Additional Fruit and Vegetable Vouchers for Pregnant WIC Clients: An Equity-Focused Strategy to Improve Food Security and Diet Quality
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Sample and Settings
2.2. Extenuating Circumstances
2.2.1. Modifications
2.2.2. Impacts
2.2.3. Responsible Parties
2.3. Intervention
2.4. Recruitment
2.5. Measures
2.6. Statistical Analysis
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Martin, J.A.; Hamilton, B.E.; Osterman, M.J.K.; Driscoll, A.K. Births: Final Data for 2019. Natl. Vital Stat. Rep. Cent. Dis. Control Prev. Natl. Cent. Health Stat. Natl. Vital Stat. Syst. 2021, 70, 1–51. [Google Scholar]
- Olson, M.E.; Diekema, D.; Elliott, B.A.; Renier, C.M. Impact of Income and Income Inequality on Infant Health Outcomes in the United States. Pediatrics 2010, 126, 1165–1173. [Google Scholar] [CrossRef] [PubMed]
- Coleman-Jensen, A.; Rabbitt, M.P.; Gregory, C.A.; Singh, A. Statistical Supplement to Household Food Security in the United States in 2020. 2021. Available online: https://www.ers.usda.gov/webdocs/publications/102076/err-298.pdf?v=8766.4 (accessed on 30 May 2022).
- Weiser, S.D.; Palar, K.; Hatcher, A.M.; Young, S.L.; Frongillo, E.A. Food Insecurity and Health: A Conceptual Framework. In Food Insecurity and Public Health; CRC Press: Boca Raton, FL, USA, 2015; pp. 23–50. [Google Scholar]
- Tarasuk, V.S.; Beaton, G.H. Women’s Dietary Intakes in the Context of Household Food Insecurity. J. Nutr. 1999, 129, 672–679. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tarasuk, V.; McIntyre, L.; Li, J. Low-Income Women’s Dietary Intakes Are Sensitive to the Depletion of Household Resources in One Month. J. Nutr. 2007, 137, 1980–1987. [Google Scholar] [CrossRef] [PubMed]
- Kline, N.; Zvavitch, P.; Wroblewska, K.; Worden, M.; Mwombela, B.; Thorn, B. WIC Participant and Program Characteristics 2020; U.S. Department of Agriculture, Food and Nutrition Service: Alexandria, VA, USA, 2022. Available online: https://www.fns.usda.gov/wic/participant-program-characteristics-2020 (accessed on 30 May 2022).
- Hamad, R.; Collin, D.F.; Baer, R.J.; Jelliffe-Pawlowski, L.L. Association of Revised WIC Food Package With Perinatal and Birth Outcomes: A Quasi-Experimental Study. JAMA Pediatr. 2019, 173, 845. [Google Scholar] [CrossRef] [PubMed]
- Berkowitz, S.A.; Curran, N.; Hoeffler, S.; Henderson, R.; Price, A.; Ng, S.W. Association of a Fruit and Vegetable Subsidy Program With Food Purchases by Individuals With Low Income in the US. JAMA Netw. Open 2021, 4, e2120377. [Google Scholar] [CrossRef] [PubMed]
- Healthy Food America. Healthy Food Pricing Incentives–Designing Successful Programs. 2019. Available online: https://www.healthyfoodamerica.org/healthy_food_pricing_incentives (accessed on 30 May 2022).
- Lee, Y.; Mozaffarian, D.; Sy, S.; Huang, Y.; Liu, J.; Wilde, P.E.; Abrahams-Gessel, S.; de Souza Veiga Jardim, T.; Gaziano, T.A.; Micha, R. Cost-Effectiveness of Financial Incentives for Improving Diet and Health through Medicare and Medicaid: A Microsimulation Study. PLoS Med. 2019, 16, e1002761. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Choi, S.E.; Seligman, H.; Basu, S. Cost Effectiveness of Subsidizing Fruit and Vegetable Purchases through the Supplemental Nutrition Assistance Program. Am. J. Prev. Med. 2017, 52, e147–e155. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ridberg, R.A.; Marpadga, S.; Akers, M.M.; Bell, J.F.; Seligman, H.K. Fruit and Vegetable Vouchers in Pregnancy: Preliminary Impact on Diet & Food Security. J. Hunger Environ. Nutr. 2021, 16, 149–163. [Google Scholar] [CrossRef]
- Orkin, A.M.; Gill, P.J.; Ghersi, D.; Campbell, L.; Sugarman, J.; Emsley, R.; Steg, P.G.; Weijer, C.; Simes, J.; Rombey, T. Guidelines for Reporting Trial Protocols and Completed Trials Modified Due to the COVID-19 Pandemic and Other Extenuating Circumstances: The CONSERVE 2021 Statement. JAMA 2021, 326, 257–265. [Google Scholar] [CrossRef] [PubMed]
- Levi, R.; Ridberg, R.; Akers, M.; Seligman, H. Survey Fraud and the Integrity of Web-Based Survey Research. Am. J. Health Promot. 2022, 36, 18–20. [Google Scholar] [CrossRef] [PubMed]
- Blumberg, S.J.; Bialostosky, K.; Hamilton, W.L.; Briefel, R.R. The Effectiveness of a Short Form of the Household Food Security Scale. Am. J. Public Health 1999, 89, 1231–1234. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lyles, C.R.; Nord, M.; Chou, J.; Kwan, C.M.L.; Seligman, H.K. The San Francisco Chinese Food Security Module: Validation of a Translation of the US Household Food Security Survey Module. J. Hunger Environ. Nutr. 2015, 10, 189–201. [Google Scholar] [CrossRef]
- Kwan, C.M.; Napoles, A.M.; Chou, J.; Seligman, H.K. Development of a Conceptually Equivalent Chinese-Language Translation of the US Household Food Security Survey Module for Chinese Immigrants to the USA. Public Health Nutr. 2015, 18, 242–250. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bikel, G.; Nord, M.; Price, C.; Hamilton, W.; Cook, J. Guide to Measuring Household Food Security; U.S. Department of Agriculture, Food and Nutrition Service: Alexandria, VA, USA, 2000. [Google Scholar]
- Rasch, G. Studies in Mathematical Psychology: I. In Probabilistic Models for Some Intelligence and Attainment Tests; Available online: https://psycnet.apa.org/record/1962-07791-000 (accessed on 30 May 2022).
- Rabbitt, M.P. Causal Inference with Latent Variables from the Rasch Model as Outcomes. Measurement 2018, 120, 193–205. [Google Scholar] [CrossRef]
- Nugent, N.B.; Shanks, C.B.; Seligman, H.K.; Fricke, H.; Parks, C.A.; Stotz, S.; Yaroch, A.L. Accelerating Evaluation of Financial Incentives for Fruits and Vegetables: A Case for Shared Measures. Int. J. Environ. Res. Public. Health 2021, 18, 12140. [Google Scholar] [CrossRef] [PubMed]
- Dimick, J.B.; Ryan, A.M. Methods for Evaluating Changes in Health Care Policy: The Difference-in-Differences Approach. JAMA 2014, 312, 2401–2402. [Google Scholar] [CrossRef] [PubMed]
- Anderson, D.; Burnham, K. Model Selection and Multi-Model Inference, 2nd ed.; Springer: New York, NY, USA, 2004; Volume 63, p. 10. [Google Scholar]
- Oronce, C.I.A.; Miake-Lye, I.M.; Begashaw, M.M.; Booth, M.; Shrank, W.H.; Shekelle, P.G. Interventions to Address Food Insecurity Among Adults in Canada and the US: A Systematic Review and Meta-Analysis. JAMA Health Forum 2021, 2, e212001. [Google Scholar] [CrossRef]
- Haslam, A.; Gill, J.; Taniguchi, T.; Love, C.; Jernigan, V.B. The Effect of Food Prescription Programs on Chronic Disease Management in Primarily Low-Income Populations: A Systematic Review and Meta-Analysis. Nutr. Health 2022, 026010602110707. [Google Scholar] [CrossRef] [PubMed]
- Ritchie, L.; Hecht, C.; Au, L.; Vital, N.; Strochlic, R.; Tsai, M.; Hecht, K.; Olague, C.; Rios, A.; Anderson, C.; et al. Research Report Informing the Future of WIC: Lessons Learned during COVID-19 from California WIC Participants; Nutrition Policy Institute, University of California: Oakland, CA, USA, 2021. [Google Scholar]
- Dasgupta, S.; Robinson, E.J.Z. Impact of COVID-19 on Food Insecurity Using Multiple Waves of High Frequency Household Surveys. Sci. Rep. 2022, 12, 1865. [Google Scholar] [CrossRef] [PubMed]
Variable | Intervention (n = 304) | Comparison (n = 466) | |
---|---|---|---|
Age, n (%) * | |||
18–25 years old | 68 (22%) | 143 (31%) | |
26–35 years old | 177 (58%) | 257 (55%) | |
36–45 years old | 58 (19%) | 60 (13%) | |
Older than 45 | 1 (0%) | 0 (0%) | |
Prefer not to answer | 0 (0%) | 6 (1%) | |
Language, n (%) | |||
English | 151 (50%) | 310 (67%) | |
Spanish | 80 (26%) | 144 (31%) | |
Chinese | 73 (24%) | 12 (3%) | |
Household size, mean (SD) | 3.1 (1.5) | 3.2 (1.5) | |
Number of children under 18 in the household, mean (SD) * | 1.17 (1.03) | 1.42 (1.04) | |
Number of children under 18 in the household, n (%) | |||
None | 95 (32%) | 103 (22%) | |
One | 103 (34%) | 151 (32%) | |
Two | 71 (23%) | 135 (29%) | |
Three | 30 (10%) | 69 (15%) | |
Prefer not to answer | 5 (2%) | 8 (2%) | |
Household Monthly Income, n (%) | |||
None | 28 (9%) | 33 (7%) | |
$1–$1000 | 78 (26%) | 119 (26%) | |
$1001–$2000 | 89 (29%) | 135 (29%) | |
$2001 or more | 60 (20%) | 95 (20%) | |
Prefer not to answer | 49 (16%) | 84 (18%) | |
Highest Educational Attainment, n (%) * | |||
Less than or some high school | 95 (31%) | 104 (22%) | |
High School diploma or GED | 114 (38%) | 218 (47%) | |
Associate’s/Bachelor’s degree or trade school | 74 (24%) | 111 (24%) | |
Advanced Degree (e.g., Master’s, Doctorate or Professional degree) | 5 (2%) | 10 (2%) | |
Prefer not to answer | 16 (5%) | 23 (5%) | |
Receive CalFresh (Yes), n (%) | 102 (34%) | 142 (30%) | |
Eligible to receive additional CVB (Yes) *, n (%) | 126 (41%) | 149 (32%) | |
Use Emergency Food Programs (at baseline), n (%) | |||
Never | 162 (53%) | 256 (55%) | |
Every day or a few times per week | 45 (15%) | 73 (16%) | |
Once a week or less | 96 (32%) | 136 (29%) | |
Prefer not to answer | 1 (0%) | 1 (0%) | |
Race and Ethnicity, n (%) ** | |||
Latino or Hispanic | 130 (44%) | 278 (62%) | |
Black or African American | 31 (11%) | 55 (12%) | |
Asian or Pacific Islander | 106 (36%) | 58 (13%) | |
White or Caucasian | 15 (5%) | 31 (7%) | |
Native American or American Indian | 0 | 4 (<1%) | |
Other race | 2 (1%) | 6 (1%) | |
Prefer not to answer | 4 (1%) | 16 (4%) | |
Food insecure at baseline (yes), n (%) | 184 (62%) | 282 (62%) | |
Days between surveys, mean (SD) | 104.27 (30) | 107.75 (32) | |
Pregnant at follow-up survey? (yes), n (%) * | 153 (60%) | 243 (68%) |
Intervention | Comparison | Difference Within Groups (Follow-Up-Baseline) | Adjusted Mean Difference for the Treatment Effect ^ | |||||
---|---|---|---|---|---|---|---|---|
Baseline | Follow-Up | Baseline | Follow-Up | Intervention | Comparison | |||
Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean [95% Confidence Interval] | ||
Fruits and vegetables, including legumes (excluding French fries) ** | 2.56 (0.95) | 2.51 (0.89) | 2.41 (1.02) | 2.40 (0.91) | 0.06 (0.97) | 0.01 (0.94) | −0.06 [−0.23–0.11] | |
Vegetables including legumes (excluding French fries) * | 1.49 (0.55) | 1.47 (0.46) | 1.38 (0.52) | 1.37 (0.44) | 0.03 (0.55) | 0.00 (0.48) | −0.02 [−0.11–0.06] | |
Fruits | 1.05 (0.56) | 1.03 (0.55) | 1.04 (0.65) | 1.02 (0.59) | 0.02 (0.67) | 0.02 (0.69) | −0.01 [−0.12–0.11] | |
Food insecurity (Rasch score) | 3.67 (2.79) | 3.47 (2.73) | 3.77 (2.88) | 3.59 (2.84) | −0.10 (2.22) | −0.16 (2.49) | 0.05 [−0.35–0.44] |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ridberg, R.A.; Levi, R.; Marpadga, S.; Akers, M.; Tancredi, D.J.; Seligman, H.K. Additional Fruit and Vegetable Vouchers for Pregnant WIC Clients: An Equity-Focused Strategy to Improve Food Security and Diet Quality. Nutrients 2022, 14, 2328. https://doi.org/10.3390/nu14112328
Ridberg RA, Levi R, Marpadga S, Akers M, Tancredi DJ, Seligman HK. Additional Fruit and Vegetable Vouchers for Pregnant WIC Clients: An Equity-Focused Strategy to Improve Food Security and Diet Quality. Nutrients. 2022; 14(11):2328. https://doi.org/10.3390/nu14112328
Chicago/Turabian StyleRidberg, Ronit A., Ronli Levi, Sanjana Marpadga, Melissa Akers, Daniel J. Tancredi, and Hilary K. Seligman. 2022. "Additional Fruit and Vegetable Vouchers for Pregnant WIC Clients: An Equity-Focused Strategy to Improve Food Security and Diet Quality" Nutrients 14, no. 11: 2328. https://doi.org/10.3390/nu14112328