- Journal List
- HHS Author Manuscripts
- PMC8277659
As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsem*nt of, or agreement with, the contents by NLM or the National Institutes of Health.
Learn more: PMC Disclaimer | PMC Copyright Notice
J Allergy Clin Immunol Pract. Author manuscript; available in PMC 2022 Jul 1.
Published in final edited form as:
J Allergy Clin Immunol Pract. 2021 Jul; 9(7): 2867–2873.e1.
Published online 2021 Jan 19. doi:10.1016/j.jaip.2020.12.026
PMCID: PMC8277659
NIHMSID: NIHMS1657586
PMID: 33359586
Mahboobeh Mahdavinia, MD., PhD.,1 Mary C. Tobin, MD.,1 Jamie L Fierstein, PhD.,2 Aame B Andy-Nweye, MD.,1 Lucy A Bilaver, PhD.,2 Susan Fox, PA,1 Andrea A. Pappalardo, MD.,4 Jialing Jiang, BS.,2 Perry A. Catlin, BS,5 Annika Chura, BS,5 Adam Robinson, BS,6 Iman Abdikarim, MS,6 Amaziah Coleman, MD,6 Christopher Warren, PhD.,3 Pamela J Newmark, BA.,2 Alexandria Bozen, BS,2 Olivia Negris, BS,2 Jacqueline A Pongracic, MD,3 Hemant P. Sharma, MD.,6 Amal H. Assa’ad, MD.,5 and Ruchi S. Gupta, MD, MPH.2,3
Author information Copyright and License information PMC Disclaimer
The publisher's final edited version of this article is available at J Allergy Clin Immunol Pract
See commentary "Moving FORWARD Toward Racial Equity in Food Allergy" in J Allergy Clin Immunol Pract, volume 9 onpage2874.
Associated Data
- Supplementary Materials
Abstract
Background:
Despite major differences in health profiles and rates of health care utilization between African American (AA) and White children with food allergy (FA), the detailed phenotypic variables that can potentially impact these outcomes have not been thoroughly studied.
Objective:
We aimed to characterize phenotypic differences such as allergies to different foods and allergic comorbidities between AA and White children with FA enrolled in the Food allergy management & Outcomes Related to White and African American Racial Differences (FORWARD) study.
Methods:
Our active, prospective, multi-center cohort study is currently enrolling AA and White children aged 0–12 years diagnosed with FA and followed by allergy/immunology clinics at four urban tertiary centers in the US. To evaluate associations between race and phenotypic variables, we used multivariable logistic regression, adjusting for important demographic and cofounding factors, as well as potential household clustering.
Results:
As of May 2020, there were 239 AAs and 425 Whites with complete intake information enrolled in the study. In comparison to Whites, we found that AAs had significantly higher adjusted odds of allergy to finfish (OR: 2.54, p<0.01) and shellfish (OR: 3.10, p< 0.001). AAs also had higher adjusted odds of asthma than Whites (asthma prevalence of 60.5% in AAs and 27.2% in Whites, OR: 2.70, p<0.001). In addition, shellfish allergy was associated with asthma, after controlling for race.
Conclusion:
Among a diverse cohort of children with physician-diagnosed FA, we observed that AA children had higher odds of allergy to shellfish and finfish, and higher rates of asthma. Interestingly, having asthma was independently associated with allergy to shellfish, after controlling for race.
Keywords: FORWARD, Food allergy, race, African American, asthma
Introduction:
Despite advancements in science and medicine, many children lack essential health-related necessities. This problem is not confined to underdeveloped or developing populations; even in advanced and developed countries such as the United States (US), medical care is not distributed equitably. Health disparities impact health outcomes, and in the case of some diseases such as food allergy (FA), the impact can be very significant. A major public health concern, FA affects approximately 8% of children in the US and its prevalence has increased in recent decades.(1–4) The impact of FA spans beyond reactions such as anaphylaxis and can result in chronic anxiety, diminished quality of life (QoL), and impaired psychosocial development.(5, 6) Recent studies from our group and others have found African American (AA) children to be at an increased risk for FA and its associated morbidities. (1, 7, 8)
Previous research indicates that FA and food sensitization are often associated with other common allergic conditions such as atopic dermatitis (AD), allergic rhinitis (AR), and asthma.(1) In children with asthma, food-allergic reactions can result in life-threatening asthma attacks, leading to hospitalization or even death. (9, 10) In fact, a previous retrospective study showed that AA children with FA had a higher rate of comorbid asthma compared with White children with FA.(7) Therefore, further understanding of the phenotype of FA in AAs may not only improve management practices for FA itself, but also can alleviate its potential detrimental impact associated with other atopic comorbidities in these children. Dietary differences of children with diverse racial backgrounds (11, 12) may also affect the development of FA. For example, racial disparities may exist in the rates of allergy to different foods and the amount of exposure in early life, the association between early life diet and atopy and FA, and the impact of FA on QoL. Previous studies based on chart review and retrospective data acquisition have shown different food allergen profiles in AA and White children. (7) However, prospective data on the association of race on allergies to different foods, adjusting for important confounding variables such socioeconomic factors is limited.
These alarming differences between racial groups prompted our multicenter collaborative group to conduct studies on FA disparity among AA and White children. The objective of FORWARD (Food Allergy Outcomes Related to White and African American Racial Differences) is to compare FA clinical and psychosocial outcomes, FA phenotypes and endotypes, and FA management practices among a large, socioeconomically and geographically diverse sample of AA and White children. Characterizing these differences will inform future interventions and policies to address the specific needs of patients with FA and their families to ultimately improve FA outcomes. In the current report we have focused on the food allergen profiles and associated comorbidities of AA and White children while investigating these differences in the context of socioeconomic disparities.
Methods
Data Source
Data were sourced from the FORWARD study, NIH-funded, multi-center prospective cohort of AA and White children with FA at four urban tertiary centers in the US. Data collection has been ongoing since 2017 and will continue through 2021. These centers include the Allergy and Immunology Divisions of Ann & Robert H. Lurie Children’s Hospital of Chicago/Northwestern University (NU/Lurie), Cincinnati Children’s Hospital Medical Center (CCHMC), Children’s National Health Systems (CNHS), and Rush University Medical Center (RUMC). The study was approved by the Institutional Review Boards of all involved institutes.
Eligibility for FORWARD includes AA and White children, 0 to 12 years of age with an allergist confirmed diagnosis of IgE-mediated FA. There were 12 participants excluded from the current analyses given that they reported race other than AA or White (i.e., Other n=10, Native Hawaiian Pacific Islander n=1, Unknown n=1). A maximum of two eligible children from each household were enrolled; 7.3% of households in the current sample had more than one child enrolled. When the analyses were performed on the sample of children from n=619 households with only a single child enrolled, the results were similar. Diagnosis of FA required convincing clinical symptoms (i.e., cutaneous, respiratory, gastrointestinal, systemic) of an IgE-mediated reaction to a specific food and either an elevated serum specific IgE measured by ImmunoCAP assay (Phadia AB, Uppsala, Sweden) based on manufacturers values or a positive skin test measured by a wheal of 3mm or above on standard skin prick test (Greer Laboratories Inc, Lenoir, North Carolina) to that specific food(s). In the case of food groups such as tree nuts, fish and shellfish, evidence of allergy to one or more types of food allergens within the group was considered positive allergy history to that food group.
The present study utilized cross-sectional data from FORWARD’s intake survey, based on participant-report (Repository Text). Specifically, during the intake visit, parents/guardians of eligible children met with research coordinators from the respective site and completed FA surveys on multiple phenotypic variables, including participant-report of physician-confirmed items on type of food allergen, food-related allergic reactions, and history/current status of atopic comorbidities. The diagnosis of comorbid conditions (including asthma, allergic rhinitis and eczema) was confirmed by chart review and consulting with their primary allergist, given that these children were active patients at one of the study sites and FA was the primary eligibility criterion for the FORWARD study. Additionally, surveys acquired participant-report of demographic information including race, ethnicity, date of birth, gender, and yearly household income.
Primary Outcomes of Interest
The primary outcomes of interest were current “top 9” food allergens and presence and/or history of atopic comorbidities. Current food allergies were based on participant report of the survey item “To which food(s) is [Child’s name] currently allergic”. Categories of “top 9” food allergens (i.e., peanuts, tree nuts, milk, egg, wheat, soy, finfish, shellfish, sesame) included positive, negative, or unknown/missing. Atopic comorbidities including asthma, eczema, and allergic rhinitis were also categorized according to positive, negative, or unknown/missing participant response. Current asthma status was based on the survey item, “Does your child have asthma?” while eczema and allergic rhinitis were based on participant report of having ever been diagnosed with these conditions
Primary Independent Variable
The primary independent variable of interest was race, based on the survey item which asked participants to report “With which race does (Child’s name), your child with food allergy primarily identify?”. Children from mixed-race background were not included in the study. Each participant was assigned to AA or White groups based on their response to NIH recommended race and ethnicity categories (NIH policy notice NOT-OD-01–053). Specifically, responses for race included “White or Caucasian”, “Black or African-American,” “American-Indian or Alaskan Native”, “Asian or Asian American,” “Native Hawaiian or Pacific Islander”, “Other”, or “Unknown”. Participants also responded to a survey item asking, “Is your child of Hispanic or Latinx origin?”. Children of White race and non-Hispanic/Latinx ethnicity were categorized as White. Children who identified as AA and non-Hispanic were categorized as AA. The present study included non-Hispanic/Latinx White and AA children whose parents self-identify as either AA or White.
Covariates
Other demographic variables of interest included gender (male/female), current age in years, and yearly household income. Current age was calculated from the participant reported date of birth and analyzed as an indicator of < 5 years of age (yes/no). Yearly household income was categorized as less than $50,000 per year, $50,000 to $99,000, or $100,000 or greater.
Statistical Analysis
Data from each respective FORWARD recruitment site were exported from Research Electronic Data Capture (REDcap) databases and pooled for the purpose of the current analyses. We used univariable statistics to describe the primary phenotypic variables of interest and demographics. We performed a complete case analyses whereby the denominator for each calculation included participants with non-missing data on the survey item of interest. Chi-square tests evaluated unadjusted associations between race (AA/White) and primary outcome indicators. We used separate multivariable logistic regressions to calculate the adjusted probability of having each of the “top 9” food allergies and/or atopic comorbidities, with White race as the reference group for all adjusted odds ratios. For inclusion into multivariable analyses, food allergies and atopic comorbidities were analyzed as binary indicators of having (or had) the condition (yes/no). All multivariable models included family-level robust standard errors to adjust for potential household clustering. Additionally, all models were adjusted for current age, gender, household income, and recruitment site. Significance tests were two-sided with an alpha level of < 0.05. All statistical analyses were performed using Stata SE Version 15.1 (StataCorp. 2017. College Station, TX.).
Results
A total of 239 AAs and 425 White children with a diagnosis of FA were included in these analyses. Demographic and clinical characteristics, as well as comparisons between the four sites are detailed in Table 1. The racial distribution of enrolled cases was different among the centers, as AA constituted a larger percentage of enrolled cases at RUMC compared with Lurie Children’s Hospital.
Table 1.
Demographic and clinical characteristics of African-American and White children with food allergy enrolled in FORWARD
| Total | Northwestern/Lurie | Rush | CCHMC | CNHS | |||||
|---|---|---|---|---|---|---|---|---|---|
| n (%) | n (%) | Odds Ratio (95% CI)7 | n (%) | Odds Ratio (95% CI) | n (%) | Odds Ratio (95% CI) | n (%) | Odds Ratio (95% CI) | |
| Individual Demographics | |||||||||
| Total n=664 | 664 (100) | 226 (100) | – | 133 (100) | – | 158 (100) | – | 147 (100) | |
| Race | |||||||||
| African-American | 239 (36.0) | 70 (31.0) | 1 (ref) | 75 (56.4) | – | 44 (27.9) | – | 50 (34.0) | – |
| White | 425 (64.0) | 156 (69.0) | 58 (43.6) | 0.35 (0.22 – 0.54)*** | 114 (72.2) | 1.16 (0.74 – 1.82) | 97 (66.0) | 0.87 (0.56 – 1.35) | |
| Gender1 | |||||||||
| Male | 411 (62.7) | 130 (57.8) | – | 89 (68.5) | – | 96 (61.9) | – | 96 (63.8) | – |
| Female | 245 (37.4) | 95 (42.2) | 1 (ref) | 41 (31.5) | 0.63 (0.40–0.99)* | 59 (38.1) | 0.84 (0.55 – 1.27) | 50 (32.9) | 0.71 (0.46 – 1.09) |
| Current Age in Years2 | |||||||||
| < 5 | 229 (35.0) | 62 (27.7) | – | 39 (30.0) | – | 59 (38.3) | – | 69 (47.3) | – |
| >=5 | 425 (65.0) | 162 (72.3) | 1 (ref) | 91 (70.0) | 0.89 (0.55 – 1.44) | 95 (61.7) | 0.62 (0.40 – 0.95)* | 77 (52.7) | 0.43 (0.28 – 0.67)*** |
| Household Demographics | |||||||||
| Total n=619 | 619 (100) | 208 (100) | – | 124 (100) | – | 153 (100) | – | 134 (100) | – |
| Income3 | |||||||||
| < $50K | 123 (22.9) | 39 (21.4) | – | 36 (34.3) | – | 30 (22.9) | – | 18 (15.0) | – |
| $50k-$99k | 94 (17.5) | 22 (12.1) | – | 20 (19.1) | – | 36 (27.4) | – | 16 (13.3) | – |
| 100k+ | 321 (59.7) | 121 (66.5) | 1 (ref) | 49 (46.7) | 0.44 (0.27 – 0.72)** | 65 (49.6) | 0.50 (0.31 – 0.79)** | 86 (71.7) | 1.27 (0.77 – 2.10) |
| Comorbidities | |||||||||
| Total | 664 (100) | 226 (100) | – | 133 (100) | – | 158 (100) | – | 147 (100) | – |
| Asthma4 | |||||||||
| Yes | 247 (38.9) | 88 (39.5) | 1 (ref) | 52 (42.3) | 1.09 (0.70 – 1.72) | 57 (38.8) | 0.94 (0.61 – 1.44) | 50 (35.2) | 0.81 (0.52 – 1.26) |
| No | 369 (58.1) | 126 (56.5) | – | 68 (55.3) | – | 87 (59.2) | – | 88 (62.0) | – |
| Outgrown | 19 (3.0) | 9 (4.0) | – | 3 (2.4) | – | 3 (2.0) | – | 4 (2.8) | – |
| Allergic Rhinitis5 | |||||||||
| Yes | 297 (46.9) | 98 (44.0) | 1 (ref) | 65 (47.5) | 1.41 (0.91 – 2.19) | 67 (45.6) | 1.06 (0.70 – 1.62) | 68 (47.9) | 1.17 (0.76 – 1.79) |
| No | 337 (53.2) | 125 (56.1) | – | 59 (43.1) | – | 80 (54.4) | – | 74 (52.1) | – |
| Eczema6 | |||||||||
| Yes | 524 (81.9) | 185 (83.0) | 1 (ref) | 90 (73.8) | 0.58 (0.34 – 0.98)* | 126 (85.1) | 1.17 (0.66 – 2.08) | 119 (83.8) | 1.06 (0.60 – 1.87) |
| No | 115 (18.1) | 38 (17.0) | – | 32 (26.2) | – | 22 (14.9) | – | 23 (16.2) | – |
Open in a separate window
1n=8 individuals (1.2%) had missing gender data; n=1 from NU/Lurie, n=3 from Rush, n=3 from CCHMC, n=1 from CNHS
2n=10 individuals (1.5%) had missing age data; n=2 from NU/Lurie, n=3 from Rush, n=4 from CCHMC, n=1 from CNHS
3n=81 households (13.1%) had missing income data or declined to answer; n=26 from NU/Lurie, n=19 from Rush, n=22 from CNHS, n=14 from CCHMC
4n=29 individuals (4.4%) had missing asthma data; n=3 from NU/Lurie, n=10 from Rush, n=11 from CCHMC, n=5 from CNHS
5n=30 individuals (4.5%) had missing data on allergic rhinitis; n=3 from NU/Lurie, n=11 from Rush, n=11 from CCHMC, n=5 from CNHS
6n=29 individuals (4.4%) had missing data on eczema; n=3 from NU/Lurie, n=11 from Rush, n=10 from CCHMC, n=5 from CNHS
7Odds ratios represent the unadjusted probability estimate of the italicized row in each recruitment center compared to NU/Lurie (reference group). Odds ratios for income are based on probability of earning > 100k versus <$50k and for asthma are based on having asthma (outgrown asthma, n=19 was excluded) versus not having asthma.
Bold values represent statistical significance;
*P < 0.05;
**P < 0.01;
***P < 0.001
The adjusted comparisons of the two races in terms of demographic factors are detailed in Table 2. Whites had a median age of 5.7 years (interquartile range [IQR]: 3.4 to 9.5) while AAs had a median age of 8.0 years (IQR: 5.2 to 11.5). Gender was similarly distributed between races. As seen in Table 2, families of AA and White children had significantly different distributions of annual household income. Among White families, 80.0% had annual incomes of $100,000 or greater, whereas 21.8% of AA families had annual incomes of $100,000 or greater.
Table 2.
Adjusted probability of demographic and clinical characteristics among African-American and White children with food allergy enrolled in FORWARD
| White | African-American | |||
|---|---|---|---|---|
| n (%) | Odds Ratio (95% CI) | n (%) | Odds Ratio (95% CI)7 | |
| Individual Demographics | ||||
| Total n=664 | 425 (100) | 239 (100) | ||
| Gender1 | ||||
| Male | 255 (60.6) | – | 156 (66.4) | – |
| Female | 166 (39.4) | 1 (ref) | 79 (33.6) | 0.72 (0.44 – 1.18) |
| Current Age in Years2 | ||||
| < 5 | 179 (42.7) | – | 50 (21.3) | – |
| >=5 | 240 (57.3) | 1 (ref) | 185 (78.7) | 3.93 (2.32 – 6.67)*** |
| Household Demographics | ||||
| Total n=619 | 398 (100) | 221 (100) | ||
| Income3 | ||||
| < $50K | 16 (4.6) | – | 107 (56.9) | – |
| $50k-$99k | 54 (15.4) | – | 40 (21.3) | – |
| 100k+ | 280 (80.0) | 1 (ref) | 41 (21.8) | 0.05 (0.03 – 0.08)*** |
| Comorbidities | ||||
| Total n=664 | 425 (100) | 239 (100) | ||
| Asthma4 | ||||
| Yes | 112 (27.2) | 1 (ref) | 135 (60.5) | 2.70 (1.65 – 4.43)*** |
| No | 286 (69.4) | – | 83 (37.2) | – |
| Outgrown | 14 (3.4) | – | 5 (2.2) | – |
| Allergic Rhinitis5 | ||||
| Yes | 158 (38.4) | 1 (ref) | 139 (62.6) | 1.47 (0.85 – 2.53) |
| No | 254 (61.7) | – | 83 (37.4) | – |
| Eczema6 | ||||
| Yes | 334 (80.9) | 1 (ref) | 186 (83.8) | 1.03 (0.55 – 1.92) |
| No | 79 (19.1) | – | 36 (16.2) | – |
Open in a separate window
1n=8(1.2%) had missing gender data; n=4 White, n=4 African-Americans
2n=10 individuals (1.5%) had missing age data; n=4 White, n=6 African-Americans
3n=81 (13.1%) households had missing income data; n=48 White, n=33 African-American
429 individuals (4.4%) had missing data on asthma; n=13 White, n=16 African-Americans
530 individuals (4.5%) had missing data on allergic rhinitis; n=13 White, n=17 African-Americans
6n=29 individuals (4.4%) had missing data on eczema; n=12 White, n=17 African-Americans
7Odds ratios represent the probability of the italicized row outcome in comparison to White children (reference group), adjusted for demographic covariates. Adjusted ratios for income are based on probability of earning >= $100k in comparison to < $50k; ratios for asthma model are based on having asthma versus not.
Bold values represent statistical significance;
*P < 0.05;
**P < 0.01;
***P < 0.001
Percentages may add to over 100 due to rounding
Race and comorbid allergic conditions:
Also shown in Table 2, AA children had significantly higher odds of comorbid asthma than White children after adjusting for gender, age, annual household income, and recruitment site (OR: 2.70, P<0.0001). The adjusted odds of other atopic comorbidities such as allergic rhinitis and atopic dermatitis were similar between the two race groups.
Race and types of foods associated with FA:
As indicated in Table 3, we found a significantly higher adjusted odds of allergy to two food group allergens in AAs compared to White children; finfish (OR [95%confidence interval (CI)] = 2.54 [1.42 – 4.56] ) and shellfish (OR[95%CI]= 3.11 [1.63 – 5.93]). Furthermore, we observed a trend towards higher odds of wheat allergy among AAs compared to Whites (OR [95%CI] = 2.10 [0.87 – 5.04], P=0.097). We also observed a trend towards a lower odds of sesame allergy among AAs compared to Whites (OR [95%CI]=0.52 [0.27 – 1.00], P=0.053).
Table 3.
Adjusted probability of current food allergies among African-American and White children with food allergy enrolled in FORWARD
| Total | White | African-American | |||
|---|---|---|---|---|---|
| n(%) | n(%) | Odds Ratio (95% CI) | n (%) | Odds Ratio1 (95% CI) | |
| Total n=664 | 664(100) | 425 (100) | 239 (100) | ||
| Number of “Top 9” Current Food Allergies | |||||
| None | 21 (3.2) | 10 (2.4) | – | 11 (4.6) | – |
| One | 224 (33.7) | 154 (36.2) | – | 70 (29.3) | – |
| Multiple (more than one) | 419 (63.1) | 261 (61.4) | 1(ref) | 158 (66.1) | 1.13 (0.70 – 1.81) |
| Type of Current Food Allergy | |||||
| Peanut | 421 (63.4) | 266 (62.6) | 1 (ref) | 155 (64.9) | 0.87 (0.55 – 1.40) |
| Milk | 158 (23.8) | 107 (25.2) | 1 (ref) | 51 (21.3) | 0.81 (0.46 – 1.4) |
| Egg | 263 (39.6) | 179 (42.1) | 1 (ref) | 84 (35.2) | 0.81 (0.51 – 1.30) |
| Wheat | 52 (7.8) | 22 (5.2) | 1 (ref) | 30 (12.6) | 2.10 (0.87 – 5.04) |
| Soy | 47 (7.1) | 23 (5.4) | 1 (ref) ^ | 24 (10.0) | 0.97 (0.42 – 2.26) |
| Sesame | 112 (16.9) | 87 (20.5) | 1 (ref) | 25 (10.5) | 0.52 (0.27 – 1.00) |
| Tree nuts (≥1) | 353 (53.2) | 218 (51.3) | 1 (ref) | 135 (56.5) | 1.27 (0.79 – 2.05) |
| Fin fish (≥1) | 92 (13.9) | 35 (8.2) | 1 (ref) | 57 (23.9) | 2.54 (1.42 – 4.56)** |
| Shellfish (≥1) | 110 (16.6) | 34 (8.0) | 1 (ref) | 76 (31.8) | 3.11 (1.63 – 5.93)*** |
Open in a separate window
1Odds ratios represent the adjusted probability of given food allergy in comparison to White children (reference group). All models adjusted for gender, age, household income, and site. Model for multiple FA evaluates adjusted probability of having multiple FA versus 1 FA.
Bold values represent statistical significance;
*P < 0.05;
**P < 0.01;
***P < 0.001
Percentages may add to over 100 due to rounding
Association of food allergens and asthma:
Children with shellfish allergy had a significantly higher odds of asthma (OR: 1.94, P=0.024) after adjusting for race, gender, age, household income, and recruitment site. Other food allergens were not associated with presence of asthma.
Discussion
In this multi-center prospective study, the adjusted odds of allergy to shellfish was significantly higher among AAs in comparison to Whites, and this association remained in magnitude when the analyses were stratified by site, lending further evidence to the robustness of this observation. It is hypothesized that sensitization to shellfish might occur through inhalation of a similarly structured protein with a common peptide chain between some inhaled allergens and shellfish; tropomyosin. (13–15) Shrimp tropomyosin (Pen a 1) and co*ckroach (Per a 7) share 80% amino acid sequencing.(13) Increase in co*ckroach allergen exposure in the home is seen with higher shrimp allergen and co*ckroach IgE. (16, 17) Positive skin test to shrimp allergen in an observant population of Jewish individuals who were allergic to co*ckroach and/or dust mite but unexposed to shellfish further confirmed that sensitivity to shellfish could be due to cross-reacting tropomyosins. (18) An increase in the level of co*ckroach allergen which is known to be higher in low socioeconomic inner-city areas where most residents are AA (19) may provoke cross sensitization for shellfish allergy in the AA children. Similarly, we have observed a higher rate of finfish allergy in AA children compared to whites. Tropomyosins are identified as allergens in commonly consumed finfish.(20) and IgE-binding to fish tropomyosins was recently demonstrated in fish allergic patients.(21, 22) While further investigation is needed to tease out the exact mechanism of this link, preventive studies to decrease the exposure of these at-risk AA children to co*ckroach can potentially prevent food allergy and improve asthma control. Further analysis of fish and shellfish as individual allergens showed significant racial differences in the unadjusted analyses for all analyzed finfish and shellfish (Table E1) which indicates that the differences are shared among most allergens in these groups. To understand whether the geographic distribution and proximity to large bodies of water can impact the association to race with seafood allergy we evaluated the relationship between race and the adjusted probability of having finfish or shellfish allergies in Chicago, Cincinnati and DC separately. We found that in all Midwest centers, Black children had significantly higher adjusted probability of both finfish and shellfish allergies. However, when the sample is restricted to only DC (our only east coast center), the adjusted probabilities are maintained in magnitude with respect to shellfish but not finfish. Given that very few children (n=15) had finfish allergy in the DC site, there is a lack of statistical power to detect these associations which could have resulted in the negative finding. Further larger studies are needed to investigate the link between AA race and seafood allergy stands in all geographic areas.
The higher prevalence of asthma in AA children with FA compared with White children with FA is an important epidemiologic finding. Asthma accompanies fatal food anaphylaxis 70 % of the time with cardiovascular collapse attributed to respiratory failure.(23, 24) What further signifies this increased rate of asthma is the fact that AA children bear an unequally higher burden of asthma-related morbidity and mortality in comparison to their White counterparts. (25) This is markedly pertinent to FA because uncontrolled asthma is a risk factor for fatal anaphylaxis in the food allergy setting (9, 10), which places these AA children at significantly greatest risk. Specifically, AA girls and boys are at a nearly two-fold and three-fold greater risk of FA-related fatal anaphylaxis than White girls and boys, respectively. (26) This is evident by previously reported increased need for immediate healthcare utilization in AA children for both ED visits (7) and hospital admissions for food-induced anaphylaxis. Unfortunately, this problem in AA children has been increasing, at least regionally, from 2008–2012 in Illinois. (27)
The significant difference in household incomes between AA and white families in our study is yet again an evidence of the economic disparities between these children. These economic burdens can directly affect social determinants of health and food access in children and have significant impacts in children with food allergy. These results emphasize the necessity of specialty programs for these AA children with FA and asthma. Programs that are multi-level, meaning that they address organizations as well as individuals within the community. Such programs could be the most helpful when addressing health disparities and access to care.(28, 29) An example of such a model that addresses the social determinants of health through an innovated Medicaid model is the Coordination of Healthcare for Complex Kids (CHECK).(30) This program included chronic diseases in children, most commonly asthma, to ensure adequate resources were available to families to navigate the complicated health system.(30, 31) Preliminary outcomes in asthma are described in the literature, and Illinois has adopted this chronic disease model by introducing policy that calls for large-scale care coordination programs that work with managed care organizations to address health disparities. These models, and others, that address health disparities at the core, can help families improve access to equitable care.(32) Similar programs are necessary to address food allergy management issues such as food insecurity and access, to reduce the risk of accidental ingestions that could put these children at greater risk.
Although this study provides important findings on the relation between race and food allergy, it has some limitations. Race is a self-reported variable in this study. Furthermore, this large cohort includes FA children from four urban allergy and immunology clinics, three in the Midwest and one on the East coast of the US; while these centers are fairly well distributed geographically, the overall participant distribution may not represent US children with FA in general. The percentage of enrolled children above age of 5 was lower at CCHMC and CNHS compared to Lurie Children hospital which represents a slightly different overall age groups of these clinics. However, as all analysis are adjusted for age, this difference would not impact the findings. Another limitation is that the number of enrolled white children is higher than AA children. AA children constituted a lower percentage of enrolled cases at Lurie Children hospital compared to RUMC, which reflects the different racial distribution of patients in these centers across the same city. Inclusion of centers with different racial break down and economic background (as evidenced by annual income) was intentional to represent a more diverse population.
In conclusion, we found that AA children had different food allergen profiles and higher prevalence of associated asthma. As FA can increase the chance of life-threatening asthma attack,(8, 9) the higher prevalence of asthma observed among AA children might increase their risk of severe respiratory reactions. These data reinforce previous findings and point to the need for implementation of strategies to create unique management tools for all children with food allergy, especially those from lower income families and AA children with comorbid asthma and eczema. Our ongoing studies on the FORWARD cohort are designed to further investigate different cultural practices and behavioral factors impacting risk factors such as early introduction of food/s by race and ethnicity. Quarterly surveys are being administered to obtain comprehensive data such as how socioeconomic and cultural difference impact the food and diet purchasing habits, accessibility to healthcare and parental/family burdens. Our goal is to understand differences by race and ethnicity in all aspects of food allergy to be able to provide individualized care to improve management and decrease FA morbidity and mortality.
Highlights Box
What is already known about this topic? Previous studies have shown that FA, food sensitization and dietary differences of children with diverse racial background and phenotype are often associated with other common allergic conditions such as atopic dermatitis, asthma and allergic rhinitis.
What does this article add to our knowledge? Higher prevalence of asthma and allergy to shellfish observed among AA children increases risk of severe potentially fatal reactions.
How does this study impact current management guidelines? Understanding the varied risk of allergy to different food allergens in AA children compared to whites, will guide our clinical practice in terms of history taking and additional testing.
Supplementary Material
1
Click here to view.(109K, doc)
2
Click here to view.(46K, pdf)
Acknowledgment:
FORWARD (NIH number: 1R01AI130348-01A1), MM is also supported by KL2TR002387-02 from NIH and Brinson Foundation grant.
Abbreviations:
| FORWARD | Food allergy management & Outcomes Related to White and African American Racial Differences |
| FA | Food allergy |
| AA | African-American |
| AD | Atopic dermatitis |
| RUMC | Rush University Medical Center |
| CCHMC | Cincinnati Children’s Hospital Medical Center |
| EMR | electronic medical records |
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: 1- All authors are supported by FORWARD (NIH number: 1R01AI130348–01A1)
2 - Mahboobeh Mahdavinia is supported by KL2TR002387–02 from NIH and Brinson Foundation grant.
References:
1. Gupta RS, Warren CM, Smith BM, Blumenstock JA, Jiang J, Davis MM, et al. The Public Health Impact of Parent-Reported Childhood Food Allergies in the United States. Pediatrics. 2018;142(6). [PMC free article] [PubMed] [Google Scholar]
2. Gupta RS, Springston EE, Warrier MR, Smith B, Kumar R, Pongracic J, et al. The prevalence, severity, and distribution of childhood food allergy in the United States. Pediatrics. 2011;128(1):e9–17. [PubMed] [Google Scholar]
3. Sicherer SH, Munoz-Furlong A, Godbold JH, Sampson HA. US prevalence of self-reported peanut, tree nut, and sesame allergy: 11-year follow-up. J Allergy Clin Immunol. 2010;125(6):1322–6. [PubMed] [Google Scholar]
4. Branum AM, Lukacs SL. Food allergy among children in the United States. Pediatrics. 2009;124(6):1549–55. [PubMed] [Google Scholar]
5. Greenhawt MFood allergy quality of life and living with food allergy. Curr Opin Allergy Clin Immunol. 2016;16(3):284–90. [PubMed] [Google Scholar]
6. Cummings AJ, Knibb RC, King RM, Lucas JS. The psychosocial impact of food allergy and food hypersensitivity in children, adolescents and their families: a review. Allergy. 2010;65(8):933–45. [PubMed] [Google Scholar]
7. Mahdavinia M, Fox SR, Smith BM, James C, Palmisano EL, Mohammed A, et al. Racial Differences in Food Allergy Phenotype and Health Care Utilization among US Children. The journal of allergy and clinical immunology In practice. 2017;5(2):352–7 e1. [PMC free article] [PubMed] [Google Scholar]
8. Liu AH, Jaramillo R, Sicherer SH, Wood RA, Bock SA, Burks AW, et al. National prevalence and risk factors for food allergy and relationship to asthma: results from the National Health and Nutrition Examination Survey 2005–2006. J Allergy Clin Immunol. 2010;126(4):798–806 e13. [PMC free article] [PubMed] [Google Scholar]
9. Roberts G, Patel N, Levi-Schaffer F, Habibi P, Lack G. Food allergy as a risk factor for life-threatening asthma in childhood: a case-controlled study. J Allergy Clin Immunol. 2003;112(1):168–74. [PubMed] [Google Scholar]
10. Wang J, Visness CM, Sampson HA. Food allergen sensitization in inner-city children with asthma. J Allergy Clin Immunol. 2005;115(5):1076–80. [PubMed] [Google Scholar]
11. Roess AA, Jacquier EF, Catellier DJ, Carvalho R, Lutes AC, Anater AS, et al. Food Consumption Patterns of Infants and Toddlers: Findings from the Feeding Infants and Toddlers Study (FITS) 2016. J Nutr. 2018;148(suppl_3):1525S–35S. [PMC free article] [PubMed] [Google Scholar]
12. Thomson JL, Tussing-Humphreys LM, Goodman MH, Landry AS. Diet quality in a nationally representative sample of American children by sociodemographic characteristics. Am J Clin Nutr. 2019;109(1):127–38. [PubMed] [Google Scholar]
13. Wong L, Huang CH, Lee BW. Shellfish and House Dust Mite Allergies: Is the Link Tropomyosin?Allergy Asthma Immunol Res. 2016;8(2):101–6. [PMC free article] [PubMed] [Google Scholar]
14. Ayuso R, Reese G, Leong-Kee S, Plante M, Lehrer SB. Molecular basis of arthropod cross-reactivity: IgE-binding cross-reactive epitopes of shrimp, house dust mite and co*ckroach tropomyosins. International archives of allergy and immunology. 2002;129(1):38–48. [PubMed] [Google Scholar]
15. Ayuso R, Lehrer SB, Reese G. Identification of continuous, allergenic regions of the major shrimp allergen Pen a 1 (tropomyosin). International archives of allergy and immunology. 2002;127(1):27–37. [PubMed] [Google Scholar]
16. Wang J, Calatroni A, Visness CM, Sampson HA. Correlation of specific IgE to shrimp with co*ckroach and dust mite exposure and sensitization in an inner-city population. J Allergy Clin Immunol. 2011;128(4):834–7. [PMC free article] [PubMed] [Google Scholar]
17. McGowan EC, Peng R, Salo PM, Zeldin DC, Keet CA. co*ckroach, dust mite, and shrimp sensitization correlations in the National Health and Nutrition Examination Survey. Ann Allergy Asthma Immunol. 2019;122(5):536–8 e1. [PMC free article] [PubMed] [Google Scholar]
18. Fernandes J, Reshef A, Patton L, Ayuso R, Reese G, Lehrer SB. Immunoglobulin E antibody reactivity to the major shrimp allergen, tropomyosin, in unexposed Orthodox Jews. Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology. 2003;33(7):956–61. [PubMed] [Google Scholar]
19. Rosenstreich DL, Eggleston P, Kattan M, Baker D, Slavin RG, Gergen P, et al. The role of co*ckroach allergy and exposure to co*ckroach allergen in causing morbidity among inner-city children with asthma. The New England journal of medicine. 1997;336(19):1356–63. [PubMed] [Google Scholar]
20. Gonzalez-Fernandez J, Alguacil-Guillen M, Cuellar C, Daschner A. Possible Allergenic Role of Tropomyosin in Patients with Adverse Reactions after Fish Intake. Immunological investigations. 2018;47(4):416–29. [PubMed] [Google Scholar]
21. Ruethers T, Taki AC, Nugraha R, Cao TT, Koeberl M, Kamath SD, et al. Variability of allergens in commercial fish extracts for skin prick testing. Allergy. 2019;74(7):1352–63. [PubMed] [Google Scholar]
22. Ruethers T, Taki AC, Karnaneedi S, Nie S, Kalic T, Dai D, et al. Expanding the allergen repertoire of salmon and catfish. Allergy. 2020. [PubMed] [Google Scholar]
23. Turner PJ, Gowland MH, Sharma V, Ierodiakonou D, Harper N, Garcez T, et al. Increase in anaphylaxis-related hospitalizations but no increase in fatalities: an analysis of United Kingdom national anaphylaxis data, 1992–2012. J Allergy Clin Immunol. 2015;135(4):956–63 e1. [PMC free article] [PubMed] [Google Scholar]
24. Mullins RJ, Wainstein BK, Barnes EH, Liew WK, Campbell DE. Increases in anaphylaxis fatalities in Australia from 1997 to 2013. Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology. 2016;46(8):1099–110. [PubMed] [Google Scholar]
25. Hyattsville MNational Surveillance of Asthma: United States, 2001–2010. : U.S. Dept. of Health and Human Services. Centers for Disease Control and Prevention: Centers for Disease Control and Prevention, National Center for Health Statistics., 2020. [Google Scholar]
26. Jerschow E, Lin RY, Scaperotti MM, McGinn AP. Fatal anaphylaxis in the United States, 1999–2010: temporal patterns and demographic associations. J Allergy Clin Immunol. 2014;134(6):1318–28 e7. [PMC free article] [PubMed] [Google Scholar]
27. Dyer AA, Lau CH, Smith TL, Smith BM, Gupta RS. Pediatric emergency department visits and hospitalizations due to food-induced anaphylaxis in Illinois. Ann Allergy Asthma Immunol. 2015;115(1):56–62. [PubMed] [Google Scholar]
28. Pappalardo AA MM. Asthma Health Disparities. In: Mahdavinia M, editor. Health Disparities in Allergic Diseases. US2020. [Google Scholar]
29. Martin MA, Press VG, Nyenhuis SM, Krishnan JA, Erwin K, Mosnaim G, et al. Care transition interventions for children with asthma in the emergency department. J Allergy Clin Immunol. 2016;138(6):1518–25. [PMC free article] [PubMed] [Google Scholar]
30. Glassgow AE, Martin MA, Caskey R, Bansa M, Gerges M, Johnson M, et al. An innovative healthcare delivery model for children with medical complexity. Journal of child health care : for professionals working with children in the hospital and community. 2017;21(3):263–72. [PubMed] [Google Scholar]
31. Pappalardo AA, Glassgow AE, Kumar HV, Martin MA. CHECK: a multi-level program to improve outcomes for urban children and youth with asthma. J Asthma. 2019:1–3. [PubMed] [Google Scholar]
32. Canino G, McQuaid EL, Rand CS. Addressing asthma health disparities: a multilevel challenge. J Allergy Clin Immunol. 2009;123(6):1209–17; quiz 18–9. [PMC free article] [PubMed] [Google Scholar]
