Association between household context and emergency hospital use in older people: a retrospective cohort study on indicators for people living alone or living with somebody with frailty, developed from routine healthcare data in England

Lloyd, Therese; Crellin, Elizabeth; Brine, Richard; Shen, Julia Y; Wolters, Arne

doi:10.1136/bmjopen-2021-059371

Cited by 5 publications

(7 citation statements)

References 25 publications

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“…Moreover, married people are more likely to have social support than those who are not given that marital status is often used as a proxy for informal social support [ 30 , 31 ]. This finding aligns with previous studies that older adults living alone had higher odds of ED admission [ 32 , 33 ], and people with multimorbidity who live alone had significantly longer inpatient days after ED admissions than those without multimorbidity [ 32 ]. These findings may be supported by the fact that multimorbid people have more care needs due to the complexity of care, as well as greater disease and symptom burdens [ 34 , 35 ].…”

Section: Discussionsupporting

confidence: 92%

Social determinants of health and emergency department visits among older adults with multimorbidity: insight from 2010 to 2018 National Health Interview Survey

Lim,

Benjasirisan,

Liu

et al. 2024

BMC Public Health

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Background Multimorbidity is prevalent among older adults and is associated with adverse health outcomes, including high emergency department (ED) utilization. Social determinants of health (SDoH) are associated with many health outcomes, but the association between SDoH and ED visits among older adults with multimorbidity has received limited attention. This study aimed to examine the association between SDoH and ED visits among older adults with multimorbidity. Methods A cross-sectional analysis was conducted among 28,917 adults aged 50 years and older from the 2010 to 2018 National Health Interview Survey. Multimorbidity was defined as the presence of two or more self-reported diseases among 10 common chronic conditions, including diabetes, hypertension, asthma, stroke, cancer, arthritis, chronic obstructive pulmonary disease, and heart, kidney, and liver diseases. The SDoH assessed included race/ethnicity, education level, poverty income ratio, marital status, employment status, insurance status, region of residence, and having a usual place for medical care. Logistic regression models were used to examine the association between SDoH and one or more ED visits. Results Participants’ mean (± SD) age was 68.04 (± 10.66) years, and 56.82% were female. After adjusting for age, sex, and the number of chronic conditions in the logistic regression model, high school or less education (adjusted odds ratio [AOR]: 1.10, 95% confidence interval [CI]: 1.02–1.19), poverty income ratio below the federal poverty level (AOR: 1.44, 95% CI: 1.31–1.59), unmarried (AOR: 1.19, 95% CI: 1.11–1.28), unemployed status (AOR: 1.33, 95% CI: 1.23–1.44), and having a usual place for medical care (AOR: 1.46, 95% CI 1.18–1.80) was significantly associated with having one or more ED visits. Non-Hispanic Black individuals had higher odds (AOR: 1.28, 95% CI: 1.19–1.38), while non-Hispanic Asian individuals had lower odds (AOR: 0.71, 95% CI: 0.59–0.86) of one or more ED visits than non-Hispanic White individuals. Conclusion SDoH factors are associated with ED visits among older adults with multimorbidity. Systematic multidisciplinary team approaches are needed to address social disparities affecting not only multimorbidity prevalence but also health-seeking behaviors and emergent healthcare access.

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Section: Discussionsupporting

confidence: 92%

Social determinants of health and emergency department visits among older adults with multimorbidity: insight from 2010 to 2018 National Health Interview Survey

Lim,

Benjasirisan,

Liu

et al. 2024

BMC Public Health

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“…Hospital and primary care data, if available, can be used to identify a group with similar characteristics, such as age and long-term conditions. But this method can also have limitations: routinely collected data may not adequately record severity of disease or social isolation or other social factors that affect emergency hospital use, 79,80 and details on comorbidities are often incomplete in hospital data. 79 Robust evaluation requires resources and skills.…”

Section: Undertake Robust Evaluation Of Outcomesmentioning

confidence: 99%

Realising the potential of community-based multidisciplinary teams: insights from evidence

Lloyd¹,

Beech²,

Wolters³

et al. 2023

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Better integration between health and social care services is a longstanding policy objective in England and other countries. Recent reforms to the NHS in England established 42 area-based integrated care systems (ICSs) to lead local efforts to develop more integrated models of care. •To realise the potential of MDTs, local implementation needs to be carefully planned and supported by ongoing monitoring and evaluation. We make four recommendations:1. Develop a clear, evidence-informed 'logic model' (or 'theory of change')for how service changes are expected to lead to improvements in care. This will help identify the support needed to make MDTs work and set meaningful goals. 2.Collect data on key aspects of the logic model as part of the implementation of MDTs. This should include resource inputs, activities and outcomes that can be accessed in a timely way. 3.Monitor inputs, activities and short-and medium-term outputs. This can provide early indications of whether the MDT is working as intended and identify opportunities to learn and course-correct. 4.Undertake robust evaluation to understand what works, for whom and in which contexts. This should include measures beyond emergency hospital use and reflect outcomes that matter to patients. Robust evaluation requires careful design, reliable data and resources -and input and support from both local and national decision makers. About the Improvement Analytics UnitThe Improvement Analytics Unit (IAU) is a unique partnership between NHS England and the Health Foundation. We evaluate complex initiatives in health care in order to support learning and improvement.

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“…These effects could be associated with environmental factors, both built or natural, that characterise the household or local area in which a person resides, and the “household composition” or characteristics of the other people who live in the same household or area [ 12 ]. Existing studies have investigated the impact of the household composition on healthcare outcomes [ 13 ], for example through examining associations between the health status of co-residents and emergency hospital admission rates in two-person households [ 14 , 15 ]. Other household characteristics are known to affect the use of health care services including financial resources available to co-habitants [ 16 ], household food security [ 17 ], household tenure [ 18 ] and the availability of household co-residents to provide care [ 19 ] and to reduce social isolation [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Impact of individual, household, and area characteristics on health and social care outcomes for people with multimorbidity: Protocol for a multilevel analysis

MacRae,

Mercer,

Lawson

et al. 2023

PLoS ONE

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Background Multimorbidity is one of the greatest challenges facing health and social care systems globally. It is associated with high rates of health service use, adverse healthcare events, and premature death. Despite its importance, little is known about the effects of contextual determinants such as household and area characteristics on health and care outcomes for people with multimorbidity. This study protocol presents a plan for the examination of associations between individual, household, and area characteristics with important health and social care outcomes. Methods The study will use a cross-section of data from the SAIL Databank on 01 January 2019 and include all people alive and registered with a Welsh GP. The cohort will be stratified according to the presence or absence of multimorbidity, defined as two or more long-term conditions. Multilevel models will be used to examine covariates measured for individuals, households, and areas to account for social processes operating at different levels. The intra-class correlation coefficient will be calculated to determine the strength of association at each level of the hierarchy. Model outcomes will be any emergency department attendance, emergency hospital or care home admission, or mortality, within the study follow-up period. Discussion Household and area characteristics might act as protective or risk factors for health and care outcomes for people with multimorbidity, in which case results of the analyses can be used to guide clinical and policy responses for effective targeting of limited resources.

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Association between household context and emergency hospital use in older people: a retrospective cohort study on indicators for people living alone or living with somebody with frailty, developed from routine healthcare data in England

Cited by 5 publications

References 25 publications

Social determinants of health and emergency department visits among older adults with multimorbidity: insight from 2010 to 2018 National Health Interview Survey

Social determinants of health and emergency department visits among older adults with multimorbidity: insight from 2010 to 2018 National Health Interview Survey

Realising the potential of community-based multidisciplinary teams: insights from evidence

Impact of individual, household, and area characteristics on health and social care outcomes for people with multimorbidity: Protocol for a multilevel analysis

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