Exploring Staffing Levels in Adult Day Services: Does Profit Status Matter?

ABSTRACT

OBJECTIVE: For more than 4 decades, adult day service centers (ADSCs) have provided long-term care services and socialization to hundreds of thousands of people in the United States. An important part of the long-term care continuum, ADSCs serve older adults and adults with disabilities, many of whom have low incomes and are racial and ethnic minorities. Yet, little is known about the quality of ADSCs. To better understand ADSC quality, we examined staffing levels, a key aspect of organizational structure. Staffing levels are an established quality measure associated with resident outcomes in nursing homes. Our study compares ADSC staffing levels between for-profit and nonprofit or government-operated ADSCs. DESIGN: Cross-sectional secondary data analysis using a nationally representative survey of ADSCs. SETTING AND PARTICIPANTS: Adult day service center (n = 573) directors completed a survey as part of the 2018 National Study of Long-Term Care Providers. METHODS: Bivariate comparisons and multivariate linear regression were used to compare staffing, measured as hours per participant day in nonprofit and for-profit ADSCs. RESULTS: Approximately 60% of ADSCs in the sample were nonprofit or government-operated and the remainder were for-profit. For-profit ADSC staffing averaged 1.5 hours per participant day and nonprofit or government-operated ADSC staffing averaged 1.9 hours per participant day. For-profit ADSCs had 15.8% (P = .047) lower hours per participant day compared with nonprofit ADSCs after controlling for center characteristics, such as Medicaid use, participant acuity, and ADSC size. CONCLUSION AND IMPLICATIONS We found that for-profit ADSCs have lower staffing levels compared with nonprofit and government-operated ADSCs. Future research is needed to understand how staffing levels relates to the quality of care in for-profit and nonprofit ADSCs and how these relationships vary by participant characteristics, such as income, race/ethnicity, and acuity.