Evaluation of a Pediatric Asthma High-risk Scoring Algorithm

ABSTRACT

Objectives: Identification of patients with asthma at increased risk for hospitalization and emergency department (ED) visits presents opportunity for intervention.

Study Design: Retrospective analysis of computerized health plan claims data.

Methods: Texas Children’s Health Plan, a large Medicaid managed care program, developed an asthma risk scoring algorithm using the clinically relevant parameters of hospitalization for asthma, ED visits for asthma, short-acting β agonist medication dispensing, inhaled corticosteroid medication dispensing, number of prescribing providers, loss to follow-up, and oral corticosteroid dispensing. The risk score performance was evaluated using 2016-2018 risk scores to predict 2017-2019 asthma hospitalizations and ED visits.

Results: We identified 107,811 unique members aged 1 to less than 18 years with an asthma diagnosis. For those aged 3 to less than 18 years, the area under the receiver operating characteristic curve (AUC) for risk score predicting hospitalization ranged from 0.72 to 0.79. For those aged 1 to less than 3 years, the AUC ranged from 0.65 to 0.69. Those with a risk score of 1 or greater accounted for 20% to 23% of pediatric members 3 to less than 18 years with asthma but 53% to 56% of asthma hospitalizations in the follow-up year. Sixteen to eighteen percent of those aged 3 to less than 18 years with a risk score of 9 or greater were hospitalized in the follow-up year.

Conclusions: Texas Children’s Health Plan asthma risk score stratifies risk of asthma hospitalization and ED visits for Medicaid-insured children. The risk score performs better for children aged 3 to less than 18 years than for those aged 1 to less than 3 years.

Am J Manag Care. 2022;28(6):254-260. https://doi.org/10.37765/ajmc.2022.88788

Takeaway Points

An asthma risk score using 7 clinically relevant parameters can stratify risk of hospitalization and emergency department visits for asthma in the following year. Risk scores can range from –1 to a maximum of 18.

We found in the group aged 3 to 17 years that:

• 16% to 18% of patients with a risk score of 9 or greater and 9.6% to 10% of patients with a risk score of 7 or 8 had an asthma hospitalization in the follow-up year, and
• 32% to 46% of patients with a risk score of 9 or greater and 34% to 38% of patients with a risk score of 7 or 8 had an asthma emergency department visit in the follow-up year.

Asthma is a common reason for potentially preventable pediatric hospital admission and emergency department (ED) visits.^1,2 Identification of patients at increased risk presents an opportunity for intervention. Risk stratification can inform decisions on prioritization of interventions such as care management, home visits, provider outreach, and specialist referral.^3-6 The Chronic Care Model stresses the importance of decision support tools as part of clinical information systems to allow for a prepared, proactive practice team that can engage the patient in productive interactions.⁷ Delivering “just-in-time” information to health care providers on what makes the patient high risk can assist the provider in proactive outreach, assessment, counseling, and treatment decisions.

The Asthma Medication Ratio measure is promoted by the National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set to assess asthma care quality.⁸ This measure considers asthma medication dispensing patterns in a cohort that meets criteria for persistent asthma. It does not account for other factors associated with increased risk for adverse asthma outcomes. In contrast, complex machine learning–based algorithms may identify patients who are high risk for reasons other than asthma and may not be generalizable outside of the setting and population that they were developed and validated on.

Texas Children’s Health Plan is a large, provider-sponsored, not-for-profit Medicaid managed care organization serving as health care payer for more than 500,000 members in eastern Texas. Texas Children’s Health Plan developed and implemented an asthma high-risk scoring algorithm in 2010. Each primary care provider in the health plan is provided with a list of their high-risk patients with asthma and details about what factors made the patient high risk. The list is updated monthly. In 2019 the risk score was added as a just-in-time best practice alert for the Texas Children’s Hospital pulmonary and general pediatric outpatient practices’ electronic medical record. The algorithm incorporates clinically relevant data readily available from computerized health plan claims databases. It was developed from risk factors that were identified from previous research on asthma hospitalization and ED visit risk. Children hospitalized for asthma are at high risk for rehospitalization in the following year, and those with 2 or more hospitalizations in a year are at very high risk.^9-11 Those with an ED visit are at high risk. More than 2 ED visits in 1 year suggests higher risk. Overuse of short-acting β agonist medication is associated with future ED visits and hospitalizations for asthma,^12,13 and very high levels of overuse are associated with increased asthma mortality.¹⁴ Adherence to inhaled corticosteroid medications is associated with decreased asthma exacerbations and decreased asthma mortality, and their use attenuates the risk associated with short-acting β agonist overuse.^12,15,16 Having 3 or more prescribing providers for asthma medications is associated with increased risk of asthma hospitalization and ED visits.¹⁷ Having 2 or more oral corticosteroid dispensing events in 1 year defines persistent asthma by National Asthma Education and Prevention Program criteria.¹⁸

The aim of this study is to evaluate Texas Children’s Health Plan’s scoring system by examining the relationship of the baseline-year risk score with asthma hospitalization and ED visits in the following year.

METHODS

All Texas Children’s Health Plan pediatric members aged 1 year to less than 18 years who had a medical claim with a diagnosis of asthma (International Classification of Diseases, Tenth Revision codes J45 and J46) from 2016 to 2019 were identified. Data for analyses were extracted from health plan computerized databases. For each year, count of hospital admissions and ED visits was determined. Hospitalizations and ED visits are considered as being for asthma if the primary diagnosis was asthma or if the secondary diagnosis was asthma and the primary diagnosis was a respiratory illness. For each year, counts of short-acting β agonist canister equivalents and inhaled corticosteroid low-dose equivalents dispensed were determined. One short-acting β agonist canister equivalent was defined as either as a dispensing of one 200-puff canister of short-acting β agonist medication or 50 unit doses of a short-acting β agonist medication solution for inhalation via nebulizer.¹⁹ A low-dose equivalent of inhaled corticosteroid medication was calculated as the amount of medication that delivers a 1-month supply of what the National Asthma Education and Prevention Program defined as the upper limit of a low dose for a child aged 5 to 11 years.¹⁸ This criterion was chosen to account for differences in inhaled corticosteroid dosing. An oral corticosteroid dispensing event is defined as a pharmacy claim submitted for dispensing of prednisolone, prednisone, or dexamethasone. Demographic data extracted included member date of birth, type of coverage (Medicaid, Children’s Health Insurance Program [CHIP], other government-funded programs), and enrollment months. Ethnicity was determined as self-report of belonging to 1 of 5 categories (African American, Alaskan/American Indian, Asian/Pacific Islander, White, Hispanic) on program enrollment.

Texas Children’s Health Plan asthma risk score was initiated in 2010. Factors selected for inclusion were based on known risk factors for adverse asthma outcomes, including history of asthma hospitalization and ED visits,^9-11 overuse of short-acting β agonist medication,^12-14 underuse of inhaled corticosteroid medication, fragmentation of care, and loss to follow-up.^12,15-17 History of hospitalization for asthma was given greater weight because it is associated with high risk for rehospitalization.^9,10 As inhaled corticosteroid adherence can decrease the risk associated with short-acting β agonist overuse,¹² additional points were added for patients with short-acting β agonist overuse in the context of inhaled corticosteroid nonadherence. The factor weights for the risk score were refined between 2010 and 2015 based on experience with algorithm performance and clinician feedback, with the goal of ensuring that those considered most at risk rose to the top of the risk score. Analyses were then performed to determine if further changes in factor weights would improve predictive validity of the algorithm. We were not able to identify modifications to factor weights that could improve the algorithm’s predictive validity. Because 2 or more oral corticosteroid dispensing events per year are a criterion for persistent asthma per National Asthma Education and Prevention Program criteria,¹⁸ analyses for this study explored adding 1 point to the risk score for 2 or more oral corticosteroid dispensing events in the baseline year. We found that this improved the area under the receiver operating characteristic curve (AUC) for predicting asthma hospitalization in the follow-up year by a small amount (addition of 0.03, 0.01, and 0.01 to the AUC for the 2016, 2017, and 2018 baseline years, respectively). We retrospectively decided to add this criterion to the risk score. This change increased both the total number of patients identified as high risk (an additional 7% to 8% of patients with asthma) and increased the proportion of patients with hospitalization in the follow-up year who were identified by baseline-year asthma risk score (an additional 6% to 9% of hospitalizations).

Asthma risk scores (Table 1) were determined from events during the 12 months of the baseline years of 2016, 2017, and 2018. Outcomes were determined for the 1 year following the baseline year. We did not exclude from analyses patients who were included in analyses of a prior year’s data. Years were defined as December 15 of the prior year to December 14 of the named year. Age was determined as age on December 14 of the named year. The asthma risk score was based on health services utilization history in the past 12 months. The risk score calculation algorithm is described in Table 1. The risk score can range from –1 to a maximum of 18 points. The risk score was recalculated for these analyses in 2020 to ensure that a consistent scoring algorithm was applied and to account for claims lag.

Exclusion criteria included being 18 years or older at end of baseline year and the presence of a severe chronic disease, including cystic fibrosis, emphysema, other nonasthma severe chronic lung diseases, malignant neoplasms, glycogen storage and other storage diseases, severe neuromuscular diseases, severe brain damage, and major organ transplantation. These severe chronic diseases would likely have complex comorbidities unrelated to asthma. Members who had 8 months or less of health plan membership in the follow-up year and who were not a health plan member in December of the follow-up year were excluded from analyses for that year.

Statistical Methods

Statistical significance of bivariate comparisons for both ordinal and continuous variables was determined using the Wilcoxon rank sum test because their distributions were highly skewed. The χ² test was used to determine the statistical significance of differences in proportions. The AUC for receiver operating characteristic curves assessing baseline-year asthma risk score in predicting events in the follow-up year was determined. Logistic regression procedures were used to test for interactions (effect-modifying variables) and to determine odds ratios. Hospitalization and ED visits were reduced to bivariate (yes/no) data for analyses of outcome events in the follow-up year. Statistical significance was accepted as 2-tailed P < .05. Statistical analyses were conducted using Stata 10.0 (StataCorp). The study was approved by the institutional review board of Baylor College of Medicine.

RESULTS

We identified 107,811 unique members aged 1 year to less than 18 years with an asthma diagnosis for the years 2016 to 2018, of whom 52,583 (48.8%) self-identified as Hispanic, 17,716 (16.4%) as African American, 12,712 (11.8%) as White, 2559 (2.4%) as Asian/Pacific Islander, and 214 (0.2%) as Native American. A total of 22,027 (20.4%) declined to state a racial/ethnic group. In their first year of health plan membership between 2016 and 2018, 96,899 (89.9%) were enrolled under Medicaid (STAR), 10,542 (9.8%) were enrolled under CHIP, and 357 (0.3%) were enrolled under other government-paid insurance programs such as Supplemental Security Income (STAR Kids). Of those unique members, 53,528 met inclusion criteria for the 2016 baseline year; 53,000 for the 2017 baseline year; and 55,056 for the 2018 baseline year (Table 2). Risk scores ranged from –1 to 15. Of health plan members with an asthma diagnosis, 0.3% to 0.5% had a hospitalization for asthma and 2% to 4% had an ED visit for asthma during the follow-up years of 2017 to 2019.

In bivariate analyses, we found that hospitalization in baseline year was associated with increased risk of both hospitalization and ED visits in the follow-up year. Short-acting β agonist dispensing and number of oral corticosteroid dispensing events in the baseline year were higher in patients who required asthma hospitalization or ED visit in the follow-up year. The number of prescribing providers was higher in the baseline year for patients who required an asthma hospitalization or ED visit in the follow-up year. Similar results were found for each baseline and follow-up year analyzed (eAppendix Table 1 [A-D] [eAppendix available at ajmc.com]).

The Texas Children’s Health Plan asthma risk score was able to predict risk of asthma hospitalization and ED visits in the follow-up year with AUCs for hospitalization in the follow-up year of 0.76, 0.72, and 0.71 (2016, 2017, and 2018 baseline years, respectively) and for ED visits in the follow-up year of 0.69, 0.67, and 0.67 (2016, 2017, and 2018 baseline years, respectively) (eAppendix Table 2 and eAppendix Figure).

We analyzed for differences in effect by subgroups. For asthma risk score predicting asthma hospitalization in the follow-up year, the AUC for the group aged 1 to less than 3 years was lower than for older age groups for baseline years 2016 and 2017 (P < .05 for interaction); the difference was not statistically significant for 2018. With stratification by age group, the AUCs for hospitalization in the follow-up year for the group aged 3 to less than 18 years were 0.79, 0.74, and 0.72 for the 2016, 2017, and 2018 baseline years, respectively (Table 3).

The risk score was able to stratify patients with asthma by level of risk of asthma hospitalization. For the 3 baseline/follow-up years analyzed, in the group aged 3 to less than 18 years, relative to those with a risk score of 0 or –1 in the baseline year, the odds ratios for asthma hospitalization in the follow-up year ranged from 2.30 to 3.49 for those with a risk score of 1 or 2 and odds ratios of 130 to 157 for those with a risk score of 9 or greater, with 16% to 18% of patients having an asthma hospitalization in the follow-up year (Table 4). Those with a risk score of 1 or greater accounted for 20% to 23% of pediatric members with asthma, but for those in the group aged 3 to less than 18 years, 59% to 71% of asthma hospitalizations in the follow-up year. For those in the group aged 1 to less than 3 years, those with a risk score of 1 or greater accounted for 45% to 55% of asthma hospitalizations (Table 4).

Relative to those with a risk score of 0 or –1 in the baseline year in the group aged 3 to less than 18 years, the odds ratios for asthma ED visit in the follow-up year ranged from 2.84 to 3.15 for those with a risk score of 1 or 2 and from 22.6 to 43.6 for those with a risk score of 9 or greater. For those with a risk score of 9 or greater, 32% to 46% had an ED visit for asthma in the follow-up year. The 20% to 23% of pediatric members with a risk score of 1 or greater accounted for 53% to 56% of ED visits in the follow-up year for the group aged 3 to less than 18 years and 47% to 51% of ED visits in the follow-up year for the group aged 1 to less than 3 years (Table 5).

DISCUSSION

Texas Children’s Health Plan’s asthma high-risk score differentiates patients with low-risk compared with high-risk asthma and stratifies patients by their level of risk for hospitalization and ED visits in the follow-up year. The results are consistent year to year. The risk score was developed and validated in a pediatric Medicaid managed care population. It uses 7 variables that would be available in the claims and pharmacy databases of most managed care organizations. The risk score was built from clinically relevant variables with weights based on what is known about risk for adverse asthma outcomes. Identification of patients at risk for potentially preventable asthma hospitalizations provides opportunity for targeted intervention. Rather than fixed cutoffs, we identified a continuum of risk, which can be useful in clinical decision-making and in resource allocation decisions.

The risk score, along with the factors that make the patient high risk, can be provided as just-in-time information to providers to assist medical decision-making.²⁰ A high-risk asthma panel report can allow a provider group to reach out to high-risk patients. A health plan can consider the highest-risk patients with asthma for care management and/or home visits.^3,21

The risk score reflects the previous 12 months of utilization. A patient who had a risk-defining event will stay on the high-risk list until 12 months have passed even if actions have been taken to bring their asthma under control. Respiratory viral infections are an important trigger of asthma exacerbations^22,23; large variation in prevalence of respiratory viral infections (such as seen during the COVID-19 pandemic) may affect predictive ability of the risk score.

We found that the risk score performed better for those aged 3 to less than 18 years than for those aged 1 to less than 3 years. However, in children aged 1 to less than 3 years, the score was able to stratify risk in a clinically important manner.

Our finding that previous hospitalization, previous ED visits, short-acting β agonist overuse, and having multiple prescribing providers are associated with increased risk of asthma ED visit and hospitalization is consistent with previous research.^9-15

Previous research from Kaiser Permanente found that risk prediction models incorporating hospitalizations, β agonist dispensing, anti-inflammatory dispensing, and number of prescribers performed better than use of 1 parameter alone and, although the asthma case definition differed from our study and their data were from 1994 and 1998, the AUC values obtained were similar to our study’s findings.^17,24

A previous study examined the predictive ability of the ratio of asthma controller medication to total asthma medication (controller + reliever) dispensing in children aged 4 to 17 years found that those with a medication ratio of less than 0.5 for commercially insured and less than 0.7 for Medicaid-insured patients were at increased risk for asthma exacerbations.²⁵

A prediction model that was developed utilizing data from 1998 and 1999 from Kaiser Permanente Southern California, using backward stepwise logistic regression for potential predictors, identified significant predictors as history of prior-year hospitalizations, β agonist dispensing, anti-inflammatory dispensing, number of prescribers, and age, obtaining an AUC of 0.78 in that model.²⁶

Machine learning algorithms developed using more than 200 variables, utilizing data from Kaiser Permanente Southern California and from Intermountain Healthcare databases, achieved AUCs of 0.82 and 0.86, respectively.^27,28 Many of the variables used in these models would not be available in the typical health insurance program’s databases.

Limitations

Limitations of our study include that the risk score was validated on a managed Medicaid and CHIP-insured pediatric population. Further research is needed to evaluate risk score performance in commercially insured populations. Asthma diagnosis was based on diagnosis codes on insurance claims submitted to the health plan, thus it is likely that there is both overdiagnosis and underdiagnosis by health care providers. We have previously found that patterns of oral corticosteroid dispensing to pediatric members of Texas Children’s Health Plan suggest overprescribing by primary care providers.²⁹ We speculate that the oral corticosteroid dispensing criterion may have greater predictive value in communities that are more selective in oral corticosteroid prescribing. Our study excluded those with less than 9 months of health plan membership in the follow-up year. Patients who have difficulty maintaining consistent health plan membership may be at higher risk. Fragmentation of care was assessed by multiple (3 or more) prescribing providers, but some providers may share a practice and such cooperative relationships would not be detected. Systemic corticosteroid medication administered at an ED visit or hospitalization would not be captured. Texas Children’s Health Plan uses the asthma risk score to provide feedback to physicians and to prioritize care management interventions, with the asthma high-risk score added as a best practice alert in the Texas Children’s Hospital electronic medical record in 2019. Different outcomes might have been observed if the information was not available to providers.

CONCLUSIONS

The Texas Children’s Health Plan asthma risk score can identify children with asthma who have an increased risk of asthma ED visits and hospitalization in the following year. The risk score stratifies patients by level of risk. In predicting hospitalization in the follow-up year, the risk score performs better in the group aged 3 to less than 18 years than in the group aged 1 to less than 3 years. This knowledge can be used to inform targeted asthma quality improvement efforts.

Acknowledgments

Jaennie Yoon, MA, MPH, assisted with data extraction from health plan computerized databases. Hua Chen, MD, PhD, reviewed the manuscript and provided helpful suggestions.

Author Affiliations: Medical Affairs, Texas Children’s Health Plan (HJF), Houston, TX; Pulmonary Medicine Service (HJF, RM, MMS) and Information Technology (EAS), Texas Children’s Hospital, Houston, TX; Section of Pulmonology, Department of Pediatrics, Baylor College of Medicine (HJF, RM, MMS), Houston, TX.

Source of Funding: None.

Author Disclosures: Dr Farber serves as associate medical director of Texas Children’s Health Plan. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (HJF, EAS, RM, MMS); acquisition of data (EAS, RM); analysis and interpretation of data (HJF, MMS); drafting of the manuscript (HJF, RM, MMS); critical revision of the manuscript for important intellectual content (HJF, EAS, RM, MMS); and statistical analysis (HJF).

Address Correspondence to: Harold J. Farber, MD, MSPH, Pulmonary Medicine Service, Texas Children’s Hospital, 6701 Fannin St, Suite 1040.00, Houston, TX 77030. Email: hjfarber@texaschildrens.org.

REFERENCES

1. McDermott KW, Jiang HJ. Characteristics and costs of potentially preventable inpatient stays, 2017. Healthcare Cost and Utilization Project statistical brief No. 259. Agency for Healthcare Research and Quality. June 2020. Accessed January 13, 2021. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb259-Potentially-Preventable-Hospitalizations-2017.pdf

2. Barnes PJ, Jonsson B, Klim JB. The costs of asthma. Eur Respir J. 1996;9(4):636-642. doi:10.1183/09031936.96.09040636

3. Farber HJ. Care management for childhood asthma: what works? Pediatr Asthma Allergy Immunol. 2009;22(3):105-110. doi:10.1089/pai.2009.0014

4. Giese JK. Evidence-based pediatric asthma interventions and outcome measures in a healthy homes program: an integrative review. J Asthma. 2019;56(6):662-673. doi:10.1080/02770903.2018.1472279

5. Campbell JD, Brooks M, Hosokawa P, Robinson J, Song L, Krieger J. Community health worker home visits for Medicaid-enrolled children with asthma: effects on asthma outcomes and costs. Am J Public Health. 2015;105(11):2366-2372. doi:10.2105/AJPH.2015.302685

6. Zeiger RS, Heller S, Mellon MH, Wald J, Falkoff R, Schatz M. Facilitated referral to asthma specialist reduces relapses in asthma emergency room visits. J Allergy Clin Immunol. 1991;87(6):1160-1168. doi:10.1016/0091-6749(91)92162-t

7. Wagner EH, Austin BT, Von Korff M. Organizing care for patients with chronic illness. Milbank Q. 1996;74(4):511-544.

8. HEDIS 2020 Volume 2: Technical Specifications for Health Plans. National Committee for Quality Assurance; 2019.

9. Farber HJ. Risk of readmission to hospital for pediatric asthma. J Asthma. 1998;35(1):95-99. doi:10.3109/02770909809055410

10. Lasmar LMLBF, Camargos PAM, Goulart EMA, Sakurai E. Risk factors for multiple hospital admissions among children and adolescents with asthma. J Bras Pneumol. 2006;32(5):391-399.

11. Gaspar AP, Morais-Almeida MA, Pires GC, et al. Risk factors for asthma admissions in children. Allergy Asthma Proc. 2002;23(5):295-301.

12. Farber HJ, Chi FW, Capra A, et al. Use of asthma medication dispensing patterns to predict risk of adverse health outcomes: a study of Medicaid-insured children in managed care programs. Ann Allergy Asthma Immunol. 2004;92(3):319-328. doi:10.1016/S1081-1206(10)61569-4

13. Schatz M, Zeiger RS, Vollmer WM, et al. Validation of a beta-agonist long-term asthma control scale derived from computerized pharmacy data. J Allergy Clin Immunol. 2006;117(5):995-1000. doi:10.1016/j.jaci.2006.01.053

14. Suissa S, Ernst P, Boivin JF, et al. A cohort analysis of excess mortality in asthma and the use of inhaled beta-agonists. Am J Respir Crit Care Med. 1994;149(3, pt 1):604-610. doi:10.1164/ajrccm.149.3.8118625

15. Castro-Rodriguez JA, Rodrigo GJ. The role of inhaled corticosteroids and montelukast in children with mild-moderate asthma: results of a systematic review with meta-analysis. Arch Dis Child. 2010;95(5):365-370. doi:10.1136/adc.2009.169177

16. Suissa S, Ernst P, Benayoun S, Baltzan M, Cai B. Low-dose inhaled corticosteroids and the prevention of death from asthma. N Engl J Med. 2000;343(5):332-336. doi:10.1056/NEJM200008033430504

17. Lieu TA, Quesenberry CP, Sorel ME, Mendoza GR, Leong AB. Computer-based models to identify high-risk children with asthma. Am J Respir Crit Care Med. 1998;157(4, pt 1):1173-1180. doi:10.1164/ajrccm.157.4.9708124

18. National Asthma Education and Prevention Program. Expert Panel Report 3: Guidelines for the Diagnosis and Management of Asthma. National Heart, Lung, and Blood Institute; 2007.

19. Idris AH, McDermott MF, Raucci JC, Morrabel A, McGorray S, Hendeles L. Emergency department treatment of severe asthma: metered-dose inhaler plus holding chamber is equivalent in effectiveness to nebulizer. Chest. 1993;103(3):665-672. doi:10.1378/chest.103.3.665

20. Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness. JAMA. 2002;288(14):1775-1779. doi:10.1001/jama.288.14.1775

21. Marshall ET, Guo J, Flood E, Sandel MT, Sadof MD, Zotter JM. Home visits for children with asthma reduce Medicaid costs. Prev Chronic Dis. 2020;17:E11. doi:10.5888/pcd17.190288

22. Jartti T, Bønnelykke K, Elenius V, Feleszko W. Role of viruses in asthma. Semin Immunopathol. 2020;42(1):61-74. doi:10.1007/s00281-020-00781-5

23. Gern JE. Viral respiratory infection and the link to asthma. Pediatr Infect Dis J. 2008;27(suppl 10):S97-S103. doi:10.1097/INF.0b013e318168b718

24. Schatz M, Zeiger RS, Vollmer WM, et al. Development and validation of a medication intensity scale derived from computerized pharmacy data that predicts emergency hospital utilization for persistent asthma. Am J Manag Care. 2006;12(8):478-484.

25. Stanford RH, Shah MB, D’Souza AO, Schatz M. Predicting asthma outcomes in commercially insured and Medicaid populations? Am J Manag Care. 2013;19(1):60-67.

26. Schatz M, Cook EF, Joshua A, Petitti D. Risk factors for asthma hospitalizations in a managed care organization: development of a clinical prediction rule. Am J Manag Care. 2003;9(8):538-547.

27. Luo G, Nau CL, Crawford WW, et al. Developing a predictive model for asthma-related hospital encounters in patients with asthma in a large, integrated health care system: secondary analysis. JMIR Med Inform. 2020;8(11):e22689. doi:10.2196/22689

28. Luo G, He S, Stone BL, Nkoy FL, Johnson MD. Developing a model to predict hospital encounters for asthma in asthmatic patients: secondary analysis. JMIR Med Inform. 2020;8(1):e16080. doi:10.2196/16080

29. Farber HJ, Silveira EA, Vicere DR, Kothari VD, Giardino AP. Oral corticosteroid prescribing for children with asthma in a Medicaid managed care program. Pediatrics. 2017;139(5):e20164146. doi:10.1542/peds.2016-4146