Demographic and Risk-Factor Differences between Users and Non-Users of Unscheduled Healthcare among Pediatric Outpatients with Persistent Asthma
Abstract
:1. Introduction
1.1. A “Holistic Framework” for Measuring SME in Childhood Asthma
- Extrinsic socioecological factors (individual demographic and risk factors, interpersonal factors, socioeconomic factors, health system factors including provider–patient/family communication on asthma management, community-level factors, and environmental-level factors), can impact:
- Intrinsic self-agency factors (patient/family activation in asthma management); to ultimately influence
- SME in childhood asthma. SME in turn is defined by both primary outcomes (unscheduled healthcare use in children with asthma) and intermediate outcomes (asthma medication adherence and symptom control).
1.2. Study Objective
2. Methods
- Individual demographic characteristics, including: age (≤12 years or >12 years); gender (male or female); race (Caucasian, African American, or Hispanic); insurance (Medicaid, private, Tricare/military, or self-pay); NHLBI asthma severity category (mild, moderate, or severe persistent);
- Individual risk factors, including: body-mass index (BMI), defined as normal (<85%), overweight (85–95%), or obese (>95%); prescription for an asthma biologic therapy, i.e., monoclonal antibody (yes or no); prescription for allergen subcutaneous immunotherapy (SCIT, yes or no); number of ‘no-shows’ for scheduled clinic visits;
- Primary outcomes of SME, i.e., healthcare use, which was assessed in terms of: a) ED visits; b) inpatient hospitalization; c) pediatric intensive care unit (PICU) hospitalization; d) unscheduled outpatient clinic visits initiated outside of scheduled visits; e) urgent care visits; and f) scheduled outpatient clinic visits;
- Intermediate outcomes of SME, including asthma symptom control measured by age-appropriate Childhood Asthma Control Test (CACTTM)or Asthma Control Test (ACTTM), noted as poor if score was ≤19, or good if >19; medication adherence, estimated as "low", "fair", or "high" from medical record documentation of prescription adherence during records review.
3. Results
4. Discussion
4.1. Strengths and Limitations of Retrospective Study
- It provides an 18-month per patient window into unscheduled healthcare use among all eligible children who serially visited an outpatient specialty clinic for persistent pediatric asthma follow-up care over eight consecutive months (February–September 2019)
- Since we evaluated children with higher asthma severity, the sample provides a basis for understanding unscheduled healthcare use in a more severely ill population.
- The practical relevance of a sample that is representative of a higher-severity patient base is best explained by the 80/20 Pareto principle on healthcare use, i.e., that 80% of unscheduled (costly) healthcare use can be attributed to the 20% severely-ill populace.
4.2. Implications for Practice and Future Research
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Centers for Disease Control and Prevention (CDC). Asthma self-management education and environmental management: Approaches to enhancing reimbursement. In National Asthma Control Program; CDC: Atlanta, GA, USA, 2014. [Google Scholar]
- National Institutes of Health National Asthma Education & Prevention Program. Guidelines for the Diagnosis and Management of Asthma. Expert Panel Report 3; United States Department of Health and Human Services: Washington, DC, USA, 2007.
- Pinnock, H.; Parke, H.L.; Panagioti, M.; Daines, L.; Pearce, G.; Epiphaniou, E.; Bower, P.; Sheikh, A.; Griffiths, C.J.; Taylor, S.J. Systematic meta-review of supported self-management for asthma: A healthcare perspective. BMC Med. 2017, 15, 64. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pinnock, H.; Epiphaniou, E.; Pearce, G.; Parke, H.; Greenhalgh, T.; Sheikh, A.; Griffiths, C.J.; Taylor, S.J. Implementing supported self-management for asthma: A systematic review and suggested hierarchy of evidence of implementation studies. BMC Med. 2015, 13, 127. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pinnock, H. Supported self-management for asthma. Breathe 2015, 11, 98–109. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pinnock, H.; Thomas, M. Does self-management prevent severe exacerbations? Curr. Opin. Pulm. Med. 2015, 21, 95–102. [Google Scholar] [CrossRef] [PubMed]
- Young, H.N.; Larson, T.L.; Cox, E.D.; Moreno, M.A.; Thorpe, J.M.; MacKinnon, N.J. The Active Patient Role and Asthma Outcomes in an Underserved Rural Community. J. Rural Health 2014, 30, 121–127. [Google Scholar] [CrossRef] [Green Version]
- Young, H.N.; Rios, M.L.; Brown, R. How Does Patient-Provider Communication Influence Adherence to Asthma Medications? Patient Educ. Couns. 2017, 100, 696–702. [Google Scholar] [CrossRef]
- Rangachari, P. A framework for measuring self-management effectiveness and healthcare use among pediatric asthma patients & families. J. Asthma Allergy 2017, 10, 111–122. [Google Scholar] [PubMed] [Green Version]
- Rangachari, P.; May, K.R.; Stepleman, L.M.; Tingen, M.S.; Looney, S.; Liang, Y.; Rockich-Winston, N.; Rethemeyer, R.K. Measurement of key constructs in a holistic framework for assessing self-management effectiveness of pediatric asthma. Int. J. Environ. Res. Public Health 2019, 16, 3060. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Oraka, E.; Iqbal, S.; Flanders, W.D.; Brinker, K.; Garbe, P. Racial and ethnic disparities in current asthma and emergency department visits: Findings from the National Health Interview Survey, 2001–2010. J. Asthma 2013, 50, 488–496. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gupta, R.S.; Carrión-Carire, V.; Weiss, K.B. The widening black/white gap in asthma hospitalizations and mortality. J. Allergy Clin. Immunol. 2006, 117, 351–358. [Google Scholar] [CrossRef] [PubMed]
- Gushue, C.; Miller, R.; Sheikh, S.; Allen, E.D.; Tobias, J.D.; Hayes, D., Jr.; Tumin, D. Gaps in health insurance coverage and emergency department use among children with asthma. J. Asthma 2019, 56, 1070–1078. [Google Scholar] [CrossRef] [PubMed]
- Gindi, R.M.; Cohen, R.A.; Kirzinger, W.K. Emergency Room Use among Adults: Early Release of Estimates from the National Health Interview Survey. 2012. Available online: https://www.cdc.gov/nchs/data/nhis/earlyrelease/emergency_room_use_january-june_2011.pdf (accessed on 10 March 2020).
- Diener-West, M. The Chi-Square Statistic. Johns Hopkins University School of Public Health. 2008. Available online: http://ocw.jhsph.edu/courses/fundepiii/PDFs/Lecture17.pdf (accessed on 8 October 2019).
- DeCamp, L.R.; Showell, N.; Godage, S.K.; Leifheit, K.M.; Valenzuela-Araujo, D.; Shah, H.; Polk, S. Parent activation and pediatric primary care outcomes for vulnerable children: A mixed methods study. Patient Educ. Couns. 2019, 102, 2254–2262. [Google Scholar] [CrossRef] [PubMed]
- Hibbard, J.H.; Stockard, J.; Mahoney, E.R.; Tusler, M. Development of the Patient Activation Measure (PAM): Conceptualizing and Measuring Activation in Patients and Consumers. Health Serv. Res. 2004, 39, 1004–1026. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hibbard, J.H.; Stockard, J.; Mahoney, E.R.; Tusler, M. Development and testing of a short form of the patient activation measure. Health Serv. Res. 2005, 40, 1918–1930. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Prey, J.E.; Qian, M.; Restaino, S.; Hibbard, J.; Bakken, S.; Schnall, R.; Rothenberg, G.; Vawdrey, D.K.; Creber, R.M. Reliability and validity of the patient activation measure in hospitalized patients. Patient Educ. Couns. 2016, 99, 2026–2033. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Street, R.L.; Makoul, G.; Arora, N.K.; Epstein, R.M. How does communication heal? Pathways linking clinician-patient communication to health outcomes. Patient Educ. Couns. 2009, 74, 295–301. [Google Scholar] [CrossRef] [PubMed]
- Zandbelt, L.C.; Smets, E.M.; Oort, F.J.; Godfried, M.H.; Haes, H.C. Medical specialists’ patient-centered communication and patient-reported outcomes. Med. Care 2007, 45, 330–339. [Google Scholar] [CrossRef] [PubMed]
- Zill, J.M.; Christalle, E.; Müller, E.; Härter, M.; Dirmaier, J.; Scholl, I. Measurement of Physician-Patient Communication—A Systematic Review. PLoS ONE 2014, 9, e112637. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zolnierek, K.B.H.; DiMatteo, M.R. Physician communication and patient adherence to treatment: A meta-analysis. Med. Care 2009, 47, 826–834. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Full Dataset | ||||
---|---|---|---|---|
"Users" (n = 25) | "Non-Users" (n = 34) | Total (n = 59) | % of Total | |
Individual Patient Demographics | ||||
Age: ≤12 years | 11 | 21 | 32 | 54% |
Age: >12 years | 14 | 13 | 27 | 46% |
Gender: Male | 17 | 24 | 41 | 69% |
Gender: Female | 8 | 10 | 18 | 31% |
Race: Caucasian | 4 | 7 | 11 | 19% |
Race: African American | 17 | 24 | 41 | 69% |
Race: Hispanic | 4 | 3 | 7 | 12% |
Insurance: Medicaid | 17 | 23 | 40 | 68% |
Insurance: Private | 7 | 9 | 16 | 27% |
Insurance: Tricare | 1 | 0 | 1 | 2% |
Insurance: Self-Pay | 0 | 2 | 2 | 3% |
Asthma Severity: MILD Persistent | 4 | 16 | 20 | 34% |
Asthma Severity: MODERATE Persistent | 11 | 14 | 25 | 42% |
Asthma Severity: SEVERE Persistent | 10 | 4 | 14 | 24% |
Individual Patient Risk Factors | ||||
BMI: Normal | 7 | 16 | 23 | 39% |
BMI: Overweight | 8 | 5 | 13 | 22% |
BMI: Obese | 10 | 13 | 23 | 39% |
Asthma Biologic-Therapy: Yes | 5 | 1 | 6 | 10% |
Asthma Biologic-Therapy: No | 20 | 33 | 53 | 90% |
Allergen SCIT: Yes | 4 | 9 | 13 | 22% |
Allergen SCIT: No | 21 | 25 | 46 | 78% |
No-Shows for Scheduled Clinic: ZERO | 5 | 14 | 19 | 32% |
No-Shows for Scheduled Clinic: ONE | 5 | 7 | 12 | 20% |
No-Shows for Scheduled Clinic: TWO | 2 | 5 | 7 | 12% |
No-Shows for Scheduled Clinic: THREE | 1 | 4 | 5 | 8% |
No-Shows for Scheduled Clinic: FOUR | 5 | 4 | 9 | 15% |
No-Shows for Scheduled Clinic: FIVE | 3 | 0 | 3 | 5% |
No-Shows for Scheduled Clinic: SIX | 1 | 0 | 1 | 2% |
No-Shows for Scheduled Clinic: SEVEN | 1 | 0 | 1 | 2% |
No-Shows for Scheduled Clinic: EIGHT | 2 | 0 | 2 | 3% |
Intermediate Outcomes of SME | ||||
Medication Adherence: Low | 7 | 10 | 17 | 29% |
Medication Adherence: Fair | 7 | 5 | 12 | 20% |
Medication Adherence: High | 11 | 19 | 30 | 51% |
Asthma Symptom Control: Poor Control | 13 | 16 | 29 | 49% |
Asthma Symptom Control: Good Control | 12 | 18 | 30 | 51% |
Full Dataset (n = 59) | Mild Persistent (n = 20) | Moderate Persistent (n = 25) | Severe Persistent (n = 14) | |
---|---|---|---|---|
p-Value | p-Value | p-Value | p-Value | |
Individual Patient Demographics | ||||
Age | 0.148 | 0.549 ¥ | 0.413 ¥ | 0.176 ¥ |
Gender | 0.831 | 1.000 ¥ | 0.389 ¥ | 1.000 ¥ |
Race | 1.000 ¥ | 0.514 | 1.000 ¥ | 0.203 ¥ |
Insurance | 0.538 ¥ | 0.727 ¥ | 1.000 ¥ | 1.000 ¥ |
Asthma Severity | 0.008 * | N/A | N/A | N/A |
Individual Patient Risk Factors | ||||
Body-Mass Index (BMI) | 0.153 | 0.418 ¥ | 0.387 ¥ | 0.097 ¥ |
Asthma Biologic Prescription | 0.074 ¥ | 0.793 ¥ | 0.440 ¥ | 1.000 ¥ |
Allergen SCIT Prescription | 0.334 ¥ | 1.000 ¥ | 1.000 ¥ | 0.874 ¥ |
No-Shows for Scheduled Clinic | 0.072 ¥ | 0.896 ¥ | 0.822 ¥ | 0.844 ¥ |
Intermediate Outcomes of SME | ||||
Medication Adherence | 0.434 | 0.636 ¥ | 0.601 ¥ | 0.748 ¥ |
Asthma Symptom Control | 0.512 | 1.000 ¥ | 0.684 ¥ | 0.222 ¥ |
Logistic Regression Dependent Variable: "User" = 1, "Non-user" = 0 | |||
---|---|---|---|
Parameter | Category ¥ | Coefficient Estimate | p-Value |
Asthma Severity | Mild | −1.4871 | 0.0168 * |
Moderate | 0.0039 | 0.9936 | |
Age | Age ≤ 12 | 0.2364 | 0.5612 |
Gender | Female | −3.2491 | 0.9736 |
Race | African American | 3.0411 | 0.9753 |
Hispanic | 2.4757 | 0.9857 | |
BMI | Normal | −0.4561 | 0.3888 |
Overweight | 0.6793 | 0.2301 | |
Allergen SCIT | No | −0.3441 | 0.5341 |
Medication Adherence | Fair | 0.2361 | 0.7078 |
High | −0.4458 | 0.3736 | |
Asthma Symptom Control | Good Control | 0.2308 | 0.5354 |
Unscheduled Healthcare Use | Frequency | % |
---|---|---|
ED Visit | ||
ED Visit: ZERO | 39 | 66% |
ED Visit: ONE | 13 | 22% |
ED Visit: TWO | 2 | 3% |
ED Visit: THREE | 1 | 2% |
ED Visit: FOUR | 2 | 3% |
ED Visit: SIX | 1 | 2% |
ED Visit: EIGHT | 1 | 2% |
Inpatient Admissions | ||
Inpatient Admission: ZERO | 51 | 86% |
Inpatient Admission: ONE | 8 | 14% |
PICU Stay | ||
PICU Stay: ZERO | 56 | 95% |
PICU Stay: ONE | 1 | 2% |
PICU Stay: THREE | 1 | 2% |
PICU Stay: FOUR | 1 | 2% |
Unscheduled Outpatient Clinic Visit | ||
Unscheduled Clinic Visit: ZERO | 55 | 93% |
Unscheduled Clinic Visit: ONE | 4 | 7% |
Urgent Care Visit | ||
Urgent Care Visit: ZERO | 47 | 80% |
Urgent Care Visit: ONE | 8 | 14% |
Urgent Care Visit: TWO | 1 | 2% |
Urgent Care Visit: THREE | 1 | 2% |
Urgent Care Visit: FOUR | 1 | 2% |
Urgent Care Visit: FIVE | 1 | 2% |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Rangachari, P.; Griffin, D.D.; Ghosh, S.; May, K.R. Demographic and Risk-Factor Differences between Users and Non-Users of Unscheduled Healthcare among Pediatric Outpatients with Persistent Asthma. Int. J. Environ. Res. Public Health 2020, 17, 2704. https://doi.org/10.3390/ijerph17082704
Rangachari P, Griffin DD, Ghosh S, May KR. Demographic and Risk-Factor Differences between Users and Non-Users of Unscheduled Healthcare among Pediatric Outpatients with Persistent Asthma. International Journal of Environmental Research and Public Health. 2020; 17(8):2704. https://doi.org/10.3390/ijerph17082704
Chicago/Turabian StyleRangachari, Pavani, Dixie D. Griffin, Santu Ghosh, and Kathleen R. May. 2020. "Demographic and Risk-Factor Differences between Users and Non-Users of Unscheduled Healthcare among Pediatric Outpatients with Persistent Asthma" International Journal of Environmental Research and Public Health 17, no. 8: 2704. https://doi.org/10.3390/ijerph17082704