Abstract
Background
As the nutritional status of people with CF (PwCF) is associated with their socioeconomic status, it is important to understand factors related to food security and food access that play a role in the nutritional outcomes of this population. We assessed the contributions of CF program-level food insecurity screening practices and area-level food access for nutritional outcomes among PwCF.
Methods
We conducted a cross-sectional analysis of 2019 data from the U.S. CF Patient Registry (CFFPR), linked to survey data on CF program-level food insecurity screening and 2019 patient zip code-level food access. Pediatric and adult populations were analyzed separately. Nutritional outcomes were assessed with annualized BMI percentiles (CDC charts) for children and BMI (kg/m2) for adults, with underweight status defined as BMIp <10% for children and BMI <18.5 kg/m2 for adults, and overweight or obese status defined as BMIp >85% for children and BMI >25 kg/m2 for adults. Analyses were adjusted for patient sociodemographic and clinical characteristics.
Results
The study population included 11,971 pediatric and 14,817 adult PwCF. A total of 137 CF programs responded to the survey, representing 71% of the pediatric sample and 45% of the CFFPR adult sample. The joint models of nutritional status as a function of both program-level food insecurity screening and area-level food access produced the following findings. Among children with CF, screening at every visit vs less frequently was associated with 39% lower odds of being underweight (OR 0.61, p=0.019), and the effect remained the same and statistically significant after adjusting for all covariates (aOR 0.61, p=0.047). Residence in a food desert was associated both with higher odds of being underweight (OR 1.66, p=0.036; aOR 1.58, p=0.008) and with lower BMIp (−4.81%, p=0.004; adjusted −3.73%, p=0.014). Among adults with CF, screening in writing vs verbally was associated with higher odds of being overweight (OR 1.22, p=0.028; aOR 1.36, p=0.002) and higher BMI (adjusted 0.43 kg/m2, p=0.032). Residence in a food desert was associated with higher odds of being underweight (OR 1.48, p=0.025).
Conclusions
Food insecurity screening and local food access are independent predictors of nutritional status among PwCF. More frequent screening is associated with less underweight among children with CF, whereas screening in writing (vs verbally) is associated with higher BMI among adults. Limited food access is associated with higher odds of being underweight in both children and adults with CF, and additionally with lower BMI among children with CF. Study results highlight the need for standardized, evidence-based food insecurity screening across CF care programs and for equitable food access to optimize the nutritional outcomes of PwCF.
Keywords: Food insecurity screening, Food access, Food deserts, Cystic fibrosis, Nutrition
INTRODUCTION
Weight optimization is an important treatment goal for people with cystic fibrosis (CF), as suboptimal weight is associated with worse pulmonary outcomes and decreased survival.1-3 However, achieving optimal nutritional status can be challenging for people with CF (PwCF). There is ample evidence that the nutritional status of PwCF is closely associated with their socioeconomic status.4-9 Therefore, it is important to understand factors related to food security and food access that play a role for nutritional outcomes in this population.
Food insecurity, measured on a household level, refers to the limited or uncertain availability of nutritionally adequate foods, with either disrupted eating patterns or reduced food intake.10 In 2016, 12% of all U.S. households experienced food insecurity, with higher prevalence in certain populations and geographic areas. Among PwCF, more than 30% report being food insecure, with higher food insecurity rates attributable to multiple factors, from higher caloric needs to disease-related disrupted educational opportunities, limited occupational choices, and unstable employment in this population.11 Food insecurity has been strongly associated with malnutrition (either undernutrition or obesity) in the general population,12-14 but research specific to CF is limited. In the general population, food insecurity is also implicated a number of adverse health outcomes, from poor glycemic control and diabetes,15,16 to hypertension and obstructive airways disease,17 to mental-physical comorbidities.18 The little CF-specific evidence available indicates potential adverse health implications for PwCF,11 although not all studies find direct associations of food insecurity with BMI or lung function.19,20
Across the CF care center network, an increasing number of programs are screening for food insecurity and linking food-insecure individuals with CF to programs and community resources for food assistance. The importance of this has been underscored by the CF Foundation-sponsored Food Security Committee assembled in December 2018. Between 2019 and 2022, the committee, comprised of CF clinicians, dietitians, nurses, social workers, researchers, and patients and family members, published data, recommendations, and resources for CF care teams and PwCF to help identify and address food insecurity.21
Preliminary evidence exists that food insecurity screening in CF care is feasible.22,23 However, addressing identified food needs may require resources typically not available in clinical settings, and referring families to nonmedical organizations to resolve food needs requires specialized training and dedicated staff.24-26 Recognizing the importance of these issues, in 2021 the CFF Food Security Committee surveyed CF programs across the country to assess and better understand the current state of food insecurity screening. Of 286 CF programs, 138 responded to the survey (48% response rate). Among those that responded, 91% reported screening for food insecurity and shared details about their screening process and the type of assistance provided to food-insecure patients and families.27
Local food environment is also an important factor for adequate nutritional intake and an independent risk factor for adverse nutritional outcomes.28 The U.S. Department of Agriculture Economic Research Service (USDA-ERS) defines food deserts as geographical areas in which residents have restricted or nonexistent access to healthy, nutritious food.29 Residence in a food desert, with limited access to full-service supermarkets or farmers markets, or difficulty getting to grocery stores due to lack of transportation or unsafe neighborhood conditions, is an important environmental correlate of nutritional intake and food insecurity.30,31 In this study, we assess the possible contributions of CF program-level food insecurity screening practices and area-level food access to nutritional outcomes among PwCF.
METHODS
Study design and patient population
We conducted a cross-sectional analysis of 2019 data from the national CF Patient Registry (CFFPR),32 linked to survey data on CF program-level food insecurity screening and zip code-level measures of food access. The study population included all individuals with a diagnosis of CF in the CFFPR, excluding pregnant people and those who had received lung transplantation.
Data collection
Data on patient nutritional status, demographics, and clinical characteristics were obtained from the CFFPR. Data on food insecurity screening were collected with a 26-question survey developed by the CFF Food Security Committee and administered to pediatric and adult CF programs in the United States (see Appendix 1). The survey was deployed in February 2021 via e-mail to CF program directors and via listserv to social workers and registered dietitians. Only one response per CF program was allowed. Survey questions asked about food insecurity screening practices before and after the COVID-19 pandemic. This analysis uses responses about food insecurity screening before the pandemic (prior to 2020).
Data on local food access were obtained by geo-linking the 2019 residential zip codes of individuals with CF in the CFFPR with food access measures (residence in a food desert) from the United States Department of Agriculture (USDA) Food Atlas.
Measures
Outcome variables
Nutritional outcomes were assessed with annualized mean BMI percentiles (CDC charts) for children and annualized mean BMI (kg/m2) for adults, with underweight status defined as BMIp <10% for children and BMI <18.5 kg/m2 for adults, and overweight or obese status defined as BMIp >85% for children and BMI >25 kg/m2 for adults.
Predictor variables
Program-level food insecurity screening practices were assessed using survey data about food insecurity screening frequency (At every visit/Less frequently), modality (In writing/Verbal), and tracking (Formal documentation in the medical record or spreadsheet/No formal documentation).
Local food access was measured using the 2019 USDA indicator of “low income, low access” (LILA) for Census tracts. Low income (LI) is defined as tracts with ≥20% poverty rate or median income <80% of the metropolitan area median income. Low access (LA) is defined as tracts where at least 500 people or 33% of population reside >1 mile (urban areas) or >10 miles (rural areas) from the nearest supermarket, supercenter, or large grocery store; vehicle access is included in the index.33 Census tracts with LILA scores of 1 are defined as food deserts. Using a crosswalk from Census tracts to 9-digit zip codes, LILA scores were calculated for 5-digit ZIP codes in the CFFPR. Specifically, for each zip code, we calculated the number of people that reside in each Census tract within that zip code by multiplying the proportion of the Census tract that falls within that zip code. This number was multiplied by either a 0 (not LILA) or a 1 (LILA). The total number of people in a zip code impacted by LILA was then divided by the total number of people residing in the zip code, to get a proportion.
Covariates
Socio-demographic covariates included age, sex (Male/Female), race/ethnicity (non-Hispanic White/non-Hispanic Black/Hispanic/Other), and health insurance (Any private/Only Public or None). Clinical covariates included genotype (F508del homozygous/F508del heterozygous/Other), CFTR modulator use (Yes/No), CF-related diabetes (Yes/No), pancreatic enzyme replacement therapy (PERT) use (Yes/No), supplemental feeding, either oral or enteral (Yes/No), and dietitian consultation (Yes/No).
Statistical analysis
Descriptive statistics, including means, standard deviations, frequencies, and proportions, were calculated for all variables. Bivariate relationships were estimated between each predictor variable and the nutritional outcomes. Logistic regression was used to examine underweight, and ordinary least squares regression was used to analyze continuous measures of nutritional outcomes. Pediatric (age 2-17 years) and adult (age ≥18 years) populations were analyzed separately. Analyses included robust standard errors that correct for clustering by CF program, zip code, and state.34 Statistical tests were two-sided and were performed using a 5% significance level (α = 0.05). Analyses were performed using Stata software, version 15 (College Station, TX: StataCorp LLC).
RESULTS
The study population included 11,971 pediatric and 14,817 adult PwCF. Characteristics of the sample, overall and by weight status, are presented in Tables 1a and 1b (pediatric and adult populations, respectively). Mean ages of the pediatric and adult patients were 10.2 years and 32.7 years, respectively. Among children, those who were older, male, or had CF-related diabetes (CFRD) were more likely to be underweight. Among adults, those who were younger, Black, with public or no insurance, CFRD, or pancreatic insufficient were more likely to be underweight. In both age groups, underweight patients were more likely to use supplemental feeding.
Table 1a.
Characteristics of the pediatric population: overall, by underweight status, and analytic sub-samples
| Overall (N=11,97 1) |
Underweight (BMIp ≤10) |
p-
value * |
Overweight/obese (BMIp >85) |
p-
value * |
Analytic sub-samples1 | ||||
|---|---|---|---|---|---|---|---|---|---|
| Yes (n=446) 3.7% |
No (n=11,52 5) 96.3% |
Yes (n=2,12 8) 17.8% |
No (n=9,84 3) 82.2% |
Program- level (N=8,458) |
Area- level (N=11,84 6) |
||||
| Socio-demographic | |||||||||
| Age, years (range 2-17) | 10.2 (4.5) | 11.7 (4.8) | 10.1 (4.5) | <0.001 | 9.88 (4.45) | 10.27 (4.51) | <0.001 | 10.0 (4.5) | 10.2 (4.5) |
| Female, % | 48.9 | 34.8 | 49.4 | <0.001 | 48.0 | 49.0 | 0.357 | 50.7 | 48.6 |
| Race/ethnici ty, % | |||||||||
| NH White | 79.1 | 79.1 | 79.1 | 0.071 | 77.2 | 79.5 | <0.001 | 80.3 | 79.8 |
| NH Black | 4.0 | 5.4 | 3.9 | 2.8 | 4.2 | 3.5 | 4.2 | ||
| Other | 4.7 | 6.1 | 4.7 | 4.2 | 4.8 | 4.6 | 4.5 | ||
| Hispanic | 12.2 | 9.4 | 12.3 | 15.8 | 11.5 | 11.7 | 11.5 | ||
| Insurance, % | |||||||||
| Any private | 52.4 | 48.4 | 52.5 | 0.089 | 51.0 | 52.7 | 0.169 | 53.5 | 52.4 |
| Public only/None | 47.6 | 51.6 | 47.5 | 49.0 | 47.3 | 46.5 | 47.6 | ||
| Clinical | |||||||||
| Genotype, % | |||||||||
| F508del heteroz | 44.7 | 42.8 | 44.8 | 0.611 | 39.0 | 46.0 | <0.001 | 44.8 | 44.8 |
| F508del homoz | 40.9 | 41.5 | 40.9 | 43.8 | 40.3 | 41.5 | 40.9 | ||
| Other | 14.3 | 15.7 | 14.3 | 17.2 | 13.7 | 13.8 | 14.3 | ||
| CFTR modulator use, % | 51.9 | 50.7 | 52.0 | 0.587 | 51.7 | 52.0 | 0.843 | 52.4 | 52.0 |
| CFRD, % | 6.4 | 11.4 | 6.2 | <0.001 | 4.4 | 6.8 | <0.001 | 6.7 | 6.4 |
| PERT, % | 84.7 | 87.0 | 84.6 | 0.170 | 73.2 | 87.2 | <0.001 | 85.4 | 84.7 |
| Dietitian consultation, % | 95.4 | 96.0 | 95.4 | 0.594 | 93.6 | 95.9 | <0.001 | 95.3 | 95.5 |
| Supplemental feeding, % | 65.9 | 88.8 | 65.0 | <0.001 | 36.6 | 72.2 | <0.001 | 65.4 | 65.8 |
Difference between underweight and not-underweight in the overall sample
The program-level analytic sub-sample includes patients in the CF Foundation Patient Registry (CFFPR) who receive care in programs that responded to the survey.
The area-level analytic sub-sample includes all patients in the CFFPR whose residential zip code was successfully matched to a zip-code food access score.
Age is shown as mean (SD).
BMI=Body Mass Index; CF=cystic fibrosis; CFRD=CF-related diabetes; CFTR= CF transmembrane regulator; NH=Non-Hispanic; PERT=Pancreatic enzyme replacement therapy
Table 1b.
Characteristics of the adult population: overall, by underweight status, and analytic sub-samples
| Overall (N=14,8 98) |
Underweight (BMI ≤18.5 kg/m2) |
p-
value * |
Overweight/obese (BMI>25 kg/m2) |
p-
value * |
Analytic sub-samples1 | ||||
|---|---|---|---|---|---|---|---|---|---|
| Yes (n=1,029 ) 6.9% |
No (n=13,86 9) 93.1% |
Yes (n=4,39 7) 29.5% |
No (n=10,501 ) 70.5% |
Program- level (n=6,651) |
Area- level (n=14,613 ) |
||||
| Socio-demographic | |||||||||
| Age, years; mean (SD) | 32.7 (12.4) | 28.7 (10.8) | 33.0 (12.4) | <0.001 | 36.9 (13.27) | 30.72(11.52) | <0.001 | 31.2 (12.3) | 32.7 (12.4) |
| Female, % | 48.0 | 52.4 | 47.8 | 0.611 | 41.4 | 50.8 | <0.001 | 49.4 | 48.1 |
| Race/ethnicity, % | |||||||||
| NH White | 88.8 | 86.9 | 88.9 | 0.003 | 89.3 | 88.5 | 0.117 | 88.8 | 88.8 |
| NH Black | 3.0 | 4.6 | 2.9 | 2.7 | 3.2 | 3.2 | 3.0 | ||
| Other | 2.3 | 3.5 | 2.2 | 2.0 | 2.5 | 2.2 | 2.3 | ||
| Hispanic | 5.8 | 4.9 | 5.9 | 6.0 | 5.8 | 5.8 | 5.8 | ||
| Insurance, % | |||||||||
| Any private | 66.8 | 49.6 | 68.1 | <0.001 | 70.3 | 65.3 | <0.001 | 66.6 | 66.9 |
| Public only/None | 33.2 | 50.4 | 31.9 | 29.7 | 34.7 | 33.4 | 33.1 | ||
| Clinical | |||||||||
| Genotype, % | |||||||||
| F508del heteroz | 44.6 | 49.8 | 44.3 | 0.009 | 35.4 | 48.5 | <0.001 | 46.0 | 44.6 |
| F508del homoz | 41.2 | 38.0 | 41.4 | 46.5 | 38.9 | 40.6 | 41.2 | ||
| Other | 14.2 | 12.2 | 14.3 | 18.1 | 12.6 | 13.4 | 14.2 | ||
| CFTR modulator use, % | 65.9 | 63.9 | 66.0 | 0.133 | 62.3 | 67.3 | <0.001 | 67.8 | 65.9 |
| CFRD, % | 31.9 | 37.5 | 31.5 | <0.001 | 29.5 | 32.7 | <0.001 | 31.3 | 31.9 |
| PERT, % | 84.6 | 92.1 | 84.0 | <0.001 | 73.6 | 89.2 | <0.001 | 86.2 | 84.7 |
| Dietitian consultatio n, % | 91.2 | 94.9 | 90.9 | <0.001 | 89.3 | 91.9 | <0.001 | 90.8 | 91.2 |
| Supplemental feeding, % | 48.0 | 81.2 | 45.5 | <0.001 | 24.7 | 57.7 | <0.001 | 49.5 | 48.0 |
Difference between underweight and not-underweight in the overall sample
The program-level analytic sub-sample includes patients in the CF Foundation Patient Registry (CFFPR) who receive care in programs that responded to the survey. The area-level analytic sub-sample includes all patients in the CFFPR whose residential zip code was successfully matched to a zip-code food access score.
Age is shown as mean (SD).
BMI=Body Mass Index; CF=cystic fibrosis; CFRD=CF-related diabetes; CFTR= CF transmembrane regulator; NH=Non-Hispanic; PERT=Pancreatic enzyme replacement therapy
Tables 1a and 1b also show the characteristics of the analytic subsamples for program-level and area-level analyses. The program-level analytic sample included individuals receiving care at CF programs that responded to the food insecurity survey (138/286 programs, or 48%): 66 pediatric CF programs, 38 adult CF programs, and 34 that cared for both pediatric and adult patients. The program-level sample was substantially smaller than the CFFPR population: 71% (8,458/11,971) of CFFPR pediatric participants and 45% (6,651/14,898) of CFFPR adult participants. There were differences in participant characteristics between responding and non-responding CF programs. Compared to patients receiving care at non-responding CF programs, those from responding CF programs had higher lung function and were more likely to have a normal weight status, be on CFTR modulators, pancreatic enzymes, and supplemental feeding, and less likely to have public or no insurance (data not shown).
The analytic sample for area-level analyses included patients whose residential zip-code was successfully matched to zip-code food access scores (99% of all pediatric and adult CFFPR participants). There were no differences between the CFFPR population and the area-level analytic sample (Tables 1a and 1b).
Program-level prevalence of food insecurity screening in pediatric, adult, and combined CF care programs was 65.6%, 60.5%, and 50%, respectively. Food insecurity screening frequency, modality, and documentation were as follows: 21.9% pediatric, 15.8% adult, and 17.6% combined programs screened at every visit vs less frequently; 37.9% pediatric, 26.5% adult, and 25.9% combined programs screened in writing; 58.3% pediatric, 55.6% adult, and 51.7% programs maintained formal documentation of screening.
Nutritional outcomes as a function of program-level food insecurity screening practices are presented in Tables 2a and 2b (pediatric and adult samples, respectively). Among children, food insecurity screening at every visit vs less frequently was associated with lower odds of being underweight (OR 0.69, p=0.041), and the relationship remained statistically significant after adjusting for sociodemographic and clinical covariates (aOR 0.64, p=0.044) (Table 2a). Sensitivity analyses revealed a statistically significant interaction between food insecurity screening frequency and health insurance type: compared to children with any private insurance, those with only public or no insurance were less likely to be screened at every visit (1.8% vs 3.1%, p=0.003) (data not shown). Formal documentation of screening was associated with lower odds of being overweight (OR 0.84, p=0.044) but the relationship was no longer significant in the adjusted model (Table 2a).
Table 2a.
Pediatric nutritional outcomes as a function of program-level food insecurity screening practices
| Food insecurity screening |
Logistic regression of underweight (<10 BMIp) |
Logistic regression of overweight (>85 BMIp) |
Linear regression of BMIp |
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model 1 Unadjusted |
Model 2 Adjusted1 |
Model 1 Unadjusted |
Model 2 Adjusted1 |
Model 1 Unadjusted |
Model 2 Adjusted1 |
|||||||
| OR | p | OR | p | OR | p | OR | p | β | p | β | p | |
| At every visit (vs Less frequently) | 0.69 * | 0.041 | 0.64 * | 0.044 | 1.00 | 0.967 | 1.18 | 0.313 | 0.64 | 0.607 | 2.16 | 0.208 |
| In writing (vs Verbal) | 1.09 | 0.669 | 0.98 | 0.942 | 1.06 | 0.480 | 1.24 | 0.117 | 0.36 | 0.760 | 1.86 | 0.259 |
| Formal documentation (vs No) | 1.17 | 0.332 | 1.10 | 0.592 | 0.84 * | 0.044 | 0.94 | 0.591 | −1.46 | 0.165 | −0.21 | 0.868 |
Adjusted for age, sex, race/ethnicity, health insurance, genotype, CFTR modulator use, CFRD, PERT, supplemental feeding, and dietitian consultation
Boldface indicates statistical significance: * p<0.05, ** p<0.01, *** p<0.001
Table 2b.
Adult nutritional outcomes as a function of program-level food insecurity screening practices
| Food insecurity screening |
Logistic regression of underweight (BMI <18.5 kg/m2) |
Logistic regression of overweight (BMI >25 kg/m2) |
Linear regression of BMI (kg/m2) |
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model 1 Unadjusted |
Model 2 Adjusted1 |
Model 1 Unadjusted |
Model 2 Adjusted1 |
Model 1 Unadjusted |
Model 2 Adjusted1 |
|||||||
| OR | p | OR | p | OR | p | OR | p | β | p | β | p | |
| At every visit (vs Less frequently) | 1.19 | 0.080 | 1.02 | 0.900 | 0.90 | 0.375 | 1.01 | 0.965 | −0.31 | 0.143 | −0.08 | 0.725 |
| In writing (vs Verbal) | 1.08 | 0.447 | 0.95 | 0.685 | 1.21 * | 0.037 | 1.42 ** | 0.001 | 0.20 | 0.320 | 0.46 * | 0.030 |
| Formal documentation (vs No) | 1.20 * | 0.043 | 1.13 | 0.226 | 1.08 | 0.338 | 1.17 | 0.209 | 0.02 | 0.925 | 0.15 | 0.523 |
Adjusted for age, sex, race/ethnicity, health insurance, genotype, CFTR modulator use, CFRD, PERT, supplemental feeding, and dietitian consultation
Boldface indicates statistical significance: * p<0.05, ** p<0.01, *** p<0.001
Among adults, screening in writing (vs verbally) was associated with higher BMI after adjusting for sociodemographic and clinical covariates (0.46 kg/m2, p=0.030) and with higher odds of being overweight in both unadjusted and adjusted analyses (OR 1.21, p=0.037; aOR 1.42, p=0.001) (Table 2b). Formal documentation of screening was associated with higher odds of being underweight (OR 1.20, p=0.043) but the relationship was no longer significant in the adjusted model (Table 2b).
Results from logistic regression of underweight and overweight status and linear regression of BMI as a function of area-level food access are presented in Tables 3a and 3b (pediatric and adult samples, respectively). Among children, residence in a food desert was associated with a 3.3-point lower mean BMIp (−3.30%, p=0.005) and remained marginally significant after controlling for covariates (−2.14%, p=0.054) (Table 3a). Among adults, residence in a food desert was associated with nearly twice the odds of being underweight (OR 1.80, p<0.001), and the relationship remained significant after adjusting for sociodemographic and clinical characteristics (aOR 1.31, p=0.025) (Table 3b). Limited food access was also associated with lower mean BMI in the adult sample (−0.32 kg/m2, p=0.025), although no longer significant in the fully controlled model (Table 3b).
Table 3a.
Pediatric nutritional outcomes as a function of area-level food access
| Food access | Logistic regression of underweight (<10 BMIp) |
Logistic regression of overweight (>85 BMIp) |
Linear regression of BMIp |
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model 1 Unadjusted |
Model 2 Adjusted1 |
Model 1 Unadjusted |
Model 2 Adjusted1 |
Model 1 Unadjusted |
Model 2 Adjusted1 |
|||||||
| OR | p | OR | p | OR | p | OR | p | β | p | β | p | |
| Food desert (LILA) | 1.39 | 0.157 | 1.23 | 0.388 | 0.95 | 0.625 | 0.99 | 0.961 | −3.30 ** | 0.005 | 2.14 | 0.054 |
Adjusted for age, sex, race/ethnicity, health insurance, genotype, CFTR modulator use, CFRD, PERT, supplemental feeding, and dietitian consultation
LILA=Low income, low access
Boldface indicates statistical significance: * p<0.05, ** p<0.01, *** p<0.001
Table 3b.
Adult nutritional outcomes as a function of area-level food access
| Food access | Logistic regression of underweight (BMI <18.5 kg/m2) |
Logistic regression of overweight (BMI >25 kg/m2) |
Linear regression of BMI (kg/m2) |
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model 1 Unadjusted |
Model 2 Adjusted1 |
Model 1 Unadjusted |
Model 2 Adjusted1 |
Model 1 Unadjusted |
Model 2 Adjusted1 |
|||||||
| OR | p | OR | p | OR | p | OR | p | β | p | β | p | |
| Food desert (LILA) | 1.80 *** | <0.001 | 1.31 * | 0.025 | 0.91 | 0.167 | 1.16 | 0.052 | −0.32 * | 0.025 | 0.238 | 0.071 |
Adjusted for age, sex, race/ethnicity, health insurance, genotype, CFTR modulator use, CFRD, PERT, supplemental feeding, and dietitian consultation
LILA=Low income, low access
Boldface indicates statistical significance: * p<0.05, ** p<0.01, *** p<0.001
Finally, we estimated unadjusted and adjusted joint models of nutritional status as a function of both predictor variables: program-level food insecurity screening and area-level food access. Results are presented in Tables 4a and 4b (pediatric and adult samples, respectively). Among children, screening at every visit vs less frequently was associated with 39% lower odds of being underweight (OR 0.61, p=0.019), and the effect remained the same and statistically significant after adjusting for all covariates (aOR 0.61, p=0.047). Residence in a food desert was associated both with higher odds of being underweight (OR 1.66, p=0.036; aOR 1.58, p=0.008) and with significantly lower mean BMIp (−4.81%, p=0.004; adjusted −3.73%, p=0.014). Among adults, screening in writing was associated with higher odds of being overweight (OR 1.22, p=0.028; aOR 1.36, p=0.002) and higher mean BMI in the fully adjusted models (0.43 kg/m2, p=0.032). Residence in a food desert was associated with higher odds of being underweight (OR 1.48, p=0.025).
Table 4a.
Pediatric nutritional outcomes as a function of both CF program-level food insecurity screening and area-level food access
| Logistic regression of underweight (<10 BMI) |
Logistic regression of overweight (>85 BMIp) |
Linear regression of EIMIp |
||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model 1 Unadjusted |
Model 2 Adjusted1 |
Model 1 Unadjusted |
Model 2 Adjusted 1 |
Model 1 Unadjusted |
Model 2 Adjusted1 |
|||||||
| OR | p | OR | p | OR | p | OR | p | β | p | β | p | |
| CF program-level screening | ||||||||||||
| At every visit (vs Less frequently) | 0.61 * | 0.019 | 0.61 * | 0.047 | 0.99 | 0.964 | 1.09 | 0.61 | 0.66 | 0.608 | 1.43 | 0.389 |
| In writing (vs Verbal) | 1.24 | 0.251 | 1.14 | 0.562 | 1.14 | 0.254 | 1.21 | 0.226 | 0.48 | 0.714 | 1.16 | 0.492 |
| Formal documentation (vs No) | 1.23 | 0.262 | 1.19 | 0.442 | 0.82 * | 0.036 | 0.89 | 0.416 | −1.89 | 0.104 | −0.92 | 0.56 |
| Food desert (LILA) | 1.66 * | 0.036 | 1.58 ** | 0.008 | 0.83 | 0.252 | 0.84 | 0.333 | −4.81 ** | 0.004 | −3.73 * | 0.014 |
Adjusted for age, sex, race/ethnicity, health insurance, genotype, CFTR modulator use, CFRD, PERT supplemental feeding, and dietitian consultation
LILA=Low income, low access
Boldface indicates statistical significance: * p<0.05, ** p<0.01, *** p<0.001
Table 4b.
Adult nutritional outcomes as a function of both CF program-level food insecurity screening and area-level food access
| Logistic regression of underweight (BMI <18.5 kg/m2) |
Logistic regression of overweight (BMI >25 kg/m2) |
Linear regression of BMI (kg/m2) |
||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model 1 Unadjusted |
Model 2 Adjusted1 |
Model 1 Unadjusted |
Model 2 Adjusted1 |
Model 1 Unadjusted |
Model 2 Adjusted 1 |
|||||||
| OR | p | OR | p | O R |
p | OR | p | β | p | β | p | |
| CF program-level screening | ||||||||||||
| At every visit (vs Less frequently) | 1.19 | 0.063 | 1.04 | 0.825 | 0.81 | 0.100 | 0.87 | 0.242 | −0.45 | 0.075 | −0.28 | 0.217 |
| In writing (vs Verbal) | 0.99 | 0.866 | 0.92 | 0.523 | 1.22 * | 0.028 | 1.36 ** | 0.002 | 0.26 | 0.178 | 0.43 * | 0.032 |
| Formal documentation (vs No) | 1.15 | 0.128 | 1.08 | 0.449 | 1.07 | 0.405 | 1.17 | 0.154 | 0.06 | 0.707 | 0.22 | 0.267 |
| Food desert (LILA) | 1.48 * | 0.025 | 1.22 | 0.265 | 1.02 | 0.778 | 1.15 | 0.124 | 0.03 | 0.898 | 0.34 | 0.089 |
Adjusted for age, sex, race/ethnicity, health insurance, genotype, CFTR modulator use, CFRD, PERT, supplemental feeding, and dietitian consultation
LILA=Low income, low access
Boldface indicates statistical significance: * p<0.05, ** p<0.01, *** p<0.001
DISCUSSION
We assessed the contributions of CF program-level food insecurity screening practices and local access to nutritional outcomes among children and adults with CF in the United States. Both food insecurity screening practices and local food access were important correlates of weight status. In joint models accounting for both factors and adjusted for covariates, more frequent screening was associated with less underweight among children; screening in writing (vs verbally) was associated with higher BMI among adults; and residence in a food desert was associated with higher odds of being underweight in both children and adults, and lower BMIp among children.
While past research has documented higher prevalence of food insecurity among PwCF compared to the general population,11 this is the first report that examines associations of food insecurity screening and food access with weight status among children and adults with CF. In the general population, food insecure households have higher odds of obesity even after adjusting for socioeconomic status.35 Whereas U.S. studies of the general population typically focus on over-nutrition and rarely examine under-nutrition, research from developing countries overwhelmingly supports the link between food insecurity and under-nutrition, including underweight and stunting.36 Our findings indicate that such association may also be present among U.S. children and adults with CF. It should be noted, however, that the proportion of underweight PwCF (3.7% children and 6.9% adults) was smaller than the proportion of overweight or obese PwCF (17.8% children and 29.5% adults). The widespread use of highly effective CFTR modulators, particularly Elexacaftor/Tezacaftor/Ivacaftor (ETI) approved by the U.S. Food and Drug Administration for ages >12 years at the end of 2019, ages 6-11 years in 2021, and ages 2-5 years in 2023, may change the CF nutritional landscape. Recent data indicate that ETI may contribute to increased prevalence of overweight and obesity among PwCF.37-42 Further research is needed to determine whether, among PwCF who use ETI, food insecurity and limited food access are risk factors for overweight and obesity as they are in the general population.
The higher prevalence of food insecurity among PwCF, along with their greater caloric needs and CF-related dietary requirements, calls for routine food insecurity screening to identify needs and provide food assistance. Past research has documented that screening for unmet needs as part of the CF care delivery is feasible,23 but important knowledge gaps exist. As highlighted by our survey, there is great variation in food insecurity screening practices across CF care teams, without established standards for screening tools or implementation protocols. Our findings suggest that more frequent screening (e.g., at each clinic visit) is associated with better nutritional outcomes for children with CF, whereas screening in writing (vs verbally) is associated with better nutritional outcomes among adults. Future research is needed to determine the optimal frequency, modality, documentation, and other attributes of food insecurity screening for children and adults with CF receiving care in multidisciplinary CF programs.
There are also profound ethical implications of food insecurity screening in clinical settings. Screening without providing food assistance or helping patients access meaningful food resources and services can cause unintentional harm, as can screening in ways that damage the patient-provider relationship.43,44 To address food insecurity among PwCF, robust food assistance programs are critical. Future work should examine nutritional outcomes across CF centers while accounting for the state-level variation in food assistance programs and medical nutrition policies.45,46
Finally, our data suggest that limited access to healthy food, as indicated by residence in a food desert, is an independent risk factor for undernutrition among children and adults with CF. The health effects of the food environment have been addressed in numerous publications..47,48 Whereas most U.S. studies focus on the associations of the food environment with obesity,49-51 our findings demonstrate that, among PwCF, residence in a food desert is also associated with underweight. Although our analyses control for race/ethnicity, health insurance type, and other characteristics, it is possible that the observed association between food desert residence and underweight status in CF captures the adverse nutritional effects of poverty in general, as the LILA measure factors in not only distance to food stores but also area-level poverty and vehicle access. Other studies show that access to high-quality food stores is particularly limited among Black and Hispanic neighborhoods and decreases further with increasing neighborhood poverty.52
The food environment, including the availability, affordability, convenience, desirably, and sustainability of foods,53,54 influences food choices, impacts dietary behaviors, and plays a role in overall health.55-58 Changes to the food environment typically require policy and system approaches, such as innovative food retail and food system enterprises to improve access to healthy food in underserved areas, or changes in food inventory and pricing to decrease unhealthy food purchases in convenience stores and increase healthy food purchases in full-service grocery stores.59 Although such population-level interventions are more difficult, they are decidedly more impactful for everyone, including PwCF. It is therefore important that CF patient advocacy organizations support progressive public policies on economic affairs, taxation, environmental regulations, food access and quality, and social welfare that can address the root causes of food insecurity and food access, contributing to improved nutritional outcomes for PwCF.
Our study has several limitations. As the CFFPR collects only residential zip codes rather than full addresses, LILA scores were calculated at the zip-code level. This is an inferior method of data aggregation resulting in less precise estimates,60 especially in areas in which concentrated poverty abuts more wealthy regions.61 Further, we do not adjust for the size, performance, or other characteristics of the CF programs. Although the CF Foundation maintains high accreditation standards, CF care across programs may vary and may be an unmeasured confounder of CF outcomes in this analysis. Relatedly, only 48% of all CF care programs responded to the survey. Compared to patients from non-responding CF programs, those from responding CF programs had better health outcomes and CF management indicators, pointing to a potential difference in program quality that may have affected our program-level analyses. We also acknowledge that food insecurity screening practices were self-reported by CF programs and may be biased. Finally, we do not assess the type or quality of food assistance provided by CF programs after the screening.
These limitations notwithstanding, this is the first study of associations between food insecurity screening practices, local food access, and nutritional outcomes among children and adults with CF in the United States. Study results have important implications for clinical practice and research. First, they highlight the need for standardized, evidence-based food insecurity screening across CF care programs to optimize nutritional outcomes in this population. The best frequency, modality, documentation, and implementation of such screening need to be determined in future research. Second, study results require examining the association between the food assistance provided – either directly by CF programs or through referrals to community resources – on CF nutritional outcomes. Relatedly, research is needed to determine the role of state-administered federal food assistance programs (SNAP and WIC) for the nutritional status of vulnerable PwCF, as well as the role of state-specific medical nutrition policies for the nutritional status of all PwCF.
Conclusions
Food insecurity screening and local food access are independently associated with nutritional status among PwCF. More frequent food insecurity screening is associated with less underweight among children with CF, whereas screening in writing (vs verbally) is associated with higher BMI among adults. Limited food access is associated with higher odds of being underweight in both children and adults with CF, and additionally with lower BMI among children with CF.
Supplementary Material
Highlights.
Screening for food insecurity by CF care programs and local food access are independent predictors of nutritional status among children and adults with CF in the United States.
More frequent food insecurity screening is associated with less underweight in children with CF, and screening in writing (vs verbally) is associated with higher BMI in adults with CF.
Residence in a food desert is associated with higher odds of underweight in both children and adults with CF, as well as with lower BMI percentile in children with CF.
Standardized, evidence-based food insecurity screening across CF care programs and equitable food access are needed to optimize the nutritional outcomes of people with CF.
Acknowledgments
This work was supported by grants from the National Institutes of Health (P30DK072482) and the Cystic Fibrosis Foundation (GUIMBE22Y7).
Footnotes
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Conflict of interest statement. Kim Reno and Cristen Clemm are employees of the Cystic Fibrosis Foundation. Georgia Brown is a person living with cystic fibrosis. All other co-authors served on the Cystic Fibrosis Foundation Food Security Committee and received a honorarium for their service.
CRediT authorship contribution statement:
Gabriela R. Oates: Conceptualization, Methodology, Data curation, Investigation, Funding acquisition, Writing – Original Draft
Julianna Bailey: Methodology, Data curation, Investigation, Writing – Review & Editing
Elizabeth Baker: Methodology, Investigation, Formal analysis, Writing – Review & Editing
Michael S. Schechter: Conceptualization, Methodology, Investigation, Writing – Review & Editing
Keith Robinson: Methodology, Data curation, Writing – Review & Editing
Kate E. Powers: Methodology, Writing – Review & Editing
Elliot Dasenbrook: Methodology, Writing – Review & Editing
Monir Hossain: Formal analysis, Writing – Review & Editing
Dixie Durham: Data curation, Writing – Review & Editing
Georgia Brown: Investigation, Writing – Review & Editing
Cristen Clemm: Data curation, Writing – Review & Editing
Kim Reno: Data curation, Writing – Review & Editing
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