Unmet Needs, Unplanned Admissions

Executive summary

England’s population is ageing, and more older adults are living with health conditions that make carrying out everyday activities difficult. Without appropriate support, these difficulties may worsen and lead to complications that require emergency hospital admission. This report examines how social care needs, such as problems with mobility, self-care or managing daily tasks, relate to emergency hospital admissions among people aged 65 and over in England. We analysed more than a decade of data from the English Longitudinal Study of Ageing (ELSA) and linked NHS hospital admission records and carried out in-depth interviews with ELSA sample members to get a better understanding of their experiences with social care. The findings show a clear pattern: older adults with social care needs are more likely to be admitted to hospital, and to be admitted more often, than those without such needs, even after taking age, health, socioeconomic circumstances and lifestyle into account.

Key findings

1. Having social care needs strongly predicts emergency hospitalisations. 
   Older adults with care needs were more likely to have at least one emergency admission, and to experience a higher number of admissions on average, than those without difficulties. Needs related to mobility and self-care (ADL) carried the highest risk. Even when needs were met through social care, people with needs had a greater likelihood and number of emergency admissions than those without social care needs. This may reflect the overall poorer health of people receiving social care, who were a minority in our sample compared to those who required social care but did not receive it.
2. Repeated hospitalisations often trigger social care provision. 
   Interviews with older adults currently receiving social care revealed that some older adults first gain access to social care following a hospital stay, often through “discharge-to-assess” pathways. This means care is reactive, arriving after a crisis rather than preventing one.
3. Hospitalisations may have been avoided with earlier support. 
   Common reasons for admission included falls, progressive lung condition flare-ups, infections and problems with hydration or medication. These conditions are often preventable with timely community support, home adaptations or better outpatient care. Delayed assessments and difficulties navigating the care system contributed to deteriorations that led to hospitalisation.
4. Older adults struggle to access or understand social care. 
   Participants reported long waits for assessments, limited knowledge of available services, difficulty accessing advice and financial barriers. These challenges sometimes led people to refuse care or miss out on care they were eligible for, potentially increasing the risk of avoidable hospital admissions.
5. Hospital experiences vary, but strain on emergency care is clear. 
   Interviews suggest that planned care was generally positive, but emergency admissions were marked by long waits, bed shortages and delays in treatment. Some participants were discharged without appropriate follow-up or referral to social care.

Policy implications

1. Prioritise early identification and prevention. 
Strengthen proactive approaches such as:

• Early assessments for people with emerging mobility or self-care difficulties.
• Rapid-response community services for infections, COPD or falls.
• Expansion of preventative home adaptations and telecare.

2. Improve access to information and advice. 
Many people did not understand what help they were entitled to, how assessments work or how social care funding operates. Better public information (as required under the Care Act 2014) would help ensure support is accessed before a crisis.

3. Reduce delays in assessments and provision. 
Long waits for assessments and care packages can leave many older adults at greater risk. Streamlined processes, better staffing and reduced backlogs could significantly lower avoidable hospitalisations.

4. Strengthen integration between health and social care. 
Discharge-to-assess models show promise but remain inconsistent. More coordination would ensure

• Post-hospital care is arranged promptly.
• Follow-up support meets actual levels of need
• Communication across services improves.

5. Target support for people living alone and those with long-term limiting health conditions. 
These groups had notably higher hospitalisation risks. Tailored interventions, such as enhanced falls prevention, medication management and community monitoring, could help reduce emergency admissions.

Conclusion

Older adults with social care needs are more likely to experience emergency hospitalisations, and to experience more of them, compared to older adults without difficulties. Provision of care does not appear to reduce emergency hospitalisations, suggesting that for some people it may be too little, too late. Timely, accessible and coordinated social care has the potential to play a critical role in preventing emergency hospital use among older adults. While many hospitalisations are likely due to medical conditions, the availability and adequacy of social care can shape whether these conditions escalate into avoidable crises. Policies that strengthen early intervention, improve access and advice, and integrate health and social care responses could reduce pressure on hospitals and improve quality of life for older adults.

Social care needs and hospital use among older adults in England: Background and context

Foreword from Age UK

Age UK has, for many years, used ELSA data to estimate the number of older people who have a need for social care which is not being met. Our most recent estimate is that this applies to 2 million older people in England. These older people need support with basic, everyday activities such as dressing, washing or eating, and do not get the help they need.  

With this report, NatCen researchers have taken a closer look at those older people and have addressed one of the key questions at the forefront of the minds of all those engaged in the conversation about the need for reform of social care services in England: what are the consequences for older people and for our stretched healthcare system of a social care system which leaves so many without the support they need?

To do so, they have made excellent use of the highest quality data available. ELSA is a unique resource which now provides more than 20 years of insight into the lives of people aged 50 and over in England. Linking this rich data source with NHS records offers a unique opportunity to understand real use of health services within the real context of older people’s lives.  

The research provides us with robust evidence that care and health needs are inextricably linked. Those older people who have care needs are more likely to be hospitalised and to be hospitalised more frequently than those who do not. These effects remain even when a range of other characteristics, including health, are taken into account.

However, the message is not completely clear. In this analysis, older people whose care needs are met are – contrary to what we might expect – more likely to be hospitalised than those who don’t get the support they need. As the researchers hypothesise, this may because older people with greater health and care needs are more likely to have their needs met.  

So, as is so often the way, the conclusion must be that more research is needed. In the meantime, this paper makes a fantastic contribution to the evidence base on the link between older people’s health and care needs, supporting the work of those advocating for the reform of social care. Social care for older people must be reformed so that older people are able to live well, independently and with dignity for as long as possible. Now, with this report, we also have robust evidence that such reforms would also be of benefit to the NHS. 

Research background and context 

England's population is ageing rapidly, with 22.3 million people over 50 in 2025 (38% of the population), projected to rise to 26.6 million by 2045 (42%) (ONS, 2025) ¹ . This demographic shift presents significant challenges for health and social care systems, particularly as healthy life expectancy is falling and stark inequalities persist by geography and deprivation, with those in deprived areas spending less of their lives in good health and having lower access to support (Reeves et al., 2025) ² . Many older adults struggle with daily activities due to health, with the 2025 GP patient survey showing that 77% of those aged 85+ reported activity limitations, and two-thirds of those aged 80+ were living with multiple long-term conditions (NHS England, 2025) ³ . The World Health Organisation's United Nations Decade of Healthy Ageing (2021–2030) emphasises the right of older people to receive care and support to maintain their functional ability—enabling them to be and do what they value (World Health Organization, 2020) ⁴ .  

In England, adult social care is arranged and funded by individuals and their families or by local council social services. Provision of social care by local councils requires completing a care needs assessment, and funding towards the cost of social care may be provided depending on income and savings. Non-means tested support is provided in some cases by the NHS and includes up to 6 weeks of care following illness or hospital discharge, or continuous care for individuals with complex and serious health conditions. In this report, we focus on social care for individuals living at home, rather than in care homes. Types of social care and support received at home include receiving help from paid carers, meals at home (meals on wheels), home adaptations and assistive household gadgets, and personal alarms and monitoring systems (telecare).

The adult social care system in England faces significant and sustained pressures driven by demographic change, funding constraints, and workforce challenges. A recent report from the Health and Social Care Committee highlights that 2 million people aged 65+ are not receiving the social care that they need, and 1 in 7 older adults have care costs of over £100,000 ⁵ . The report states that local authority budgets are struggling to meet the increasing demand for social care, the care provider market is struggling to cover costs, and care workers are underpaid, leading to a large number of vacancies and high turnover rates. The 2024 British Social Attitudes survey showed that public satisfaction with social care stands at just 13%, with 53% dissatisfied, reflecting widespread concern about the sector's capacity to meet current and future needs (Taylor et al., 2025) ⁶ . Social care reform in England has been attempted repeatedly, with the Health Foundation identifying over 25 relevant social care commissions, select committee inquiries and white papers published between 1997 and 2024 (Allen et al., 2024) ⁷ . Most previous proposals agreed on the need for more funding, a fairer charging system, better workforce pay and conditions, and improved coordination with health services (Allen et al., 2024). However, implementation has repeatedly stalled due to cost concerns (Allen et al., 2024). In January 2025, the UK government announced the launch of the Casey Commission, an independent commission on adult social care chaired by Baroness Louise Casey, which is due to conclude in 2028 (gov.uk) ⁸ . The broad aim of the Commission is to find solutions to the deep-rooted challenges in adult social care, such as inconsistent standards of care, an overstretched workforce, and limited support for unpaid carers, by establishing what citizens require from social care for a sustainable future, building cross-party consensus, and ultimately creating a ‘national care service’ (UK Parliament) ⁹ .

Understanding the relationship between social care needs and hospital use is particularly critical in England, where the NHS faces mounting pressure from avoidable hospitalisations amongst older adults. In 2023/24, there were over 1.1 million emergency admissions among older people for potentially avoidable reasons such as falls and exacerbations of chronic conditions that should not normally require an admission (NHS England, 2025 and 2024) ¹⁰ . The likelihood of attending A&E and being admitted increases with age, with one in five people aged 75+ experiencing an emergency readmission within 30 days of discharge (Reeves et al., 2025). When social care needs go unmet, older people may experience deterioration in their health and functional status, increasing their risk of these preventable complications. Despite growing demand in the population, social care needs are not being met for everyone who request these services. In 2023/24, there were 1.4 million new requests for support from older people to Adult Social Service Departments, but over half resulted in no services provided or only provision of universal services and signposting elsewhere (NHS England, 2024) ¹¹ .  The Association of Directors of Adult Social Services (ADASS) Spring survey of local councils found that over 370,000 people were waiting for a care needs assessment or care to start in March 2025 ¹² . The Nuffield Trust showed that in June 2025, 85% of patients experiencing delayed discharge were aged 65+, and these delays were often due to waits for social care or community support (Nuffield Trust, 2025) ¹³ . These delays contribute to bed shortages, reduced capacity to address acute medical needs, and increased risks to patient safety and dignity (Reeves et al., 2025). These figures demonstrate that unmet social care needs can have cascading effects across the health and social care services.

With England's ageing population and limited social care resources, understanding how various social care needs—met or unmet—affect hospitalisation patterns is key to developing integrated care policies that help reduce preventable hospital admissions and support older adults' independence and wellbeing. The English Longitudinal Study of Ageing (ELSA) provides extensive information on various aspects of social care for older adults in England, including the prevalence and type of social care needs, and the levels of care receipt. ELSA is a longitudinal study that tracks the health, social, and economic circumstances of individuals aged 50 and over every two years (ELSA, n.d) ¹⁴ . By linking ELSA data with NHS hospital records across an 11-year period up to 2024, we had a unique opportunity to investigate the dynamic relationship between social care needs and hospital admission patterns in the ELSA sample over time.  

A previous study using ELSA-NHS linked data found that individuals who reported receiving care in the 2012-2013 data collection wave of ELSA had an increased risk of hospital admissions over the next 5 years compared to individuals receiving no care (Maharani et al., 2024) ¹⁵ . This finding suggests that care recipients represent a particularly high-risk group, with further research needed to understand the reasons for the increased number of hospitalisations in this group, and whether they could benefit from targeted interventions to avoid potentially avoidable hospitalisations. Further research is also needed to examine whether hospitalisations are higher in individuals who have the need for social care (regardless of whether they receive care or not), compared to those who don’t have the need for social care and are able to perform everyday activities without assistance.  

In the current study, we compared the likelihood and the number of emergency hospital admissions between ELSA sample members aged 65+ who have social care needs, and those who have no social care needs. We further distinguish between individuals with social care needs who have mobility and self-care (ADL ¹⁶ ) needs who (1) mostly receive help for their needs (met needs), or (2) mostly don’t receive help for their needs (unmet needs). We also look separately at individuals who have difficulties with everyday independent living tasks (IADL ¹⁷ ) but haven’t got any self-care or mobility needs. For 4,808 sample members across 5 waves of data collection, we extracted individuals’ care need status in each wave and used modelling to predict the likelihood and the number of hospitalisations over the 2-year period following each wave. We also carried out in-depth interviews with 13 sample members who had social care needs (both for which they received and didn’t receive help) to better understand their social care needs, the support that they were receiving for these needs and their experiences of care and hospitalisations.  

Methodology

Data  

ELSA Waves 6-10

ELSA has been collecting data on the dynamics of health, social, wellbeing and economic circumstances in the English population aged 50+ since 2002, with 11 waves (data collection periods) collected to date. Data is collected from the same sample members every two years, with new sample members added on a regular basis to ensure that the sample remains representative.  For our analysis on the relationship between social care needs and hospitalisations, we used ELSA data collected from 4,808 sample members aged 65+ who took part between 2012 and 2023, across 5 waves of data collection. These sample members had taken part in at least one data collection, had consented to health record linkage and had linked hospital records. The latest ELSA wave (2023 – 2024) was used to report the prevalence of social care needs but was not included in the analysis because linked hospitalisation data were available up until the start of 2024.

Hospital episodes admitted patient care (HES-APC) data  

Hospitalisation data were obtained from Hospital Episode Statistics (HES) ¹⁸ , provided by NHS England and linked to ELSA. HES is an administrative dataset of inpatient admissions, outpatient appointments and Accident & Emergency attendance records in England. HES contains information about diagnoses, operations, admissions, discharges and some sociodemographic characteristics. Data covered the period May 2012 – March 2024.  

This study used the HES Admitted Patient Care (APC) dataset, and this was filtered by admission type to only include records where the patient was admitted via emergency. Emergency admissions encompass a range of scenarios including immediate care via emergency departments, mental health crisis teams, general practitioners, consultant clinics, bed bureaus, transfers between providers, planned home births, and other urgent or unspecified methods.

Data linkage  

ELSA data from 2012 – 2024 was linked to the HES-APC data. NHS England used the following criteria to match ELSA respondents to their health records:  

• NHS number
• Date of birth
• Sex
• Postcode (and address)
• Name

87% of ELSA participants gave consent for their data to be linked to their NHS records. The success of linkage was high, with 79% of ELSA participants having some HES-APC data returned and a further 6% having no record of a hospital admission during that time.  

Derivation of likelihood and frequency of hospital admission data was extracted for each participant by ELSA wave. The time period for each wave was between the start date for the current wave and the start date of the next wave. This was then linked to the participant’s interview data on care needs for that wave. The time period between waves is approximately two years.  

Likelihood of admissions was derived as a binary variable that was equal to 1 where the individual had a least one emergency hospital admission during the given time period.

Frequency of admission was derived as a count variable and was based on Continuous Inpatient Spells (CIPs). The HES-APC data contains records of hospital admission episodes. An episode can be defined as a period of continuous care from a single consultant. However, this may not include the whole hospital stay because if a patient’s care is taken over by another consultant, then a new episode begins. The whole length of stay in a hospital is defined as a spell. The spell ends when the patient is discharged or transferred to another hospital or dies.  Therefore, when a patient is transferred to another hospital, one spell ends, and a new one begins but they have not yet been discharged. To ensure that the whole period of a patient being in hospital was captured, CIPs were derived. The CIP includes the entire continuous care period including transfers to other providers.  

Social care needs definition

ELSA contains questions on the presence of difficulties with everyday activities, and whether the respondent receives help with them. The questions cover the following areas and 12 activities:

1. Mobility – basic movement activities

• Walking 100 yards
• Climbing a flight of stairs

2. Activities of Daily Living (ADL) - basic self-care tasks that are fundamental to independent living and personal care:

• Bathing/washing - ability to wash oneself
• Dressing - putting on and taking off clothes
• Eating - feeding oneself
• Toileting - using the toilet
• Mobility / transferring – getting in/out of bed
• Mobility / transferring –walking across a room

3. Instrumental Activities of Daily Living (IADL) - more complex tasks required for independent living in the community, but not essential for basic self-care:

• Managing medications - taking medication correctly
• Managing money - paying bills, keeping track of expenses
• Shopping - grocery shopping
• Doing work around the house and garden

An activity was classed as a social care need if the sample member reported experiencing difficulty with the activity. For each ELSA data collection wave that a sample member was interviewed in, we considered a need as being ‘met’ if the sample member reported that they had difficulty with that activity and received help with it. We considered a need as being ‘unmet’ if the sample member reported that they have difficulty with that activity and do not receive help with it. Therefore, each activity coded as:

1. No need: no difficulty  
2. Met need: experiences difficulty and receives help  
3. Unmet need: experiences difficulty and does not receive help  

Taking into account all 12 activities, for each data collection wave that they were interviewed in, sample members were categorised into one of the following ‘social care needs’ groups for analysis purposes:

1. No needs: have no difficulties across the 12 activities
2. Unmet needs: not receiving help for at least half or all of their ADL/mobility needs  
3. Met needs: receiving help for more ADL/mobility needs than the number of ADL/mobility needs for which help is not received.  
4. IADL needs only: no unmet or met ADL/ mobility needs and one or more IADL needs for which help is either received or not received

Individuals who required only assistance with instrumental activities of daily living (IADL) were assigned to a separate group, as we anticipated that these needs would be less likely to raise the risk of hospitalisation compared to needs related to activities of daily living (ADL) or mobility.  

Sample members could be in the same or different social care needs groups across the different data collection waves they were interviewed in. For example, a sample member could be in the ‘no needs’ group in waves 5 and 6, change to the ‘unmet needs’ group in wave 7, and change again to the ‘met needs’ group in wave 8.  

Regression modelling and statistical analysis  

The aim of our analyses was to investigate potential associations of hospitalisation with social care needs, after controlling for other factors that are likely to be associated with hospitalisation. We performed two separate analyses to predict likelihood of hospitalisation (Model 1) and frequency of hospitalisation (Model 2) from social care needs group (no needs, IADL needs only, met needs, unmet needs).  Both models adjusted for the following covariates:  

• demographic factors (age, sex, ethnicity)
• socioeconomic factors (area deprivation, educational qualification, non-housing wealth)
• health and lifestyle factors (limiting long-term health conditions, falls since last interview, smoking)
• household and environmental factors (presence of another person in the household, rural or urban location)
• data collection wave

The analysis dataset included 14,232 person-wave observations from 4,808 unique individuals across the 5 ELSA data collection waves. Of the 4,808 individuals, 23% participated in one wave and 77% participated two or more waves.

Model for examining the likelihood of hospitalisation

We examined whether people in different social care need groups (no needs, met needs and unmet needs) had different chances of being hospitalised. To do this, we used a statistical method called a Generalised Estimating Equation (GEE) model. This approach is particularly useful when analysing data from the same people measured multiple times over several years, as was the case in our study (Liang & Zeger, 1986; Wang, 2014) ¹⁹ .

The challenge with this type of data is that measurements from the same person tend to be related to each other—for example, someone who was hospitalised in one year might be more likely to be hospitalised again in a later year. GEE models handle this issue by accounting for these connections between measurements while still producing reliable results about overall patterns across the entire study population (Hubbard et al., 2010) ²⁰ .

In our analysis, we grouped all measurements by individual participants and tested different ways of accounting for the relationships between their repeated measurements. We used a statistical quality measure - Independence model Criterion (QIC0), to determine which approach fitted our data best (Pan, 2001) ²¹ . We found that measurements from the same person taken at different times were largely independent of each other once we accounted for other factors in our analysis. Thus, we have specified independence working correlation structure in the final GEE model.

Independence correlation structure found in our data makes sense for two main reasons. First, the study collected data every two years (or longer if someone missed a survey round), which is enough time for circumstances to change substantially between measurements. Second, hospitalisation appears to be driven primarily by factors that change over time—such as a person's age, current health status, social care needs status and living situation—rather than by unchanging personal characteristics. Once we included these time-varying factors in our analysis, there was little remaining connection between a person's measurements across different time points.

Model for examining the frequency of hospitalisation

We also examined the association between social care needs group and the number of hospitalisations. To do this, we used a statistical method called negative binomial regression. This approach is specifically designed for counting events—such as the number of times someone is hospitalised—particularly when the data show a pattern common in healthcare: most people have no hospitalisations or just one or two, whilst a smaller number of people have many hospitalisations (Hilbe, 2011) ²² .

A simpler statistical method called Poisson regression exists for counting events, but it makes an assumption that didn't fit our data well: it assumes that the average number of events and the spread of the data around that average are the same (variance equals the mean). Our data, in line with what is seen in typical healthcare data, showed much more variation than this assumption allows—a characteristic statisticians call "overdispersion" (Cameron & Trivedi, 2013) ²³ . We found substantial overdispersion in our data (theta = 0.858), consistent with other studies which used Hospital Episodes data (Bottle et al., 2017) ²⁴ . The negative binomial model handled this extra variation by including an additional parameter that captured how spread out the data were, leading to more reliable results for our data.

As the same people were surveyed multiple times over several years, measurements from the same person could be related to each other. To address this, we used a statistical adjustment called cluster-robust standard errors, which accounted for the fact that multiple measurements came from the same individuals (Cameron & Miller, 2015) ²⁵ . We found that approximately 8.5% of the variation in hospitalisation numbers was due to differences between people (rather than changes within the same person over time), indicating moderate clustering in the data. This adjustment ensured our results remained valid even though we were analysing repeated measurements from the same individuals.

Qualitative interviews  

To complement the quantitative analysis and explore participants' experiences in greater depth, we conducted semi-structured qualitative interviews with a purposively selected subsample of ELSA participants. Participants were selected to ensure representation across key characteristics identified in the quantitative analysis: met and unmet social care needs, age, gender, living arrangements and number of hospitalisations.  

A total of 13 interviews were conducted remotely via telephone or Microsoft Teams video conferencing, depending on participant preference, between 27th of October and 20th of November 2025. Each interview lasted around 30 minutes on average and explored participants' experiences of living with social care needs, their use of formal and informal support, experiences of hospitalisation, and perceptions of the relationship between social care provision and hospital use. All interviews were audio-recorded with participant consent, transcribed using transcription function on MS Teams, and analysed using the Framework approach. Ethical approval for the qualitative component was obtained from NatCen Ethics Committee, and all participants provided informed consent prior to participation.

Participants were purposively selected to be representative across demographics, living arrangements, and numbers of hospitalisations. Table 1 provides further detail of the qualitative participants, their health conditions and reasons for hospitalisation in the past five years. 

^{*Note: COPD – Chronic obstructive pulmonary disease}

Limitations

For our quantitative analysis, we focused on hospitalisations during the time period between the start date for the current wave and the start date of the next wave. An alternative would have been to use the time period in between the respondent’s interview date in one wave, and their interview date in the next wave (or the start of the next wave if they didn’t take part in the next way). The benefit of our chosen approach is that the length of time was consistent across respondents within a wave. However, it presents the limitation that individuals could have been hospitalised during the time between the start of the wave and their interview taking place and may have had a different care needs status at the time of their hospitalisation than they reported in the interview. For example, someone could have reported having unmet care needs that led to them being hospitalised, and the hospitalisation could have triggered the provision of social care to this person. In this situation, the individual would subsequently report having met needs during the ELSA interview, and the hospitalisation would be associated with a met needs status despite occurring when the person’s needs were unmet. In the latest ELSA wave with linked hospitalisation data (wave 10), a quarter of hospitalisations took place before the interview, suggesting that only a minority of cases were affected by this limitation.  

Prevalence of social care needs in ELSA wave 11 

The following section focuses on the social care needs of participants in ELSA Wave 11, which is the most recent completed data collection period that ran from October 2023 to July 2024. This section is based on the unweighted data for 7,719 respondents. The weighted data produced very similar results, but we report unweighted data here as it includes a larger number of respondents (only ELSA sample members who were originally invited to join ELSA and still live in private households receive a weight, whereas any partners who join ELSA during the course of the study or respondents who move into care homes do not). 

Prevalence of care needs  

In wave 11, the proportion of sample members aged 50+ in each care needs group was:  

• No needs: 74%
• IADL needs only (difficulties with everyday independent living activities): 3%
• Met needs (mobility/self-care needs with help received for all or more than half): 5%
• Unmet needs (mobility/self-care needs with no help received with all or at least half): 18%

Types of care needs  

The social care needs of the ELSA participants can be classified into 3 different types: mobility needs, IADL (difficulties with everyday independent living activities) and ADL (self-care needs). We found that there were differences across the different types of needs in whether they tended to be met or unmet. Figure 1 shows the percentage of participants aged 50+ who reported needing help with each activity, as well as the portion of participants that stated if the need was met or unmet. Mobility and ADL needs tended to be unmet, with the exception of eating, whereas IADL needs tended to be met.  

Care needs by age  

Figure 2 shows the proportion of care needs by age. The majority of respondents aged between 50 and 85 had no social care needs. Among respondents aged 85+, the proportion with no social care needs decreased with age, and the proportion of those with care needs increased. The age group of 85+ was highlighted in a recent report by Age UK (Reeves et al., 2025) ²⁶  as being the most likely to need health and care services. 

Figure 1. Social care need status in the latest ELSA data collection period (2023 - 2024) 

Met and unmet needs by type in Wave 11

^{The percentages and baseline N for Figure 1 can be found in Appendix C.}

Figure 2. Social care needs group distribution by age in the latest ELSA data collection period (2023 - 2024) 

Proportion of care needs by age in Wave 11 (Ages 50+)

^{The data on which Figure 2 is based can be found in Appendix D.} 

Patterns of social care needs and hospitalisations across 5 ELSA waves

To look at patterns of social care needs and hospitalisations we focused on the period from 2012 to 2023 (ELSA waves 6-10) which was the period that the latest linked data were available for. This analysis only included participants aged 65+, due to the smaller number of people with care needs in the 50-64 age group, which disproportionally lowered the mean age of the ‘no needs’ group compared to the care needs groups.  

Prevalence of social care needs

Across all 5 waves combined (with individuals who took part in multiple waves counted multiple times), the proportion of sample members aged 65+ in each care needs group was:

• No needs: 57%
• IADL needs only (difficulties with everyday independent living activities): 4%
• Met needs (mobility/self-care needs with help received for all or more than half): 8%
• Unmet needs (mobility/self-care needs with no help received with all or at least half): 31%

The proportion of sample members with no needs is lower than in wave 11 because the 50-64 age groups is excluded.

We also found that there were consistent patterns for changes in needs status across the waves. Out of 3,795 sample members who took part in 2 or more waves, the top three patterns (accounting for 57% of sample members) across waves were:

• No needs across all waves (35%)
• Change from no needs to unmet needs (12%)
• Unmet needs across all waves (11%)

Among sample cases in the met needs group, 70% reported that the care received meets their needs all of the time, 24% reported that it usually meets their needs, 5% reported that is sometimes meets their needs, and only 1% reported that it hardly ever meets their needs.  

Hospitalisations in each care need group  

Figure 3 shows the percentage of participants admitted to hospital by care needs group across the 5 waves. A lower proportion of sample members with no care needs were admitted to hospital, compared with sample members who had care needs. This was the case for those with met and unmet mobility and / or ADL (self-care) needs across all data collection periods. Furthermore, this was also the case for the IADL needs only group (difficulties with everyday independent living activities) for all waves except wave 10. A higher proportion of sample members were admitted to hospital in the most recent waves across all care need groups, potentially due to the impact of the COVID-19 pandemic.

Figure 3. Hospital admissions for each social care needs group for respondents aged 65+ in 5 ELSA waves covering 2012-2023 (‘ELSA waves’ 6-10) 

Hospital admission percentage by needs group 

^{The data on which Figure 3  is based can be found in Appendix D.} 

Descriptive characteristics of the social care needs groups

Table 2 shows characteristics of the sample for each social care needs group. It includes individuals aged 65+ participating between 2012 and 2023. Participants were excluded from analysis in the data collection periods where we were missing HES-APC data or interview data about their social care needs.  

^{*Note: Ambulatory Care–Sensitive Conditions (ACSC) flag identifies hospital admissions that are considered potentially avoidable because the condition could, in principle, be managed effectively in primary care or community settings.
** Note: Net total non-housing wealth consisting of savings and investments, after subtracting financial debt.} 

Hospitalisation likelihood and frequency  

The likelihood of hospitalisation and the number of hospitalisations was higher for the groups with care needs, compared to the no needs group. Within the care needs groups, these metrics are highest for the met needs group, followed by the unmet needs group, and lowest for the IADL needs only group. Only 31% of those with no needs were hospitalised, compared to 44 - 58% in the care needs groups. The mean number of hospitalisations is 0.50 in the no needs group and ranges between 0.82 -1.36 in the care needs groups.

Association between care needs and ACSC hospitalisations

The percentage of sample members with a flag for potentially avoidable hospital admissions (ACSCs) is higher for the groups with care needs (20 – 24%) compared to the no needs group (16%). An exploratory analysis showed that the overall effect of need status on the likelihood of being hospitalised with an ASCS condition was statistically significant (Type III Wald χ²(3) = 38.23, p < .001). Individuals with no needs had significantly lower odds of ACSC hospitalisation compared with those whose needs were met (OR = 0.59, p < .001) and those with unmet needs (OR = 0.59, p < .001). No statistically significant differences were observed between individuals with no needs and those with IADL needs only (OR = 0.74, p = .27).

Number of needs

The met needs group had a higher number of mobility/ADL needs (M = 3.5) and IADL needs (M = 1.6) compared to the unmet needs group (mobility/ADL M=2.4; IADL M=0.9). The IADL needs only group had a mean of 1.3 needs.  

Demographic factors

The no needs group had a lower mean age than the groups with needs (M = 74), and this was four years younger than the mean age for the met and unmet needs group (M = 78), and  3 years younger than the IADL group (M = 77). There was a larger proportion of males in the no needs group (51%) compared to the groups with needs (range 38 – 41%). The proportion of non-White sample members was similar across the groups (range 2 – 3%).

Socioeconomic factors

Mean wealth was lower amongst those with care needs (range £86,800 - £121,000) than those with no needs  (£190,000). We also found that those with care needs tend to live in areas with higher deprivation and to have lower levels of educational qualifications.  

Health and lifestyle factors

Within ELSA, we collect information about limiting long-term health conditions that participants might have. We define a long-term health condition as anything that has troubled the respondent or is likely to affect them over a period of time. These conditions are much more common within the care needs groups. Only 26% of those within the no needs group had a limiting long-term health condition, in comparison to 67% for those in the IADL group, 86% for those in the met needs group and 78% for those in the unmet needs group.

Prevalence of falls was also higher within the care needs groups. 24% of those within the No needs group had a fall in the last two years, in comparison to between 35% to 53% within the care needs groups.  

There was also a slight difference in the prevalence of smokers within each care needs group. 7% of those with No needs or IADL needs only were smokers, whereas between 9% and 10% of those in the met and unmet needs group were smokers.

Household and environmental factors

We also looked at household structure within each needs group. Living alone was more common in the IADL only (44%) and unmet needs (45%) groups. In comparison, 29% of those in the no needs group and 26% in the met needs group lived alone.  

Urban living is slightly more common among those with care needs with between 75% and 78% of those with care needs living in an urban area, compared to 73% of those with no needs.  

Reasons for hospital admissions  

To understand the reasons that people were being admitted to hospital and to look at the trends within these, we looked at the primary diagnosis for each hospital spell. The primary diagnosis in the APC dataset uses ICD10 coding (WHO, 2019) ²⁷  and the Chapter level description was matched on the diagnosis code. We then looked at the top 5 reasons for hospitalisation by social care needs group. The figures represent percentage of people within the social care need group which were hospitalised for the specified condition.  

The top 5 most prevalent reasons for hospitalisations for each group, accounting for over 2/3 of group members (ranging from 68 – 71%), were:

• No needs group: Circulatory diseases (19.4%) and abnormal findings (17.2%) are the top two reasons, followed by injuries (11.2%), digestive diseases (10.9%), and respiratory diseases (10.4%).
• IADL needs only: Circulatory (17.5%) and respiratory (17.0%) diseases are top, with injuries (14.7%) and abnormal findings (14.4%) also prominent, and digestive diseases accounting for 7.0%.  
• Met needs: Respiratory diseases (17.7%) are the leading cause, followed by circulatory (16.1%), abnormal findings (15.4%), injuries (10.4%), and genitourinary diseases (8.0%).
• Unmet needs: Circulatory (18.6%) and respiratory (16.1%) diseases top the list, with abnormal findings (15.7%), injuries (10.6%), and digestive diseases (8.6%) also significant.  

The category of abnormal findings (full title is ‘symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified further’) included “syncope and collapse” as the top diagnosis for the no needs and IADL needs only groups, while “tendency to fall, not elsewhere classified” was most common for met and unmet needs groups.

Summary of trends

Diseases of the circulatory system and diseases of the respiratory system were consistently among the top causes of hospitalisation across all groups, though their ranking and percentage varied slightly. Injury, poisoning, and certain other consequences of external causes and symptoms, signs, and abnormal clinical/lab findings not elsewhere classified were also frequent reasons for admission in all groups. Those with met or unmet needs showed a slightly higher prevalence of respiratory diseases and falls-related diagnoses, potentially reflecting greater vulnerability and complexity in their health status.  

Understanding hospitalisations through social care needs

Likelihood of hospitalisation (Model 1)

The Generalized Estimating Equations (GEE) model showed that having social care needs was a significant predictor of being hospitalised, after controlling for demographic factors (age, sex, ethnicity), socioeconomic factors (area deprivation, educational qualification, non-housing wealth), health and lifestyle factors (limiting long-term health conditions, falls since last interview, smoking), household and environmental factors (presence of another person in the household, rural or urban location), and the data collection wave. Controlling for these ensures that the model takes into account other factors that might have an influence on the outcome measure – likelihood of hospitalisation. Full results are shown in Appendix 3. Results are reported as Odds Ratios with 95% confidence intervals. Odds Ratios are used to look at the likelihood of being hospitalised in one group in comparison to the likelihood of it happening in another group.  

Individuals with care needs had significantly higher odds of being hospitalised compared to those with no care needs. Relative to the no needs group, the odds of hospitalisation were 2.09 times higher (109% increase) for those with met needs, 1.43 times higher (43% increase) for those with unmet needs, and 1.26 times higher (26% increase) for those with IADL needs only.

Other characteristics that were significantly associated with an increase in the odds of being hospitalised were:

• Older age: each additional year is associated with 4% increase  
• Being male: 19% increase compared to females
• Having a limiting long-term health condition: 45% increase compared to those without such conditions
• Living alone: 17% increase compared to those living with others
• Having had a fall recently: 24% increase compared to those who hadn’t
• Taking part in a more recent ELSA data collection period: each successive wave was associated with a 35% increase  
• Smoking: 16% increase compared to non-smokers

The predicted likelihood of hospitalisation was higher for individuals with care needs compared to individuals without care needs (Figure 4). While controlling for demographic, socioeconomic, health, lifestyle, household and environmental factors, the probability of being hospitalised was 26% (CI: 23.9 – 28.1) for those with no social care needs. This increased to 31% (CI: 26.5 – 34.9) for the IADL needs only group, 33% (CI: 30.5 – 36.3) for the unmet needs group, and 42% (CI: 38.0 – 46.6) for the met needs group. 

Figure 4. Predicted probabilities of emergency hospital admission for each social care needs group for adults aged 65+ 

Predicted probability of unplanned hospitalisations by care needs status

^{All other variables held at mean/modal values (95% CI based on robust SEs)}

Frequency of hospitalisation (Model 2)

The negative binomial regression model showed that having social care needs was a significant predictor of frequency of hospitalisations, after controlling for demographic factors (age, sex, ethnicity), socioeconomic factors (area deprivation, educational qualification, non-housing wealth), health and lifestyle factors (limiting long-term health conditions, falls since last interview, smoking), household and environmental factors (presence of another person in the household, rural or urban location), and the data collection wave. Appendix B presents the full model results. Results are reported as incidence rate ratios (IRRs) with 95% confidence intervals.  

Individuals with care needs experienced significantly more hospitalisations than those with no care needs. Compared to the no needs group, those with met needs had nearly twice as many hospitalisations (95% more), those with unmet needs had 44% more, and those with IADL needs only had 25% more.

The predicted number of hospitalisations was higher for individuals with care needs compared to individuals without care needs (Figure 5). Looking at the effect of social care group on the frequency of emergency hospitalisations while controlling for demographic, socioeconomic, health, lifestyle, household, and environmental factors, the predicted number of hospital admissions was 0.37 (CI: 0.33 - 0.42) for those with no social care needs. This increased to 0.47 (CI: 0.39 - 0.42) for the IADL only group, 0.54 (CI: 0.47 - 0.61) for the unmet needs group, and 0.73 (CI: 0.62 – 0.84) for the met needs group. 

Figure 5. Predicted number of emergency hospital admissions by each social care needs group for people aged 65+

Predicted probability of hospitalisations by care needs status

^{All other variables held at mean/modal values (95% CI based on clustered SEs)} 

Other characteristics that were significantly associated with an increase in the number of hospitalisations were:

• Older age: each additional year is associated with 3% increase  
• Being male: 22% increase compared to females
• Having a limiting long-term health condition: 39% increase compared to those without such conditions
• Living alone: 17% increase compared to those living with others
• Having had a fall recently: 17% increase compared to those who hadn’t
• Taking part in a more recent ELSA data collection period: each successive wave was associated with a 23% increase  
• Smoking: 18% increase compared to non-smokers
• Having a qualification that is less than O level or equivalent: 10% increase compared to those with degree-level qualifications 

Qualitative analysis findings

During qualitative interviews, participants were asked to self-assess whether their care needs were ‘met’ or ‘unmet’. Out of the 13 interview participants, five described their care needs as ‘unmet’, six as ‘met’ and two as ‘partially met’. In the interviews, we asked participants about their ability complete basic movement activities (e.g., walking 100 yards, climbing a flight of stairs), essential self-care activities (e.g., dressing, bathing, eating) as well as more complex daily tasks (e.g., housework and gardening, managing money). All eight individuals with self-declared ‘met’ or ‘partially met’ care needs were limited in their ability to one or more of these daily tasks. Six of the eight individuals received regular help either formally or informally, though in two cases this was from their elderly spouse, so it was not clear if the care they are able to offer is sufficient. The other two did not receive regular help.  

Among the eight participants who reported having their care needs ‘met’ or ‘partially met’ in their last ELSA interview, four received care from friends and family, two paid for it privately and only one received publicly funded care (i.e., they were in a nursing home). Of the two individuals who reported paying privately for care, one was paying out of pocket for a carer to come in every day. They described having “fought for years” to have a daily carer and finally managed to get this during the COVID-19 pandemic when their family members stopped visiting. The other individual was paying for her husband’s carer to help with household tasks (and so the care was not for her but for their spouse). The eighth participant did not have an informal or format carer but had had adaptations made to their house by social services.

In the group of participants who reported their needs being unmet in their last ELSA interview, there was one instance where the participant had a privately funded carer visit them once a day. This was organised by social services following emergency hospitalisation for heart failure. However, they did not perceive that once a day was enough for their condition and believed that they should receive two daily visits. This participant had severe mobility limitations and had waited 18 months for an assessment for home adaptations. The other four participants in the ‘unmet’ care needs group did not receive support from friends and family. Three out of these four participants were men living alone, and one was a female living alone.  

We asked participants about the care they currently receive and whether they need more support. The participants with ‘met/partially met’ care needs gave several reasons for not wanting or not requesting more care:

• perception that the current level of care from care professionals and medical staff is not good enough
• financial barriers of care  
• feeling that their current needs are met by either informal or formal care
• wanting to hold on to current levels of independence
• acceptance of old age and a belief that frequent hospitalisations are a part of it

Understanding more about older adults’ self-assessment of care needs would be a valuable area of further qualitative research. Such a research project could inspect personal, demographic and contextual drivers (e.g., strong identity around personal independence, low expectations of care, normalisation of lack of support, differences by gender, age, ethnicity, marital status and living arrangements).    

Avoidable and unavoidable hospital admissions

Looking at the reasons for hospitalisation (Table 1), avoidable hospitalisations (hospitalisations that could have been prevented with an earlier medical or care intervention due to their acute nature) were the most common reason for admissions in our qualitative sample. This included UTIs, chest infections, pneumonia, chronic obstructive pulmonary disease (COPD), falls, fractured hips (usually a consequence of a fall), collapses, fluid problems, breathing problems, bowel issues, abscess removal, and kidney stones. These conditions could often be managed with earlier detection, medication management, preventative or maintenance care and environmental or functional support at home.  

A minority of the reasons cited were unavoidable in that they could not have been prevented with an earlier medical or care intervention due to their acute nature. This included instances of amputation, heart failure, bypass surgery, bowel operations and cancer-related hospital care. Other reasons for hospital admission could be considered partially avoidable, i.e., the timing or severity could have been reduced with an earlier medical or care intervention. This included stroke, heart conditions, kidney issues, hip replacement and arthritis. This was also a small segment of our qualitative sample.

Avoidable and unavoidable hospital admissions by participant group

Among the five participants who reported ‘unmet’ care needs in their last ELSA interview, in three instances hospitalisations were not related to the individuals’ chronic health conditions, i.e., the hospitalisations were for a different, new reason. In one example, a participant with COPD and epilepsy had been hospitalised for a fall and a fractured hip (usually preventable with environmental or functional support at home). In a second example, an individual with arthritis and breathing problems was admitted to hospital for abscess removal (preventable with early antibiotic treatment). Lastly, a participant with a fitted pacemaker and mobility issues was admitted for bowel blockage (preventable through diet and hydration).

Among the participants who reported ‘met’ or ‘partially met’ needs in their last ELSA interview, seven out of eight participants had been hospitalised for reasons that were not related to the individuals’ chronic health condition. In one, a participant with COPD and asthma was admitted to hospital for a shoulder surgery after a fall (usually preventable with environmental or functional support at home). In another case, a participant with COPD and heart failure had been admitted to hospital for pneumonia and kidney issues (preventable with early treatment of respiratory symptoms, vaccinations, dietary changes).  

Taken together, the differences between individuals who were categorised into the ‘met/partially met’ and ‘unmet’ care needs groups based on their last ELSA interview were minor. Participants describing their care needs as ‘met/partially met’ were in many instances admitted to hospital for avoidable reasons such as falls, pneumonia and UTIs. This speaks to the earlier finding that participants’ self-assessment of care needs may not be accurate and that in reality they may have benefitted from earlier medical and care intervention.  

This pattern is illustrated by the experience of a respondent whose ELSA survey answers indicated help for most social care needs was being received, placing the individual within the ‘met needs’ group, yet the qualitative account revealed repeated requests for additional support were made but not met. Despite receiving one care visit per day, this level of support was not sufficient to meet the respondent’s needs, ultimately leading to a fall and subsequent hospital admission. The respondent believes that with more timely and adequate help, such an incident—and the subsequent hospitalisation—could have been prevented:

I've asked the social services, please increase my care. I phoned up the agency and said look, I'm really bad and need someone else…I fell and I woke up in a pool of blood. And it was my nose. But no one came out to check me. I had to pick myself up and get on with it.

^{(R3, met care needs group)}

This underscores how gaps in support and delayed intervention can directly contribute to the prevalence of avoidable hospital admissions among older adults, even for those who appear to have their care needs met.

Experiences of hospitalisations

In interviews, participants were asked to feed back on their experience of hospitalisations regarding the term of stay, discharge and outpatient care. The reported experiences were mixed to negative and varied by whether they had been admitted for avoidable or unavoidable reasons.

Two participants whose care needs were classified as ‘met’ based on their last ELSA interview and who had been hospitalised for an unavoidable, acute medical need (e.g., bypass surgery, cancer-related care) had a relatively positive experience of hospitalisation and were discharged in the agreed timeframe. One further participant with ‘met’ needs had shoulder surgery after a fall (normally avoidable reason with sufficient functional/environmental support at home) but was similarly discharged as planned. These experiences likely speak to the fact that cancer treatments and surgeries are pre-planned and so the in-patient experience is less affected by issues related to lack of beds and staffing in emergency care.

Participants who had been hospitalised for avoidable reasons described more negative experiences. These included four cases of hospital stays being longer than anticipated (for COPD exacerbation, heart failure, bowel operation and kidney stones – in all cases, the delay was due to the participants’ condition not improving as expected) and five cases of care being affected by lack of NHS staff or beds. These negative experiences likely stem from being admitted to emergency care where the NHS faces the most acute pressures on staff and resources. Notably, in two of the five cases affected by lack of staff and beds the participants still spoke positively of the staff and the care they received in emergency care, noting that the staff had done everything they could for them.

Looking more closely at the cases affected by lack of NHS staff or beds in emergency care, this included cases where a participant did not have access to a speech and language therapist following a stroke; a participant with a serious UTI needing to come to hospital every four hours due to lack of beds; a participant needing an abscess removed being “left on a trolley” for hours due to lack of beds, which also contributed to an extended hospital stay; a participant hospitalised for urinary issues sharing a room with another patient who attacked them during their stay as well as waiting a long time to be seen and witnessing staff arguing about who is responsible for their care; and a participant who had been hospitalised several times for ongoing collapses waiting many hours to be seen by a doctor and feeling that an appropriate assessment was not made due to staff being stressed and overworked.  

One participant described the hospitalisation experience in the following way:

Yeah, I've got examined. Well clocked in for whatever a better word got examined. Left on a trolley for God knows how many hours... More time on the trolley and eventually found a bed. Then they moved me. Then they moved me again and then they moved me again and then finally I came to rest in the ward.

^{(R11, unmet care needs group)}

One other reported negative experience was a case of participant having been admitted to hospital for a chest infection and contracting COVID-19 during their hospital stay. This made their underlying condition worse and led to symptoms of Long Covid following hospitalisation.

Experiences of discharge, outpatient care and care referrals

We asked participants about their experiences of discharge and outpatient care and whether they had been offered a care package or a referral to an assessment by the hospital. Four out of the 13 participants had been offered a care package either by the hospital during discharge or by social services close to the hospital admission. Among these, one said they had been hospitalised once for a UTI and once for COPD, and both times they had not been offered outpatient care or a care package/referral but had been offered financial support by social services. Another participant had been hospitalised for kidney issues and for pneumonia and placed in a care home while they waited for a care package. A third participant was admitted for heart failure and offered no outpatient care (which they found sufficient) but had been offered a care package because of friend’s intervention and now had one privately funded carer visit them daily (which they found insufficient). Lastly, a participant who has been admitted several times in the past six months for collapsing said they often had to discharge themselves and that a care package had not been offered by the hospital. A family member had insisted on needs assessment, which social services had offered, but which the participant refused. Instead, the participant wanted a more comprehensive assessment for what is causing their condition and felt they had not yet received this from medical staff. This is illustrated by the participant’s account below:

Right, this has been a very difficult area because when I was in hospital, my eldest sister, who I think feels a great responsibility to keep me upright and going, sort of got hold of the social services. And they started to send people around to talk to me about care and offer care and things like that… I rejected it because my sister was going on about it and I got all the paperwork and it's just not what I want.

^{(R6, unmet care needs group)}

Nine out of 13 interview participants said they had not been offered a care package or a referral to adult social care following hospitalisation. In three of these nine cases, the participants felt that this was sufficient considering either the condition they were admitted for or their current level of care. All three cases were from the ‘met’ care needs group. Two of them had received outpatient care, and one not due to their cancer being in remission.

In six cases where a care referral had not been made, the participants either viewed this negatively or acceptingly. In one illustrative case, a participant from the ‘unmet’ care needs group who had been hospitalised for kidney stones did not think their care needs had changed due to this condition, and described going back to “how things were” pre-hospitalisation; perhaps not realising that a care referral might have been appropriate had the medical staff understood or queried about the full context of their care needs, including significant mobility issues. This acceptance and normalisation of lack of support was evident in four other participant cases, though two of them had received outpatient care. The final participant expressed frustration that they were discharged in worse health than when they were admitted due to contracting COVID-19, and that no care or follow-up support was offered.  

There were no clear patterns in the post-hospitalisation experiences of participants by either ‘met/partially met’ versus ‘unmet’ care groups or by avoidable versus unavoidable hospitalisations. This suggests that that there is local and case-by-case variation in what is offered to older adults following hospitalisation, and that personal and contextual reasons are also at play (e.g., financial constraints, apathy about the current system).

Case study

We have included a case study of one of the participants to illustrate the complex interplay between personal health needs, caring responsibilities, and the challenges of accessing appropriate support for older adults living in the community. Participant A’s experience highlights how gaps in both health and social care provision—such as the lack of timely emergency medication, insufficient information about entitlements, and financial barriers to formal care—can lead to avoidable hospital admissions and increased strain on individuals who are already managing multiple care roles. Her story highlights the real-life consequences of inconsistent support and the need for clearer, more accessible pathways to care for people with health conditions and those acting as primary carers. 

Participant A is 74 years old woman and lives in a small village in the Midlands. She is currently living in a bungalow with her husband and has friends who live nearby. She has a family but does not like to depend on them for support. Participant A is the primary caregiver for her husband, who has Alzheimer’s disease and a history of heart attacks and blood cancer. She reports that attending medical appointments without her husband is challenging due to his Alzheimer’s-related anxiety. Additionally, overnight appointments or hospital stays are difficult because she needs to assist her husband with his medication. 

Participant A has chronic obstructive pulmonary disease (COPD) and arthritis, which she reports have a continuous impact on her daily life. She experiences significant mobility challenges and difficulty with self-care tasks such as walking, climbing stairs, bathing, and dressing. To manage these limitations, she uses aids like a walking stick and relies on her husband’s assistance for dressing.  

Participant A was hospitalised for COPD and a urinary tract infection (UTI), which she believes could have been avoided. She feels the UTI could have been treated at home with IV fluids, but instead she had to travel to the hospital every four hours over five days to have the cannula replaced for the drip, as she was not offered a hospital bed. She reports that this made her feel worse. Regarding COPD, she states that she should have received emergency medication—such as steroids and penicillin—to prevent hospitalisation. She was told pharmacies had stopped issuing emergency packs, but later a pharmacist informed her this was incorrect and that she should have been given the medication. 

"When they stopped issuing the emergency packs, I started having more admissions to hospital."

When Participant A was admitted to hospital for emergency COPD treatment, she was offered a care package on discharge by social services. However, she discovered that she would need cover the costs herself, so she declined. One social worker in the hospital identified additional funds to help cover the cost, but Participant A reported that the money had not yet been received and did not specify the source. 

"I don't want to spend all that savings and support at this time."

Participant A currently pays privately for a carer to visit their household weekly and provide additional support for her husband, which she finds beneficial. Although the care is primarily for her husband, it enables her to visit friends who are terminally ill and offer them support. Participant A arranged this care independently, without assistance from the hospital. She also receives advice over the phone from the Alzheimer’s Society, which she finds helpful. The carer that visits their household has helped her apply for Carer’s Allowance, but this has not yet been granted.

Conclusion

Having social care needs is associated with a higher likelihood and frequency of emergency hospitalisation

Our study shows a strong and consistent link between social care needs and emergency hospital use among older adults. People aged 65 and over with social care needs were significantly more likely to be admitted to hospital and had more admissions than those without needs, even after adjusting for demographic, socioeconomic, health, lifestyle, household and environmental factors. Among those with social care needs, the likelihood and frequency of admission was higher for those with mobility and self-care needs (ADL), and lower for people whose needs were limited to everyday independent living tasks (IADL). Receiving help did not appear to protect against hospitalisation: people with no care needs consistently had the lowest admission rates, when compared with people with care needs, regardless of whether needs were met.  

The interplay between social care receipt and emergency hospitalisations  

In our sample, among individuals with mobility and ADL (self-care) needs, only 8% reported receiving help for all or most of their needs, while 31% had unmet needs. People whose needs were met had a higher likelihood of being hospitalised when compared with all other groups: no needs, unmet needs and ADL needs, even after controlling for health status and falls. This aligns with previous research showing that people receiving care had higher admission risks than those receiving none (Maharani et al., 2024). The met needs group also tended to have more severe needs and a higher proportion of limiting long-term conditions or recent falls. However, even after adjusting for these factors, they had the highest number of hospitalisations. This may suggest that repeated hospitalisations could trigger care provision, with hospital discharge acting as a gateway to formal support. The ‘discharge-to-assess’ pathway exemplifies this process, where health and social care practitioners plan post-discharge care and conduct long-term needs assessments ²⁸ . The qualitative interviews with ELSA sample members provided additional evidence supporting this possibility. Interview data reflected a complex interplay between social care needs and hospitalisations, whereby repeated hospitalisations can lead to the provision of social care for some individuals. One limitation of the quantitative analysis should be noted, as we extracted hospitalisations between the start of each survey fieldwork wave rather than between ELSA interviews, for a minority of cases some hospitalisations were recorded before individuals reported their needs as being met, meaning some admissions may have occurred while needs were actually unmet (see section 2.6 Limitations).

Another possible explanation for the high likelihood and frequency of hospitalisations in people whose social care needs were mostly or entirely met according to their ELSA survey responses (met needs group) is that the assistance they received may not have fully addressed all their requirements, thereby increasing their vulnerability to hospitalisation. The qualitative accounts also provided more context on this. Although generally happy with the care they were receiving, older people often reflected that it was not easy to get the help they needed when they needed it. There were individual accounts which detailed a long wait for care needs assessment, and then having to wait further to receive the care services once the needs were assessed.  

Experiences of the social care system

The qualitative findings from the interviews provided further insights on ELSA sample members’ experiences of social care. The findings highlighted that some people, even after being offered care or support packages post-discharge, were unable to accept them due to personal or financial reasons, a desire for independence, complications and insufficiencies in the package offered or low expectations of the system. Qualitative accounts also reflected people’s acceptance or normalisation of limited support and repeated hospitalisations, particularly among those with ongoing or recurring health issues.  

Another important finding from the qualitative interviews was that before the first contact with social services people were not well informed about the support, services and funding available to them. Despite the critical role of social care, public awareness and understanding of these services remain limited, especially when compared to the NHS services. Research by the Health Foundation showed that a quarter of older people did not know about the quality of social services in their area (The Health Foundation, 2025) ²⁹ .  The Care Act 2014 states that local authorities should provide clear, accessible information and advice about care and support services, including funding options. However, NHS England report also showed that about one third (32%) of people using Adult Social Care services report finding it difficult to access the information and advice (NHS England, 2024). Additional barriers to adequately meeting social care needs are highlighted by research from the National Institute for Health and Social Care Research (NIHR, 2021), which found that navigating the social care system requires a range of skills—including information seeking, needs assessment, financial planning, and contract management—that many individuals may not possess. These findings highlight that timely access to care, and comprehensive information about services and funding available are essential to ensure that social care provision happens in a timely manner in order to prevent health conditions worsening, and as a result, hospitalisations.

Experiences of hospitalisation

Qualitative interviews with ELSA sample members also provided valuable context about their hospitalisation experiences. Many participants described negative experiences during hospitalisation, particularly in cases of unplanned admissions, which were often exacerbated by system pressures such as staff shortages and bed availability. While at hospital delays in access to therapies, prolonged treatment waits, and inadequate assessments were mentioned, among the qualitative interviews with participants in the ‘met/partially met’ care need group. These accounts suggest that individuals’ self-assessment of care needs may not always fully reflect the true level of support required, and that earlier or more comprehensive intervention could have prevented some hospitalisations.

Reasons for hospitalisations

Both quantitative and qualitative analyses revealed that reasons for hospital admission were broadly similar across individuals with and without social care needs. However, the quantitative analysis showed that admissions for Ambulatory Care–Sensitive Conditions (ACSC)—which are potentially avoidable with effective primary or community care—tended to be more frequent among those with social care needs. Qualitative evidence reinforced the notion that gaps in community and outpatient care, as well as inconsistent discharge planning and referral practices, may contribute to these avoidable admissions. In their interviews some individuals reported not being offered follow-up care, care packages, or social care referrals after hospitalisation, regardless of their level of need, suggesting significant variation and inconsistency in post-hospital support.

Policy implications

Our findings suggest that older adults may need earlier, more coordinated social care to prevent avoidable hospital admissions. Interviews with ELSA sample members revealed that care often arrives too late—after a crisis or hospital stay—rather than before problems escalate. Moving to a proactive model may be essential to reducing avoidable hospitalisations.  

We propose that efforts to reform social care should include these four key priorities:

• Timely, preventative support – Shift from reactive, post-hospital care to early intervention for people with emerging mobility or self-care difficulties.
• Clear and accessible information – Ensure older adults and families understand what support is available and how to access it, as required under the Care Act 2014.
• Integrated health and social care – Strengthen links between hospitals, community services and social care so that transitions are seamless and support is consistent.
• Addressing inequities – Tailor support for people who live alone, have long-term limiting health conditions, and have limited mobility.

Better integration and earlier intervention could potentially reduce avoidable admissions and improve the quality of life of older adults. Continued efforts to improve timely access, clear information and coordination between health and social care will be essential to prevent hospitalisation and support better outcomes for older adults in England. 

Appendix

^{Note: N = 14,232 person-wave observations. SE = Standard Error (cluster-robust, clustered by participant ID). IRR = Incidence Rate Ratio.
Reference categories: No care needs, Female, IMD Quintile 1 (least deprived), Degree-level qualifications, No falls, No limiting condition, Non-smoker, Urban, East Midlands, White ethnicity, Living with others.
***p < 0.001, **p < 0.01, *p < 0.05, †p < 0.10.}

Acknowledgments

We thank Martin Mitchell for leading on the design and implementation of the qualitative component of the study. We are also grateful to Brian Beech, Giorgio Di Gessa, Nicholas Steel, and Julie Byles for their valuable advice and feedback on the design, methodology and results. It should be noted that these individuals were not asked to endorse the content or conclusions of this report. The authors bear sole responsibility for the final content, any errors in the analysis, or misinterpretations of the findings. We would also like to thank Alina Haque, Honor Mitcheson, Gabriel Fung and Noemie Bourguignon for the research assistance offered in this project, including carrying out and summarising qualitative interviews.