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Public Health ; 185: 324-331, 2020 Aug.
Article in English | MEDLINE | ID: mdl-32726729

ABSTRACT

OBJECTIVES: Social needs interventions in medical settings aim to mitigate the effects of adverse social circumstances on health outcomes by connecting vulnerable patients with resources. This study examined the relationship between intervention dosage and the success of resource connections using data from a social needs intervention in multiple clinical settings across the US. STUDY DESIGN: The intervention uses a case management approach to connect patients with unmet needs to resources and services in the community. Intervention dosage was conceptualized as the number of contacts between the navigator and the patient, categorized as direct contact (phone vs. in person) and indirect contact (initiated by the navigator vs. patient). Success of the intervention was conceptualized as 'none,' 'partial,' or 'optimal' for each patient, based on the number of social needs the resource connections addressed. METHODS: Administrative data were extracted for 38,404 unique patients who screened positive for unmet resource needs between 2012 and 2017. Owing to the large sample size, statistical corrections were made to reduce type I error. RESULTS: Multinomial logistic regression analyses showed that higher intervention dosage was related to greater success of resource connections, after adjusting for the patient and site characteristics, and the number of needs (odds ratios ranged from 1.62 to 2.89). In-person contact, although received by only 25% of the patients, was associated with the highest probability of optimal success. CONCLUSIONS: This study demonstrates a feasible way to conceptualize an intervention dose for a social needs intervention that uses a case management approach and has implications for how intervention delivery may improve success of resource connections.


Subject(s)
Case Management/statistics & numerical data , Health Resources/statistics & numerical data , Social Support , Adult , Female , Humans , Logistic Models , Male , Middle Aged , Telephone , United States , Vulnerable Populations
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