Spatial association patterns between post-acute care services and acute care facilities in the United States

Abstract

Background There is increasing demand for post-acute care services, which is amplified by the COVID-19 pandemic. Aims We studied the pattern of spatial association between post-acute care services and acute care facilities and evaluated how geographic variability could influence their use. Methods We compiled data on CMS-certified acute care and critical access hospitals and post-acute health care services (nursing homes, home health care services, inpatient rehabilitation facilities, long-term care hospitals, and hospice facilities). We used the colocation quotient (CLQ) to measure the magnitude and direction of association (clustering or segregation) between post-acute care providers and hospitals. This metric allows pairwise comparison of categorical data; a value <1 indicates spatial segregation and a value >1 spatial clustering. Unity marks the lack of spatial dependence (random distribution). Results With the exception of nursing homes (CLQ 1.26), all other types of post-acute care providers are spatially segregated from rural critical access hospitals. Long-term care facilities ranked first (had the lowest global CLQ, 0.06), hospice facilities ranked last (had the highest global CLQ estimate, 0.54). Instead, post-acute care services either clustered with (inpatient rehabilitation 2.76, long-term care 2.10, nursing homes 1.37) or were only weakly segregated (home health care 0.86) from acute care hospitals. Home health care (1.44), hospice services (1.46), and nursing homes (1.08) spatially clustered with the same category of services. Results were robust in the sensitivity analysis and we provided illustrative examples of local variation for the states of MA and IA.

The Relative Importance of Hospital Discharge and Patient Composition in Changing Post-Acute Care Utilization and Outcomes Among Medicare Beneficiaries

“…24 , 25 While all SNFs across the country experienced higher number of case outbreaks during the pandemic, 13 , 20 for some rural patients, SNF might be the only option if any PAC service was needed. 17 , 18 , 24 , 25 , 36 For some patients and families, professional supports were critical when caregiving at home was not an option. 11 , 26 , 29 Recently developed telemonitoring service might be a solution to the geographic disparities in access to home-based services.…”

“…18 , 27 , 30 For example, there are fewer home health agencies in rural than urban areas, rural patients may find it more restricted when making PAC choices. 24 , 25 While all SNFs across the country experienced higher number of case outbreaks during the pandemic, 13 , 20 for some rural patients, SNF might be the only option if any PAC service was needed. 17 , 18 , 24 , 25 , 36 For some patients and families, professional supports were critical when caregiving at home was not an option.…”

“…Second, changes in patient composition also resulted in changes in PAC utilization (ie, admissions across PAC facility types), especially via hospital discharge patterns (ie, to home vs various types of PAC facilities). The changes in hospital discharge patterns and the overall PAC utilization during the pandemic could come from at least 3 sources: (1) changes that can be explained by the patient demographics and clinical characteristics in general (eg, sicker patients need more intensive treatment) 6 – 8 ; (2) changes that can be explained by the differential effects of patient-level characteristics during the pandemic relative to the previous period (eg, patients living alone were even more likely to use PACthan those who were living with others during the pandemic than before) 21 – 25 ; (3) changes during the pandemic that are independent of patient characteristics (eg, changes in care delivery and practice). 15 , 18 These sources of variation and the relative contribution to the overall PAC utilization changes are not well understood.…”

Espaço E Covid-19:

“…Ainda em relação aos padrões espaciais relacionados à pandemia, destaca-se outra importante preocupação que mobilizou a atenção dos cientistas: a capacidade dos sistemas de saúde de responder à demanda gerada pela doença. Um grupo de estudiosos se dedicou a realizar análises geoespaciais para avaliar a demanda em contraste com a capacidade dos hospitais de referência para o tratamento da COVID-19, de modo a gerar subsídios para otimizar a gestão do provisionamento dos serviços de saúde (LISBINSKI;DIAS, 2020;SILALAHI et al, 2020;REQUIA et al, 2020;ZIAKAS;MYLONAKIS, 2020) NOUVELLET et al, 2021;MORAES et al, 2020;IACUS et al, 2020;ZHENG et al, 2020). Ainda neste primeiro grupo de estudos, detectou-se uma significativa preocupação com análises de redes viárias em associação com dados de fluxos de passageiros e a identificação dos pontos nodais dessas redes, ou hotspots -com grande potencial de disseminação da doença (JIA et al, 2021;MOREIRA;SANTOS, 2020;CHANG et al, 2021;SEVERO et al, 2021;CHEN HAONAN et al, 2020a;GAN et al, 2020; Nesses estudos diversas formas de mobilidade são retratadas, com destaque para os efeitos da migração MOREIRA;SANTOS, 2020;COELHO et al, 2020;XINLIANG et al, 2020), movimentos pendulares interregionais ou interurbanos NIU et al, 2020;FORTALEZA et al, 2021;MOREIRA;SANTOS, 2020;COELHO et al, 2020;ZHENG et al, 2020) e, sobretudo, deslocamentos intraurbanos (GAN et al, 2020;SEVERO et al, 2021;CHANG et al, 2021;ESEN;YAMAN, 2021;JIA et al, 2021;…”