Sensitivity and specificity of obesity diagnosis in pediatric ambulatory care in the United States

Sensitivity of Clinical Pediatric Obesity Diagnosis Documented in Electronic Health Records

Objective. This study tested the sensitivity of obesity diagnosis in electronic health records (EHRs) using body mass index (BMI) classification and identified variables associated with obesity diagnosis. Methods. Eligible children aged 2 to 18 years had a calculable BMI in 2017 and had at least 1 visit in 2016 and 2017. Sensitivity of clinical obesity diagnosis compared with children's BMI percentile was calculated. Logistic regression was performed to determine variables associated with obesity diagnosis. Results. Analyses included 31 059 children with BMI at or above 95th percentile. Sensitivity of clinical obesity diagnosis was 35.81%. Clinical obesity diagnosis was more likely if the child had a well visit, had Medicaid insurance, was female, Hispanic or Black, had a chronic disease diagnosis, and saw a provider in a practice in an urban area or with academic affiliation. Conclusion. Sensitivity of clinical obesity diagnosis in EHR is low. Clinical obesity diagnosis is associated with nonmodifiable child-specific factors but also modifiable practice-specific factors.

Association between Weight Status and Mental Health Service Utilization in Children and Adolescents

BACKGROUND

Previous literature reports inconsistent associations between obesity and mental health. The objective of this study was to determine the association between weight status and mental health service utilization in Ontario children and youth.

METHODS

A cross-sectional study of children 0 to 18 years, identified using primary care electronic medical records from the EMRPC database in Ontario, Canada was conducted. Height and weight data were extracted to calculate BMI and linked to administrative data on mental health related outpatient visits, emergency department visits, and hospitalizations. Multivariable logistic regression models were performed.

RESULTS

A total of 50,565 children were included. Overall, 2.2% were underweight, 70.4% had a normal weight, 18.3% were overweight, 6.9% had obesity and 2.2% had severe obesity. 28.2% of all children had at least one mental health visit. Multivariable analyses showed children with overweight, obesity, and severe obesity were 1.11 (95% CI 1.05-1.17), 1.18 (95% CI 1.08-1.27) and 1.39 (95% CI 1.22-1.59) times more likely to have an outpatient mental health visit compared to children with normal weight.

CONCLUSION

Increased weight status was associated with mental health related outpatient visits and emergency department visits. This study may inform policy makers' planning of mental health resources for children with obesity and severe obesity.

Developmental-Behavioral Pediatricians' Diagnosis and Coding of Overweight and Obesity in Children with Autism Spectrum Disorder

OBJECTIVE

The prevalence of obesity in autism spectrum disorder (ASD) is high, and managing obesity in children with ASD can be challenging. The study's objective was to examine developmental-behavioral pediatricians' (DBPs) coding practices for overweight/obesity in children with ASD and patient characteristics associated with coding.

METHODS

We analyzed the clinical data on children with ASD with at least 1 visit at one of 3 developmental-behavioral pediatrics network sites between January 2010 and December 2011. Weight status was calculated using body mass index z-scores. For children meeting the criteria for overweight/obesity, we assessed the frequency of weight-related ICD-9 diagnosis codes at DBP visits, used multivariable logistic regression to determine characteristics associated with the presence of these codes, and examined the prevalence of weight-related codes relative to other diagnosis codes.

RESULTS

The sample included 4542 children, ages 2 to 19 years. 15.5% of children met the criteria for overweight, 14.7% for obesity, and 6.3% for severe obesity. Of children meeting the criteria for overweight/obesity/severe obesity, 7.5% had a weight-related code documented at their visits. Children with obesity or severe obesity and older children had higher odds of having a weight-related code. Compared with not being on medications, atypical antipsychotics use was significantly associated with increased odds of having a weight-related code. Of 3802 unique ICD-9 diagnosis codes documented at any visit during the study period, only 4% were related to weight.

CONCLUSION

Few children meeting the criteria for overweight/obesity had documented weight-related codes. Weight-related coding was more likely for children with obesity, who were older, and those taking atypical antipsychotics.

Characteristics of Correctly Identified Pediatric Obesity and Overweight Status and Management in an Academic General Pediatric Clinic

This study identified and characterized the rates of documentation and guideline-based management of overweight and obese children within an academic pediatric clinic through a retrospective electronic medical record review of 7422 well-child visits in 2016. Diagnosis and treatment were analyzed by patient's weight diagnosis, sex, age, and provider training level. The percentages of correctly identified severely obese (90.2%), obese (77.0%), and overweight (42.0%) children were much higher than in previous retrospective chart reviews; however, less than 30% of children were referred for more intensive weight management to a dietitian or pediatric obesity weight management program. Increased provider training level was associated with a lower adherence to pediatric obesity guidelines. Strategic modifications to electronic medical records that automatically offer body mass index-associated weight diagnoses with a link to treatment pathways and resources are needed to facilitate improved compliance with current pediatric obesity guidelines in the primary care setting.

Underdiagnosis and Lower Rates of Office Visits for Overweight/Obese Pediatric Patients in Rural Compared with Urban Areas

OBJECTIVES

This study compared the number of children enrolled in Medicaid in rural and urban areas of South Carolina with an overweight/obesity diagnosis and the mean rates of office visits with overweight/obesity diagnosed.

METHODS

Medicaid claims data from 2012 for children in three South Carolina counties, categorized as urban, rural high resource, and rural low resource, were used to identify those who had been diagnosed as being overweight/obese during any encounter. Logistic and Poisson regressions were performed to predict whether overweight/obese children in each county would receive an overweight/obesity visit diagnosis and to calculate the mean rate of total office visits with an overweight/obesity diagnosis in each county.

RESULTS

A total of 1233 children enrolled in Medicaid were diagnosed as being overweight/obese at any encounter in the designated counties. Well visits with overweight/obesity diagnosed varied significantly, with 42.6%, 28%, and 11% in urban, rural high-resource counties, and rural low-resource counties, respectively (P < 0.01). In the logistic regression rural high-resource children (adjusted odds ratio 0.58, 95% confidence interval 0.38-0.88) and rural low-resource children (adjusted odds ratio 0.16, 95% confidence interval 0.09-0.28) were less likely than urban children to be diagnosed as being overweight/obese at a well visit. All of the children had a low number of total office visits with overweight/obesity diagnosed. When comparing the counties, urban children (1.22 visits per year) had more visits than rural low-resource children (0.75 visits per year, P < 0.01) and rural high-resource children (0.89 visits per year, P < 0.01).

CONCLUSIONS

Overweight/obesity is underdiagnosed in rural children enrolled in Medicaid in South Carolina, which affects the number of children who receive help to manage their weight. Interventions to overcome barriers of diagnosis and management are necessary to address childhood obesity properly.

How Accurately Do Both Parents and Health Professionals Assess Overweight in Children?

BACKGROUND

An accurate perception of weight status is important to prevent childhood obesity.

OBJECTIVE

We investigated whether parents and health professionals accurately identify children's weight status.

METHODS

On the 2012 National Health and Nutrition Examination Survey: National Youth Fitness Survey, parents and health professionals rated the same child's weight status as overweight or healthy. The sensitivity and specificity of their answers were computed by comparing parents' and health professionals' ratings to the age growth chart from the US Centers for Disease Control and Prevention.

RESULTS

Participants were 1571 children. Parents' sensitivity and specificity were 0.386 and 0.992, respectively. Health professionals' sensitivity and specificity were 0.343 and 0.981, respectively.

CONCLUSIONS

Parents and health professionals demonstrate low sensitivity in identifying children's weight status. Health professionals should use the Centers for Disease Control and Prevention categories to more accurately identify children who are overweight.

ICD-9 diagnosis codes have poor sensitivity for identification of preexisting comorbidities in traumatic fracture patients: A study of the National Trauma Data Bank

BACKGROUND

The use of large national databases for clinical research has increased recently in the field of trauma care as they allow study of rare events without the logistical difficulties of a prospective study. However, many of these databases use administrative billing codes, such as International Classification of Disease-9th Rev. (ICD-9) codes, to identify preexisting patient comorbidities. While the accuracy of these billing codes for research purposes has previously been called into question, this has not been studied in a trauma population.

METHODS

All patients with proximal tibia fractures in the 2011 and 2012 American College of Surgeons' National Trauma Data Bank were reviewed. Rates of 12 individual comorbidities in this population were computed using both ICD-9 diagnosis codes and also National Trauma Data Bank chart-abstracted variables. The sensitivity was computed for ICD-9 coding of each comorbidity taking chart-abstracted data elements as criterion standard. With the use of multivariate logistic regression, controlling for age and Injury Severity Score (ISS), the odds ratio for mortality was computed for each comorbidity, using both ICD-9 diagnoses and chart-abstracted diagnoses.

RESULTS

A total of 32,441 patients with proximal tibia fractures were identified. The sensitivities of ICD-9 billing codes for the comorbidities analyzed ranged from 18.8% for previous myocardial infarction to 2.4% for alcoholism. In individual multivariate analyses of each comorbidity, there was a change in the statistical significance of the odds ratio for mortality for 6 of 12 comorbidities analyzed.

CONCLUSION

Researchers and those evaluating research in the field of trauma should carefully consider the accuracy of data elements in future studies, especially ICD-9-coded comorbidity diagnoses.

Limitations of administrative databases in spine research: a study in obesity

BACKGROUND CONTEXT

The use of national inpatient databases for spine surgery research has been increasing. Unfortunately, without firsthand knowledge of each specific database, it can be difficult to judge the validity of such studies. Large databases that rely on administrative data, such as International Classification of Diseases, Ninth Revision (ICD-9) codes, may misrepresent patient information and could thus affect the results of studies that use these data.

PURPOSE

The present study uses obesity, an easily quantified and objective variable, as an example comorbidity to assess the accuracy of ICD-9 codes in the setting of their continued use in spine database studies.

STUDY DESIGN/SETTING

A cross-sectional study at a large academic medical center.

PATIENT SAMPLE

All patients spending at least one night in the hospital as an inpatient between April 1, 2013 and April 16, 2013. Obstetrics and gynecology, psychiatry, and pediatric patients were excluded.

OUTCOME MEASURES

Proportion of patients for whom ICD-9 obesity diagnosis codes assigned at hospital discharge match chart-documented body mass index (BMI).

METHODS

The medical record was reviewed for each patient, and obesity ICD-9 codes were directly compared with documented BMI.

RESULTS

The study included 2,075 patients. Of 573 "obese" patients (calculated BMI 30-39.9), only 109 received the correct code (278.00), giving this ICD-9 code a sensitivity of 0.19. Of 174 "morbidly obese" patients (calculated BMI >40), only 84 received the correct code (278.01), giving this ICD-9 code a sensitivity of 0.48.

CONCLUSIONS

Using obesity as an example, this study highlights the potential errors inherent to using ICD-9-coded databases for spine surgery research. Should a study based on such data use "obesity" as a variable in any analyses, the reader should interpret these results with caution. We further suggest that obesity is likely not the only comorbidity to which these results apply. As database research continues to represent an increasing proportion of publications in the field of spine surgery, it is important to realize that study outcomes can be skewed by data accuracy, and, thus, should not be blindly accepted simply by virtue of large sample sizes.

Administrative database concerns: accuracy of International Classification of Diseases, Ninth Revision coding is poor for preoperative anemia in patients undergoing spinal fusion

STUDY DESIGN

Cross-sectional study.

OBJECTIVE

To objectively evaluate the ability of International Classification of Diseases, Ninth Revision (ICD-9) codes, which are used as the foundation for administratively coded national databases, to identify preoperative anemia in patients undergoing spinal fusion.

SUMMARY OF BACKGROUND DATA

National database research in spine surgery continues to rise. However, the validity of studies based on administratively coded data, such as the Nationwide Inpatient Sample, are dependent on the accuracy of ICD-9 coding. Such coding has previously been found to have poor sensitivity to conditions such as obesity and infection.

METHODS

A cross-sectional study was performed at an academic medical center. Hospital-reported anemia ICD-9 codes (those used for administratively coded databases) were directly compared with the chart-documented preoperative hematocrits (true laboratory values). A patient was deemed to have preoperative anemia if the preoperative hematocrit was less than the lower end of the normal range (36.0% for females and 41.0% for males).

RESULTS

The study included 260 patients. Of these, 37 patients (14.2%) were anemic; however, only 10 patients (3.8%) received an "anemia" ICD-9 code. Of the 10 patients coded as anemic, 7 were anemic by definition, whereas 3 were not, and thus were miscoded. This equates to an ICD-9 code sensitivity of 0.19, with a specificity of 0.99, and positive and negative predictive values of 0.70 and 0.88, respectively.

CONCLUSION

This study uses preoperative anemia to demonstrate the potential inaccuracies of ICD-9 coding. These results have implications for publications using databases that are compiled from ICD-9 coding data. Furthermore, the findings of the current investigation raise concerns regarding the accuracy of additional comorbidities. Although administrative databases are powerful resources that provide large sample sizes, it is crucial that we further consider the quality of the data source relative to its intended purpose.

Factors affecting subspecialty referrals by pediatric primary care providers for children with obesity-related comorbidities

OBJECTIVE

To determine referral patterns from pediatric primary care to subspecialists for overweight/obesity and related comorbidities.

METHODS

We used the National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey to identify overweight/obesity and 5 related comorbidities in primary care visits between 2005 and 2009 by children 6 to 18 years. The primary outcome was whether the visit ended in referral. We used multivariable analysis to examine factors associated with referral.

RESULTS

We identified 34,225 database visits. A total of 17.1% were with overweight (body mass index=85th to 94th percentile) or obese (body mass index≥95th percentile) patients. A total of 7.1% of primary care visits with overweight/obese children ended in referral. Referral was more likely when obesity was the reason for visit (odds ratio=2.83; 95% confidence interval=1.61-4.97) but was not associated with presence of a comorbidity (odds ratio=1.35; 95% confidence interval=0.75-2.44).

CONCLUSIONS

Most overweight or obese children are not referred, regardless of comorbidity status. One reason may be low levels of appropriate diagnosis.