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. 2023 Feb 10;18(2):e0277074. doi: 10.1371/journal.pone.0277074

Women experiencing homelessness and mental illness in a Housing First multi-site trial: Looking beyond housing to social outcomes and well-being

Patricia O’Campo 1,2,*, Rosane Nisenbaum 1,2, Anne G Crocker 3,4,5, Tonia Nicholls 6,7, Faith Eiboff 8, Carol E Adair 9,10
Editor: Janet E Rosenbaum11
PMCID: PMC9916643  PMID: 36763583

Abstract

Objective

There is scant research on the effectiveness of permanent supportive housing for homeless women with mental illness. This study examines the effectiveness of Housing First with an unprecedentedly large sample of homeless women from five Canadian cities, and explore baseline risk factors that predict social, health and well-being outcomes over a 24 month-period.

Methods

The At Home/Chez Soi multi-site randomized controlled Housing First trial recruited over 600 women between October 2009 and July 2011. This is a post-hoc subgroup exploratory analysis of self-identified women with at least one follow-up interview who were randomized to Housing First (HF) (n = 374) or treatment-as-usual (TAU) (n = 279) and had at least one follow-up interview. Linear mixed models and generalized estimating equations were used after multiple imputation was applied to address missing data.

Results

At the end of follow-up, the mean percentage of days spent stably housed was higher for women in the intervention 74.8% (95%CI = 71.7%–77.8%) compared with women in the treatment-as-usual group, 37.9% (95%CI = 34.4%–41.3%), p<0.001. With few exceptions, social and mental health outcomes were similar for both groups at 6-, 12-, 18- and 24-months post-enrollment. Suicidality was a consistent predictor of increased mental health symptoms (beta = 2.85, 95% CI 1.59–4.11, p<0.001), decreased quality of life (beta = -3.99, 95% CI -6.49 to -1.49, p<0.001), decreased community functioning (beta = -1.16, 95% CI -2.10 to -0.22, p = 0.015) and more emergency department visits (rate ratio = 1.44, 95% CI 1.10–1.87, p<0.001) over the study period. Lower education was a predictor of lower community functioning (beta = -1.32, 95% CI -2.27 to -0.37, p = 0.006) and higher substance use problems (rate ratio = 1.27, 95% CI 1.06–1.52, p = 0.009) during the study.

Conclusions

Housing First interventions ensured that women experiencing homelessness are quickly and consistently stably housed. However, they did not differentially impact health and social measures compared to treatment as usual. Ensuring positive health and social outcomes may require greater supports at enrolment for subgroups such as those with low educational attainment, and additional attention to severity of baseline mental health challenges, such as suicidality.

Trial registration

International Standard Randomized Control Trial Number Register Identifier: ISRCTN42520374.

Introduction

Homelessness in Canada is primarily experienced by men, yet data show that over one-third of the homeless people in this country are women and close to 90% of the single parents who are homeless are women [13]. Mental health and substance use disproportionately affect women experiencing homelessness. Between 20–50% experience major depressive disorder and major depressive episodes as demonstrated in systematic reviews [4], between 30–40% experience post-traumatic stress disorder [5, 6], as many as one in five report moderate-to-high levels of suicidality [6], and a large proportion experience exceptional vulnerability to an array of health and social risks [611], including premature mortality [12]. A recent meta-analysis of observational studies in high-income countries reports that three-quarter of homeless individuals have a mental illness diagnosis with no differences by gender [13].

Women who are homeless have different health and social vulnerabilities compared to homeless men. They have higher rates of distress including suicidal ideation [14], greater experiences of trauma prior to and during episodes of homelessness including more adverse childhood events (ACEs) such as physical and sexual abuse and trauma-related disorders [6, 1522], few options for effective woman-centred services and treatments for these traumatic events and other ailments, all of which predispose them to homelessness [14, 2327]. They are also substantially more likely than homeless men to engage in survival sex work and are thus more vulnerable to exploitation, violence, infectious disease, gynecological problems and unwanted pregnancies [6, 14]. Homeless women with children express considerable traumatic distress because of frequent child apprehensions [28]. Because women who are homeless often seek shelter in informal settings such as couch surfing or sleeping rough, they are less visible and have less access to services [14, 29, 30] and have received less attention in research and policy and practice.

Program planners and policy makers operate in a context of too little gender-specific data on programs and interventions and weak studies addressing the experiences of women [3, 4, 31]. As such, there have been calls for more research that moves away from gender-neutral approaches (i.e., examining men’s and women’s experiences with the same lens, as if there are no gender differences) [14, 3133] towards more gender-sensitive frameworks, including women-only analyses that capture the variability around the unique challenges women who are homeless face and accelerating evidence-informed practice with this population [3, 29, 34, 35]. For interventions, there is a need for studies with large enough samples to examine whether and how programs work for women [4, 3641].

Currently, with some exceptions [42], what we do know about housing interventions for women experiencing homelessness comes from small subsamples in longitudinal observational studies [34, 43], randomized trial pilot [44], or qualitative evaluations [32, 45]. A large multi-site supportive housing demonstration project for chronically homeless adults in 11 US communities, with 173 women as part of the sample, reported substantial improvements in housing stability but no gender differences in this and other service utilization or clinical outcomes over two years of follow-up [46]. In an observational study of an integrated service system initiative in 18 sites across the US, women (N = 2727) demonstrated greater improvements in family relationships and social support compared to men (N = 4502) over 18-months of follow-up [42].

Housing First, a supportive housing approach with demonstrated success in transitioning individuals experiencing chronic homelessness and mental illness into permanent housing, has negligible evidence on outcomes for women [4750]. The Housing First model is based on harm reduction principles of choice and self-determination, and provides immediate access to scattered-site housing through the provision of rent supplements and flexible recovery-oriented supports [49, 51]. Participation in Housing First is not contingent on participation in services or requirements of ‘housing readiness’ (e.g., sobriety or medication compliance) [49].

The At Home/Chez Soi randomized trial conducted in five cities across Canada provides an unprecedented opportunity to examine the impact of a housing first initiative among women for several reasons. First, it is the largest randomized trial of a Housing First initiative and includes an unprecedented sample of several hundred women. This large sample size facilitates a woman only analysis to focus on variability within this subsample and factors that are relevant for women living with homelessness such as sources of trauma or parental roles. A woman only analysis, too rarely seen in studies of homelessness, ensures that the focus remains on women within the sample as gender analyses largely focus on comparing experiences with those of men. With 24 months of follow-up and novel risk variables not previously examined in a Housing First analysis we can provide unique point-in-time and longitudinal evidence not previously considered because of prior sample size and variable availability limitations.

The present study employs the largest longitudinal sample of women experiencing homelessness and mental illness in a Housing First randomized controlled trial (RCT) and extends the follow up duration typically seen to 24 months. Our objectives were: (i) To compare change at the end of the study in health and social outcomes between women who received and did not receive the Housing First intervention; ii) To examine whether the Housing First intervention leads to better housing stability over 24 months, and health and social and outcomes between 6- to 24-months post enrolment; and, (iii) To explore baseline demographic, social, health measures, and ACEs as predictors of key study outcomes during the study period.

Methods

Methods and measures for this analysis have been extensively described elsewhere [48, 5254]. Briefly, the Canadian At Home/Chez Soi five city RCT randomized Housing First among adults experiencing homelessness and serious mental illness [53]. Participants randomized to the Housing First (HF) intervention received scattered-site housing and either moderate or high needs-based services to support their recovery and integration into the community (i.e., Intensive Case Management or Assertive Community Treatment). Participants randomized to treatment-as-usual (TAU) had access to housing and services through other community programs. Each site had additional focus on a unique population (e.g., ethno-racial populations in Toronto and Indigenous populations in Winnipeg) and had specialized services to address their needs. Fidelity assessments ensured strong adherence to program theory of change [55]. Information about the study, such as the randomization process and a description of the intervention, was provided to all study participants prior to enrolment. An informed consent process was created to ensure that participants had the time and information to understand the complexities of the trial and follow-up activities. All participants provided written informed consent prior to enrolment. All enrolment and consent documents were harmonized across sites, however, each site had separate approval from relevant university/site Research Ethics Boards.

Over 24 months, participants were interviewed every 3 months to assess housing stability and every 6 months to evaluate health and social outcomes. Previous results from 2,148 participants (including 683 women) demonstrated that those in the HF arm showed positive improvements in housing stability, quality of life, and community functioning compared to participants in usual care [46, 48, 52]. This paper reports on an exploratory analysis of 653 participants who self-identified as women and had at least one follow-up after their baseline assessment (96%), including 588 followed to 24 months. Across all sites, 374 participants were from the HF group and 279 from the TAU group (S1 Fig in S1 File).

Outcomes and other study variables

We describe in this section the outcome variables and any other scales used in this analysis. Stable housing was defined as “living in one’s own room, apartment, or house, or with family, with an expected duration of residence of 6 months or more or tenancy rights” while enrolled in the study, taking into account participants’ move in/out dates for each type of residence (e.g., street place, unstable residence, stable residence, institution) [56]. The percentage of days stably housed during the 24-month period was calculated as the total number of days stably housed divided by the total number of days for which any type of residence data were provided by the participant over 24 months and varied between 0% and 100% [48, 53]. Unlike the other outcomes, housing stability was measured at the end of the follow-up period for several reasons. First, the treatment group establishes stable housing within the first 1–3 months of enrolment and housing status rarely changes after that point in time. A small proportion of the treatment group was not consistently stably housed throughout the duration of the program; thus, this single cumulative measure captures housing stability better than if we had measured it at each follow-up time point. In sum, this cumulative housing stability is the best measure for the outcome of housing as it does not demonstrate incremental changes over the 24-month follow-up period.

The other outcomes were assessed via in-person interviews at baseline, 6-, 12-, 18, and 24 months post-enrollment.

The Lehman Quality of Life Interview 20 (QoLI-20) measured participants’ condition-specific quality of life. Twenty items scored on a 7-point Likert scale define a total quality of life and domains of family, finance, leisure, living, safety, social, and overall quality of life. In this work, we used the total score calculated as the sum of all 20 items, which ranges from 20 to 140, with higher scores indicating better quality of life [57].

Community functioning was measured using the Multnomah Community Ability Scale (MCAS), a 17-item scale capturing total community functioning and degree of clinician/observer-rated functional ability in domains of health, adaptation, social skills, and behavior. In this work, we used the total score calculated as the sum of all 17 times, ranging from 17–85, with higher scores indicating less impairment [48].

The Colorado Symptom Index (CSI) assessed how often specific psychiatric symptoms were experienced, from ‘at least every day’ to ‘not at all’. Fourteen self-report items were summed (range 14 to 40), with lower scores reflecting fewer symptoms [58, 59].

Emergency department (ED) visits were measured using self-reported counts in the past 6 months. The number of past-month substance-related problems were measured via the 5-item Global Assessment of Individual Needs Short Screener (GAIN-SS); higher counts correspond to greater problem severity [53].

The Community Integration Scale (CIS) measured self-reported psychological integration with the immediate community on a scale from 4–20; physical integration used counts of previous month social activities (e.g., attending a concert, meeting people for coffee) on a scale of 0–7. Higher scores on both reflect greater integration [60].

Baseline and predictor variables included data collected on traumatic events that occurred before the age of 18 years using the Adverse Childhood Experiences module assessing ten early childhood adversities (yes/no items): emotional, physical or sexual abuse; emotional or physical neglect; household substance use, household mental illness and household criminal justice involvement; mother treated violently, and parental separation or divorce [61]. Scores ranged from 0 to 10 with higher scores indicating greater childhood adversity. We created two categories representing high levels of reported ACEs, ≥5 adversities, and four or fewer including none [62, 63]. Mental health diagnoses and levels of suicidality were assessed via the MINI Neuropsychiatric Interview 6.0 administered as part of the baseline enrolment interview [64]. No, low, moderate and high levels of suicidality were dichotomized into ‘no/low’ and ‘moderate/high’ suicidality. Information about all other variables (e.g., demographics, financial support of children, sources of income) was obtained via self-report during the in-person baseline interviews.

Statistical analyses

Descriptive statistics for a wide range of descriptors were selected to characterize the sample by treatment group. Longitudinal outcomes were analyzed using linear mixed analysis of repeated measures models for continuous outcomes and generalized estimating equations for count outcomes (Poisson distribution for CIS-Physical Integration and negative binomial distribution for GAIN-SS and ED Visits), assuming an unstructured correlation matrix for the repeated measures. Percentage of days stably housed over the 24-months follow-up (range 0–100%) was modeled as a continuous outcome in a linear model. We also performed a sensitivity analysis by considering the proportion (0–1) of days stably housed and fitting a fractional logistic model. The fractional logistic model, which uses a quasi-likelihood function for estimation, was introduced by Papke and Woolridge (1996) [65] and is appropriate for proportions when the values of 0 and 1 are admissible and observable. The fractional logistic model predicts the mean of the proportion conditional on covariates. To ensure that the predicted mean is also between 0 and 1, the logit model for the mean is considered. The parameters estimated from this model can be exponentiated to yield odds ratios and 95% confidence intervals for the association between stable housing during the 24 months follow-up and covariates.

For the first objective, for each group, the adjusted change from baseline to 24-months follow-up was estimated by mean differences and 95% confidence intervals (continuous outcomes), and rate ratios and 95% confidence intervals (count outcomes). We fit models that included treatment group (fixed effect, HF vs TAU), categorical time (24-months vs baseline), interaction of treatment x time, and study city and need level at baseline (high vs. moderate) as covariates.

For the second objective, in a more detailed examination of possible differential effect over time, we tested for differences between HF and TAU at each follow-up time point from 6- to 24-months, adjusting for baseline outcomes. Except for percentage (or proportion) of days stably housed over 24 months, models included treatment group, categorical time (6-, 12-, 18-, 24-months), interaction of treatment x time, and city, need level and baseline outcome values as covariates. At each time point, we compared HF vs TAU by estimating mean differences and 95% confidence intervals or rate ratios and 95% confidence intervals. For percentage (or proportion) of days stably housed over 24-months we included treatment group, city, need level and percentage of days stably housed in the three months prior to baseline as covariates.

For the third objective, the differences between HF and TAU were no longer the focus. Rather we took advantage of the large sample of women in a study on homelessness to identify predictors of our outcomes. To accomplish this we expanded the models from the second objective and added the following baseline predictors: age at enrolment (continuous), education (1 = less than high school, 0 = high school or higher), duration of homelessness (1 = ≥3, 0 = <3 years), whether minor children were being supported by the participant (1 = yes, 0 = no), level of suicidality (1 = moderate or high, 0 = mild or none), and high level of ACEs at baseline (1 = ≥5, 0 = <5 ACEs). These predictors were chosen because they represent a knowledge gap or have yielded inconsistent findings on longitudinal studies of women experiencing homelessness. We were unable to include ethnicity or Indigenous identity due to multicollinearity with study city.

Multiple imputation with chained equations (MICE) was used to handle missing data from loss to follow-up, withdrawal, skipped interviews, nonresponse on specific items, and systematically measured lack of interviewer confidence in participant responses. We imputed all outcomes simultaneously and MICE imputation models included all variables needed to address three objectives [66]. Although outcome missing rates varied between 0.53% to 31.9% (S1 Table in S1 File), approximately 70% of the participants had at least one missing value in at least one outcome in at least one point in time. Therefore, we chose 70 imputations following guidelines given by White et al. (2010) that the percentage of cases with incomplete data should be similar to the number of imputations generated [66]. Comparisons between observed and imputed data via diagnostic plots were used to assess the quality and plausibility of the imputations [67].

Data were imputed using Stata software (mi impute chained, Stata Statistical Software: Release 13. College Station, TX: StataCorp LP) and analyzed using SAS (PROC MIXED, GENMOD, GLIMMIX, PROC MIANALYZE, SAS 9.4, SAS Institute Inc, Cary, NC). SAS Syntax is provided in S2 Table in S1 File. Figures were created using the ggplot2 package in R software version 3.4.0 (R Core Team (2017) [68]. All statistical tests were two-tailed and significance was defined as p < .05. No adjustments for multiple testing were applied [69].

Results

We took advantage of the rich data collected in this study and present a broad array of participant demographic, social, and health characteristics at enrolment (Table 1). Fifteen percent of the women were born outside of Canada (median time in Canada: TAU = 25yrs; HF = 21yrs). Three percent of women were employed at baseline and 90% received social assistance of some type as a source of income. Around 40% (n = 274) reported having minor children, and of these, 22% (n = 60) were providing full or partial financial support to the child(ren). The most prevalent mental illnesses were depressive episodes, PTSD, and alcohol or substance dependence. Approximately 40% had moderate-to-high suicidality, around 40% had post-traumatic stress disorder, about 60% reported ≥5 ACEs, and the majority had three or more co-morbid medical conditions.

Table 1. Baseline characteristics of women participants in the Canadian At Home/Chez Soi study by randomization group (N = 653).

Characteristics Treatment as Usual (N = 279) Housing First (N = 374)
N % N %
Age (M ± SD), y 40.2 ± 11.7 39.7 ± 11.2
Ethnic or cultural identity
 Aboriginal 76 27.2 95 25.4
 Ethno-racial 67 24.0 81 21.7
 White 136 48.8 198 52.9
Education
 Less than high school 130 46.9 202 54.3
 Completed high school 57 20.6 61 16.4
 Some post-secondary school 90 32.5 109 29.3
Marital status
 Married/Partnered 22 7.9 16 4.3
 Divorced/Separated/Widowed 74 26.5 106 28.4
 Single/Never married 183 65.6 251 67.3
Country of birth, & length of time in Canada for immigrants
 Born in Canada 235 84.8 315 84.2
 Immigrant, 10 years or less 8 2.8 12 3.2
 Immigrant, more than 10 years 34 12.3 47 12.6
Employment status
 Unemployed (unemployed, volunteer work, retired, student, housewife/husband, other) 270 96.8 362 97.3
 Employed (including self-employed and special work program) 9 3.2 10 2.7
Sources of income
 Earnings from regular/casual work or busking 16 5.7 18 4.8
 Unemployment insurance 22 7.9 26 7.0
 Social assistance (disability, welfare, pension, personal needs allowance) 245 87.8 338 90.4
 Selling papers, crafts, pan-handling, squeegee, or $ from recycling (bottles, scrap metal) 16 5.7 22 5.9
 Other (loans, theft, sex work, help from family or friends) 12 4.3 10 2.7
Housing status
 Absolutely Homeless 215 77.1 296 79.1
 Precariously housed 64 22.9 78 20.9
Lifetime duration of homelessness (M ± SD)a, y 3.8 ± 4.6 3.8 ± 4.8
 Less than 3 years duration 155 57.8 211 58.0
 3 years or more duration 113 42.2 153 42.0
Has minor children (≤18 yrs of age)
 0 154 56.0 221 59.1
 1–2 81 29.4 110 29.4
 3 or more 40 14.6 43 11.5
Has minor children (≤18 yrs of age) & provides full/partial support
 0 88 72.7 125 82.2
 1 20 16.5 16 10.5
 2 or more 13 10.7 11 7.2
Relationship to minor children (≤ 18 yrs of age)
 Biological parent 121 100.0 151 98.7
 Partner’s, adoptive or other adult relative’s child 0 0 5 3.4
MCASb score (M ± SD) 60.7 ± 8.0 61.8 ± 7.8
MINIc diagnostic categories
 Depressive episode 170 60.9 218 58.3
 Manic or hypomanic episode 50 17.9 55 14.7
 Post-traumatic stress disorder 126 45.3 145 38.8
 Panic disorder 94 33.7 102 27.3
 Mood disorder with psychotic features 54 19.4 54 14.5
 Psychotic disorder 81 29.0 95 25.4
 Alcohol dependence 97 34.8 108 28.9
 Substance dependence 134 48.0 167 44.7
Suicidality
 Not suicidal 46 16.5 62 16.6
 Low 119 42.7 170 45.5
 Moderate 50 17.9 70 18.7
 High 64 22.9 72 19.3
Adverse Childhood Experiences Scale, mean (SD) 5.1 3.0 5.2 3.2
 Less than 5 95 39.9 137 40.5
 5 or more 143 60.1 201 59.5
Comorbid medical conditions
 Less than 3 51 18.6 100 27.0
 3 or more 223 81.4 270 73.0
Study Component
 Moderate Needs 144 51.6 232 62.0
 High Needs 135 48.4 142 38.0

a Median (IQR), y 2.0 (0.8–5.0) for Treatment as Usual and 2.0 (0.7–5.0) for Housing First;

b MCAS: Multnomah Community Ability Scale. Possible scores range from 17 to 85, with lower scores indicating more disability.;

c MINI: The Mini-International Neuropsychiatric Interview

Table 2 displays results from models examining changes from baseline to 24 months for those in HF and TAU arms for each of our social and well-being outcomes, and comparing groups with respect to housing stability over the 24-month period. Over the 24-month follow-up period, substantial improvements were observed for all the outcomes for both HF and TAU participants. Of note, mean total QoLI-20 scores improved 16.0 points (95%CI = 13.6–18.4) and 13.4 points (95%CI = 10.6–16.2) in the HF and TAU groups, respectively, and number of ED visits decreased by approximately 60% in both groups.

Table 2. Post-imputation pooled change in health and social measures from baseline to 24 months among women living with mental illness and homelessness, adjusted for study city, and need level (ACT or ICM).

Housing First (HF) (N = 374) Treatment as Usual (TAU) (N = 279) p-value for differences between HF and TAU
Continuous Outcomes (95% CI) Mean Change from Baseline to 24 Months 95% CI Mean Change from Baseline to 24 Months 95% CI p
Mental illness symptom severity (CSIa) -6.3 -7.5 to -5.0 -6.9 -8.4 to -5.4 0.534
Community functioning (MCASb) 3.8 2.8–4.9 4.8 3.6–6.0 0.236
Condition-specific QoL (QoLI-20c) 16.0 13.6–18.4 13.4 10.6–16.2 0.178
Psychological community integration (CISd) 2.0 1.5–2.4 2.0 1.4–2.6 0.941
Count Outcomes Rate Ratio Comparing 24 Months with Baseline 95% CI Rate Ratio Comparing 24 Months with Baseline 95% CI p
Physical community integration in past month (CISd) 0.97 0.86–1.08 1.03 0.92–1.14 0.439
Severity of substance problems in past month (GAIN-SSe) 0.79 0.67–0.93 0.83 0.70–0.98 0.688
Number of emergency department visits in past 6 months 0.38 0.29–0.50 0.37 0.27–0.51 0.926

Note. Housing outcome is mean proportion of days spent stably housed.

a CSI: Colorado Symptom Index. Possible scores range from 14 to 70, with higher values indicating greater symptom severity.

b MCAS: Multnomah Community Ability Scale. Possible scores range from 17 to 85, with lower values indicating more disability.

c QoLI-20: Lehman Quality of Life Interview 20. Possible scores range from 20 to 140, with higher values indicating greater quality of life.

d CIS: Community Integration Scale. Possible scores range from 4 to 20 for the Psychological component and 0–7 for the Physical component, with higher values indicating greater integration.

e GAIN-SS: Global Assessment of Individual Needs Short Screener. Possible scores range from 0 to 5 with higher values indicating greater problem severity.

These improvements over the 24-month period are more clearly illustrated in Fig 1 for the HF intervention and TAU groups for all social and health unadjusted outcomes at each observation point from baseline to 24-months. For all outcomes, except CIS-Physical where scores remained flat throughout the follow-up, both groups experienced similar significant change from baseline to 24-months (Fig 1).

Fig 1. Health and social outcomes among women who are homeless and mentally ill, unadjusted means and 95% confidence intervals over 24 months of follow-up by study time point and treatment group.

Fig 1

Note. N = 653. CSI = Colorado Symptom Index; MCAS = Multnomah Community Ability Scale; Lehman Quality of Life Interview 20 = QoLI-20; CIS = Community Integration Scale; GAIN-SS = Global Assessment of Individual Needs Short Screener.

The mean percentage of days spent stably housed during follow-up for women receiving HF was 74.8% (95%CI = 71.7%–77.8%) compared with 37.9% (95%CI = 34.4%–41.3%) TAU, a difference of 36.9% (95%CI = 32.4%–41.4%, p<0.001). In addition, the odds of stable housing during follow-up was over 5 times higher among those in the HF group than in the TAU group (OR = 5.09, 95% CI = 4.08–6.35, p<0.001). Table 3 displays changes over time in greater detail through results of the models to assessing whether social and health outcomes were better in HF vs. TAU groups at each follow-up point. For condition-specific quality of life, significant improvements of 7.1 and 6.1 points were observed in the HF group at the 6- and 12-month follow-ups, respectively. The HF group also showed significant improvements in psychological community integration by 0.7 points at the 6-month follow-up. However, in the last year of the trial, the ED visits fell faster for the TAU group (0.6 visits) compared to the HF group (1.1 visits) at 18 months, with a rate ratio of 1.81 (95%CI = 1.26–2.60).

Table 3. Post-imputation pooled comparisons between housing first and treatment as usual at 6, 12, 18 and 24 months for women in the At Home/Chez Soi trial adjusted for study city, need level (ICM or ACT) and baseline outcome valuesf.

Month Housing First Treatment as Usual Comparison
Continuous Outcome Mean 95% CI Mean 95% CI Mean Difference 95% CI p
Mental illness symptom severity (CSIa) 6 35.8 34.8–36.8 36.8 35.6–38.0 -1.0 -2.5 to 0.5 0.183
12 34.6 33.6–35.6 35.3 34.1–36.6 -0.7 -2.3 to 0.8 0.364
18 33.8 32.7–35.0 34.1 32.7–35.4 -0.2 -2.0 to 1.5 0.785
24 33.7 32.6–34.8 33.4 32.1–34.7 0.3 -1.4 to 2.0 0.737
Community functioning (MCASb) 6 64.9 64.1–65.7 64.9 64.0–65.9 -0.02 -1.2 to 1.2 0.975
12 65.0 64.2–65.8 64.3 63.4–65.3 0.7 -0.6 to 1.9 0.299
18 65.6 64.8–66.5 64.9 63.8–65.9 0.8 -0.6 to 2.1 0.267
24 65.5 64.7–66.4 65.9 64.9–66.9 -0.4 -1.7 to 0.9 0.593
Condition-specific QoL (QoLI-20c) 6 84.4 82.4–86.4 77.3 75.0–79.6 7.1 4.1–10.1 < .001
12 87.4 85.4–89.4 81.3 79.0–83.7 6.1 3.1–9.2 < .001
18 84.8 82.8–86.9 82.2 79.9–84.6 2.6 -0.4 to 5.6 0.091
24 86.8 84.6–89.0 84.3 81.8–86.7 2.5 -0.7 to 5.8 0.124
Psychological community integration (CISd) 6 12.4 12.0–12.7 11.8 11.4–12.3 0.5 -0.1 to 1.1 0.077
12 12.8 12.4–13.2 12.1 11.6–12.5 0.7 0.1 to 1.3 0.017
18 12.7 12.3–13.1 12.3 11.8–12.7 0.4 -0.2 to 1.0 0.194
24 12.9 12.5–13.2 12.7 12.2–13.1 0.2 -0.4 to 0.8 0.458
Count Outcome Rate 95% CI Rate 95% CI Rate Ratio 95% CI p
Physical community integration in past month (CISd) 6 2.1 1.9–2.3 2.1 1.9–2.3 0.98 0.87–1.12 0.789
12 1.9 1.7–2.1 2.0 1.8–2.2 0.95 0.84–1.09 0.492
18 2.0 1.8–2.1 2.0 1.8–2.2 0.99 0.87–1.12 0.858
24 1.9 1.7–2.0 2.0 1.8–2.2 0.91 0.80–1.03 0.148
Severity of substance problems in past month (GAIN-SSe) 6 1.2 1.0–1.4 1.2 1.0–1.5 0.97 0.78–1.20 0.767
12 1.3 1.1–1.5 1.2 1.0–1.4 1.09 0.87–1.37 0.452
18 1.1 0.9–1.3 1.2 0.9–1.4 0.74 1.20–0.63 0.628
24 1.1 0.9–1.2 1.0 0.9–1.2 1.00 0.79–1.27 0.977
Number of emergency department visits in past 6 months 6 1.2 0.9–1.4 1.4 1.0–1.9 0.80 0.55–1.16 0.241
12 1.1 0.7–1.4 1.1 0.7–1.6 0.96 0.58–1.57 0.858
18 1.1 0.8–1.4 0.6 0.5–0.8 1.81 1.26–2.60 0.001
24 0.8 0.6–0.9 0.7 0.5–0.9 1.13 0.83–1.55 0.426

a CSI: Colorado Symptom Index. Possible scores range from 14 to 70, with higher values indicating greater symptom severity.

b MCAS: Multnomah Community Ability Scale. Possible scores range from 17 to 85, with lower values indicating more disability.

c QoLI-20: Lehman Quality of Life Interview 20. Possible scores range from 20 to 140, with higher values indicating greater quality of life.

d CIS: Community Integration Scale. Possible scores range from 4 to 20 for the Psychological component and 0–7 for the Physical component, with higher values indicating greater integration.

e GAIN-SS: Global Assessment of Individual Needs Short Screener. Possible scores range from 0 to 5 with higher values indicating greater problem severity.

f Housing stability is not included in this table as we did not examine it at each follow-up point.

Table 4 no longer focuses on differences between HF and TAU as results presented here identify predictors of our outcomes. Moderate to high suicidality had the most consistent association to poorer health and service utilization outcomes at 24 months follow-up. Compared to those with no or low suicidality at baseline, moderate-to-high suicidality at baseline was associated with nearly three points on the CSI indicating greater mental health symptom severity (beta = 2.85, 95%CI = 1.59–4.11, p<0.001), QoLI-20 mean scores were almost four points lower indicating reduced quality of life (beta = -3.99, 95%CI = -6.49 to -1.49, p = 0.002), and almost 1.5 more ED visits (rate ratio = 1.44, 95%CI = 1.10–1.87, p = 0.007). Low education was also associated with several outcomes. Compared to those with higher education, women with less than a high school level of education at baseline predicted lower community functioning (MCAS) scores (beta = -1.32, 95%CI = -2.27 to -0.37, p = 0.006), 15% fewer social activities (CIS) in the previous month (rate ratio = 0.85, 95%CI = 0.77–0.93, p<0.001), and more severe substance-related problems at 24 months follow-up (rate ratio = 1.27, 95%CI = 1.06–1.52, p = 0.009). Having been homeless for ≥3 years at baseline was associated with lower percentage days stably housed at 24-months (beta = -6.1%, 95%CI = -11.0% to -1.3%, p = 0.013; OR = 0.74, 95% CI 0.58–0.94, p = 0.0132). An ACE score of 5 or more was associated with a 1.5 point increase in mental illness symptom severity (95%CI = 0.25–2.76, p = 0.019). Finally, we explored and found no evidence that baseline levels of ACEs and suicidality, separately, were effect modifiers for any of the above analyses.

Table 4. Post-imputation pooled coefficient estimates (95% confidence interval) for predictors in linear mixed models for repeated measures for continuous outcomes and rate ratios (95% confidence interval) for generalized estimating equations models for count outcomes for the sample of women in the At Home/Chez Soi study (N = 653).

Mental illness symptom severity (CSI) Community functioning (MCAS) Condition-specific QoL (QoLI-20) Psychological community
integration (CIS)
Est 95% CI p-val Est 95% CI p-val Est 95% CI p-val Est 95% CI p-val
Age (years) -0.01 -0.06–0.04 0.680 -0.02 -0.060-.02 0.310 -0.06 -0.17–0.04 0.250 -0.01 -0.03–0.00 0.140
Less than high school 0.21 -0.98–1.40 0.731 -1.32 -2.27 to -0.37 0.006 0.87 -1.56–3.30 0.480 0.15 -0.26–0.55 0.476
3 or more years of homelessness 0.91 -0.27–2.09 0.132 -1.75 -2.72 to -0.78 < .001 -0.75 -3.14–1.64 0540 -0.07 -0.48–0.33 0.728
Supports ≥1 child under 18 years 0.11 -1.89–2.10 0.917 0.62 -0.96–2.20 0.442 0.47 -3.52–4.47 0.820 0.48 -0.20–1.17 0.168
Moderate or high suicidality at baseline 2.85 1.59–4.11 < .001 -1.16 -2.10 to -0.22 0.015 -3.99 -6.49 to -1.49 < .001 -0.30 -0.71–0.11 0.150
5 or more ACE 1.50 0.25–2.76 0.019 0.21 -0.75–1.17 0.666 -0.56 -3.14–2.02 0.670 0.31 -0.12–0.73 0.157
Physical community integration in past month (CIS) (N = 653) Severity of substance problems in past month (GAIN-SS) (N = 653) Number of emergency department visits in past 6 months (N = 653) Stable housing (N = 653)
95% CI p-val RR 95% CI p-val RR 95% CI p-val beta* or Odds ratio** 95% CI p-val
Age (years) 1.0 1.0–1.0 0.834 0.99 0.98–1.00 0.003 1.00 0.99–1.01 0.920 0.16 -0.04–0.36 0.122
1.01 1.00–1,02 0.1021
Less than high school 0.85 0.77–0.93 < .001 1.27 1.06–1.52 0.009 1.11 0.83–1.49 0.474 -2.78 -7.43–1.88 0.242
0.87 0.69–1.09 0.222
3 or more years of homelessness 0.98 0.93–1.11 0.720 1.21 1.03–1.43 0.021 0.99 0.76–1.29 0.951 -6.14 -10.9–1.32 0.013
0.74 0.58–0.94 0.013
Supports ≥1 child under 18 years 1.10 0.96–1.27 0.176 1.05 0.80–1.39 0.715 0.92 0.67–1.26 0.598 5.57 -2.17–13.3 0.158
1.31 0.89–1.92 0.171
Moderate or high suicidality at baseline 1.02 0.93–1.12 0.634 1.06 0.90–1.27 0.465 1.44 1.10–1.87 <0.001 -1.82 -6.49–2.80 0.440
0.91 0.73–1.15 0.432
5 or more ACE 1.04 0.95–1.15 0.356 1.16 0.96–1.39 0.114 1.05 0.77–1.42 0.772 2.931.16 2.05–7.92 0.90–1.48 0.249 0.245

Each model includes treatment group, categorical time, interaction of treatment x time, city, need level, baseline outcome values and all baseline predictors. For percentage or proportion of days stably housed over 24 months, time and interaction treatment by time are not included in linear* and fractional logistic models**

Discussion

This is the first analysis of women’s outcomes in an RCT of Housing First. Prior studies and trials on housing interventions for people who experience homelessness often included very small numbers of women or few variables reflecting women’s unique needs and experiences that have precluded documentation of the heterogeneity of experiences and in-depth understanding of this population.

In this Canadian multi-site study, the largest longitudinal sample of women experiencing homelessness to date, the intervention group experienced housing stability for 75% of the 24-month follow-up, as compared to 38% for women in the TAU group, a finding similar to previously published reports of At Home/Chez Soi participants, the majority of whom were men [48, 52]. Even if the large proportion of time spent stably housed for women who received Housing First compared to the TAU group did not translate into greater improvements in health and social outcomes by the end of the study, there were some early differences favoring the treatment group in the first year for quality of life and psychological community integration. Immediate placement in housing and early housing stability for HF compared to TAU [70] participants likely contributed to faster improvements in these outcomes.

While ED visits declined in a similar fashion for HF and TAU over the 24-month follow-up, an unanticipated finding was a significant reduction in ED visits of 0.5 visits at 18 months for the TAU group over the HF group. For this rare count outcome, the dip in ED visits for TAU at 18 months could be a chance improvement as both groups had similar levels of ED visits by 24 months. Analyses of the full At Home/Chez Soi trial data did not show differences for this outcome [48]. One possibility is that uncertainty around continued housing as the trial was ending may have created high levels of stress in the Housing First group, but if this were the case, we would have likely seen similar changes in other study outcomes or among men in the analyses of the full sample.

Since improvements in outcomes over the 24-month period were similar for both the HF and TAU groups, we moved beyond the intention-to-treat analysis and examined baseline predictors for our outcomes across the full sample. We focused on baseline suicidality, ACEs, and parental responsibilities as predictors- variables rarely examined in past studies of supported housing or Housing First [34, 4244, 71, 72]. As might be expected, women with ≥5 ACEs or moderate-to-high levels of suicidality at baseline showed poorer improvement by 24 months compared to those with low ACE or suicidality scores. Moderate-to-high suicidality was a strong predictor of poorer outcomes at 24 months, including mental health, quality of life, community integration, and ED visits. Past cross-sectional studies of combined samples of homelessness men and women have demonstrated similar links between suicidality, childhood and adult trauma, substance abuse, and mental health [20, 21, 73]. However, while cross-sectional associations between suicidality and substance among women who are homeless have been reported in the literature [19, 74] our study is the first to examine these associations longitudinally.

Contrary to what we might have anticipated on the importance of ACEs in predicting multiple study outcomes given past research [75], we found that high levels of ACEs only predicted significantly worse scores on mental illness severity (CSI). This is consistent with a recent study of the full sample of At Home/Chez Soi where ACEs total scores predicted several mental health conditions at 24-months [50]. Our choice of a cutoff representing high numbers of ACEs, while informed by existing literature, may have limited our ability to detect an association. Alternatively, ACEs may be limited in capturing relevant adverse conditions. A recent study has suggested that an expanded ACEs tool might be used to capture important domains currently missing such as severe poverty or family loss and separation [76], which might be particularly relevant in capturing sources of trauma and adversity for women experiencing homelessness. Our findings also suggested that low educational attainment at baseline was a significant predictor of worse mental health, community integration, and substance use outcomes independently of the other variables in the model. Low educational attainment may be a proxy for other factors such as low health literacy which might impact management of health conditions and requires further investigation [77]. Providing economic support for dependent children was not related to any of our study outcomes.

Although the At Home/Chez Soi support services were important for intervention participants to remain stably housed and make gains in health and social standing, these supports and services were not designed in this Housing First program to be tailored to women’s unique needs. Psychological housing stability which includes considerations of safety, security and feelings of home, is particularly important for women while the At Home/Chez Soi focused predominantly on material housing stability [78]. Women who are unstably housed or homeless need services that address trauma and promote informal peer support networks [32, 79]. Although staff were trained to be responsive to individual needs, the At Home/Chez Soi intervention was not specifically designed to provide the latter two resources. In addition, more than 40% of our participants were homeless for more than three years at enrolment. Two years of stable housing with mental health supports may not have been sufficient to resolve chronic distress from being homeless with co-morbid health, trauma and substance use problems, particularly given the high rates of reported PTSD and ACEs [14].

The lack of greater improvements in the HF arm in this analysis might have been due to the intensive follow-up schedule that could have led to a Hawthorne effect among the TAU group, and higher loss to follow-up in the TAU group might have contributed to smaller differences between groups at 24 months. Despite our sample of homeless women being relatively large, it may not have been sufficient to detect small effects. The At Home/Chez Soi study did not capture gender-sensitive outcomes specific to women experiencing homelessness and therefore we may have missed important changes, such as those associated with positive social support, increased safety from crime and victimization, or strengthening of relationships with children, family or friends [3, 80].

Our study adds to the growing yet sparse literature on the characteristics and needs of women experiencing homelessness and the effectiveness of interventions to eliminate homelessness. Given that women make up more than one third of the homeless population, and their needs differ from that of men [3, 14, 27, 31], it is important that their experiences with homelessness and recovery receive ample focus to better inform the design and implementation of interventions that meet their unique needs. Moreover, the homelessness epidemic has only worsened in the context of the COVID pandemic so the issues remain relevant and these new analyses can provide insights [81].

Supporting information

S1 File

(DOCX)

Data Availability

The data used in this paper from four of the five sites were made available for use by our team via a data sharing agreement for analysis purposes only and not for making the data publicly available. As such, data for all five sites is available from a third party. Researchers may contact the Observational Epidemiology & Qualitative Research unit at the Health Research Solutions HUB at Unity Health Toronto (https://www.hubresearch.ca/services/observational-epidemiology-qualitative-research/) where requests for the data can be submitted.

Funding Statement

This research has been made possible through a financial contribution from Health Canada (https://www.canada.ca/en/health-canada.html). The views expressed herein solely represent the authors. The Mental Health Commission of Canada (https://www.mentalhealthcommission.ca/English) oversaw the conduct of the study and provided training and technical support to the service teams and research staff throughout the project. However, the funder had no role in the analysis and interpretation of the data, preparation, review or approval of the manuscript.

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Decision Letter 0

Janet E Rosenbaum

9 Dec 2021

PONE-D-21-13748Women experiencing homelessness and mental illness in a Housing First multi-site trial. Looking beyond housing to social outcomes and well-being.PLOS ONE

Dear Dr. O'Campo,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Regarding missingness, it’s important to address the missing data mechanisms in more depth than currently, and also whether these mechanisms result in data that are missing at random, missing completely at random, or even not missing at random. Given the diverse reasons for missingness, it is possible that more than one type of missingness is present in the data.  

70% of data had at least one missing value. With such a large percent like 70%, a reader might think that is in the entire dataset. It seems from context and supplementary table 1 that this was all variables that are used in this paper’s analysis, so please say that more clearly.  Also given that 70% of data used in the study had at least one missing value, please cite research about the implications of MI in data with such a large percent missing.  

One reviewer asked for sensitivity analysis for imputed versus non-imputed. Diagnostics of MI method may be appropriate.  

With regard to sample size calculation, an alternative to a standard power calculation would be Gelman and Carlin type-M and type-S errors, which is less centered on p-values.  

The effects of the experiment have been displayed as p-values.  An effect size measure such as Hedge’s g or Cohen’s d in addition to the differences that are in the tables 2-4. These effect size measures may give a clearer picture of the true results of the experiment.

P-values have been displayed to 3 digits in all tables. That’s too many digits and gives false precision.  Just one significant digit is fine. For instance, in table 2 the p-values would be 0.001 (as is currently there), 0.5, 0.2, 0.2, 0.9, 0.4, 0.7, 0.9.

Figure 1 is really nice display of results, but the treated and control groups should be shifted slightly so the confidence intervals are clearer and don’t overlap— e.g., intervention at 0.5 months below each time point and control at 0.5 months above each time point. Many of these continuous variable outcomes in figure 1 probably have complex distributions, and it may be helpful to show the distributions of these variables for treatment versus control, such as using a violin plot or similar data display, rather than the point and CI shown in figure 1.

The tables are somewhat hard to read with many lines, and they may benefit from using data displays such as dumbbell plots for comparisons of the treatment vs control.

The mixed models should report evaluation of assumptions of the models, such as linear regression, Poisson regression.

Table 2 shows percent of the time housed over all visits. However, the other measures in table 2 are a comparison of baseline to 24 months adjusted for city and need level, which leaves out the middle 3 follow-up visits.  It seems like these observations in table 2 could be more completely displayed as an adjusted version of Figure 1 that includes all time points adjusted for city and need level.  

Table 4 shows mixed model effects that leaves out the treatment effect and treatment x time, among other variables. It seems that this table should at least include treatment effect coefficients.  Omitting the main effect of treatment doesn’t avoid table 2 fallacy. The authors should address whether table 2 fallacy may apply here. 

Please submit your revised manuscript by Jan 23 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Janet E Rosenbaum, Ph.D.

Academic Editor

PLOS ONE

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Overall, the study sheds important light on the effectiveness of Housing First intervention among women with mental illness. However, there were a few areas that could be strengthened. In the introduction section, beyond the scant literature on women in permanent supportive housing programs, the authors should consider strengthening the significance of the study. A clearly stated rationale is warranted for the study.

The methods section includes instruments that have strong psychometric properties in the literature. However, the authors do not report any reliability coefficient of the instruments. Further, no testing of group differences on demographic characteristics were reported. The reasons for selecting predictor and control variables are unclear.

Reviewer #2: This manuscript reports a secondary analysis of the original Canadian five city Housing First trial focused solely on female participants. The authors largely repeat the analyses previously reported, but with a specific focus on women; they also add an examination of specific risk factors that might be associated with differential outcomes for women.

Because of the size and innovation of the original study, the secondary analysis is still able to provide some new insights and considerations in an important subsample. As the authors note, many investigators have argued for separate studies or at least distinguishing analyses to understand the numerous differences reported among men and women experiencing homelessness. Moreover, the homelessness epidemic has only worsened in the context of the COVID pandemic so the issues remain relevant if these new analyses can provide insights.

Several strengths are evident besides the original sample size and randomization. First, the authors do include novel risk/resilience factors that might alter the outcomes for women experiencing homelessness such as the caretaking status of dependent children, exposure to ACES, and others. These factors have not been sufficiently considered in earlier studies of Housing First. Secondly, the authors examined group by time effects and group differences at each interval to assess the relative changes from the intervention. Thirdly, they provide detailed reporting on the individual outcomes and the relative importance or size of the changes.

Some changes would also strengthen the manuscript. The most important is better clarity and continuity among the introduction, the results and the discussion. Para 3 of the intro makes a strong argument for providing data for women experiencing homelessness. However, para 5 points to studies that tried and found no differences. Why will this study be different than those studies?

Similarly, in the results, the authors found intervention women did NOT differ on most assessments with the exception of stably housed days (the primary intervention). However, this is different than the original finding of the five city study where all participants (men and women together) had improved quality of life and integration. No comment or discussion on this ‘lessening’ of effects for women is provided.

This theme continues into the discussion where the authors conclude with the need for more studies of women even though they find no clear benefit from separate analysis in this example nor in the cited studies in their introduction. Reconciling their results, introduction and discussion seem like a critical piece because it is the main contribution of the study rather than some of the ancillary factors they look at.

Tables 3 and 4 are difficult to read given the multitude of comparisons that are made.

Reviewer #3: Thank you for the opportunity to review this important work. This study examined the social, health, and housing needs of women experiencing homelessness. It will make an important contribution to the evidence base as the experiences of this population have received little attention. The strengths of this study include a large sample of women experiencing homelessness in the context of a multi-site randomized controlled Housing First trial, a women-only analysis, and baseline factors and outcomes that have not been fully explored in past research. However, there are some comments that need to be addressed, including questions about the statistical analysis, and adding to the discussion section.

Is the manuscript technically sound? And do the data support the conclusions?

Yes, the manuscript is technically sound, and the methods and analysis are clearly described to allow for replication. Data are also consistent with discussion section and conclusions. There are a few items that needs to be addressed:

- Please describe baseline predictors and how they were coded in the methods section

- For women with children in the sample, were any living with their children? What type of support was provided to children? Financial? Social?

- Study findings are very interesting and surprising, particularly the lack of significant findings associated with health and social outcomes. Can prior research provide an explanation for the decline in ED visits for the TAU group? Were there any demographic characteristics that could have influenced the findings (marital status, racial/ethnic group, sources of income, having children, mental health diagnosis, comorbid conditions)?

- The first half of the discussion section is a bit thin and reads more like a results section than a discussion section. Consider cutting down sentences that restate the results and incorporate more explanation of findings using past research.

- This sentence is confusing: “Contrary to what we might have anticipated on the importance of ACEs on multiple study outcomes based upon prior demonstrating the association of ACEs with victimization and criminal justice involvement, in our study high baseline ACE scores only predicted significantly worse scores on mental illness severity (CSI).” (Line 298-300)

Has the statistical analysis been performed appropriately and rigorously?

Yes, the statistical analysis has been performed appropriately and rigorously. Please address the following comments:

- Please add statistical results for linear mixed models to the abstract

- Consider running a sensitivity analysis comparing imputed vs. non-imputed data

- Why were no adjustments made to account for multiple tests?

- Please include sample size calculations

Have the authors made all data underlying the findings in their manuscript fully available?

Yes, all data used in the manuscript has been made fully available.

Is the manuscript presented in an intelligible fashion and written in standard English?

Yes, the manuscript is clear and well-written.

**********

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Reviewer #1: No

Reviewer #2: Yes: Kelly J. Kelleher, MD, MPH

Reviewer #3: Yes: Dr. Alexia Polillo

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PLoS One. 2023 Feb 10;18(2):e0277074. doi: 10.1371/journal.pone.0277074.r002

Author response to Decision Letter 0


8 Mar 2022

Here we respond to the specific comments raised by the Editor. These are also included in our “Response to Reviewer” document along with supplemental figures/tables.

Comment: Regarding missingness, it’s important to address the missing data mechanisms in more depth than currently, and also whether these mechanisms result in data that are missing at random, missing completely at random, or even not missing at random. Given the diverse reasons for missingness, it is possible that more than one type of missingness is present in the data.

Answer: Missing data mechanisms have been widely presented and discussed in the literature. Although the Missing Completely at Random mechanism simplifies analyses, it is quite difficult to find real life applications that satisfy its assumption [VanBuuren2012], and therefore we did not consider it in our data.

We did however consider the Missing at Random (MAR) Mechanism, where the probability of missingness depends only on observed data but not on the data that are missing. That is, after controlling for or stratifying by observed variables, missingness is random. We followed recommendation from Van Buuren 2012 and Barnard 1999 to include in our imputation models a high the number of predictors of the incomplete variables in order to make the MAR assumptions more plausible.

Van Buuren, S. (2012), Flexible Imputation of Missing Data. Chapman & Hall/CRC, Boca Raton, FL.

Barnard J, Meng XL Applications of multiple imputation in medical studies: from AIDS to NHANES. Statistical Methods in Medical Research. 8:17-36, 1999

Comment: 70% of data had at least one missing value. With such a large percent like 70%, a reader might think that is in the entire dataset. It seems from context and supplementary table 1 that this was all variables that are used in this paper’s analysis, so please say that more clearly. Also given that 70% of data used in the study had at least one missing value, please cite research about the implications of MI in data with such a large percent missing.

Answer: We followed recommendations from White et al, 2010 and Horton et al. 2007 who suggest that depending on the number and type of variables, and amount of missing data, 50 to 100 imputations yield better estimates. However, a rule of thumb was provided by White et al. 2010 that the number of imputations should be at least 100 x (percent of incomplete cases) and we applied this recommendation to our data analysis approach.

Horton, N. & Kleinman, K.P. (2007). Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models. The American Statistician 61(1): 79-90. Software appendix: http://www.math.smith.edu/muchado-appendix.pdf

White, I.R., Royston, P., and Wood, A.M. (2010). Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine 30:377-399. (Includes Stata codes)

Comment: One reviewer asked for sensitivity analysis for imputed versus non-imputed. Diagnostics of MI method may be appropriate.

Answer: As requested, we provide a sample of multiple imputation diagnostic plots as suggested by Van Buuren, 2012 at the end of this document.

We also added the following sentence to the methods: Comparisons between observed and imputed data via diagnostic plots were used to assess the quality and plausibility of the imputations.

Van Buuren, S. (2012), Flexible Imputation of Missing Data. Chapman & Hall/CRC, Boca Raton, FL.

Comment: With regard to sample size calculation, an alternative to a standard power calculation would be Gelman and Carlin type-M and type-S errors, which is less centered on p-values.

Answer: Because ours was a post-hoc subgroup exploratory analysis of the main study, the sample size calculation which was predetermined for the main hypotheses for the main study involving the full sample, does not apply here. As such, for the analyses we present here, we would like to emphasize that our study was hypothesis generating and therefore power calculations do not apply. The original sample sizes were calculated for the original study, please refer to protocol in Goering et al.

Goering PN, Streiner DL, Adair C, Aubry T, Barker J, Distasio J, Hwang SW, Komaroff J, Latimer E, Somers J, Zabkiewicz DM. The At Home/Chez Soi trial protocol: a pragmatic, multi-site, randomised controlled trial of a Housing First intervention for homeless individuals with mental illness in five Canadian cities. BMJ Open. 2011 Nov 14;1(2):e000323

Comment: The effects of the experiment have been displayed as p-values. An effect size measure such as Hedge’s g or Cohen’s d in addition to the differences that are in the tables 2-4. These effect size measures may give a clearer picture of the true results of the experiment.

Answer: We are familiar with Cohen’s d and the Hedge’s g correction as alternate ways of illustrating differences between treatment and control groups. We did not use Cohen’s d in any of our dozens of publications that emerged from this randomized trial as in our experience it is not as common in health or medical studies as it would be in psychology. In fact, a quick review of evaluations or randomized trials published in Plos One over a one-year period (back to Dec 2020, see citations below) demonstrated that these studies report findings in a similar way to which we presented our findings. Evaluations presented mean values of outcomes for treatment and controls groups along with 95% confidence intervals and p-values. Some studies add in the t-statistics. As such, we feel that adding Cohen’s d values were not necessary.

Hajek P, Przulj D, Pesola F, McRobbie H, Peerbux S, Phillips-Waller A, et al. (2021) A randomised controlled trial of the 5:2 diet. PLoS ONE 16(11): e0258853.

Ngugi P, Babic A, Were MC (2021) A multivariate statistical evaluation of actual use of electronic health record systems implementations in Kenya. PLoS ONE 16(9): e0256799.

Müller SA, Diallo AOK, Rocha C, Wood R, Landsmann L, Camara BS, et al. (2021) Mixed methods study evaluating the implementation of the WHO hand hygiene strategy focusing on alcohol based handrub and training among health care workers in Faranah, Guinea. PLoS ONE 16(8): e0256760.

Andersen TO, Dissing AS, Varga TV, Rod NH (2021) The SmartSleep Experiment: Evaluation of changes in night-time smartphone behavior following a mass media citizen science campaign. PLoS ONE 16(7): e0253783.

Greene MC, Likindikoki S, Rees S, Bonz A, Kaysen D, Misinzo L, et al. (2021) Evaluation of an integrated intervention to reduce psychological distress and intimate partner violence in refugees: Results from the Nguvu cluster randomized feasibility trial. PLoS ONE 16(6): e0252982.

Heimgartner N, Meier S, Grolimund S, Ponti S, Arpagaus S, Kappeler F, et al. (2021) Randomized controlled evaluation of the psychophysiological effects of social support stress management in healthy women. PLoS ONE 16(6): e0252568.

Dardas A, Williams A, Wang L (2021) Evaluating changes in workplace culture: Effectiveness of a caregiver-friendly workplace program in a public post-secondary educational institution. PLoS ONE 16(5): e0250978.

O’Donnell A, Wilson L, Bosch JA, Borrows R (2020) Life satisfaction and happiness in patients shielding from the COVID-19 global pandemic: A randomised controlled study of the ‘mood as information’ theory. PLoS ONE 15(12): e0243278.

Comment: P-values have been displayed to 3 digits in all tables. That’s too many digits and gives false precision. Just one significant digit is fine. For instance, in table 2 the p-values would be 0.001 (as is currently there), 0.5, 0.2, 0.2, 0.9, 0.4, 0.7, 0.9.

Answer:We respectfully disagree with this suggestion to report p-values using 1 significant digit. We not only feel this is imprecise but this practice is not aligned with what we discovered in our review of studies published in Plos One. Studies reviewed (see item above) reported p-values using 3 digits or sometimes more significant digits. Therefore, we have not reduced the number of significant digits we report for our p-values in our tables and text.

Comment: Figure 1 is really nice display of results, but the treated and control groups should be shifted slightly so the confidence intervals are clearer and don’t overlap— e.g., intervention at 0.5 months below each time point and control at 0.5 months above each time point. Many of these continuous variable outcomes in figure 1 probably have complex distributions, and it may be helpful to show the distributions of these variables for treatment versus control, such as using a violin plot or similar data display, rather than the point and CI shown in figure 1.

Answer:Thank you for your appreciation of our figures. The figures are intended to augment the study findings in the tables as Table 3 presents results of multivariate analyses. The Figures with the unadjusted results are intended to be a visual supplement to Table 3 as the differences or lack thereof are easier to grasp quickly in the figures. While CIs are included in the Figures to help identify whether the values at any one time point differ between treatment and TAU, the data in the Tables reporting multivariate regression findings should be the primary source of information about differences and significance of those differences. To change the scales on the figures to further separate the trend lines for the treatment and TAU would create larger figures that would take up too much space and is not necessary given that the multivariate findings are presented in Table 3.

Comment: The tables are somewhat hard to read with many lines, and they may benefit from using data displays such as dumbbell plots for comparisons of the treatment vs control.

Answer:We have added formats to the table to increase readability. In particular, we have shaded every other row in Tables 1, 3 and 4. We have added lines between each outcome to more clearly delineate the findings for each outcome and co-variate. We believe the format changes have considerably increased the readability of the Tables. Moreover the figures we provided as supplementary information about Table 3 is a visual aid to rapidly understanding the findings from the analysis of these trial data.

Comment: The mixed models should report evaluation of assumptions of the models, such as linear regression, Poisson regression.

Answer:To respond to this suggestion we have added the following text in the Statistical analysis section: Normality of residuals was graphically checked for linear mixed models. The decision to fit a negative binomial rather than a Poisson model was based on whether over-dispersion (, ie, if the variance was much larger than the mean) was present in the count outcome data.

Comment: Table 2 shows percent of the time housed over all visits. However, the other measures in table 2 are a comparison of baseline to 24 months adjusted for city and need level, which leaves out the middle 3 follow-up visits. It seems like these observations in table 2 could be more completely displayed as an adjusted version of Figure 1 that includes all time points adjusted for city and need level.

Answer:There were several reasons why our study measured housing success as cumulative stability at 24 months and we now state this in the methods. First, given the Housing First Theory of Change and Program Manual, stable housing is established within the first 1-3 months of enrolment for those in the treatment arm and housing status rarely changes after that point in time. Thus, we wanted to align with the program’s theory of change. Second, to measure housing status at different time points over the 24 months means that we would have seen very little change for those in the treatment arm and a single cumulative measure made more sense for this outcome. This measure captures overall stability and instability in that a small proportion of the treatment group was not stably housed throughout and this single measure captures that information. Third, this single measure captures the overall housing stability or instability across the two groups over a long period of time and for this outcome, knowing the housing status at different time points in the study is less relevant to the overall goals of the program. In sum, we chose the best measure for the outcome of housing even if it did not match how we approached other outcomes such as quality of life which is expected to demonstrate incremental changes over the 24-month follow-up period.

Comment: Table 4 shows mixed model effects that leaves out the treatment effect and treatment x time, among other variables. It seems that this table should at least include treatment effect coefficients. Omitting the main effect of treatment doesn’t avoid table 2 fallacy. The authors should address whether table 2 fallacy may apply here.

Answer:Table 4 presents the findings for the third objective of the paper (see bottom of page 7 of the manuscript for the objective) intended to “explore baseline demographic, social, health measures, and ACEs as predictors of key study outcomes during the study period.” This objective is not concerned with differences between treatment and TAU. In fact, we include that objective because few differences between treatment and TAU were reported and we can, therefore, take advantage of this large sample of homeless women to learn more about predictors of our key outcomes. Consequently, our analysis for this objective does not account for treatment group membership by design. We do not address the issue of type 2 fallacy for this objective as it does not apply. However, we have made our intent clearer in the statistical analyses, see top of page 11 and in the results on page 13

Comment: 3. Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified what type you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.

Answer:We have expanded the description of the consent process and Ethics Board approval process on page 8 of the revised manuscript.

Comment: 4. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.

Answer:We tried upon our initial submission to create a new entry online for our funding agency, the Mental Health Commission of Canada. However, when we try to add this new funding agency to your database the system freezes and cannot seem to complete the task. Therefore we did not do this task but are willing if we get assistance to do so. We did also try to email the generic help email but received an unhelpful response that we should enter the information into the field with no guidance on how to overcome our problem.

Comment: When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

Answer:There was no grant number for this award.

Attachment

Submitted filename: Response to Reviewers PONE-D-21-13748.docx

Decision Letter 1

Janet E Rosenbaum

11 May 2022

PONE-D-21-13748R1

Women experiencing homelessness and mental illness in a Housing First multi-site trial: Looking beyond housing to social outcomes and well-being.

PLOS ONE

Dear Dr. O'Campo,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

We sent this manuscript for statistical review, and please see and address these comments.

Please submit your revised manuscript by Jun 10 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Janet E Rosenbaum, Ph.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

Reviewer #4: (No Response)

********** 

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Partly

********** 

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: No

********** 

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: No

********** 

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

********** 

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: This revised manuscript is a sub-analysis of the Five City Housing First trial conducted in Canada focused on participants identifying as women. The original manuscript contained many strengths as the largest report of a trial focused on homeless women to date, an important set of outcome measures not previously considered in this population, and a thoughtful approach to the comparison.

The revision adds further strength. The authors have clarified their rationale and communication about their express purpose, the status of the extant literature, and how their findings are related to the original study analysis. In general, this reviewer finds the authors have done a very strong job of responding to the critiques of the reviewers and improved their manuscript in the process.

The only question raised by the changes is whether or not the additional tables combined with the figures is overwhelming to the reader or whether some of this material would be better placed as online supplements. The figures are particularly valuable, but the tables are still quite long.

Reviewer #3: (No Response)

Reviewer #4: Please see comments on attached PDF.

Please, make the original data available by clearly stating where that can be found. By data I mean here the raw data in tabular form with all demographics and scoring (not aggregated). If sensitive data cannot be shared, please state so and remove that data from the data table. If none of the data can be shared because consent to do so was not obtained from the participants, please clearly state so and select "no" in the upload form when asked if the data is available.

There are several points to be clarified/tackled in the manuscript, as reported in the comments on the attached PDF. In brief:

- Some missing details should be provided for some aspects of the statistical analysis.

- The manuscript should clearly state the exploratory nature of the study.

- Data should be made available, barring ethical considerations (in which case a clear statement should be added in the manuscript).

- More appropriate analyses should be used for the time series data, or statistical significance testing should be dropped altogether.

********** 

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: Yes: K Kelleher

Reviewer #3: Yes: Alexia Polillo

Reviewer #4: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Attachment

Submitted filename: PONE-D-21-13748_R1_reviewer.pdf

PLoS One. 2023 Feb 10;18(2):e0277074. doi: 10.1371/journal.pone.0277074.r004

Author response to Decision Letter 1


11 Jun 2022

A summary of the points Reviewer 4 raised in the May 11, 2022 email includes:

- Some missing details should be provided for some aspects of the statistical analysis.

- The manuscript should clearly state the exploratory nature of the study.

- Data should be made available, barring ethical considerations (in which case a clear statement should be added in the manuscript).

- More appropriate analyses should be used for the time series data, or statistical significance testing should be dropped altogether.

All of these points along with the additional detailed feedback from Reviewer 4 contained in comment boxes in the PDF are addressed in updated "Response to Reviewer" file provided in the attached files as instructed by the Editor. Please see that file for the specific responses and descriptions of revisions made to the manuscript.

Attachment

Submitted filename: Response to Reviewer PONE-D-21-13748.docx

Decision Letter 2

Janet E Rosenbaum

6 Jul 2022

PONE-D-21-13748R2Women experiencing homelessness and mental illness in a Housing First multi-site trial: Looking beyond housing to social outcomes and well-being.PLOS ONE

Dear Dr. O'Campo,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Reviewer 2 had a small number of additional comments that they have addressed with respect to your responses.

Please submit your revised manuscript by Aug 20 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Janet E Rosenbaum, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: All comments have been addressed

Reviewer #4: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Yes

Reviewer #4: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

Reviewer #4: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #3: No

Reviewer #4: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #3: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: Thank you for addressing my comments and the opportunity to review this paper. No additional comments.

Reviewer #4: See comments in attached PDF.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #3: No

Reviewer #4: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Attachment

Submitted filename: PONE-D-21-13748_R2_reviewer-R2.pdf

Decision Letter 3

Janet E Rosenbaum

20 Oct 2022

Women experiencing homelessness and mental illness in a Housing First multi-site trial: Looking beyond housing to social outcomes and well-being.

PONE-D-21-13748R3

Dear Dr. O'Campo,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Janet E Rosenbaum, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: All comments have been addressed

Reviewer #4: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Yes

Reviewer #4: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #3: Yes

Reviewer #4: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #3: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: Thank you for addressing our comments and providing us with the syntax. No further questions on my end.

Reviewer #4: Overall, clarity has much improved. There are still a few points that need to be clarified. See comments in attached PDF.

Another point not mention among the comments on the PDF is that sometimes p-values are reported in the text, some other times they are not and it's not entirely clear why. The authors may which to clarify this in the text with sentences like "for p-values see ...". Note that generally it is not enough to provide p-values only, but tables and reporting should also contain the value of the statistic and DFs used to get the specific p value.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #3: No

Reviewer #4: No

**********

Attachment

Submitted filename: PONE-D-21-13748_R3_reviewer.pdf

Acceptance letter

Janet E Rosenbaum

22 Dec 2022

PONE-D-21-13748R3

Women experiencing homelessness and mental illness in a Housing First multi-site trial: Looking beyond housing to social outcomes and well-being.

Dear Dr. O'Campo:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Janet E Rosenbaum

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 File

    (DOCX)

    Attachment

    Submitted filename: Response to Reviewers PONE-D-21-13748.docx

    Attachment

    Submitted filename: PONE-D-21-13748_R1_reviewer.pdf

    Attachment

    Submitted filename: Response to Reviewer PONE-D-21-13748.docx

    Attachment

    Submitted filename: PONE-D-21-13748_R2_reviewer-R2.pdf

    Attachment

    Submitted filename: Response to Reviewer PONE-D-21-13748.docx

    Attachment

    Submitted filename: PONE-D-21-13748_R3_reviewer.pdf

    Data Availability Statement

    The data used in this paper from four of the five sites were made available for use by our team via a data sharing agreement for analysis purposes only and not for making the data publicly available. As such, data for all five sites is available from a third party. Researchers may contact the Observational Epidemiology & Qualitative Research unit at the Health Research Solutions HUB at Unity Health Toronto (https://www.hubresearch.ca/services/observational-epidemiology-qualitative-research/) where requests for the data can be submitted.


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