Abstract
Objective
To examine the association between the use of community-based social assistance programs (CB-SAPs) and the reduction of household food insecurity among de novo food-aid seekers in Quebec, Canada.
Study design
Prospective Cohort Study.
Methods
A longitudinal observational study was conducted using a sample of 915 newly registered food-aid seekers in Quebec's food banks from The Pathways cohort study (2018–2020). The outcome was any reduction in the severity of Household Food Insecurity. Exposures included three CB-SAPS:1) using food donations, 2) using food-management related CB-SAPs (other than food donations), and 3) using CB-SAPs unrelated to food. We used Longitudinal Targeted Maximum Likelihood Estimation (LTMLE) to estimate the Relative Risk (RR) and LTMLE for working Marginal Structural Models to estimate Average Additive Treatment Effects (ATE) of the relationship between the use of CB-SAPs and Household Food Insecurity.
Results
The use of CB-SAPs showed a trend towards reduction of Household Food Insecurity. Compared to households using exclusively food banks at baseline, households with multiple-food-acquisition (Multiple AFS) health-promoting practices were more likely to reduce (in the relative scale) Household Food Insecurity by using: food donations (RR: 1.30; 95 %CI:1.01, 1.60); food-management related CB-SAPs (RR: 1.28; 95 %CI:1.03, 1.58); and CB-SAPs unrelated to food (RR: 1.33; 95 %CI:1.03, 1.62). Multiple AFS showed a reduction in the Household Food Insecurity (absolute) scale, especially among food-management related CB-SAPs users (ATE: −0.24; 95 %CI: 0.43, −0.04).
Conclusions
CB-SAPs use contributes to reducing Household Food Insecurity. This contribution varies depending on the food-acquisition health-promoting practices of food-aid seeker households.
Keywords: Food supply, Food donations, Community-based social assistance programs, Food security
Highlights
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Limited evidence exists on the effects of CB-SAPs usage on food insecurity.
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CB-SAPs usage contribute to reducing food insecurity among new food-aid seekers.
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CB-SAPs usage’ effects on food insecurity depend on household food-acquisition health-promoting practices.
1. Introduction
In 2022, almost 1.5 million Canadians lived in households who experienced severe food insecurity. Several definitions of food insecurity include the inadequate or insecure access to food, the reduced food intake or their variety, often due to lack of financial resources and could be characterized by disrupted eating patterns (Li et al., 2023; Polsky and Dividon, 2024). In Canada, the level of household food insecurity is classified as marginal, moderate or severe, derived from 18-points score Household Food Security Survey Module (HFSSM) within the Canadian Community Health Survey (CCHS). The HFSSM is a validated measure that inquiries about worrying about running out of food, limited selection, compromises in quality and quantity of food, and food intake due to financial resources in the past 12 months (Health Canada, 2007; Polsky and Dividon, 2024). Severe household food insecurity (HFI) has been associated with malnutrition (Pereira et al., 2022), cardiometabolic risk factors (Liu et al., 2021; Te et al., 2021; da Silva et al., 2020), mental health issues (Cain et al., 2022; Zahid et al., 2021), and mortality (Gundersen et al., 2018; Walker et al., 2019). Approximately 20 % of Canadian households experiencing HFI request food from community organizations providing direct food donations (i.e., raw or canned food) to those in need (Tarasuk et al., 2019). In Quebec, these organizations may also serve as a gateway to other Community-Based Social Assistance Programs (CB-SAPs), which encompass facilitating access to essential services, fostering skill development, providing language training, offering technical assistance, and aiding in job searches (Ticala, 2020). CB-SAPs may play a crucial role in addressing key health determinants such as food access, education, social support, and employment (Baker & Brownson, 1998; Jourdan et al., 2022). Although several concerns have been raised about the role of community organizations offering food donations in Canada, including questioning their inability to address root causes of HFI (McIntyre et al., 2015; Tarasuk et al., 2014), the quality and quantity of food and services they provide (Loopstra & Tarasuk, 2013), their potential adverse effect on governments’ commitment to reduce HFI, and the stigma associated with their usage (Riches, 2002; Riches & Tarasuk, 2014); evidence regarding the specific contribution of CB-SAPs use on HFI reduction remains insufficient (Loopstra, 2018).
Qualitative research indicates that people at risk of hunger adopt several copings and adaptative strategies to improve food security (Pinard et al., 2016). Furthermore, coping strategies, including the decision to use social assistant programs, their way to use them, and the ultimate outcome in terms of HFI will depend on context, individual and household characteristics and the type of strategies available or offered (Chaudhuri et al., 2021; Davies, 2016; Pinard et al., 2016). Among individuals seeking food aid to address unmet basic needs, these practices involve problem-solving and decision-making processes crucial to gaining control over determinants of health.These strategies can also be defined as household health-promoting practices because they encompass actions undertaken by individuals aiming at contributing not only to their self-care but to contribute to promote the health of their household -as a whole- and the one of its members, even in instances where these preventive or mitigating practices may not possess all the essential attributes of traditional ‘health promotion practices’ (Jourdan et al., 2022; Kulbok, 1992).
Assuming the preventing and mitigating strategies are household health-promoting practices, food-aid seeker households could adopt formal or informal health-promoting practices. Formal health-promoting practices could involve participation in the market economy or utilization of governmental programs, while informal practices might entail participation in the informal or social economy. Informal household health-promoting practices could be related to food acquisition or management, as well as non-food-related. Household food-acquisition health-promoting practices attempt to meet the fundamental need of obtaining food for all household members, including the utilization of alternative food sources (i.e., places where people can obtain free or lower-priced food) (Campbell & Desjardins, 1989). Household health-promoting practices related to food-management include the use of CB-SAPs aimed at improving household food management skills and fostering social support networks (e.g., collective kitchens). Household health-promoting practices unrelated to food aim to increase household resources and capabilities, such as the use of CB-SAPs designed to contribute to the development of individuals’ basic life skills within a household. Household health-promoting practices and examples of CB-SAPs in Québec are illustrated in Fig. 1.
Fig. 1.
Household health-promoting practices among new food-aid seekers and examples of community resources.
Estimating the effects of CB-SAPs is challenging due to their open systems nature, characterized by having multiple dynamic, adaptative, and interacting components (Jolley, 2014; Sharareh & Wallace, 2022). Moreover, CB-SAPs usage is contingent upon the socioeconomic and political context in which they are implemented, their quality and capacity, as well as the characteristics of the users, including their assets, resources, capabilities, and household health-promoting practices (Jolley, 2014; Potvin et al., 2012; Roncarolo et al., 2015; Rouffignat et al., 2001). However, quantitative evidence exploring the relationship between CB-SAPs usage and any level of food insecurity is limited in terms of study design, scope, or analytical approaches. For instance, most observational studies are cross-sectional (Loopstra, 2018; Oldroyd et al., 2022; Rizvi et al., 2021; Roncarolo et al., 2016) and fail to consider contextual factors or food-aid seeker households' health promoting-practices, yielding inconsistent results (Loopstra, 2018; Oldroyd et al., 2022). Additionally, existing literature predominantly focuses on the use of individual CB-SAP related to food (i.e., food donations, collective kitchens, community gardens, cooking training or food-related workshops) without considering the concurrent use of other CB-SAPs (Cheyne et al., 2020; Loopstra, 2018; Roncarolo et al., 2016). To address these research gaps and to provide a comprehensive understanding of the contribution of CB-SAPs use on HFI, this study examined the association between CB-SAPs use and the reduction of HFI (any downward shift from any level of household food insecurity) among newly registered food-aid seekers in Quebec's food banks. We also examined whether this relationship varied across profiles of household food-acquisition health-promoting practices.
2. Materials and methods
2.1. Data
We analyzed data from the first-year follow-up of the Pathways cohort study, a prospective longitudinal cohort of 1001 newly enrolled food bank users aged between 18 and 63 years. Individuals over 63 years old were excluded given that additional social assistance programs are in place to ensure improvement of HFI. Participants were recruited from 106 community organizations offering food donations in rural, suburban, and urban settings of Quebec, Canada. Questionnaires were administered in-person, by phone, or online to the household lead or the individual responsible for the food arrangements in the household, at baseline 2018/2020 and one year after 2019/2021. The follow-up response rate was 74.4 %. Further information regarding the community organizations involved and the recruitment process for The Pathways cohort study can be found elsewhere (Roncarolo et al., 2023). The analytical sample of this study was 915 participants, as 86/1001 participants reported not experiencing food insecurity at baseline and were excluded.
2.2. Outcome
Household food insecurity reduction was defined as any reduction in the severity of HFI over the year following baseline assessment. One binary variable reflecting the reduction of HFI was created based on the results of the Household Food Security Survey Module (HFSSM) of the Canadian Community Health Survey at baseline and follow-up3. The HFSSM measures the severity of HFI over the past 12 months based on 10-item and 8-item food security scales for adults and children respectively, with scores ranging from 0 to 10 for adults and 0–8 for children (Polsky and Dividon, 2024; Health Canada, 2007; Uppal and Canada). A zero score indicates food security; a score of one indicates marginal HFI; scores between two and four for children or between two and five for adults denote moderate HFI; and scores above four for children or above five for adults indicate severe HFI. A reduction in HFI indicated a downward shift in the class of food insecurity. Thus, including a shift from: severe HFI to food security; severe HFI to moderate or marginal HFI; moderate HFI to food security or marginal HFI; or marginal HFI to food security.
2.3. Exposures
The exposures of interest were the following household health-promoting practices: use of food donations, use of food-management CB-SAPs, and use of CB-SAPs not related to food during the year following baseline, with binary indicators over time (use of the program was denoted At = 1; non-use of the program was denoted At = 0). The use of food donations and food-management CB-SAPs refers to monthly use after the baseline assessment. The use of food-management CB-SAPs refers to the use of at least one of the following CB-SAPs: collective kitchens, food buying groups, class or workshops on cooking or food-related topics. The use of these programs was measured by asking the participant, “During the past 12 months, did you use (name of the program)?”. If the answer was yes, in either case of food donations or food-management CB-SAPs, the participant was asked about the monthly use [yes/no] of this program [1, …, 12]. To avoid low cell counts, we converted monthly use of food donations and monthly use of food-management CB-SAPs into quarterly use. The use of CB-SAPs not related to food refers to the annual use of the following CB-SAPs: individual follow-up, job search service, educational courses, and personal development activities.
2.4. Covariates
Profiles of household food-acquisition health-promoting practices at the baseline. This variable was created using the following five information items related to the utilization of alternative food sources obtained at enrollment: 1) Type of food bank visited (i.e., food banks offering food donations and capacity-building programs [CBP-FBs] or food banks only offering food donations [FD-FBs]); 2) Use of fruit and vegetable (F&V) markets during the summer; 3) Growing one's own food during the summer; 4) Food donation usage frequency; and 5) Travel time to the most used grocery store. Consequently, three profiles characterized by variations in food donation use, frequency, and travel time to the primary grocery store across settings (urban, semi-urban, and rural) were identified: 1) FB-Exclusive-users (those reporting at baseline exclusively visiting food banks), 2) FB + F&V-Market-users (those reporting at baseline visiting food banks and F&V markets during the summer), and 3) Multiple-AFS-users (those reporting at baseline visiting food banks while using F&V markets and growing their own food during the summer). Details regarding items assessment and profiles computation have been summarized in a previous paper (Pérez et al., 2024).
Other covariates were selected based on their expected contribution to both household health-promoting practices and HFI (Figure A.1, Supplementary appendix). Baseline covariates were socio-demographic characteristics (including gender, age, self-reported race/ethnicity, household composition, and household educational level). Self-reported race/ethnicity refers to the participant's answer to their preferred race/ethnicity self-identification and was dichotomized into “White” and “Other” categories. The later includes people self-identified as Black, Asian, Indigenous, and others, which due to small cell counts for some racialized groups were collapsed to avoid violation of anonymity. Other variables include length of time using food banks prior to study enrollment, setting, size of community organization from which participants were recruited. To account for potential changes during the COVID-19 pandemic, we included an indicator variable ‘COVID-19 measures’ intended to capture whether the data was collected before or during the COVID-19 pandemic, and HFI in the year preceding the study. Time-varying covariates included annual household income, major life events during the past 12-months, and physical and mental health. In models examining the effects of food donations, the use of CB-SAPs not related to food and those related to food-management during the 12 months of the study were also included as time-invariant confounders. In models examining the use of CB-SAPs not related to food and those related to food-management, food donations usage during the 12-months of the study was included as a time-varying variable. Details on the measurement of all covariates are given in Table A.1 (Supplementary appendix). Participants who did not participate in the follow-up were marked as censored (Ct = 1 denoted censored and Ct = 0 denoted uncensored).
2.5. Analytical strategy
To help ensure the robustness of our estimation, we used methods from the causal inference framework to estimate the global population average treatment/intervention effects (ATEs) of each type of CB-SAP (i.e., food donations, food-management CB-SAPs, CB-SAPs not related to food) on the reduction of household food insecurity. This analysis quantified the difference in the probability of reducing HFI that would be observed if all participants used the given type of CB-SAP throughout the study versus if all of them did not use the program, P [Y1=1]−P [Y0=1]. Then, we conducted stratified models for each profile of household food-acquisition health-promoting practices (i.e., Exclusive-FB-users, FB + F&V-Market-users, and Multiple-AFS-users) identified at baseline. The ATEs were estimated using Longitudinal targeted maximum likelihood estimation (LTMLE), a doubly robust method that allows for handling repeated measured outcomes accounting for time-varying confounding and censoring simultaneously.
Second, we quantified the associations between the use of each type of CB-SAPs and HFI reduction conditional on baseline-profiles of household food-acquisition health-promoting practices using LTMLE for working marginal structural models (MSM). In these models, we used the profile of Exclusive-FB-users that did not use other programs as a category of reference to estimate if an additional quarterly use of each program increased the probability of reducing household food insecurity among FB + F&V-Market-users and Multiple-AFS-users. All models were adjusted by mentioned covariates. Of note, despite the use of causal inference methods to ensure robustness and address methodological challenges presented with standard statistical methods, the results from our analysis should be interpreted as associational. All analyses were performed using the ltmle package in R-Studio version 4.2.1 (R Core Team. R, 2019).
2.6. Missing data
Missing data at baseline was low (<3.2 %). We used multiple imputation by chained equations (MICE) (100 sets) to impute missing data on annual household income, age, major life events, health status, and country of birth. CB-SAPs items presented a small percentage (<2.8 %) of missing data and were imputed using the random hot deck imputation method.
2.7. Sensitivity analysis
To examine whether the association between the use of each CB-SAP and the reduction of household food insecurity was modified by the interaction between baseline-user's profiles of household food-acquisition health-promoting practices at baseline and quarterly use of CB-SAPs, we included an interaction term between the baseline profiles and time by CB-SAPs usage in the working marginal structural models. Since the original scale of food insecurity is a numeric scale, we conducted the analysis using a continuous outcome and estimating the Average Additive Treatment Effect of the use of CB-SAPs and reduction of food insecurity. Given the transformation of the outcome into a numeric scale it is noteworthy clarifying that estimates from models using the continuous outcomes indicating a reduction (i.e., negative sign) are interpreted as reduction in the scale of HFI, hence as a reduction of HFI or improvement on food security, and only absolute/additive estimates are possible. This, conversely, to the use of a binary outcome with reduction of HFI = 1, where an increase or positive sign in absolute/additive estimates are desired and where relative estimates can be obtained.
3. Results
Table 1 summarizes participant characteristics at baseline and follow-up. Among the 679 participants retained in the study, 43 % reported reductions in HFI (n = 290) at follow-up. Overall, participants who experienced a reduction in food insecurity at follow-up reported better mental and physical health compared to those who did not.
Table 1.
Descriptive statistics of participants at baseline and follow-up (n = 915).
| Characteristics | Baseline (n = 915) | Follow-up (n = 679) |
|
|---|---|---|---|
| Reduction [n = 290; 43 %] | No reduction [n = 389; 57 %] | ||
| Age, y, median [IQR] | 40.0 [32.0; 51.0] | 40.0 [31.0; 50.8] | 41.0 [33.0; 52.0] |
| Sex Assigned at Birth [%] | |||
| Female | 562 [61.4] | 193 [66.6] | 251 [64.5] |
| Male | 353 [38.6] | 97 [33.4] | 138 [35.5] |
| Self-reported Race/Ethnicity [%] | |||
| White | 698 [76.3] | 210 [72.4] | 293 [75.3] |
| Other | 217 [23.7] | 80 [27.6] | 96 [24.7] |
| Mental health, median [IQR] | 40.7 [31.5; 48.6] | 44.4 [36.9; 52.6] | 41.2 [32.6; 51.1] |
| Physical health, median [IQR] | 47.9 [35.2; 56.1] | 51.3 [39.5; 57.5] | 46.5 [33.2; 55.3] |
| Major life events, median [IQR] | 3.0 [2.0; 5.0] | 2.0 [1.0; 3.0] | 3.0 [1.0; 4.0] |
| Household composition [%] | |||
| Couple (with or without children) | 231 [25.2] | 93 [32.1] | 97 [24.9] |
| Single-parent home | 204 [22.3] | 65 [22.4] | 91 [23.4] |
| Single (living alone or with others) | 480 [52.5] | 132 [45.5] | 201 [51.7] |
| Household educational level [%] | |||
| Secondary level or less | 638 [69.7] | 179 [61.7] | 271 [69.7] |
| Post-secondary studies | 277 [30.3] | 111 [38.3] | 118 [30.3] |
| Annual household income [%] | |||
| ≤ $14,999 | 578 [63.2] | 148 [51.0] | 238 [61.2] |
| ≥ $15,000 | 337 [36.8] | 142 [49.0] | 151 [38.8] |
| Length of FB use time at baseline, median [IQR] | |||
| One or less than one month | 340 [37.2] | 105 [36.2] | 160 [41.1] |
| Two or more months | 575 [62.8] | 185 [63.8] | 229 [58.9] |
| COVID-Measure s [%] | |||
| Non | 343 [50.5] | 147 [50.7] | 196 [50.4] |
| Yes | 336 [49.5] | 143 [49.3] | 193 [49.6] |
| Community organization size, median [IQR] | 14.0 [8.0; 24.0] | 14.0 [9.0; 23.0] | 13.0 [7.0; 21.0] |
| Setting [%] | |||
| Rural-Suburban | 402 [42.9] | 162 [55.9] | 203 [52.2] |
| Urban | 513 [56.1] | 128 [44.1] | 186 [47.8] |
| Household food security status | |||
| Food security | – | 93 [32.1] | 0 [0.0] |
| Marginal food insecurity | 84 [9.2] | 92 [31.7] | 18 [4.6] |
| Moderate food insecurity | 368 [40.2] | 105 [36.2] | 164 [42.2] |
| Severe food insecurity | 463 [50.6] | 0 [0.0] | 207 [53.2] |
| Baseline Profiles of health-promoting practices related to food acquisition | |||
| Exclusive-FB user | 287 [31.4] | 80 [27.6] | 119 [30.6] |
| FB + F&V-Market users | 381 [41.6] | 118 [40.7] | 180 [46.3] |
| Multiple-AFS users | 247 [27.0] | 92 [31.7] | 90 [23.1] |
| CB-SAPs related to food-management [%] | |||
| Non-use | 628 [68.6] | 221 [76.2] | 294 [75.6] |
| Use | 287 [31.4] | 69 [23.8] | 95 [24.4] |
| CB-SAPs not related to food [%] | |||
| Non-use | 421 [46.0] | 173 [59.7] | 233 [59.9] |
| Use | 494 [54.0] | 117 [40.3] | 156 [40.1] |
| Food donation use [%] | 915 [100.0] | 235 [81.0] | 330 [84.8] |
Notes: FB = food bank; FV = fruit and vegetable; CB-SAPs = community-based social assistance. Totals may not add to 100 % due rounding.
Table 2 presents the sociodemographic characteristics and CB-SAPs use by baseline profiles of household food-acquisition health-promoting practices and HFI reduction. Multiple-AFS-users exhibited the highest proportion of participants reporting a reduction in HFI (51 %), along with a higher representation of women (83.7 %), participants with an annual household income ≥ CAD 15,000, and users of CB-SAPs related to food-management (27.2 %). In contrast, FB + F&V-market users had the highest proportion of participants reporting a reduction in HFI and were participants self-identifying racially as being other than White (47.5 %), living as a couple (35.6 %), having completed post-secondary studies (45.6 %), reporting an annual household income ≤ CAD 15,000 (58.5 %), and using CB-SAPs not related to food (44.9 %). Further details regarding sociodemographic characteristics and CB-SAPs usage across user's baseline profiles of household food-acquisition health-promoting practices by HFI at baseline and follow-up are presented in Tables A.2, A.3, A.4, A.5, and A.6 of the supplementary appendix.
Table 2.
Sociodemographic characteristics and distribution of the use of community-based social assistance programs among user's baseline profiles of food-acquisition health-promoting practices by food insecurity reduction at follow-up (n = 679).
| Characteristics | Exclusive-FB user |
FB + FV-Market users |
Multiple-AFS users |
|||
|---|---|---|---|---|---|---|
| (n = 199; 29 %) |
(n = 298; 44 %) |
(n = 182; 27 %) |
||||
| HFI Reduction |
No HFI reduction |
HFI Reduction |
No HFI reduction |
HFI Reduction |
No HFI reduction |
|
| [n = 80; 40 %] | [n = 119; 60 %] | [n = 118; 40 %] | [n = 180; 60 %] | [n = 92; 51 %] | [n = 90; 49 %] | |
| Age,years, median [IQR] | 40.0 [31.0; 53.2] | 42.0 [33.0; 52.0] | 39.5 [32.0; 47.8] | 40.5 [32.8; 52.2] | 39.0 [31.8; 51.0] | 42.5 [33.2; 51.0] |
| Gender[%] | ||||||
| Female | 46 [57.5] | 75 [63.0] | 70 [59.3] | 105 [58.3] | 77 [83.7] | 71 [78.9] |
| Male | 34 [42.5] | 44 [37.0] | 48 [40.7] | 75 [41.7] | 15 [16.3] | 19 [21.1] |
| Self-reported Race/Ethnicity[%] | ||||||
| White | 64 [80.0] | 90 [75.6] | 62 [52.5] | 130 [72.2] | 84 [91.3] | 73 [81.1] |
| Other | 16 [20.0] | 29 [24.4] | 56 [47.5] | 50 [27.8] | 8 [8.7] | 17 [18.9] |
| Mental health, median [IQR]∗ | 43.7 [36.1; 50.6] | 41.0 [32.7; 50.6] | 45.2 [37.7; 53.2] | 41.0 [31.9; 51.2] | 44.4 [36.0; 52.8] | 41.5 [35.1; 51.7] |
| Physical health,median [IQR]∗ | 49.2 [40.9; 54.6] | 46.3 [34.9; 54.8] | 52.9 [40.5; 57.8] | 45.6 [33.0; 54.8] | 51.5 [36.5; 56.8] | 48.6 [31.4; 57.0] |
| Major life events,median [IQR]∗ | 2.0 [1.0; 3.0] | 3.0 [1.5; 4.0] | 2.0 [1.0; 3.0] | 2.0 [1.0; 4.0] | 2.0 [1.0; 4.0] | 3.0 [2.0; 4.0] |
| Household composition[%] | ||||||
| Couple (with or without children) | 23 [28.7] | 29 [24.4] | 42 [35.6] | 45 [25.0] | 28 [30.4] | 23 [25.6] |
| Single-parent home | 9 [11.2] | 27 [22.7] | 27 [22.9] | 37 [20.6] | 29 [31.5] | 27 [30.0] |
| Single (living alone or with others) | 48 [60.0] | 63 [52.9] | 49 [41.5] | 98 [54.4] | 35 [38.0] | 40 [44.4] |
| Household educational level[%] | ||||||
| Secondary level or less | 60 [75.0] | 90 [75.6] | 63 [53.4] | 124 [68.9] | 56 [60.9] | 57 [63.3] |
| Post-secondary studies | 20 [25.0] | 29 [24.4] | 55 [45.6] | 56 [31.1] | 36 [39.1] | 33 [36.7] |
| Annual household income[%]∗ | ||||||
| ≤14.999$ | 40 [50.0] | 72 [60.5] | 69 [58.5] | 121 [67.2] | 39 [42.4] | 45 [50.0] |
| ≥15.000$ | 40 [50.0] | 47 [39.5] | 49 [41.5] | 59 [32.8] | 53 [57.6] | 45 [50.0] |
| Length of FB use time before the study, median [IQR] | ||||||
| One or less than one month | 27 [33.8] | 44 [37.0] | 39 [33.1] | 76 [42.2] | 39 [42.4] | 40 [44.4] |
| Two or more months | 53 [66.2] | 75 [63.0] | 79 [66.9] | 104 [57.8] | 53 [57.6] | 50 [55.6] |
| COVID-Measure s[%]∗ | ||||||
| Non | 34 [42.5] | 53 [44.5] | 59 [50.0] | 88 [48.9] | 54 [58.7] | 55 [61.1] |
| Yes | 46 [57.5] | 66 [55.5] | 59 [50.0] | 92 [51.1] | 38 [41.3] | 35 [38.9] |
| Community organization size, median [IQR] | 13.0 [8.0; 18.0] | 13.0 [9.0; 21.0] | 14.0 [9.0; 28.0] | 12.0 [7.0; 21.0] | 15.5 [9.0; 22.0] | 14.0 [6.2; 22.5] |
| Setting[%] | ||||||
| Rural-Suburban | 57 [71.7] | 84 [70.6] | 26 [22.0] | 49 [27.2] | 45 [48.9] | 53 [58.9] |
| Urban | 23 [28.3] | 35 [29.4] | 92 [78.0] | 131 [72.8] | 47 [51.1] | 37 [41.1] |
| CB-SAPs related to food management[%]∗ | ||||||
| Non-use | 64 [80.0] | 94 [79.0] | 90 [76.3] | 135 [75.0] | 67 [72.8] | 65 [72.2] |
| Use | 16 [20.0] | 25 [21.0] | 28 [23.7] | 45 [25.0] | 25 [27.2] | 25 [27.8] |
| CB-SAPs not related to food[%]∗ | ||||||
| Non-use | 49 [61.3] | 74 [62.2] | 65 [55.1] | 104 [57.8] | 59 [64.1] | 55 [61.1] |
| Use | 31 [38.7] | 45 [37.8] | 53 [44.9] | 76 [42.2] | 33 [35.9] | 35 [38.9] |
| Food donation use[%]∗ | 65 [81.2] | 96 [80.7] | 99 [83.9] | 159 [88.3] | 71 [77.2] | 75 [83.3] |
Note: ∗: Measured or created at time 1; FB = food bank; FV = fruit and vegetable; AFS = alternative food sources; CB-SAPs = community-based social-assistance program.
Table 3 presents the overall and profile specific Risk difference (RD), Relative Risk (RR) and 95 % confidence intervals (CIs) for the association between the use of each of the three types of CB-SAPs (i.e., Food Donations, Other CB-SAPs related to food management and Other CB-SAPs unrelated to food management) and household food insecurity reduction (i.e., food security improvement), according to the determined user's baseline profiles of household food-acquisition health-promoting practices. Results are from the estimation through the LTMLE using a cumulative structure comparing participants who used the intervention to those who did not use the intervention during the study period. Overall, we observed a small cumulative food security improvement in the absolute scale for users of food donations (5 %; 95 %CI = −7 %, 17 %) and users of CB-SAPs unrelated to food management (6 %; 95 %CI = −5 %, 17 %). Likewise, there was a relative increase in HFI reduction for users of food donations (RR = 1.12; 95 %CI = 0.86, 1.46) and CB-SAPs unrelated to food management (RR = 1.14; 95 %CI = 0.89, 1.47). However, both absolute and relative estimates included the null value. The stratified analysis among each of the user's baseline profile of household food-acquisition health-promoting practices (i.e., users of food banks exclusively, users of Frutis & Vegetables Markets, or users of Multiple sources) showed mostly a trend towards improvement in the reduction of HFI in both, relative and absolute scales, but all estimates included the null values.
Table 3.
Risk difference (RD), Relative Risk (RR) and 95 % confidence intervals (CI) for the association between the use of different community-based social assistance programs(CB-SAPs) and household food insecurity (HFI) reduction, overall and by user's profiles of household food-acquisition health-promoting practices at baseline.
| Overall and specific user's baseline profile HFI reduction by type of CB-SAP used | ||||
|---|---|---|---|---|
| Use of Food Donations | RR | (95 % CI) | RD | (95 % CI) |
| Overall | 1.12 | (0.86, 1.46) | 0.049 | (-0.070, 0.168) |
| Exclusive-FB user | 0.95 | (0.54, 1.67) | −0.020 | (-0.228, 0.188) |
| FB + FV-Market users | 1.40 | (0.91, 2.15) | 0.145 | (-0.060, 0.350) |
| Multiple-AFS users | 1.06 | (0.69, 1.61) | 0.030 | (-0.200, 0.261) |
| Use of CB-SAP- Related to Food Management | RR | (95 % CI) | RD | (95 % CI) |
| Overall | 1.00 | (0.63, 1.60) | 0.000 | (-0.199, 0.199) |
| Exclusive-FB user | 0.81 | (0.28, 2.34) | −0.092 | (-0.606, 0.422) |
| FB + FV-Market users | 1.46 | (0.53, 3.99) | 0.123 | (-0.153, 0.399) |
| Multiple-AFS users | 0.87 | (0.51, 1.49) | −0.077 | (-0.396, 0.242) |
| Use of CB-SAPs Unrelated to Food | RR | (95 % CI) | RD | (95 % CI) |
| Overall | 1.14 | (0.89, 1.47) | 0.067 | (-0.053, 0.167) |
| Exclusive-FB user | 1.26 | (0.80, 1.99) | 0.114 | (-0.115, 0.343) |
| FB + FV-Market users | 1.12 | (0.75, 1.66) | 0.043 | (-0.112, 0.199) |
| Multiple-AFS users | 1.00 | (0.61, 1.66) | 0.001 | (-0.277, 0.279) |
Results from Longitudinal Targeted Maximum Likelihood Estimation (LTMLE) considering a cumulative use of any CB-SAP intervention, overall and according to each baseline user's profile. RR= Risk Ratio; RD Risk Difference, here equivalent to the Average Additive Treatment Effect (ATE); CI= Confidence interval; FB = food bank; FV = fruit and vegetable; AFS = alternative food sources; CB-SAPs = community-based social-assistance program.
Table 4 shows the results from the LTMLE for working marginal structural models (MSM), comparing users of each CB-SAP to non-users by the type of baseline household food-acquisition user's profiles, accounting for each quarterly use of food donations. Compared to the Exclusive-FB-users (reference profile), users who were classified at baseline as Multiple AFS users were more likely to reduce HFI, regardless of the intervention used (RR: 1.30; 95 % CI:1.01, 1.60) for Food donations use, (RR: 1.28; 95 % CI:1.03, 1.58) for CB-SAPs related to Food and (RR: 1.33; 95 % CI:1.03, 1.62) for CB-SAPs unrelated to food management. Similarly, compared to the Exclusive-FB-users, users who were classified at baseline as Multiple AFS users had a higher probability of reducing HFI regardless the CB-SAP used. There was no difference in HFI reduction between the FB + FV Market users and Exclusive FB users in both, the relative and absolute scales. Although there was a trend towards an increase in HFI reduction by trimester use in all interventions, the estimates included the null value.
Table 4.
Relative Risk and 95 % confidence intervals for the association between the use of different community-based social assistance programs (CB-SAPs) and household food insecurity reduction, according to the baseline profiles of household food-acquisition health-promoting practices at enrollment.
| HFI reduction by type of CB-SAP used and specific user's baseline profile | ||||
|---|---|---|---|---|
| Food Donation | RR | 95 %CI | RD | 95 %CI |
| Exclusive-FB user | Ref. | – | Ref. | – |
| FB + FV-Market users | 0.99 | (0.76, 1.25) | −0.009 | (-0.021, 0.293) |
| Multiple-AFS users | 1.30 | (1.01, 1.60) | 0.367 | (0.011, 0.921) |
| Trimester use | 1.17 | (0.90, 1.46) | 0.188 | (-0.097, 0.627) |
| CB-SAP- Related to Food Management | RR | 95 %CI | RD | 95 %CI |
| Exclusive-FB user | Ref. | – | Ref. | – |
| FB + FV-Market users | 0.95 | (0.73, 1.19) | −0.052 | (-0.224, 0.199) |
| Multiple-AFS users | 1.28 | (1.03, 1.58) | 0.343 | (0.024, 0.824) |
| Trimester use | 1.01 | (0.64, 1.45) | 0.012 | (-0.289, 0.573) |
| CB-SAPs Unrelated to Food | RR | 95 %CI | RD | 95 %CI |
| Exclusive-FB user | Ref. | – | Ref. | – |
| FB + FV-Market users | 0.95 | (0.70, 1.24) | −0.045 | (-0.255, 0.281) |
| Multiple-AFS users | 1.33 | (1.03, 1.62) | 0.406 | (0.035, 0.984) |
| Trimester use | 1.09 | (0.95, 1.23) | 0.091 | (-0.052, 0.269) |
Results from LTMLE with working marginal structural models (MSM) including trimester (quarterly) intervention use. RR= Risk Ratio; RD Risk Difference, here equivalent to the Average Additive Treatment Effect (ATE); RR and RD obtained directly from the transformation of the resulting log-Odds from the logistic MSM-LTMLE models. CI= Confidence interval; FB = food bank; FV = fruit and vegetable; AFS = alternative food sources; CB-SAPs = community-based social-assistance program.
3.1. Sensitivity analyses
Table A7 presents the RRs and 95 % CIs derived from models including the interaction term between user's baseline profiles of household food-acquisition health-promoting practices and the use of each CB-SAP over time. Although results suggest trends towards improvement in HFI with variations across baseline profiles of household food-acquisition health-promoting practices, all estimates cover the null value.
Fig. 2 shows the results of the average additive treatment effect (ATE) between the use of different community-based social assistance programs (CB-SAPs) and household food insecurity reduction, using both, the HFI scale as binary outcome (Top panel) and a continuous outcome (Bottom panel). For ease of comparison, we reiterate that estimates with positive signs (showing an increase) in the absolute/additive scale from models using the binary outcome (reduction of HFI = 1) indicate an improvement on food security. Conversely, estimates indicating a reduction (i.e., negative sign) from models using the continuous outcomes are interpreted as reduction in the scale of HFI, hence an improvement on food security. Fig. 2A shows the ATE using LTMLE cumulative approach for the overall ATE and among profile specific populations using the HFI reduction dichotomous outcome (Table 3). Fig. 2B shows results from the LTMLE for working MSM, comparing users of each CB-SAP to non-users, by the type of baseline household food-acquisition user's profiles, accounting for each quarterly use of food donations (Table 4). Fig. 2C shows the ATE using LTMLE cumulative approach for the overall ATE and among profile specific populations. Overall, compared to non-users, there is a trend for reduction in the scale of food insecurity (i.e., HFI improvement) with the use of all CB-SAPs and across all baseline household food-acquisition user's profiles, most accentuated among users of food donation strategies (ATE = −0.83; 95 %CI: −1.53, −0.14). Fig. 2D shows results from the LTMLE for working MSM, comparing users of each CB-SAP to non-users, by the type of baseline household food-acquisition user's profiles, accounting for each quarterly use of food donations. Compared to the Exclusive-FB-users (reference profile), users who were at baseline classified as Multiple AFS users were more likely to show a reduction in the HFI scale, especially among the users of CB-SAPs related to food management (ATE: −0.24; 95 % CI: 0.43, −0.04). Overall, results using the binary and continuous outcomes consistently show a trend towards reduction of HFI among the de novo food-aid seekers using CB-SAPs. The improvement in HFI (i.e., improvement of food security) is more pronounced in the additive scale using continuous outcomes for food donation users but is consistently shown form Multiple AFS users with both, binary and continuous outcomes, strengthening the complementary role of both approaches. Additional details are presented in Supplementary Table A8.
Fig. 2.
Mean average additive treatment effects (ATE) and 95 % CIs of the use of community-based social assistance programs (CB-SAPs) on the reduction of household food insecurity by baseline user's profiles of household food-acquisition health-promoting practices.
4. Discussion
This study aimed to examine the association between CB-SAPs use and the reduction of household food insecurity among newly registered food-aid seekers in Quebec's food banks over time, and whether this association varied by baseline profiles of household food-acquisition health-promoting practices. Our findings suggest that the use of CB-SAPs might contribute to reducing household food insecurity among de novo social assistance food bank users. Each type of CB-SAPs appears to have different contributions to the reduction of household food insecurity, and these contributions may vary depending on the user's specific baseline profile of household food-acquisition health-promoting practices. The results on the use of food donations provide robust longitudinal evidence, reinforcing the limited existing literature indicating that the use of this program is linked to the reduction of food insecurity (Cheyne et al., 2020; Roncarolo et al., 2016).
Our analyses revealed two original findings about the influence of CB-SAPs usage on food insecurity. Firstly, our results suggest that the effectiveness of food donations in reducing food insecurity among new food-aid seekers is contingent not only upon the type of community organization offering food donations that they visit, but also on the household food-acquisition health-promoting practices adopted by this population. Given that Exclusive-FB and FB + F&V-Market users had fewer resources and capabilities to cope with hardships (Roncarolo et al., 2016), their reliance on food assistance may persist over time as they adopt long-term health-promoting practices, such as using CB-SAPs not related to food to overcome food shortages. This is contrary to the findings of Rizvi et al. (2021), who reported an association between improved food security and visiting food banks offering a choice model of food distribution and those integrated within Community Resource Centers, but not visiting food banks offering other CB-SAPs (Rizvi et al., 2021). This difference in findings could potentially be explained by the smaller sample size in their study and their global analytical approach, which did not account for household food-acquisition health-promoting practices and the use of other CB-SAPs among new food-aid seekers.
Second, our analysis suggests that the use of food donations, food-management CB-SAPs or CB-SAPs not related to food appear to play a role in HFI reduction, particularly among new food-aid seekers who adopt multiple household food-acquisition health-promoting practices at the beginning of their enrollment in a food bank. In contrast, there is less household food insecurity reduction among FB + F&V-Market-users. This result could be explained by the fact that the effects of using CB-SAPs related to food-management or non-food-related CB-SAPs may require more time to be developed and sustained to generate meaningful results, particularly when new food-aid seekers face several constraints in resources and capabilities (Ezekekwu et al., 2022). Thus, a comprehensive follow-up process extending beyond one year from their enrollment in the food bank would be necessary. Especially, to understand whether household food insecurity decreases among Exclusive-FB and FB + F&V-Market users and explore the mechanisms associated to any changes, including the lag time consideration (Ezekekwu et al., 2022; Li et al., 2023).
4.1. Strengths and limitations
The findings from this study should be interpreted considering some limitations. Firstly, despite adjusting for several relevant confounders, we lacked access to data regarding the institutional availability of alternative food sources and quality of CB-SAPs, which together with potential physical accessibility limitations, are potential unmeasured or unknown confounders related to the user's profiles or the use of interventions and the HFI outcome that were not accounted for. Secondly, the self-reported retrospective nature of the CB-SAPs use measures introduces the possibility of recall bias. It is noteworthy to indicate that COVID-19 related sanitary measures were in place during the second wave of follow-up interviews, for which most of the second half of follow-up interviews were conducted over the phone or online. Thus, not only affecting the recalling but potentially affecting the use of the CB-SAPs and any other practices with potentially unpredictable effects beyond the scope of this study. However, we acknowledge the multidimensional effects of the COVID-19 pandemic may have affected HFI differently among different populations and hence compromise our study's external validity (Lamarche et al., 2021). Thirdly, the relatively small sample size may have impacted the precision of our estimates and limited the power to detect main and interaction effects. Here we used only baseline and end-point information about health status, properly accounted for in our models. However, we are cognizant that the relationship between health status and HFI as mediator should data allows it can be further studied. Finally, the variability of CB-SAPs across settings restricts the transferability of our findings. However, to address this variability, we considered differences between the type of CB-SAPs offered by community organizations to create the baseline profiles of household food-acquisition health-promoting practices. Additionally, all models were adjusted by the size of the community organization and setting in which the participant was recruited (urban, rural, suburban).
This study builds upon previous qualitative studies, indicating that trajectories of use of social assistance programs and the relationship with food insecurity are not linear and are context dependent, providing important insights into the household health-promoting practices of new food-aid seekers to understand their relationship with CB-SAPs usage. The Pathways cohort study achieved a commendable response rate (74 %), considering the high level of vulnerability of this population. Although the interpretation of our results should not be considered as causal, to the best of our knowledge, this is the first study that uses LTMLE to robustly estimate the association between CB-SAPs usage and the reduction of household food insecurity, accounting for both baseline and time-varying confounders, while considering household food-acquisition health-promoting practices among new food-aid seekers.
For policy making, these results have the opportunity to be highly intervenable, especially by targeting individuals or households at incipient stages of food insecurity or vulnerability. To develop or adapt food security interventions it is necessary to better identify what are the specific needs of new food-aid seekers and their household food-acquisition health-promoting practices. This can be achieved partially through investing on strengthening the infrastructure for registration and monitoring systems among community organizations offering food donations. A strengthened system will have an improved documentation and monitoring of the needs and practices and could contribute to ensure that such needs are met accordingly. Likewise, it is evident that food donations’ use plays different roles in the process of reducing food insecurity, according to the different household-specific characteristics (i.e., profiles) of de novo food-aid seekers. Consequently, public health interventions must ensure equitable access to nutritious food among this population accordingly. Our findings suggest that CB-SAPs may potentially reduce HFI among new food-aid seekers who already use several alternative food sources (i.e., Multiple-AFS users). Hence, public health practitioners and policymakers should focus on tackling other drivers of HFI (e.g., financial and educational improvement for job acquisition, etc.), not only providing food (Metta et al., 2021).
5. Conclusion
This study elucidates how the association between CB-SAPs usage and the reduction of household food insecurity varies according to household food-acquisition health-promoting practices among de novo food-aid seekers. It emphasizes the need to recognize differences among the household food-acquisition health-promoting practices of new food-aid seekers, the use of holistic approaches not limited to food-management, and accounting for such differences when analyzing the effects of CB-SAPs. Interventions aimed at enhancing food security should identify and target the context specific household food-acquisition health-promoting practices of food-aid seekers and capitalize on them to improve household food security.
CRediT authorship contribution statement
E.J. Pérez: Writing – original draft, Visualization, Validation, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. M. Carabali: Writing – review & editing, Validation, Supervision, Methodology, Formal analysis, Data curation, Conceptualization. G. Mercille: Writing – review & editing, Validation, Supervision, Resources, Funding acquisition. M.P. Sylvestre: Writing – review & editing, Validation, Methodology. R. Blanchet: Writing – review & editing, Validation, Investigation. F. Roncarolo: Writing – review & editing, Validation, Resources, Project administration, Investigation, Funding acquisition, Data curation, Conceptualization. M. Schnitzer: Writing – review & editing, Validation, Methodology. L. Potvin: Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.
Ethical approval
This study was approved by the Health Research Ethical Review Panel of the Université de Montréal Cert. n. CERSES-18–074-D. The study was conducted in accordance with the local legislation and institutional requirements. Participants provided written consent at the beginning of the study.
Funding
This study is funded by CIHR, Canadian Institutes of Health Research (PJT 155936), with complementary funding from the Quebec Ministry of Health and Social Services, Fondation du Grand Montréal and Fondation Vanier. Support for community organizations recruitment was also provided by the Greater Montreal Foundation and Mission Inclusion. Elsury Pérez is supported by a CIHR Frederick Banting and Charles Best Canada Graduate Scholarship (CGS-D). Mabel Carabali holds a FRQS Junior 1 Investigator Award and holds a Canada Research Chair Tier 2 in Methods to Address Health Inequalities (CRC-2023-00012-101748). Marie-Pierre Sylvestre holds a FRQS Junior 2 Investigator Award. Louise Potvin holds the Canada Research Chair in Community Approaches and Health Inequalities (CRC 950–232541).
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The authors would like to thank Food Banks of Quebec for their collaboration in the study and all community organizations and partners who facilitated the recruitment of participants.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.ssmph.2025.101859.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
Data availability
The datasets generated and analyzed during the current study are not publicly available due to confidentiality agreements with community organizations and participants. They are available from the corresponding author in response to reasonable requests.
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Supplementary Materials
Data Availability Statement
The datasets generated and analyzed during the current study are not publicly available due to confidentiality agreements with community organizations and participants. They are available from the corresponding author in response to reasonable requests.