Abstract
PURPOSE
Latino pediatric patients have a higher prevalence of obesity, but less is known about how factors related to nativity are associated with obesity in youth. We examined the prevalence of childhood and adolescent obesity in primary care over time by nativity status for Latino and non-Hispanic White children.
METHODS
In this serial cross-sectional analysis, we used electronic health records from a multi-state network of community health centers which included data from clinics in 21 US states for patients aged 9 to 17 years from 2012 through 2020 in at least 1 of 4 nonmutually exclusive cross sections. We estimated the adjusted odds and prevalence of having obesity (ie, body mass index [BMI] at the 95th percentile or greater for age and sex) at all encounters during each cross section by ethnicity and nativity status.
RESULTS
The sample included a total of 147,376 patients. In the 2012-2013 cross section, 38,697 children and adolescents had at least 1 BMI measurement recorded compared with 72,747 in the 2018-2020 cross section. US-born Latino children had higher odds of having obesity than non-Hispanic White children. Foreign-born Latino and non-Hispanic White children had lower prevalence of obesity in each cross section compared with US-born Latino children (with a range from 20.4% [95% CI, 16.9%-23.8%] to 32.7% [95% CI, 31.6%-33.9%]).
CONCLUSIONS
Differences in the prevalence of documented childhood and adolescent obesity by nativity status exist in this sample of community health center patients. This demonstrates opportunity for primary care practice to further consider patients’ background and culture when providing obesity care and cardiovascular and metabolic disease prevention.
Key words: health care disparities, emigrants and immigrants, child, adolescent
INTRODUCTION
Pediatric obesity is a risk factor for adult obesity and subsequent or early development of chronic conditions.1-4 Studies show that childhood and adolescent obesity prevalence differs by ethnicity,5,6 but there are less robust nativity data on obesity in Latino pediatric populations accessing primary health care. Place of birth, culture, and acculturation (ie, modification of one’s culture based on time spent among another culture)7 can affect how families incorporate health-promotion behaviors, the absence of which can contribute to childhood obesity.8,9 Researchers have expressed a need to incorporate cultural factors and aculturation due to emigration or immigration into obesity measurement for Latino children.7 Social factors also contribute, eg, risk for food insecurity is prevalent in Latino populations due to income, job insecurity, and barriers to federal programs for healthy food.10
Research has shown that electronic health record data are useful in identifying obesity in patients,11 allowing examination of obesity trends in populations accessing primary care. Community-based health centers (CHCs) provide primary care to patients that are often more underserved and medically complex than the general population; roughly 32% of patients are Latino which is a greater than the 19% making up the US population.12 They strive to provide equitable care for the communities they serve by hiring people from the communities.13 This can increase language and race/ethnicity concordance between patients and health care workers, a factor shown to increase utilization.14 Birthplace is not collected in a standard way in clinical practice. However, since CHCs have been found to be safe places to disclose personal information15 they may have more, or more accurate, birth country information than that collected in other clinical settings, making this setting meaningful for nativity research. The goals of this paper were to report the trends of pediatric obesity by ethnicity and nativity status in a national network of CHCs, to examine whether nativity is related to obesity, and to better equip primary care clinicians that provide care to similar patients.
METHODS
We used electronic health record data from the OCHIN, Inc network of CHCs (1,311 clinics in 21 US states) of Latino and non-Hispanic White patients aged 9 to 17 years, with 1 or more body mass index (BMI) measurements from January 1, 2012 through December 31, 2020. To evaluate trends in obesity prevalence over time and for ease of interpretation, we split our study period into 4 cross sections (2012-2013, 2014-2015, 2016-2017, 2018-2020) in a serial–cross-sectional study design. Patients only needed to have data in 1 cross section to be included. This study was approved by the Oregon Health & Science University’s Institutional Review Board.
The outcome variable was a binary indicator denoting whether or not the child had obesity (defined as the 95th percentile or greater for age and sex)16 at all BMI measurements within each cross section. The main independent variable combined self-reported ethnicity and nativity status into 3 mutually exclusive groups: foreign-born Latino, US-born Latino, and non-Hispanic White to examine disparities. Covariates included sex, and within each cross section: age at first visit, clinic visits per year, insurance, household income, pregnancy, location of clinic by US state, and neighborhood-level social deprivation index score (composite measure of social factors that identifies areas with increased disadvantage).17 Supplemental Table describes covariates in detail.
Statistical Analysis
For each of the 4 cross sections, we conducted a separate generalized estimating equation (GEE) logistic regression to account for patient clustering within clinics, including indicators for ethnicity/nativity groups and the above-listed covariates. For all models, we report covariate-adjusted odds ratios (aOR), predicted probabilities (adjusted prevalence, derived from GEE parameter estimates), and 95% CIs by ethnicity/nativity groups. Statistical tests were 2-sided with α = 0.05 and conducted using Stata 15 (StataCorp LP) and RStudio 2023.12.1+402 (Poist, PBC).
RESULTS
The sample consisted of data from 147,376 patients throughout the entire study period split into 4 cross sections (Table 1). Sample sizes varied across cross sections (2012-2013 had 67,742; 2014-2015 had 100,209; 2015-2016 had 118,445; and 2018-2020 had 191,182).
Table 1.
Patient Characteristics by Ethnicity and Nativity Group and Cross Section for 2012-2020
| Characteristics | 2012-2013 (n = 38,697) | 2014-2015 (n = 51,425) | 2016-2017 (n = 57,036) | 2018-2020 (n = 72,747) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Non-Hispanic White | Foreign-born Latino | US-born Latino | Non-Hispanic White | Foreign-born Latino | US-born Latino | Non-Hispanic White | Foreign-born Latino | US-born Latino | Non-Hispanic White | Foreign-born Latino | US-born Latino | |
| No. | 36,669 | 630 | 1,398 | 43,803 | 1,859 | 5,763 | 45,141 | 3,016 | 8,879 | 57,923 | 4,203 | 10,621 |
| Age category, No. (%), y | ||||||||||||
| 9-11 | 10,514 (28.7) | 148 (23.5) | 543 (38.8) | 12,734 (29.1) | 427 (23.0) | 2,401 (41.7) | 13,628 (30.2) | 770 (25.5) | 3,800 (42.8) | 18,548 (32.0) | 1,337 (31.8) | 4,438 (41.8) |
| 12-14 | 11,675 (31.8) | 194 (30.8) | 428 (30.6) | 13,788 (31.5) | 592 (31.8) | 1,852 (32.1) | 14,194 (31.4) | 858 (28.4) | 2,756 (31.0) | 18,133 (31.3) | 1,153 (27.4) | 3,319 (31.2) |
| 15-17 | 14,480 (39.5) | 288 (45.7) | 427 (30.5) | 17,281 (39.5) | 840 (45.2) | 1,510 (26.2) | 17,319 (38.4) | 1,388 (46.0) | 2,323 (26.2) | 21,242 (36.7) | 1,713 (40.8) | 2,864 (27.0) |
| Sex, No. (%), male | 17,222 (47.0) | 292 (46.3) | 627 (44.8) | 20,708 (47.3) | 885 (47.6) | 2,735 (47.5) | 21,305 (47.2) | 1,455 (48.2) | 43,69 (49.2) | 27,105 (46.8) | 2,070 (49.3) | 5,137 (48.4) |
| Visits per year, No. (%) | ||||||||||||
| <1 | 5,110 (13.9) | 67 (10.6) | 144 (10.3) | 7,347 (16.8) | 125 (6.7) | 388 (6.7) | 7,273 (16.1) | 275 (9.1) | 698 (7.9) | 14,742 (25.5) | 757 (18.0) | 2,571 (24.2) |
| 1 to 3 | 15,963 (43.5) | 230 (36.5) | 531 (38.0) | 18,795 (42.9) | 568 (30.6) | 2,093 (36.3) | 19,261 (42.7) | 1,189 (39.4) | 3,701 (41.7) | 22,994 (39.7) | 1,712 (40.7) | 4,319 (40.7) |
| >3 to 5 | 7,243 (19.8) | 136 (21.6) | 304 (21.7) | 8,283 (18.9) | 349 (18.8) | 1,316 (22.8) | 8,190 (18.1) | 576 (19.1) | 2,025 (22.8) | 8,901 (15.4) | 761 (18.1) | 1,606 (15.1) |
| >5 | 8,353 (22.8) | 197 (31.3) | 419 (30.0) | 9,378 (21.4) | 817 (43.9) | 1,966 (34.1) | 10,417 (23.1) | 976 (32.4) | 2,455 (27.6) | 11,286 (19.5) | 973 (23.2) | 2,125 (20.0) |
| Health insurance, No. (%) | ||||||||||||
| Never insured | 7,305 (19.9) | 164 (26.0) | 83 (5.9) | 5,875 (13.4) | 166 (8.9) | 111 (1.9) | 4238 (9.4) | 262 (8.7) | 175 (2.0) | 4,497 (7.8) | 525 (12.5) | 337 (3.2) |
| Some private | 8,610 (23.5) | 52 (8.3) | 44 (3.1) | 10,752 (24.5) | 324 (17.4) | 186 (3.2) | 11,193 (24.8) | 551 (18.3) | 279 (3.1) | 13,590 (23.5) | 204 (4.9) | 331 (3.1) |
| Some public | 19,497 (53.2) | 379 (60.2) | 1,226 (87.7) | 25,864 (59.0) | 1,283 (69.0) | 5,339 (92.6) | 28,254 (62.6) | 1,877 (62.2) | 8,187 (92.2) | 37,305 (64.4) | 3,174 (75.5) | 9,563 (90.0) |
| Some private and public | 1,257 (3.4) | 35 (5.6) | 45 (3.2) | 1,312 (3.0) | 86 (4.6) | 127 (2.2) | 1,456 (3.2) | 326 (10.8) | 238 (2.7) | 2,531 (4.4) | 300 (7.1) | 390 (3.7) |
| Income as percent of federal poverty level, No. (%) | ||||||||||||
| Always ≥138 | 4,182 (11.4) | 28 (4.4) | 55 (3.9) | 5,377 (12.3) | 47 (2.5) | 107 (1.9) | 7,050 (15.6) | 93 (3.1) | 258 (2.9) | 10,564 (18.2) | 162 (3.9) | 375 (3.5) |
| Always <138 | 17,914 (48.9) | 542 (86.0) | 1,177 (84.2) | 21,369 (48.8) | 1,027 (55.2) | 1,995 (34.6) | 22,171 (49.1) | 2,360 (78.2) | 4,051 (45.6) | 28,986 (50.0) | 3,403 (81.0) | 5,622 (52.9) |
| Some ≥138 & some <138 | 1,242 (3.4) | 23 (3.7) | 23 (1.6) | 1,534 (3.5) | 31 (1.7) | 63 (1.1) | 2,581 (5.7) | 88 (2.9) | 188 (2.1) | 4,491 (7.8) | 227 (5.4) | 602 (5.7) |
| Never documented | 13,331 (36.4) | 37 (5.9) | 143 (10.2) | 15,523 (35.4) | 754 (40.6) | 3,598 (62.4) | 13,339 (29.5) | 475 (15.7) | 4,382 (49.4) | 13,882 (24.0) | 411 (9.8) | 4,022 (37.9) |
| Ever pregnant, No. (%) | 326 (0.9) | 42 (6.7) | 59 (4.2) | 343 (0.8) | 54 (2.9) | 76 (1.3) | 309 (0.7) | 86 (2.9) | 109 (1.2) | 345 (0.6) | 179 (4.3) | 138 (1.3) |
| Social deprivation index category, No. (%) | ||||||||||||
| Low (1-39) | 5,518 (15.0) | 8 (1.3) | 67 (4.8) | 5,957 (13.6) | 22 (1.2) | 125 (2.2) | 5,534 (12.3) | 42 (1.4) | 166 (1.9) | 6,568 (11.3) | 80 (1.9) | 242 (2.3) |
| Medium (40-70) | 7,871 (21.5) | 75 (11.9) | 140 (10.0) | 7,708 (17.6) | 122 (6.6) | 424 (7.4) | 7,031 (15.6) | 133 (4.4) | 635 (7.2) | 8,232 (14.2) | 152 (3.6) | 605 (5.7) |
| High (71-100) | 5,447 (14.9) | 255 (40.5) | 568 (40.6) | 4,920 (11.2) | 527 (28.3) | 2,041 (35.4) | 4,537 (10.1) | 1,086 (36.0) | 2,439 (27.5) | 5,335 (9.2) | 1,053 (25.1) | 1,979 (18.6) |
| Never documented | 17,833 (48.6) | 292 (46.3) | 623 (44.6) | 25,218 (57.6) | 1,188 (63.9) | 3,173 (55.1) | 28,039 (62.1) | 1,755 (58.2) | 5,639 (63.5) | 37,788 (65.2) | 2,918 (69.4) | 7,795 (73.4) |
Note: The states included in the study were Alaska, California, Connecticut, Florida, Georgia, Idaho, Indiana, Massachusetts, Minnesota, Montana, Nevada, New Jersey, New Mexico, North Carolina, Ohio, Oregon, South Carolina, Texas, Utah, Washington, and Wisconsin.
Table 2 shows aOR for each cross section. Across all years, US-born Latino children had significantly higher odds of having obesity at all BMI measurements than non-Hispanic White children with the largest difference in 2018-2020. Foreign-born Latino children did not have significantly different odds than non-Hispanic White children during any cross section.
Table 2.
Adjusted OR of Having Obesity at Every BMI Measurement in a Cross Section
| Category | 2012-2013 aOR (95% CI) | 2014-2015 aOR (95% CI) | 2016-2017 aOR (95% CI) | 2018-2020 aOR (95% CI) |
|---|---|---|---|---|
| Non-Hispanic White | ref | ref | ref | ref |
| Foreign-born Latino | 0.89 (0.70-1.13) | 0.91 (0.77-1.07) | 0.85 (0.71-1.01) | 0.91 (0.80-1.02) |
| US-born Latino | 1.33 (1.11-1.60) | 1.41 (1.26-1.60) | 1.27 (1.14-1.41) | 1.48 (1.37-1.59) |
aOR = adjusted odds ratio; BMI = body mass index; OR = odds ratio; ref = reference.
Note: Generalized estimating equations logistic regression models adjusted for age at first visit in cross section, sex, insurance over the study period, household income over the study period, visits per year, pregnancy during study period, social deprivation index category, and state.
Figure 1 shows prevalence of obesity for each cross section. For 2012-2013, prevalence of obesity was lowest for foreign-born Latino children at 20.4% (95% CI, 16.9%-3.8%) and non-Hispanic White children at 20.8% (95% CI, 20.0%-21.6%), and the highest obesity prevalence was found for US-born Latino children at 28.1% (95% CI, 25.0%-31.2%). Prevalence of obesity increased in all groups over time. By 2018-2020, foreign-born Latino children had the lowest prevalence of obesity at 23.0% (95% CI, 21.2%-24.8%), followed by non-Hispanic White children at 23.3% (95% CI, 22.8%-23.8%), then US-born Latino children at 32.7% (95% CI, 31.6%-33.9%).
Figure 1.
Adjusted Prevalence of Having Obesity at Every BMI Measurement in a Cross Section
BMI = body mass index. Note: Generalized estimation equation logistic regression models adjusted for age at first visit in cross section, sex, insurance over the study period, household income over the study period, visits per year, pregnancy during study period, social deprivation index category, state.
DISCUSSION
We examined the prevalence of obesity in children and adolescents accessing care in US CHCs over time by nativity status. We found that foreign-born Latino and non-Hispanic White children had less obesity than US-born Latino children, a trend that continued over time. Latino individuals have historically had higher prevalence of obesity compared with non-Hispanic White people.18 Also, US-born Latino children with either US-born or foreign-born caregivers had a higher risk of obesity compared with foreign-born caregiver/child dyads.19 Lower prevalence of obesity in the foreign-born Latino group could be partly due to less uptake of unhealthy US diets compared with the host community,7,20-22 including less intake of ultra-processed foods.23 Stress, trauma from immigration-related factors, and migration itself, can also contribute to obesity.24 There is an opportunity for primary care practitioners to consider the positive health factors that could contribute to less obesity (eg, more physical activity, certain diets or food customs, other factors), and emphasize these strengths in their counseling of foreign-born children and parents. While our analyses did adjust for neighborhood-level social deprivation, we do not have nativity data more granular than country (eg, characteristics of the city people immigrated from) and this is a topic for future research. Family physicians should engage caregivers around relevant health behaviors, including diet, exercise, and screen time, while still being cognizant of cultural differences and promoting protective health behaviors.
Our findings also underscore the need for data disaggregation by nativity and generation in health care data. Further disaggregating data from Latino children without birthplace information into generations in the US may yield more nuanced findings that would assist health care professionals in assessing risk and treating obesity.
Limitations included that data came from CHCs so may not be generalizable to all children. Yet, a disproportionately high number of Latino children receive care at CHCs12 – making them a relevant setting for this study. Many Latino children do not have country of birth documented in the electronic health record and were not included in this study. However, these data are sensitive and while CHCs have been found to be places where people are comfortable reporting sensitive information,15 it would be unethical to require collection because that could lead to people choosing not to seek care. Additionally, state- and local-level anti-immigration policies and enforcement have been shown to affect health care–seeking behavior.25,26 Future qualitative work could enhance these incomplete data. Other work conducted in this network has explored health and health care among those who do not report place of birth.27 Future studies could identify innovative approaches to generalize to others without this information.28 Additionally, BMI may not be the most accurate measure of obesity (the reason we required all measurements to be 95th percentile or greater as documented obesity), but for children it does account for differences by age and sex,16 and is a measure that is routinely collected at most clinic visits in this network. Some factors that affect obesity (eg, parental education level, cultural practices), as well as the variable of the amount of time living in the United States, are not included in the electronic health record data and are therefore not included in the analysis.
CONCLUSION
We found differences in the prevalence of pediatric obesity by nativity status among CHC patients. There is opportunity for primary care practitioners to further consider patients’ background and culture when providing care and promoting healthy behaviors seen in some groups to all groups. Innovative health-promotion programs adapting to different needs of foreign-born and US-born Latino populations, such as Salud America!,29 should be implemented, and focused on managing and lowering childhood obesity in this population.
Supplementary Material
Acknowledgments
The research reported in this work was powered by PCORnet. PCORnet has been developed with funding from a Patient-Centered Outcomes Research Institute and conducted with the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Research Network (CRN). ADVANCE is a CRN in PCORnet led by OCHIN in partnership with Health Choice Network, Fenway Health, University of Washington, and Oregon Health & Science University. ADVANCE’s participation in PCORnet is funded through the PCORI Award RI-OCHIN-01-MC.
Footnotes
Conflicts of interest: authors report none.
Funding support: This work was supported by grants to J.H. from the National Institute on Minority Health and Health Disparities (R01MD014120) and the National Cancer Institute (R01CA258464).
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