Disparities in Nutrition Counseling at Pediatric Wellness Visits in South Carolina

Abstract

Background:

In 2007, an Expert Committee recommended that dietary patterns be assessed at each wellness visit and that counseling on diet and nutrition be provided to all children. Few studies have examined the “uptake” of obesity prevention practices into pediatric primary care. This study aimed to describe patterns of nutrition counseling among children at wellness visits in South Carolina between 2008 and 2017 and determine whether sociodemographic disparities existed.

Methods:

The sample included 123,864 children 2–18 years of age who had a wellness visit at one of South Carolina's four major health care systems between January 1, 2008, and December 31, 2017. Documentation of nutrition counseling was defined by the International Classification of Diseases (ICD)-9/10 codes. A matched sample design and conditional logistic regression were used to examine sociodemographic disparities in children who did and did not receive nutrition counseling.

Results:

Nutrition counseling was documented at 3.55% of wellness visits. Significant sociodemographic disparities were found, including that African American and Hispanic children were less likely to receive counseling than white or non-Hispanic children. Differences were also found by urban/rural residence, health insurance, and BMI. Despite guidelines, ICD 9/10 code indicating diagnosis of overweight or obesity was documented for only 12.2% of children.

Conclusions:

Nutrition counseling was rarely documented in a large sample of electronic medical record (EMR) data from pediatric wellness visits in South Carolina—a state heavily burdened by childhood obesity. Children's BMIs were infrequently recorded, which may be a barrier to tracking BMI over time. Sociodemographic and geographic differences in nutrition counseling may exacerbate disparities in childhood obesity.

Background

Rates of childhood obesity in the United States¹ have risen rapidly in recent decades,² with recent estimates indicating that 18.5% of US children are obese (BMI ≥95th percentile), and 29.9% are overweight or obese (BMI ≥85th percentile).^3,4 Early obesity may place children on a developmental trajectory that leads to a lifetime of obesity and its numerous comorbidities.^5,6 Children with obesity are at risk for obesity in adulthood, which carries an increased risk for a number of adverse health conditions that have far-reaching effects over the life course,⁷ including hypertension, coronary heart disease, stroke, type 2 diabetes, dyslipidemia, and cancer.^8,9 Prevention and treatment of childhood obesity are important because limited evidence-based strategies for long-term adult weight loss exist.^8,9

A number of behavioral, social, environmental, and genetic variables have been implicated in the recent rise in obesity rates. Insufficient physical activity,¹⁰ high availability of energy-dense foods and sugar-sweetened beverages,^10,11 and insufficient intake of fruits and vegetables may contribute to the increase in childhood obesity rates.¹² The impact of these factors, however, is not felt equally across populations; certain sociodemographic groups, including racial and ethnic minorities, are at a heightened risk for obesity likely due to both structural (e.g., economic, built environment) and sociocultural factors.^13,14 Higher obesity rates are one of the ways that health inequalities for children from communities of color and lower socioeconomic status are manifested early in childhood.¹⁵ For example, rates of obesity are higher among African American children than white children, with 20.2% of African American youth estimated to be obese compared with 14.3% of white youth,³ and these racial differences begin appearing in early childhood.^16,17

Socioeconomic status (SES) has also been linked to childhood obesity risk,¹⁸ with lower SES children nearly 40% more likely to be obese than their higher SES peers.¹⁹ Geographic disparities in childhood obesity also exist, with the “Deep South” region of the United States bearing a disproportionate burden of the nation's obesity.²⁰ In particular, the rural South has the highest levels of children with overweight and obesity in the United States,²¹ with previous research suggesting that perceptions of neighborhood safety,²² high-fat and high-calorie diets,²³ and a weaker health care infrastructure may all play a role in this disparity.²⁴

Primary care has been suggested as an opportune setting for obesity prevention, as the majority of US children (96%) have an established health care provider, and most children (75%) have had contact with a physician in the past six months.²⁵ Pediatric primary care providers play an influential role in children's health, with annual preventive (i.e., well-child) visits serving as an opportune time to assess children's health behaviors, convey messages about obesity prevention, and link to appropriate resources.^26,27 However, the role of primary care in the prevention and treatment of obesity is relatively understudied.²⁸

In 2007, an Expert Panel established clear guidelines for clinical practice in the prevention and treatment of childhood obesity. These guidelines recommend that primary care providers assess all children for obesity risk, provide obesity prevention messages, and suggest weight control interventions for children with excess weight.²⁹ Specific recommendations call for primary care providers to (1) assess weight status at least yearly for all children and adolescents, including calculating height, weight, and BMI and plotting these measures on a standard growth chart; (2) classify weight status for all children ages 2–18 using recommended BMI classifications; (3) assess diet and physical activity patterns at each well-child visit; (4) obtain a family history for all children to aid in assessing for obesity risk; (5) complete an obesity-specific physical examination for all children with overweight or obese BMI classifications; and (6) consider further diagnostic tests for children who are overweight or obese.²⁹

Despite the potential benefits that these prevention and management guidelines may offer, research is still needed to assess current practice patterns of US primary care providers for childhood obesity prevention, screening, and management, and to examine the extent to which the 2007 Expert Panel on Childhood Obesity recommendations are being implemented.³⁰ Furthermore, given the significant existing disparities in childhood obesity, research is needed to determine whether current recommendations are being implemented uniformly across racial, ethnic, and socioeconomic groups.

Thus, this study aimed to determine the extent to which nutrition counseling is documented in electronic medical records ³¹ using ICD coding for pediatric wellness visits in South Carolina, a state in the Southern United States with high rates of childhood obesity. A secondary goal of the study was to examine potential sociodemographic disparities in the provision of nutrition counseling using EMR data.

Methods

Design and Setting

The study adopted a cross-sectional design and used EMR data from the Health Sciences of South Carolina Clinical Data Warehouse (HSSC CDW). The HSSC CDW is a data repository composed of clinical data from 2007 to the present from ∼2.8 million individual patients from South Carolina^29,30—a state with an estimated total population of 5.1 million.³¹ The repository is composed of EMR from the state's four major health care systems (i.e., Greenville Health System, Medical University of South Carolina, Palmetto Health, Spartanburg Regional Health care System)—which span across the major geographic regions of South Carolina. This research was approved by the University of South Carolina Institutional Review Board.

Participants

The total sample included 123,864 children between 2 and 18 years of age who had a wellness visit (i.e., well-child visit), as defined by the International Classification of Diseases (ICD-9/10) codes ICD-9-CM V20.2 (routine infant or child health check) or ICD-9-CM V70.0 (routine general medical examination at a health care facility) and Current Procedural Terminology codes 99382, 99383, 99384, and 99385 between January 1, 2008, and December 31, 2017.

Measures

Demographic variables were extracted from medical records obtained on the same day as the wellness visit. Demographic variables extracted from the EMR included the child's race, ethnicity, county, health insurance type, and BMI percentile for age and gender. County of residence was extracted from the EMR, and urban/rural classification was designated based on the National Center for Health Statistics Urban/Rural Classification Scheme for Counties.³² Health insurance type was classified as commercial, government, self-pay/uninsured, or other. Children's height and weight data were recorded at well-child visits, and weight status was designated as obese (BMI = >95th percentile), overweight (BMI = >85th to 95th percentile), healthy (BMI = >5th to 85th percentile), or underweight (BMI <5th percentile). Receipt of nutrition counseling at the wellness visit was defined by ICD-9/10 codes for provision of dietary counseling and surveillance (ICD-9-CM V65.3 and ICD-10-CM Z71.3).

Statistical Analysis

Coded instances of nutrition counseling were examined using descriptive statistics, with a total of 3044 unique children having received nutrition counseling. Given that nutrition counseling was a rare outcome, a matched sample design was used to examine sociodemographic disparities.³² Cases (i.e., incidences of nutrition counseling) were matched to randomly selected controls (i.e., children who did not receive nutrition counseling) from the full EMR data set based on gender and age at referral. Controls were matched based on age and gender characteristics at the date of the clinic visit. After excluding children without a valid BMI value, the analytic sample included 1429 matched pairs. Conditional logistic regression was then used to examine differences between cases (i.e., children provided with nutrition counseling) and matched controls (i.e., children matched for age and gender who were not provided with nutrition counseling).

Results

Characteristics of the total population, patients receiving nutrition counseling, and patients not receiving nutrition counseling are provided in Table 1. Nutrition counseling was coded as occurring at 3.55% of well-child visits, with 3044 unique children receiving counseling (Table 2). Among children who received nutrition counseling, 53.2% were girls. A majority of youth (66.3%) who received nutrition counseling had government-funded insurance (e.g., Medicaid). The majority of pediatric wellness visits did not have a recorded weight status; BMI was recorded in the EMR for only 40.9% of children. Among all patients without a recorded weight status, 3.73% received nutrition counseling. Among all patients with a recorded weight status, 4.79% received nutrition counseling. Ancillary analyses indicated that there was statistical significance between children with a recorded BMI and children with no BMI data for age, race, ethnicity, and insurance status, but not for gender (Appendix Table A1).

Table 1.

Characteristics of Pediatric Wellness Visits in Select SC Hospital Systems, 2008–2017

Characteristics	Total population, n = 123,864 n (%)	Participant visits where patients received nutrition counseling, n = 4812 n (%)	Participant visits where patients did not receive nutrition counseling, n = 119,052 n (%)
Gender
Female	62,139 (50.17)	2479 (51.52)	59,660 (50.11)
Male	61,725 (49.83)	2333 (48.48)	59,392 (49.89)
Age at first recorded visit
Young child (2 to <6 years)	42,782 (34.54)	1166 (24.23)	41,616 (34.96)
Child (6 to <12 years)	41,164 (33.23)	1800 (37.41)	39,364 (33.06)
Adolescent (12 to 18 years)	39,918 (32.23)	1846 (38.36)	38,072 (31.98)
Race
African American	73,142 (59.05)	1935 (40.21)	71,207 (59.81)
White	23,312 (18.82)	2223 (46.20)	21,089 (17.71)
Other	27,410 (22.13)	654 (13.59)	26,756 (22.47)
Ethnicity
Hispanic	24,504 (19.78)	526 (10.93)	23,978 (20.14)
Non-Hispanic	86,547 (69.87)	3925 (81.57)	82,622 (69.40)
Unknown	12,813 (10.34)	361 (7.50)	12,452 (10.46)
County designation^a
Metropolitan (urban)	120,287 (97.11)	4369 (90.79)	115,918 (97.37)
Nonmetropolitan (rural)	3577 (2.89)	443 (9.21)	3134 (2.63)
Health insurance type
Commercial	15,920 (12.85)	1453 (30.20)	14,467 (12.15)
Government	100,717 (81.31)	3249 (67.52)	97,468 (81.87)
Self-pay/uninsured	4313 (3.48)	91 (1.89)	4222 (3.55)
Other	2914 (2.35)	19 (0.39)	2895 (2.43)
Weight status^b
Underweight (<5th percentile)	837 (0.68)	140 (2.91)	697 (0.59)
Healthy weight (5th to <85th percentile)	9632 (7.78)	414 (8.60)	9218 (7.74)
Overweight (85th to <95th percentile)	2739 (2.21)	89 (1.85)	2650 (2.23)
Obese (≥95th percentile)	4695 (3.79)	215 (4.47)	4480 (3.76)
Missing	105,961 (85.55)	3954 (82.17)	102,007 (85.68)

Urban/rural classification was based on the National Center for Health Statistics Urban/Rural Classification Scheme for Counties, which characterizes counties using (1) the Office of Management and Budget's description of metropolitan and micropolitan statistical areas, (2) the population of metropolitan statistical areas, and (3) the location of populations within metropolitan statistical areas of 1 million or more people.

Children's weight status was categorized based on the Centers for Disease Control and Prevention's BMI-for-age percentile growth charts.

Table 2.

Characteristics of Pediatric Patients Receiving Nutrition Counseling in Select SC Hospital Systems, 2008–2017 (n = 3044)

Characteristics	Pediatric patients, n (%)
Gender
Female	1618 (53.15)
Male	1426 (46.85)
Age at first recorded visit
Young child (2–6 years)	747 (24.54)
Child (6–12 years)	1086 (35.68)
Adolescent (12–18 years)	1211 (39.78)
Race
African American	1312 (43.10)
White	1296 (42.58)
Other	436 (14.32)
Ethnicity
Hispanic	342 (11.24)
Non-Hispanic	2440 (80.16)
Unknown	262 (8.61)
County designation
Metropolitan (urban)	2774 (91.13)
Nonmetropolitan (rural)	270 (8.87)
Health insurance type
Commercial	928 (30.49)
Government	2017 (66.26)
Self-pay/uninsured	84 (2.76)
Other	15 (0.46)
Weight status
Underweight (<5th percentile)	105 (3.45)
Healthy weight (5th to <85th percentile)	246 (8.08)
Overweight (85th to <95th percentile)	172 (5.65)
Obese (≥95th percentile)	721 (23.69)
Missing	1800 (59.13)

Differences in nutrition counseling were found by race, ethnicity, urban/rural residence, health insurance status, and BMI status. Table 3 includes results from a multivariate logistic regression model predicting the likelihood of receiving nutritional counseling. Adjusted for all other covariates in the table, when compared with white children, African American children were less than half as likely to receive nutrition counseling (odds ratio [OR] = 0.41; 95% confidence interval [CI] = 0.32–0.51). Children who reported any other racial identity (e.g., American Indian, more than one race) were also significantly less likely than white children to receive nutrition counseling (OR = 0.57, 95% CI = 0.38–0.84). In terms of ethnicity, Hispanic children were 38% less likely to receive nutrition counseling than non-Hispanic children (OR = 0.62, 95% CI = 0.42–0.93). Children from rural areas were more than twice as likely to receive nutrition counseling as those from urban areas (OR = 2.18; 95% CI = 1.49–3.19). Uninsured children were 49% less likely to receive counseling than children with private insurance (OR = 0.51; 95% CI = 0.29–0.91). No significant differences in provision of nutrition counseling were found between children with government and private insurance (OR = 0.88; 95% CI = 0.70–1.10).

Table 3.

Multivariate Association of Patient Characteristics with Nutrition Counseling, n = 1429 Pairs

Characteristics	Adjusted OR (95% CI)
Race^a
African American	0.41 (0.32–0.51)
Other	0.57 (0.38–0.84)
Ethnicity^b
Hispanic	0.62 (0.42–0.93)
Unknown	0.63 (0.42–0.96)
County designation^c
Nonmetropolitan (rural)	2.18 (1.49–3.19)
Health insurance type^d
Government	0.88 (0.70–1.10)
Self-pay/uninsured	0.51 (0.29–0.91)
Other	0.57 (0.14–2.34)
Weight status^e
Obese (≥95th percentile)	3.21 (2.62–3.93)
Overweight (85th to <95th percentile)	1.71 (1.32–2.21)
Underweight (<5th percentile)	5.03 (3.39–7.45)

White.

Non-Hispanic.

Metropolitan (urban).

Commercial.

Healthy weight (BMI between 5th and <85th percentile).

CI, confidence interval; OR, odds ratio.

Compared with children with a healthy weight, children who were underweight, overweight, or obese were more likely to receive nutrition counseling. Underweight children were more than five times as likely to receive nutrition counseling as children with a healthy weight (OR = 5.03, 95% CI = 3.39–7.45). Children with obesity were more than three times as likely to receive counseling as healthy-weight children (OR = 3.21; 95% CI = 2.62–3.93). Children who were overweight were 71% more likely to receive nutrition counseling as children with a healthy weight (OR = 1.71, 95% CI = 1.32–2.21).

Discussion

Nutrition counseling may be an underutilized tool in primary care for the prevention and treatment of childhood obesity. Findings from the current study suggest missed opportunities for intervention and disparities in the provision of nutrition counseling in South Carolina. Pediatric providers across four major health care systems rarely coded nutrition counseling as occurring, despite national recommendations to offer these services at every well-child visit.²⁹ Consistent with previous studies of EMR documentation, this study found that providers also did not regularly record children's BMIs within their EMR, which may limit their ability to track BMI across childhood and intervene when appropriate.^31,33,34

Significant disparities were also found in the sociodemographic characteristics of children who received nutrition counseling and those who did not. Differences in the provision of recommended screening and counseling may exacerbate already-existing disparities in childhood obesity rates among various racial, ethnic, and socioeconomic groups.³⁵ For example, despite an increased risk for obesity,^17,36 African American children in the sample were less likely than white children to receive nutrition counseling. Obesity disparities are often manifested early in childhood.^16,17 Findings suggest that children with a healthy weight were less likely to receive nutrition counseling than those who were underweight, overweight, or had obesity, yet expert recommendations call for nutrition counseling for all children, regardless of children's weight status.²⁹ Ensuring that discussions about healthy diet and nutrition are a routine aspect of pediatric care for all children may help promote maintenance of healthy weight and reduce disparities in obesity. In addition, health care providers and office teams should ensure that EMR systems facilitate the collection and reporting of BMI data, as well as document other health promotion efforts (e.g., nutrition counseling, physical activity counseling) for which providers have not historically billed.³⁷

Limitations to the study exist. While one aim of this study was to determine how well current recommendations for child nutrition counseling were being implemented in South Carolina, the four hospital systems from which data were collected may not be representative of the entire state. These concerns may be mitigated, in part,³⁸ by the use of a large data set that included more than 123,000 wellness visits. Furthermore, this study relied on EMR data rather than direct observation or provider/patient surveys. EMRs have been hailed as valuable sources of primary data for observational studies, as they avoid common challenges in patient recruitment, data collection, and generalizability.³⁹ Studies suggest that they may provide comparable estimates to self-report of certain chronic health conditions.⁴⁰ This study is strengthened by its use of a data warehouse and a subsequent large and diverse patient sample. The finding of low incidence of EMR coding for some variables (e.g., weight status) highlights an important direction for health care practice.

Last, the cross-sectional analysis used by this study was intended to provide a snapshot of nutrition counseling at well-child visits, and matching of case and controls was based on gender and age characteristics at the time of the well-child visit, resulting in the possibility that a single child who received nutrition counseling at different time points could have been included multiple times. This concern is mitigated largely by the fact that nutrition counseling was not repeated for the majority (70.0%, n = 2131) of children, with smaller numbers of children receiving nutrition counseling on two (20.6%, n = 627) or more visits (9.4%, n = 286). A future longitudinal study using a sample that has more repeat visits would be useful to show if changes in nutrition counseling occurred over time and across BMI status over a child's development.

Conclusions

Given the rapid rise in childhood obesity,² its adverse health outcomes, and financial impacts, current practices in obesity prevention must be better understood so as to move toward a state of “best practice.” Despite the guidelines for clinical practice outlined by the 2007 Expert Panel on Childhood Obesity, little research has studied the implementation of these recommendations. Effective childhood obesity prevention strategies are crucial to reducing the risk that children develop myriad adverse obesity-related health outcomes, including early mortality. Assessing how expert recommendations have been implemented by pediatric primary care practitioners may help to identify missed opportunities for intervention.

These findings emphasize the importance of providing recommended obesity prevention strategies in primary care, as well as documenting their use through EMR systems. Recent recommendations call for documenting weight status in EMR at medical encounters as an important part of assessing weight over the developmental trajectory.⁴¹ In light of these recommendations, the present study highlights how missing BMI is yet another missed opportunity for integrating obesity prevention into wellness visits. Furthermore, understanding sociodemographic disparities in clinical obesity prevention practices is important to reduce current racial, ethnic, and income-related differences in childhood obesity. Future research is needed to examine how providers make decisions about providing nutrition counseling and coding health promotion services.

Footnotes

Funding Information

The University of South Carolina Office of the Vice President for Research through the ASPIRE: Advanced Support for Innovative Research Excellence grant. Work was also supported by the SC SmartState Center for Healthcare Quality Junior Scholars Program.

Author Disclosure Statement

No competing financial interests exist.

Appendix Table A1. BMI by Demographics

Characteristics	With BMI, n = 17,903 n (%)	Without BMI, n = 105,961 n (%)	Chi-square tests (p-value)
Gender			0.3479
Female	9040 (50.5)	53,099 (50.1)
Male	8863 (49.5)	52,862 (49.9)
Age at first recorded visit			<0.0001
Young child (2 to <6 years)	4759 (26.6)	38,023 (35.9)
Child (6 to <12 years)	6772 (37.8)	34,392 (32.5)
Adolescent (12–18 years)	6372 (35.6)	33,546 (31.7)
Race			<0.0001
African American	9813 (54.8)	63,329 (59.8)
White	3489 (19.5)	19,823 (18.7)
Other	4601 (25.7)	22,809 (21.5)
Ethnicity			<0.0001
Hispanic	4146 (23.2)	20,358 (19.2)
Non-Hispanic	13,494 (75.4)	73,053 (68.9)
Unknown	263 (1.5)	12,550 (11.8)
County designation			<0.0001
Metropolitan (urban)	17,291 (96.6)	102,996 (97.2)
Nonmetropolitan (rural)	612 (3.4)	2965 (2.8)
Health insurance type			<0.0001
Commercial	2777 (15.5)	13,143 (12.4)
Government	14,753 (82.4)	85,964 (81.1)
Self-pay/uninsured	346 (1.9)	3967 (3.7)
Other	27 (0.2)	2887 (2.7)

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