case-finding tool. Significant variables in bivariate analysis were included in a multivariate logistic regression analysis and a backward elimination procedure was applied. A receiver operating characteristic (ROC) curve was developed to identify the most appropriate threshold score to identify patients with complex health needs. Results Two hundred ninety patients participated in the study. The multivariate analysis yielded a six-question tool, COmplex NEeds Case-finding Tool – 6 (CONECT-6), which evaluates the following variables: low perceived health; limitations due to pain; unmet needs; high self-perceived complexity; low income; and poor social support. With a threshold of two or more positive answers, the sensitivity was 90% and specificity 66%. The positive and negative predictive values were 49 and 75% respectively. Conclusions The case-finding process is the essential characteristic of case management effectiveness. This study presents the first case-finding tool to identify adult patients with chronic conditions and complex health needs in ED.

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Examine the association between key determinants identified by the chronic disease indicator framework and multimorbidity by rural and urban settings

Journal of Multimorbidity and Comorbidity ◽

10.1177/26335565211028157 ◽

2021 ◽

Vol 11 ◽

pp. 263355652110281

Author(s):

John S. Moin ◽

Richard H. Glazier ◽

Kerry Kuluski ◽

Alex Kiss ◽

Ross E.G. Upshur

Keyword(s):

Chronic Disease ◽

Low Income ◽

Rural Areas ◽

Chronic Conditions ◽

Significant Risk ◽

Administrative Databases ◽

Multivariable Logistic Regression Model ◽

Urban Settings ◽

Rural And Urban ◽

Indicator Framework

Background: Multimorbidity, often defined as having two or more chronic conditions is a global phenomenon. This study examined the association between key determinants identified by the chronic disease indicator framework and multimorbidity by rural and urban settings. The prevalence of individual diseases was also investigated by age and sex. Methods: The Canada Community Health Survey and linked health administrative databases were used to examine the association between multimorbidity, sociodemographic, behavioral, and other risk factors in the province of Ontario. A multivariable logistic regression model was used to conduct the main analysis. Results: Analyses were stratified by age (20–64 and 65–95) and area of residence (rural and urban). A total sample of n = 174,938 residents between the ages of 20–95 were examined in the Ontario province, of which 18.2% (n = 31,896) were multimorbid with 2 chronic conditions, and 23.4% (n = 40,883) with 3+ chronic conditions. Females had a higher prevalence of 2 conditions (17.9% versus 14.6%) and 3+ conditions (19.7% vs. 15.6%) relative to males. Out of all examined variables, poor self-perception of health, age, Body Mass Index, and income were most significantly associated with multimorbidity. Smoking was a significant risk factor in urban settings but not rural, while drinking was significant in rural and not urban settings. Income inequality was associated with multimorbidity with greater magnitude in rural areas. Prevalence of multimorbidity and having three or more chronic conditions were highest among low-income populations. Conclusion: Interventions targeting population weight, age/sex specific disease burdens, and additional focus on stable income are encouraged.

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