Economic impacts and cost-effectiveness of housing first interventions for people experiencing homelessness
Bibliographic record
Abstract
Background. Housing First (HF) provides rent supplements and supports to help homeless individuals with mental illness obtain stable housing. A 2015 literature review reported that HF was associated with cost offsets, however, they tended to be less than the cost of the intervention. Since then, additional studies, including the finalized cost-effectiveness analyses of the At Home/Chez Soi (AHCS) trial, have been published.AHCS recruited participants in 5 Canadian cities from October 2009 to June 2011 and followed them for up to 24 months. At baseline, participants were classified as high-needs (HN) or moderate-needs (MN). HN participants were randomized to receive either HF with Assertive Community Treatment (ACT) or Treatment as Usual (TAU), while MN participants were randomized to receive either HF with Intensive Case Management (ICM) or TAU, until at least March 2013. In Montreal, HF services were reduced after March 2013. Participants’ use of services over 6 months 4 years after baseline was collected between February 2014 and March 2015.Objectives. The goals of this thesis were to (1) provide an updated review of the economic impacts of HF and (2) evaluate the cost-effectiveness of HF in Montreal 48 months post-baseline. Methods. (1) A systematic review was performed on MEDLINE, Google, and the Homeless Hub repository, from January 2007 to December 2022. Study characteristics and results were extracted from selected studies; (2) Effectiveness was measured using the number of days of stable housing and days in one’s own apartment. The cost-effectiveness of HF with ACT compared to TAU and HF with ICM compared to TAU were evaluated.Results. (1) Twenty-one studies were retained. Shelter, emergency department, and inpatient costs decreased with HF, while impacts on other health and justice costs were inconsistent. Among studies that reported the cost of the intervention, 2 of the 3 pre–post studies reported a decrease in net costs with HF. The 3 quasi-experimental studies with a comparison group reported an increase in net costs. Four of 5 experimental studies reported an increase in net costs, while one, conducted in France, reported cost offsets equal to the cost of the intervention. Two modeling studies projected that HF would be associated with decreased or marginally higher net costs over 10- and 35-year horizons. (2) 362 participants were included. At 43-48 months, in the HF with ACT group, 34.6% of participants received rent supplements, 7.1% received ACT, and 25.6% received both services. Corresponding percentages for the HF with ICM group were all 17.7%. The average cost for the HF with ACT group ($71,859 (95% CI: $52,300, $83,900)) was higher than for the TAU group ($67,448 ($45,000, 84,900)), and effectiveness was similar (200 (155, 237) vs 195 (151, 240)) when using days of stable housing. The average cost was slightly lower for the HF with ICM group than for TAU ($42,894 ($32,900, $44,600) vs $44,301 ($33,400, $48,800)) while effectiveness was greater (274 (253, 293) vs 225 (190, 257)). Effectiveness measured as days in an apartment was greater for the HF group for both need levels. The incremental cost-effectiveness ratio (ICER) was $873 per day of stable housing (undefined, $3,150) for HF with ACT, while HF with ICM was dominant (undefined, $356). When the measure of effectiveness was changed to days in an apartment, the ICER was $54 per day in an apartment (undefined, $2,842) for HF with ACT, while HF with ICM remained dominant (undefined, $71). At up to $250 per day of stable housing, HF with ACT had a 40% chance of being cost-effective, vs 96% for HF with ICM. Conclusion. The updated literature review, like the previous one, suggests that over a 2-year horizon, HF leads to significant cost offsets that are usually less than but may equal the intervention cost. The results appear to vary according to context. The results of the cost-effectiveness analysis suggest that HF can be cost saving and remain more effective following a reduction of services at 43-48 months for MN participants. A greater proportion of HN participants may require continuance of HF for the intervention to remain cost-effective
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".