Predicting Food Insecurity in a Pediatric Population Using the Electronic Health Record
Open accessAbstract
Introduction: More than 5 million children in the United States experience food insecurity (FI), yet little guidance exists regarding screening for FI. A prediction model of FI could be useful for healthcare systems and practices working to identify and address children with FI. Our objective was to predict FI using demographic, geographic, medical, and historic unmet health-related social needs data available within most electronic health records. Methods: This was a retrospective longitudinal cohort study of children evaluated in an academic pediatric primary care clinic and screened at least once for FI between January 2017 and August 2021. American Community Survey Data provided additional insight into neighborhood-level information such as home ownership and poverty level. Household FI was screened using two validated questions. Various combinations of predictor variables and modeling approaches, including logistic regression, random forest, and gradient-boosted machine, were used to build and validate prediction models. Results: A total of 25,214 encounters from 8521 unique patients were included, with FI present in 3820 (15%) encounters. Logistic regression with a 12-month look-back using census block group neighborhood variables showed the best performance in the test set (C-statistic 0.70, positive predictive value 0.92), had superior C-statistics to both random forest (0.65, p < 0.01) and gradient boosted machine (0.68, p = 0.01), and showed the best calibration. Results were nearly unchanged when coding missing data as a category. Conclusions: Although our models could predict FI, further work is needed to develop a more robust prediction model for pediatric FI.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Census block-level data with 12-month lookback yielded best performance for predicting food insecurity in a pediatric population.
“Census block‐level data with 12‐month lookback yielded best performance; among first pediatric studies using ML to predict food insecurity; further refinement needed for populations with infrequent care”
Rigdon et al. [31] supplemented EHR data with American Community Survey neighborhood data at the census block level.
“Rigdon et al. [31] supplemented EHR data with American Community Survey neighborhood data at the census block level.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649 · see citing works
ranger : A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
Wright, M. N., & Ziegler, A. (2017) · Journal of Statistical Software · doi:10.18637/jss.v077.i01 · Cited by 3,814 · see citing works
Development and Validity of a 2-Item Screen to Identify Families at Risk for Food Insecurity
Hager, E. R., Quigg, A. M., Black, M. M., Coleman, S. M., Heeren, T., Rose-Jacobs, R., Cook, J. T., de Cuba, S. A. E., Casey, P. H., Chilton, M., et al. (2010) · Pediatrics · doi:10.1542/peds.2009-3146 · Cited by 1,538 · see citing works
Promoting food security for all children
COUNCIL ON COMMUNITY PEDIATRICS, COMMITTEE ON NUTRITION, Gitterman, B. A., Chilton, L. A., Cotton, W. H., Duffee, J. H., Flanagan, P., Keane, V. A., Krugman, S. D., Kuo, A. A., et al. (2015) · Pediatrics · doi:10.1542/peds.2015-3301 · Cited by 433 · see citing works
Smith, S. D., Malinak, D., Chang, J., Pérez, M. T. A., Perez, S., Settlecowski, E., Rodriggs, T., Hsu, M., Abrew, A., & Aedo, S. (2016) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2016.12.007 · Cited by 80 · see citing works
Cullen, D. L., Attridge, M. M., & Fein, J. A. (2020) · Academic Pediatrics · doi:10.1016/j.acap.2020.04.006 · Cited by 69 · see citing works
Enhancing accurate identification of food insecurity using quality-improvement techniques.
Burkhardt, M. C., Beck, A. F., Conway, P. H., Kahn, R. S., & Klein, M. D. (2012) · Pediatrics · doi:10.1542/peds.2011-1153 · Cited by 56 · see citing works
Ortiz-Marrón, H., Ortiz-Pinto, M. A., Lanza, M. U., Pujadas, G. C., Pino, V. V., Cortés, S. B., Gascón, T. G., & Gavín, M. O. (2022) · BMC Public Health · doi:10.1186/s12889-022-14308-0 · Cited by 47 · see citing works
Machine learning applied to electronic health record data in home healthcare: A scoping review
Hobensack, M., Song, J., Scharp, D., Bowles, K. H., & Topaz, M. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104978 · Cited by 44 · see citing works
Kogan, E., Didden, E., Lee, E., Nnewihe, A. N., Stamatiadis, D., Mataraso, S. J., Quinn, D. A., Rosenberg, D., Chehoud, C., & Bridges, C. R. (2022) · International Journal of Cardiology · doi:10.1016/j.ijcard.2022.12.016 · Cited by 34 · see citing works
Effects of Food Insecurity on Asthma Outcomes in the Pediatric Emergency Department
Hendrickson, M. A., O’Riordan, M. A., Arpilleda, J. C., & Heneghan, A. M. (2010) · Pediatric Emergency Care · doi:10.1097/pec.0b013e3181fb0dab · Cited by 17 · see citing works
Chronic Food Insecurity in US Families With Children
Insolera, N. E. (2023) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2022.5820 · Cited by 11 · see citing works
Li, Z., Gogia, S., Tatem, K. S., Cooke, C., Singer, J., Chokshi, D. A., & Newton-Dame, R. (2022) · Medical Care · doi:10.1097/mlr.0000000000001807 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 19
- [2]Thomas MMC, Miller DP, Morrissey TW. Food insecurity and child health. Pediatrics. 2019; 144(4): e20190397. 31501236
- [3]Sandoval VS, Jackson A, Saleeby E, Smith L, Schickedanz A. Associations between prenatal food insecurity and prematurity, pediatric health care utilization, and postnatal social needs. Acad Pediatr. 2021; 21(3): 455– 461. 33253934 PMC8026536
- [5]Palakshappa D, Garg A, Peltz A, Wong CA, Cholera R, Berkowitz SA. Food insecurity was associated with greater family health care expenditures in the US. 2016-17. Health Aff (Millwood). 2023; 42(1): 44– 52. 36623217 PMC10926282
- [7]Bernal J, Frongillo EA, Jaffe K. Food insecurity of children and shame of others knowing they are without food. J Hunger Environ Nut. 2016; 11(2): 180– 194.
- [8]Palakshappa D, Doupnik S, Vasan A, et al. Suburban families’ experience with food insecurity screening in primary care practices. Pediatrics. 2017; 140(1): e20170320. 28634248
- [12]Gonzalez JV, Hartford EA, Moore J, Brown JC. Food insecurity in a pediatric emergency department and the feasibility of universal screening. West J Emerg Med. 2021; 22(6): 1295– 1300. 34787554 PMC8597682
- [13]Oates GR, Lock L, Tarn V, et al. Electronic screening for unmet social needs in a pediatric pulmonary clinic: acceptability and associations with health outcomes. Pediatr Pulmonol. 2023; 58(5): 1444– 1453. 36721912
- [17]Breiman L. Random forests. Machine Learning. 2001; 45(1): 5– 32.
- [19]Chen T, Guestrin C. XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
- [22]Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011; 12(1): 77. 21414208 PMC3068975
- [23]Hosmer DW, Lemesbow S. Goodness of fit tests for the multiple logistic regression model. Commun Stat - Theory and Methods. 1980; 9(10): 1043– 1069.
- [24]van Buuren S, Groothuis-Oudshoorn K. Mice: multivariate imputation by chained equations in R. J Stat Softw. 2011; 45(3): 1– 67.
- [25]Josse JPN, Scornet E, Varoquaux G. On the consistency of supervised learning with missing values, July 3, 2020, Accessed April 19, 2023, https://arxiv.org/abs/1902.06931. · arXiv
- [27]Lang M, Binder M, Richter J, et al. mlr3: a modern object-oriented machine learning framework in R. J Open Source Soft. 2019; 1903: 4.
- [28]Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMC Med. 2015; 13(1): 1. 25563062 PMC4284921
- [35]Rigdon J, Montez K, Palakshappa D, et al. Social risk factors influence pediatric emergency department utilization and hospitalizations. J Pediatr. 2022; 249: 35– 42 e34. 35697140 PMC11210599
- [37]Liang K-Y, Zeger Sl. Longitudinal data analysis using generalized linear models. Biometrika. 1986; 73(1): 13– 22.
- [38]Fitzmaurice G, Laird N, Ware J. Applied Longitudinal Analysis. John Wiley & Sons; 2012.
- [39]McGee G, Haneuse S, Coull BA, Weisskopf MG, Rotem RS. On the nature of informative presence bias in analyses of electronic health records. Epidemiology. 2022; 33(1): 105– 113. 34711733 PMC8633193
Cited by
Sorted by citations
Improving personalized healthcare with automated longitudinal EHR analysis
Pal, G. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106010 · Cited by 2 · see citing works
Length of look-back periods in studies using administrative data: a scoping review
Chisholm, M., Navuluri, S., Ogundolie, M., Boykin, M., Chopra, S., Sodhi, S. S., Kaplan, S., Gaviser, A., Poteat, T., Robinson, W. R., et al. (2026) · Epidemiologic Reviews · doi:10.1093/epirev/mxaf019 · Cited by 1 · see citing works
Razzano, A. V. (2026) · Health Science Reports · doi:10.1002/hsr2.73083
- Authors
- Rigdon, J., Montez, K., Palakshappa, D., Brown, C., Downs, S. M., Albertini, L. W., & Taxter, A.
- Affiliations
- Wake Forest University Health Sciences (US)
- Nationwide Children's Hospital (US)
- Corresponding author
- Taxter, Alysha
- ORCID
- Venue
- Journal of Clinical and Translational Science
- Publisher
- Cambridge University Press (CUP)
- Published
- 2024
- Volume
- 8
- Issue
- 1
- Pages
- e195-e195
- ISSN
- 2059-8661 (ISSN-L)
- PMC
- PMC11626594
- OpenAlex
- W4403840513
- Cited by
- 3
- References
- 13
- Citations by year
- 2026: 2 · 2025: 1
- FWCI
- 1.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of Clinical and Translational Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (UL1TR001420)
- National Heart, Lung, and Blood Institute (K23HL146902)
- National Center for Advancing Translational Sciences
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (K23HD099249)
- Topics
- Health Sciences › Health Professions › General Health Professions › Food Security and Health in Diverse Populations
- Health Sciences › Health Professions › General Health Professions › Child and Adolescent Health
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Keywords
- food insecurity, children, prediction model, electronic health records, social determinants of health, neighborhood poverty, pediatric primary care, missing data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
