Of everything spent on ongoing care, this shows how much goes to care homes versus supporting people in their own homes (home-care visits, direct payments, supported living). It matters because most people prefer to stay in their own homes, and community care usually costs less per person than a care-home bed. A high share means a care-home-heavy model — potentially more expensive and less in line with what people want; a low share means a community-based approach. It is a strategy signal, not a simple good/bad score.
| name | asc_share_care_home_vs_community |
|---|---|
| dataset | 07-adult-social-care-finance |
| kind | constructed |
| type | numeric |
| unit | share (0–1) |
| grain | council (CASSR), keyed on GEOGRAPHY_CODE |
| role | feature |
| source | Formula: GCE(LTC, Residential + Nursing, both age bands) / GCE(LTC, all settings, both age bands); complement = community-based share (Home Care + Direct Payments + Supported Living + Other + Supported Accommodation). Inputs: asc_gce_long_term_care setting-level rows (sum the two AgeBand rows per setting; LTC total from the SupportSetting='99' age-band rows) |
| period | FY 2024-25 |
| missingness | NA where component cells are suppressed (`[x]`); treat `[x]` as NA, never zero |
| score_note | Unique strategy-mix signal (care home vs community) tied to cheaper community-weighted models. |
| peer_group | CASSRs (the 153 social-care authorities) — not yet scored on the leaderboard |
Balance of long-term care spend delivered in care homes vs in people's own homes/community — a key strategy variable. Community-weighted models are usually cheaper per person and preferred by users. Requires summing age-band rows per setting since setting-level rows exist only per age band.