Association of Affordable Care Act Implementation With Ambulance Utilization for Asthma Emergencies in New York City, 2008-2018 | Asthma | JAMA Network Open | JAMA Network
[Skip to Navigation]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Navigation Landing]
Figure 1.  Interrupted Time Series Analysis of EMS Dispatches in New York City at the Citywide Level Before and After Implementation of Insurance Expansion on January 1, 2014
Interrupted Time Series Analysis of EMS Dispatches in New York City at the Citywide Level Before and After Implementation of Insurance Expansion on January 1, 2014

Abbreviations: EMS, emergency medical services.

Figure 2.  Geographical Analysis Comparing Change in Uninsured Rate and Change in Rate of Asthma Dispatches Within Zip Codes in New York City
Geographical Analysis Comparing Change in Uninsured Rate and Change in Rate of Asthma Dispatches Within Zip Codes in New York City

Larger circles indicate greater reduction in uninsured rate, while orange color indicates greater reduction in rate of asthma dispatches.

Table 1.  EMS Dispatch Data and Population Demographic Characteristics Before and After Implementation of the ACA at the Citywide Level
EMS Dispatch Data and Population Demographic Characteristics Before and After Implementation of the ACA at the Citywide Level
Table 2.  Multivariable Linear Regression Model for Asthma Emergency Medical Services Dispatch Rate at the Citywide Level
Multivariable Linear Regression Model for Asthma Emergency Medical Services Dispatch Rate at the Citywide Level
Table 3.  Zip Code–level Sensitivity Analysis From a Generalized Estimating Equation Model for Rate of Emergency Medical Services Dispatches for Asthma Emergencies
Zip Code–level Sensitivity Analysis From a Generalized Estimating Equation Model for Rate of Emergency Medical Services Dispatches for Asthma Emergencies
1.
Wang  HE, Mann  NC, Jacobson  KE,  et al.  National characteristics of emergency medical services responses in the United States.   Prehosp Emerg Care. 2013;17(1):8-14. doi:10.3109/10903127.2012.722178PubMedGoogle ScholarCrossref
2.
Courtemanche  C, Friedson  A, Koller  AP, Rees  DI.  The Affordable Care Act and ambulance response times.   J Health Econ. 2019;67:102213. doi:10.1016/j.jhealeco.2019.05.010PubMedGoogle Scholar
3.
Jena  AB, Mann  NC, Wedlund  LN, Olenski  A.  Delays in emergency care and mortality during major US marathons.   N Engl J Med. 2017;376(15):1441-1450. doi:10.1056/NEJMsa1614073PubMedGoogle ScholarCrossref
4.
Wilde  ET.  Do emergency medical system response times matter for health outcomes?   Health Econ. 2013;22(7):790-806. doi:10.1002/hec.2851PubMedGoogle ScholarCrossref
5.
Ahn  KO, Shin  SD, Cha  WC, Jun  C, Lee  TS, Pirrallo  RG.  A model for the association of the call volume and the unavailable-for-response interval on the delayed ambulance response for out-of-hospital cardiac arrest using a geographic information system.   Prehosp Emerg Care. 2010;14(4):469-476. doi:10.3109/10903127.2010.497895PubMedGoogle ScholarCrossref
6.
Guth  M, Garfield  R, Rudowitz  R. The effects of Medicaid expansion under the ACA: updated findings from a literature review. Kaiser Family Foundation; 2020. Accessed May 13, 2020. https://www.kff.org/medicaid/report/the-effects-of-medicaid-expansion-under-the-aca-updated-findings-from-a-literature-review/
7.
Taubman  SL, Allen  HL, Wright  BJ, Baicker  K, Finkelstein  AN.  Medicaid increases emergency-department use: evidence from Oregon’s Health Insurance Experiment.   Science. 2014;343(6168):263-268. doi:10.1126/science.1246183PubMedGoogle ScholarCrossref
8.
Ginde  AA, Lowe  RA, Wiler  JL.  Health insurance status change and emergency department use among US adults.   Arch Intern Med. 2012;172(8):642-647. doi:10.1001/archinternmed.2012.34PubMedGoogle ScholarCrossref
9.
Sharma  AI, Dresden  SM, Powell  ES, Kang  R, Feinglass  J.  Emergency department visits and hospitalizations for the uninsured in Illinois before and after Affordable Care Act insurance expansion.   J Community Health. 2017;42(3):591-597. doi:10.1007/s10900-016-0293-4PubMedGoogle ScholarCrossref
10.
Courtemanche  C, Friedson  AI, Rees  DI.  Association of ambulance use in New York City with the implementation of the patient protection and Affordable Care Act.   JAMA Netw Open. 2019;2(6):e196419. doi:10.1001/jamanetworkopen.2019.6419PubMedGoogle Scholar
11.
Gotanda  H, Kominski  G, Tsugawa  Y.  Association between the ACA medicaid expansions and primary care and emergency department use during the first 3 years.   J Gen Intern Med. 2020;35(3):711-718. doi:10.1007/s11606-019-05458-wPubMedGoogle ScholarCrossref
12.
Klein  EY, Levin  S, Toerper  MF,  et al.  The effect of Medicaid expansion on utilization in Maryland emergency departments.   Ann Emerg Med. 2017;70(5):607-614.e1. doi:10.1016/j.annemergmed.2017.06.021PubMedGoogle ScholarCrossref
13.
Hernandez-Boussard  T, Burns  CS, Wang  NE, Baker  LC, Goldstein  BA.  The Affordable Care Act reduces emergency department use by young adults: evidence from three states.   Health Aff (Millwood). 2014;33(9):1648-1654. doi:10.1377/hlthaff.2014.0103PubMedGoogle ScholarCrossref
14.
Smulowitz  PB, Lipton  R, Wharam  JF,  et al.  Emergency department utilization after the implementation of Massachusetts health reform.   Ann Emerg Med. 2011;58(3):225-234.e1. doi:10.1016/j.annemergmed.2011.02.020PubMedGoogle ScholarCrossref
15.
Hodgson  K, Deeny  SR, Steventon  A.  Ambulatory care-sensitive conditions: their potential uses and limitations.   BMJ Qual Saf. 2019;28(6):429-433. doi:10.1136/bmjqs-2018-008820PubMedGoogle ScholarCrossref
16.
Markovitz  BP, Andresen  EM.  Lack of insurance coverage and urgent care use for asthma: a retrospective cohort study.   BMC Public Health. 2006;6:14. doi:10.1186/1471-2458-6-14PubMedGoogle ScholarCrossref
17.
Gushue  C, Miller  R, Sheikh  S,  et al  Gaps in health insurance coverage and emergency department use among children with asthma.   J Asthma. 2019;56(10):1070-1078. doi:10.1080/02770903.2018.1523929Google ScholarCrossref
18.
Szilagyi  PG, Dick  AW, Klein  JD,  et al.  Improved asthma care after enrollment in the State Children’s Health Insurance Program in New York.   Pediatrics. 2006;117(2):486-496. doi:10.1542/peds.2005-0340PubMedGoogle ScholarCrossref
19.
Sin  DD, Svenson  LW, Cowie  RL, Man  SFP.  Can universal access to health care eliminate health inequities between children of poor and nonpoor families?: a case study of childhood asthma in Alberta.   Chest. 2003;124(1):51-56. doi:10.1378/chest.124.1.51PubMedGoogle ScholarCrossref
20.
Nguyen  T, Lurie  M, Gomez  M, Reddy  A, Pandya  K, Medvesky  M.  The National Asthma Survey, New York State: association of the home environment with current asthma status.   Public Health Rep. 2010;125(6):877-887. doi:10.1177/003335491012500615PubMedGoogle ScholarCrossref
21.
Fire Department of New York City. EMS Incident Dispatch Data. NYC Open Data. Published 2020. Accessed May 21, 2020. https://data.cityofnewyork.us/Public-Safety/EMS-Incident-Dispatch-Data/76xm-jjuj
22.
Alexander  M. Reviving EMS: restructuring emergency medical services in New York City. Published November 25, 2018. Accessed October 15, 2019. https://cbcny.org/research/reviving-ems
23.
von Elm  E, Altman  DG, Egger  M, Pocock  SJ, Gøtzsche  PC, Vandenbroucke  JP; STROBE Initiative.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.   Ann Intern Med. 2007;147(8):573-577. doi:10.7326/0003-4819-147-8-200710160-00010PubMedGoogle ScholarCrossref
24.
Priority Dispatch. Discover ProQA. Priority Dispatch Corp. Accessed May 21, 2020. https://prioritydispatch.net/discover_proqa/
25.
US Census Bureau. American Community Survey (ACS). Accessed May 21, 2020. https://www.census.gov/programs-surveys/acs
26.
US Environmental Protection Agency. AirData website. Published 2020. Accessed May 21, 2020. https://aqs.epa.gov/aqsweb/airdata/download_files.html
27.
Mirabelli  MC, Beavers  SF, Chatterjee  AB, Moorman  JE.  Age at asthma onset and subsequent asthma outcomes among adults with active asthma.   Respir Med. 2013;107(12):1829-1836. doi:10.1016/j.rmed.2013.09.022PubMedGoogle ScholarCrossref
28.
Guarnieri  M, Balmes  JR.  Outdoor air pollution and asthma.   Lancet. 2014;383(9928):1581-1592. doi:10.1016/S0140-6736(14)60617-6PubMedGoogle ScholarCrossref
29.
Alhanti  BA, Chang  HH, Winquist  A, Mulholland  JA, Darrow  LA, Sarnat  SE.  Ambient air pollution and emergency department visits for asthma: a multi-city assessment of effect modification by age.   J Expo Sci Environ Epidemiol. 2016;26(2):180-188. doi:10.1038/jes.2015.57PubMedGoogle ScholarCrossref
30.
Eisner  MD, Katz  PP, Yelin  EH, Shiboski  SC, Blanc  PD.  Risk factors for hospitalization among adults with asthma: the influence of sociodemographic factors and asthma severity.   Respir Res. 2001;2(1):53-60. doi:10.1186/rr37PubMedGoogle Scholar
31.
Thakur  N, Oh  SS, Nguyen  EA,  et al.  Socioeconomic status and childhood asthma in urban minority youths. The GALA II and SAGE II studies.   Am J Respir Crit Care Med. 2013;188(10):1202-1209. doi:10.1164/rccm.201306-1016OCPubMedGoogle ScholarCrossref
32.
Linden  A.  A comprehensive set of postestimation measures to enrich interrupted time-series analysis.   Stata J. 2017;17(1):73-88. doi:10.1177/1536867X1701700105Google ScholarCrossref
33.
Linden  A.  Conducting interrupted time-series analysis for single- and multiple-group comparisons.   Stata J. Published online June 1, 2015. doi:10.1177/1536867X1501500208Google Scholar
34.
Cumby  RE, Huizinga  J.  Testing the autocorrelation structure of disturbances in ordinary least squares and instrumental variables regressions.   Econometrica. 1992;60(1):185-195. doi:10.2307/2951684Google ScholarCrossref
35.
OpenStreetMap. Accessed June 9, 2020. https://www.openstreetmap.org/
36.
Nawar  EW, Niska  RW, Xu  J.  National Hospital Ambulatory Medical Care Survey: 2005 emergency department summary.   Adv Data. 2007;(386):1-32.PubMedGoogle Scholar
37.
US Department of Health Human Services. National Hospital Ambulatory Medical Care Survey: 2016 Emergency Department Summary Tables. Published online 2016:38. Accessed October 27, 2020. https://www.cdc.gov/nchs/data/nhamcs/web_tables/2016_ed_web_tables.pdf
38.
Guilbert  TW, Garris  C, Jhingran  P,  et al.  Asthma that is not well-controlled is associated with increased healthcare utilization and decreased quality of life.   J Asthma. 2011;48(2):126-132. doi:10.3109/02770903.2010.535879PubMedGoogle ScholarCrossref
39.
Bel  EH.  Clinical Practice. Mild asthma.   N Engl J Med. 2013;369(6):549-557. doi:10.1056/NEJMcp1214826PubMedGoogle ScholarCrossref
40.
Cloutier  MM, Hall  CB, Wakefield  DB, Bailit  H.  Use of asthma guidelines by primary care providers to reduce hospitalizations and emergency department visits in poor, minority, urban children.   J Pediatr. 2005;146(5):591-597. doi:10.1016/j.jpeds.2004.12.017PubMedGoogle ScholarCrossref
41.
Schatz  M, Rachelefsky  G, Krishnan  JA.  Follow-up after acute asthma episodes: what improves future outcomes?   Proc Am Thorac Soc. 2009;6(4):386-393. doi:10.1513/pats.P09ST6PubMedGoogle ScholarCrossref
42.
Park  HJ, Byun  MK, Kim  HJ,  et al.  Regular follow-up visits reduce the risk for asthma exacerbation requiring admission in Korean adults with asthma.   Allergy Asthma Clin Immunol. 2018;14(1):29. doi:10.1186/s13223-018-0250-0PubMedGoogle ScholarCrossref
43.
Hill  J, Arrotta  N, Villa-Roel  C, Dennett  L, Rowe  BH.  Factors associated with relapse in adult patients discharged from the emergency department following acute asthma: a systematic review.   BMJ Open Respir Res. 2017;4(1):e000169. doi:10.1136/bmjresp-2016-000169PubMedGoogle Scholar
44.
Al-Jahdali  H, Ahmed  A, Al-Harbi  A,  et al.  Improper inhaler technique is associated with poor asthma control and frequent emergency department visits.   Allergy Asthma Clin Immunol. 2013;9(1):8. doi:10.1186/1710-1492-9-8PubMedGoogle ScholarCrossref
45.
Al-Muhsen  S, Horanieh  N, Dulgom  S,  et al.  Poor asthma education and medication compliance are associated with increased emergency department visits by asthmatic children.   Ann Thorac Med. 2015;10(2):123-131. doi:10.4103/1817-1737.150735PubMedGoogle ScholarCrossref
46.
US Centers for Disease Control and Prevention. Do you have an Asthma Action Plan? Published April 13, 2020. Accessed June 4, 2020. https://www.cdc.gov/asthma/actionplan.html
47.
Coffman  JM, Cabana  MD, Halpin  HA, Yelin  EH.  Effects of asthma education on children’s use of acute care services: a meta-analysis.   Pediatrics. 2008;121(3):575-586. doi:10.1542/peds.2007-0113PubMedGoogle ScholarCrossref
48.
McGovern  CM, Redmond  M, Arcoleo  K, Stukus  DR.  A missed primary care appointment correlates with a subsequent emergency department visit among children with asthma.   J Asthma. 2017;54(9):977-982. doi:10.1080/02770903.2017.1283697PubMedGoogle ScholarCrossref
49.
Dolan  E, Prezant  D, Thomson  K, Thottam  B, Napoli  A, McPherson  A. FDNY’s Computerized Triage Software. FDNY Pro. Published 2017. Accessed August 25, 2019. https://www.fdnypro.org/fdny-computerized-triage-software/
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    Views 1,175
    Citations 0
    Original Investigation
    Emergency Medicine
    November 11, 2020

    Association of Affordable Care Act Implementation With Ambulance Utilization for Asthma Emergencies in New York City, 2008-2018

    Author Affiliations
    • 1Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
    • 2Department of Emergency Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
    • 3Department of Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
    • 4Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
    • 5Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
    • 6Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
    JAMA Netw Open. 2020;3(11):e2025586. doi:10.1001/jamanetworkopen.2020.25586
    Key Points

    Question  What is the association between insurance expansion and emergency medical services (EMS) dispatches for an ambulatory care–sensitive condition like asthma?

    Findings  In this cohort study including 217 303 EMS dispatches for asthma emergencies in New York City, implementation of the Patient Protection and Affordable Care Act was associated with a decrease in calls for asthma emergencies. In adjusted models, larger decreases in the uninsured rate were associated with larger decreases in the asthma EMS dispatch rate.

    Meaning  The findings of this study suggest that insurance expansion may lead to improved outpatient management of ambulatory care–sensitive conditions like asthma, resulting in decreased utilization of EMS.

    Abstract

    Importance  Emergency medical services (EMS) are an essential component of the health care system, but the effect of insurance expansion on EMS call volume remains unclear.

    Objective  This study investigated the association between health insurance expansion and EMS dispatches for asthma, an ambulatory care–sensitive condition. We hypothesized that insurance expansion under the Patient Protection and Affordable Care Act (ACA) would be associated with decreased EMS dispatches for asthma emergencies.

    Design, Setting, and Participants  This cohort study examined 14 865 267 ambulance calls dispatched within New York City from 2008 to 2018, including 217 303 calls for asthma-related emergencies, and used interrupted time series analysis to study the change in the annual incidence of EMS dispatches for asthma emergencies after implementation of the ACA. Multivariable linear regression examined the association between the uninsured rate and the incidence of asthma-related dispatches, controlling for population demographic characteristics and air quality index.

    Exposures  Implementation of ACA on January 1, 2014.

    Main Outcomes and Measures  Incidence of EMS dispatches for asthma emergencies per 100 000 population per year (ie, asthma EMS dispatch rate) as classified by the 911 call–taker.

    Results  In this study of 217 303 EMS dispatches for asthma-related emergencies, there was a decrease in the asthma EMS dispatch rate after implementation of the ACA, from a mean (SD) of 261 (24) dispatches per 100 000 population per year preintervention to 211 (47) postintervention (P = .047). This decrease in asthma EMS dispatch rate after ACA implementation was significant on interrupted time series analysis. Prior to 2014, the annual asthma EMS dispatch rate was increasing by 11.8 calls per 100 000 population per year (95% CI, 6.1 to 17.4). After ACA implementation, the asthma EMS dispatch rate decreased annually by 28.5 calls per 100 000 population per year (95% CI, −37.6 to −19.3), a significant change in slope from the preintervention period (P < .001). Multivariable linear regression, controlling for percentage of individuals younger than age 18 years, degree of racial/ethnic diversity, median household income, and air quality index, found that a 1% decrease in the citywide uninsured rate was associated with a decrease of 98.9 asthma dispatches per 100 000 population per year (95% CI, 5.72-192.10; P = .04).

    Conclusions and Relevance  Insurance expansion within New York City under the ACA was associated with a significant reduction in the asthma EMS dispatch rate. Insurance expansion may be a viable method to reduce EMS utilization for ambulatory care–sensitive conditions such as asthma.

    Introduction

    Emergency medical services (EMS) in the US respond to millions of calls each year,1 delivering prehospital care to patients with conditions ranging from life-threatening emergencies to minor injuries. Most EMS systems have fixed resources, including a limited number of personnel and ambulances. A strain on these systems because of increased utilization or other factors can result in delayed care and even increased mortality for patients with serious emergencies.2-5 For this reason, changes in EMS utilization, such as those triggered by health insurance changes, could have important implications for EMS systems.

    Prior studies of the effects of health insurance expansion on emergency services utilization have yielded conflicting results.6 Insurance expansion, including through the 2014 implementation of the Patient Protection and Affordable Care Act (ACA), was shown in some studies to increase the number of visits to emergency departments.7-9 Similarly, ACA implementation was associated with an increase in EMS utilization for minor injuries in New York City (NYC).10 However, other studies11-14 have demonstrated no change after insurance expansion or even a decrease in utilization of emergency services. This reduction in utilization could be driven by improved management of ambulatory care–sensitive conditions, a category of chronic diseases that can result in emergency department visits if not properly controlled through primary care.15 Asthma has been extensively studied as an example of an ambulatory care–sensitive condition, and lack of insurance is a risk factor for developing asthma exacerbations that require emergency care.16-19 However, it is unknown whether EMS utilization for ambulatory care–sensitive conditions would decrease due to the assumed improvement in primary care management related to insurance expansion.

    To investigate the association of insurance expansion with EMS utilization for ambulatory care–sensitive conditions, we studied ambulance dispatches for asthma emergencies within the NYC EMS system from 2008 through 2018 (ie, before and after implementation of the ACA in 2014). Because of its high prevalence, asthma is a particularly important disease to study in NYC.20 Furthermore, asthma can be rapidly controlled through primary care interventions, including improved maintenance and rescue therapy, meaning that expanded access to primary care could in theory reduce asthma exacerbations within the time scale of this study. An assessment of the association of health policy changes with ambulance utilization for a highly prevalent disease in a high-volume EMS system could provide valuable insights for public health. We hypothesized that insurance expansion under the ACA would be associated with decreased EMS dispatches for asthma emergencies.

    Methods
    Study Design, Data Sources, and Setting

    We performed a cohort study using interrupted time series analysis in addition to linear modeling to study the association between insurance expansion under the ACA and EMS dispatches for asthma emergencies. The primary data source was a publicly available database of EMS incident dispatch data encompassing every 911 call resulting in an ambulance dispatch within NYC from January 1, 2008, to December 31, 2018.21 NYC has a centralized 911 dispatch system that captures every 911 call within city limits regardless of the responding agency.22 This system ensures that all EMS calls are captured within the data set. The data sets and analytic plan were reviewed by the Harvard Longwood Campus institutional review board and deemed exempt from informed consent requirements because all data included in this analysis are freely available to the public. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.23

    The computer-aided dispatch system used in NYC includes asthma as a distinct call type category, as opposed to other systems that may not differentiate between respiratory concerns.24 The specific dispatch codes used by the Fire Department of New York that we classified as asthma included ASTHFC (asthma attack associated with fever and cough), ASTHFT (asthma attack associated with fever and positive travel history), ASTHMA (asthma attack), ASTHMB (asthma attack), and ASTHMC (asthma attack associated with critical condition). For comparison with a nonambulatory care–sensitive condition, we repeated this procedure on all EMS dispatches classified as STAB to indicate stabbings throughout the study period. Because this data set only contains deidentified EMS incident dispatch data, individual patient demographic information including age, sex, race, and insurance status are not available.

    The EMS dispatch data set was enriched with several other population-level data sets. The zip code of each incident location was used to link each dispatch with local estimates of population size, percentage of individuals without health insurance coverage, percentage of individuals under age 18 years, degree of racial/ethnic diversity (defined as the percentage of zip code residents who identify as non-Hispanic White only), median household income, and air quality index (AQI). These variables were compiled from 5-year estimates generated by annual American Community Survey (ACS) data from 2012 to 2018 (where the 5-year estimates from 2012 were applied to 2008-2012),25 with the exception of daily AQI estimates, which were provided by the US Environmental Protection Agency (EPA).26 ACS data were organized both at the county (ie, borough) level to compute citywide estimates weighted by county population, and at the zip code level for greater geographical resolution. Although ACS data would accommodate even greater geographical resolution, such as at the census tract level of analysis, the publicly available EMS dispatch data used for this study does not specify location beyond zip code in order to maintain the privacy of deidentified patients. Daily AQI measurements within each borough were averaged together and assigned to each corresponding zip code. In the absence of EPA measurements of AQI in Brooklyn, we used measurements from the nearest site in Queens.

    Once compiled, this composite data set was used to compute estimates of asthma-related emergency incidence at the citywide and zip code levels. At the zip code level, the analysis was restricted to the 168 zip codes in NYC with a population greater than 10 000 as of 2018. Given that the ACA was implemented on January 1, 2014, we designated 2008 to 2013 as the preintervention period and 2014 to 2018 as the postintervention period.

    Statistical Analysis

    Descriptive statistics were calculated for NYC, and comparisons of measures pre-ACA and post-ACA implementation were made using 2-tailed t tests. The primary outcome measure was the asthma EMS dispatch rate, which is the rate of ambulance dispatches for asthma emergencies per 100 000 population per year. We conducted an interrupted time series analysis of the annual citywide asthma EMS dispatch rate. As a sensitivity analysis, we conducted an interrupted time series analysis using all EMS dispatches over the same time period to determine if the changes observed were associated with underlying trends. We also repeated the interrupted time series analysis for stabbings as an example of a nonambulatory care sensitive condition. Stabbings were chosen because such incidents are easily identifiable, have been recorded as a discrete call type throughout the full study period, and are less likely to be confounded by other factors (eg, gunshot wound incidence can be affected by changes in firearm control legislation).

    To further evaluate patterns in asthma EMS dispatch rate, we examined the association between uninsured rate and citywide asthma EMS dispatch rate using linear regression models, controlling for the population-level factors of percentage of individuals under age 18 years, degree of ethnic and racial diversity, median household income, and AQI. These variables were chosen from prior literature as potential confounders.27-31 Ordinary least-squares regression models were fit with Newey-West standard errors to adjust for autocorrelation and heteroskedasticity.32,33 After fitting ordinary least-squares models, we used the Cumby-Huizinga test34 for autocorrelation and included the appropriate lag term as indicated. Models allowed for both a trend (ie, slope) and level (ie, intercept) change post-ACA implementation. We repeated the linear regression models at the zip code level to test whether this association would hold across the 168 zip codes that compose NYC while controlling for other key factors. We fit generalized estimating equations to handle clustering by zip code, assuming working exchangeable correlation structure and robust standard errors. This analysis included a greater number of data points given its inclusion of space in addition to time in order to accommodate the inclusion of our 5 covariates; the models controlled for the same factors, which were also included at the zip code, rather than citywide, level. Changes in uninsured rate by zip code and asthma EMS dispatch rate were mapped to visually compare the relationship between these 2 variables. For all analyses, P < .05 was considered statistically significant. All analyses were performed in Stata version 15.0 (StataCorp) and R version 3.6.2 (R Foundation for Statistical Computing). Tableau Desktop version 2019.3 (Tableau Software) was used to graph zip code–level data on a crowdsourced web map produced by OpenStreetMap.35

    Results

    A total of 14 865 267 EMS calls were dispatched during the study period, including 217 303 (1.5%) calls for asthma-related emergencies. The overall incidence of total EMS dispatches per 100 000 population per year at the citywide level increased during the study period from a mean (SD) of 15 471 (413) prior to ACA implementation to 17 143 (685) postimplementation (P < .001) (Table 1). After ACA implementation, there was a significant decrease in the asthma EMS dispatch rate (mean [SD], 261 [24] vs 211 [47] per 100 000 population per year; P = .047).

    There were several changes in population demographics within NYC during the study period at the citywide level (Table 1). The uninsured rate decreased after ACA implementation from 14.2% to 11.0% (−3.2%; 95% CI, −5.1% to −1.4%; P = .003). Median income increased from $52 734 to $57 214 ($4480; 95% CI, $1493-$7468.20; P = .008), while there were slight decreases in the percentage of the population under age 18 years (−0.48%; 95% CI, −0.67% to −0.29%; P < .001) and the percentage of the population who were non-Hispanic White individuals (−1.3%; 95% CI, −1.7% to −0.97%; P < .001). There was no significant change in the level of air pollution in the city, as represented by the AQI (1.0%; 95% CI, −3.8 to 1.8; P = .44).

    Interrupted time series analysis demonstrated a significant decrease in the incidence of asthma dispatches after ACA implementation at the citywide level (Figure 1). Prior to 2014, the annual asthma EMS dispatch rate was increasing by 11.8 calls per 100 000 population per year (95% CI, 6.1 to 17.4). After ACA implementation, the asthma EMS dispatch rate decreased annually by 28.5 calls per 100 000 population per year (95% CI, −37.6 to −19.3), a significant change in slope from the preintervention period (P < .001). By contrast, there was a significant increase of all EMS dispatches following ACA implementation (change in slope, 180; 95% CI, 57 to 302; P = .01). The EMS dispatch rate for stabbings, a nonambulatory care–sensitive condition, did not change significantly after implementation of the ACA (change in slope, 4.3; 95% CI, −4.6 to 13.2; P = .29).

    Unadjusted linear regression analysis revealed a significant positive association between the citywide annual uninsured rate and asthma EMS dispatch rate (coefficient, 17.8; 95% CI, 10.6 to 25.0; P < .001), with an adjusted r2 = 0.75. This association persisted on multivariable linear regression analysis, where a 1% decrease in the citywide uninsured rate was associated with a mean decrease of approximately 99 asthma EMS dispatches per 100 000 population per year (98.91; 95% CI, 5.72-192.10; P = .04) (Table 2).

    We repeated the multivariable linear regression model at the zip code-level. This model showed a similar significant and positive association between uninsured rate and asthma EMS dispatch rate, where a 1% decrease in the uninsured rate was associated with a mean decrease of 10.25 asthma dispatches per 100 000 population per year (95% CI, 7.26-13.23; P < .001) (Table 3). Finally, in the geographic analysis based on zip codes within NYC, geographical overlap was noted between regions of the city with greater reductions in uninsured rate and regions with reductions in the asthma EMS dispatch rate (Figure 2). Zip codes within the Bronx and most of Brooklyn had the most consistent decreases in both the uninsured rate and the asthma EMS dispatch rate.

    Discussion

    In this cohort study of EMS dispatches for asthma emergencies in NYC, the asthma EMS dispatch rate significantly decreased in the years after implementation of the ACA. By comparison, the EMS dispatch rate for stabbings, a nonambulatory care–sensitive condition, remained unchanged. There was also an increase in EMS utilization overall during the study period, which mirrors an ongoing national trend of increasing EMS transports.36,37 The decrease in the asthma EMS dispatch rate was significantly associated with annual citywide decreases in the uninsured rate, which were most pronounced in the years following implementation of the ACA. Furthermore, this association persisted after controlling for age, race/ethnicity, median household income, and AQI. The citywide association between the decreased uninsured rate and decreased asthma EMS dispatch rate also held at the zip code level after controlling for local estimates of the same set of key factors. Therefore, our findings consistently demonstrate an association between insurance expansion delivered by the ACA and decreasing asthma EMS dispatch rate in NYC.

    The mechanism by which health insurance expansion is associated with fewer asthma-related emergencies severe enough to require EMS response is most likely related to improved outpatient management of the disease. Asthma is a chronic medical condition that is associated with acute exacerbations if not properly controlled.38 Because effective chronic care for patients with asthma can decrease exacerbations,39,40 increased access to primary care through insurance expansion is likely to improve the control of asthma and reduce exacerbations. The ways in which insurance expansion for asthma patients can reduce emergency services utilization include improved control therapy (such as inhaled corticosteroids) to reduce exacerbations; improved rescue therapy (such as inhaled beta-agonists) to de-escalate exacerbations; reduced cost of these medications; improved follow-up after an exacerbation41-43; and increased access to counseling regarding the avoidance of triggers, proper medication administration, and the formulation of an asthma action plan.44-47 Both missed primary care appointments and gaps in insurance are associated with increased emergency department utilization among children with asthma.17,48 Together, these prior findings provide possible mechanisms for the reduction in EMS activations for asthma within NYC in the years after implementation of the ACA.

    In contrast to our results, 2 previous studies by Courtemanche et al2,10 reported that ACA implementation was associated with increased EMS dispatches within the NYC EMS system. The first showed that ambulance dispatches for minor injuries increased following implementation of the ACA,10 and the second described an overall increase in all calls of relatively lower severity over the same time period.2 The authors attributed these trends to insurance expansion on the grounds that health insurance insulated patients from the costs of utilizing EMS.10 Unlike asthma, minor injuries are not sensitive to ambulatory care, so increased access to primary care services would not have had an effect on the incidence of minor emergencies. We therefore believe that increased ambulance utilization for minor injuries does not have any bearing on our explanations for the asthma findings. Moreover, our finding that the decreased asthma EMS dispatch rate was associated not just with the implementation of ACA in 2014 but also with insurance expansion itself on an annual basis (as opposed to other changes that might have coincided with 2014) further supports our conclusion that improved access to care may have contributed to lower rates of asthma emergencies in NYC.

    Limitations

    This study has several potential limitations. Asthma emergencies were identified based on dispatch information rather than on a clinician’s assessment of the patient. However, inaccuracies in assessing the patient’s chief concern over the telephone are unlikely to have changed from year to year or based on insurance status. In addition, there have been changes in EMS dispatch call classification algorithms used by the Fire Department of New York during the study period, such as during the implementation of a new computerized triage system in 2017.49 However, we are not aware of relevant changes in classification procedures that coincided with 2014. Even if such changes did occur, they would not directly affect our analyses that examine uninsured rates rather than year as the primary independent variable, and which revealed the same finding. Moreover, such policy changes would be implemented citywide and would therefore not affect our analyses at the zip code level. Another limitation is that individual patients may be captured multiple times within the EMS dispatch data set, resulting in nonindependence. In addition, the zip code of each EMS incident is not necessarily the zip code in which the patient resides, which may result in misclassification of covariates. It is also possible that other interventions took place in 2014 besides the implementation of ACA that could have contributed to the change in ambulance dispatches observed on interrupted time series analysis. However, we have not identified any public health or policy measures dedicated to controlling asthma that were implemented within NYC around 2014. Moreover, it is possible that changes in EMS utilization might not necessarily reflect changes in emergency department utilization. Future studies should be completed to evaluate whether these trends can be observed in emergency department utilization for asthma in NYC, as well as whether similar trends can be observed for the broader category of all respiratory complaints. Finally, it is important to note that there are inherent limitations in using interrupted time series analysis to make causal claims, so we can only demonstrate an association between insurance expansion under the ACA and a decrease in the asthma EMS dispatch rate.

    Conclusions

    In summary, EMS is an important component of the health care system, but there is a paucity of studies on the effects of health policy decisions on EMS systems. This study demonstrates the ability to use EMS administrative data to study the implications of policy decisions on EMS utilization. Whereas single hospital site or system data sets do not capture incidents presented to external institutions, and whereas insurance claims data sets only capture incidents among their particular patient cohort (which can skew the sample and limit generalizability), EMS administrative data sets can provide a full assessment of acute care utilization patterns at a population level within a particular geographic area. For policymakers, the study supports the growing body of evidence that insurance expansion improves the control of ambulatory care–sensitive conditions such as asthma, as shown by significant decreases in the utilization of emergency services for these conditions. It also illustrates that the association of insurance expansion with EMS call volume may vary depending on the type of emergency. Ambulatory care–sensitive conditions should be targeted by policy makers hoping to reduce EMS utilization through insurance expansion.

    Back to top
    Article Information

    Accepted for Publication: September 17, 2020.

    Published: November 11, 2020. doi:10.1001/jamanetworkopen.2020.25586

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Peters GA et al. JAMA Network Open.

    Corresponding Author: Carlos A. Camargo, MD, DrPH, Emergency Medicine Network, Department of Emergency Medicine, Massachusetts General Hospital, 125 Nashua St, Ste 920, Boston, MA 02114 (ccamargo@partners.org).

    Author Contributions: Dr Peters had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Peters and Ordoobadi contributed equally to this work.

    Concept and design: Peters, Ordoobadi, Wong, Avillach, Camargo.

    Acquisition, analysis, or interpretation of data: Peters, Ordoobadi, Cash, Avillach, Camargo.

    Drafting of the manuscript: Peters, Ordoobadi, Wong, Avillach.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Peters, Ordoobadi, Cash, Camargo.

    Obtained funding: Peters.

    Supervision: Wong, Avillach, Camargo.

    Conflict of Interest Disclosures: None reported.

    Funding/Support: This work was completed with support from Amazon Web Services Cloud Credits for Research (grant PS_R_POC_FY2019_Q2_Harvard_Medical_Peters). This work was conducted with biostatistical consulting support from Harvard Catalyst | The Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, National Institutes of Health Award UL 1TR002541) and financial contributions from Harvard University and its affiliated academic healthcare centers.

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Harvard Catalyst provided consulting services regarding statistical tools used in the study but did not have any role in the design and conduct of the study; collection, management, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

    Disclaimer: The content of this study is solely the responsibility of the authors and does not necessarily represent the official views of Harvard Catalyst, Harvard University and its affiliated academic healthcare centers, or the National Institutes of Health.

    Additional Information: Map data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org.

    References
    1.
    Wang  HE, Mann  NC, Jacobson  KE,  et al.  National characteristics of emergency medical services responses in the United States.   Prehosp Emerg Care. 2013;17(1):8-14. doi:10.3109/10903127.2012.722178PubMedGoogle ScholarCrossref
    2.
    Courtemanche  C, Friedson  A, Koller  AP, Rees  DI.  The Affordable Care Act and ambulance response times.   J Health Econ. 2019;67:102213. doi:10.1016/j.jhealeco.2019.05.010PubMedGoogle Scholar
    3.
    Jena  AB, Mann  NC, Wedlund  LN, Olenski  A.  Delays in emergency care and mortality during major US marathons.   N Engl J Med. 2017;376(15):1441-1450. doi:10.1056/NEJMsa1614073PubMedGoogle ScholarCrossref
    4.
    Wilde  ET.  Do emergency medical system response times matter for health outcomes?   Health Econ. 2013;22(7):790-806. doi:10.1002/hec.2851PubMedGoogle ScholarCrossref
    5.
    Ahn  KO, Shin  SD, Cha  WC, Jun  C, Lee  TS, Pirrallo  RG.  A model for the association of the call volume and the unavailable-for-response interval on the delayed ambulance response for out-of-hospital cardiac arrest using a geographic information system.   Prehosp Emerg Care. 2010;14(4):469-476. doi:10.3109/10903127.2010.497895PubMedGoogle ScholarCrossref
    6.
    Guth  M, Garfield  R, Rudowitz  R. The effects of Medicaid expansion under the ACA: updated findings from a literature review. Kaiser Family Foundation; 2020. Accessed May 13, 2020. https://www.kff.org/medicaid/report/the-effects-of-medicaid-expansion-under-the-aca-updated-findings-from-a-literature-review/
    7.
    Taubman  SL, Allen  HL, Wright  BJ, Baicker  K, Finkelstein  AN.  Medicaid increases emergency-department use: evidence from Oregon’s Health Insurance Experiment.   Science. 2014;343(6168):263-268. doi:10.1126/science.1246183PubMedGoogle ScholarCrossref
    8.
    Ginde  AA, Lowe  RA, Wiler  JL.  Health insurance status change and emergency department use among US adults.   Arch Intern Med. 2012;172(8):642-647. doi:10.1001/archinternmed.2012.34PubMedGoogle ScholarCrossref
    9.
    Sharma  AI, Dresden  SM, Powell  ES, Kang  R, Feinglass  J.  Emergency department visits and hospitalizations for the uninsured in Illinois before and after Affordable Care Act insurance expansion.   J Community Health. 2017;42(3):591-597. doi:10.1007/s10900-016-0293-4PubMedGoogle ScholarCrossref
    10.
    Courtemanche  C, Friedson  AI, Rees  DI.  Association of ambulance use in New York City with the implementation of the patient protection and Affordable Care Act.   JAMA Netw Open. 2019;2(6):e196419. doi:10.1001/jamanetworkopen.2019.6419PubMedGoogle Scholar
    11.
    Gotanda  H, Kominski  G, Tsugawa  Y.  Association between the ACA medicaid expansions and primary care and emergency department use during the first 3 years.   J Gen Intern Med. 2020;35(3):711-718. doi:10.1007/s11606-019-05458-wPubMedGoogle ScholarCrossref
    12.
    Klein  EY, Levin  S, Toerper  MF,  et al.  The effect of Medicaid expansion on utilization in Maryland emergency departments.   Ann Emerg Med. 2017;70(5):607-614.e1. doi:10.1016/j.annemergmed.2017.06.021PubMedGoogle ScholarCrossref
    13.
    Hernandez-Boussard  T, Burns  CS, Wang  NE, Baker  LC, Goldstein  BA.  The Affordable Care Act reduces emergency department use by young adults: evidence from three states.   Health Aff (Millwood). 2014;33(9):1648-1654. doi:10.1377/hlthaff.2014.0103PubMedGoogle ScholarCrossref
    14.
    Smulowitz  PB, Lipton  R, Wharam  JF,  et al.  Emergency department utilization after the implementation of Massachusetts health reform.   Ann Emerg Med. 2011;58(3):225-234.e1. doi:10.1016/j.annemergmed.2011.02.020PubMedGoogle ScholarCrossref
    15.
    Hodgson  K, Deeny  SR, Steventon  A.  Ambulatory care-sensitive conditions: their potential uses and limitations.   BMJ Qual Saf. 2019;28(6):429-433. doi:10.1136/bmjqs-2018-008820PubMedGoogle ScholarCrossref
    16.
    Markovitz  BP, Andresen  EM.  Lack of insurance coverage and urgent care use for asthma: a retrospective cohort study.   BMC Public Health. 2006;6:14. doi:10.1186/1471-2458-6-14PubMedGoogle ScholarCrossref
    17.
    Gushue  C, Miller  R, Sheikh  S,  et al  Gaps in health insurance coverage and emergency department use among children with asthma.   J Asthma. 2019;56(10):1070-1078. doi:10.1080/02770903.2018.1523929Google ScholarCrossref
    18.
    Szilagyi  PG, Dick  AW, Klein  JD,  et al.  Improved asthma care after enrollment in the State Children’s Health Insurance Program in New York.   Pediatrics. 2006;117(2):486-496. doi:10.1542/peds.2005-0340PubMedGoogle ScholarCrossref
    19.
    Sin  DD, Svenson  LW, Cowie  RL, Man  SFP.  Can universal access to health care eliminate health inequities between children of poor and nonpoor families?: a case study of childhood asthma in Alberta.   Chest. 2003;124(1):51-56. doi:10.1378/chest.124.1.51PubMedGoogle ScholarCrossref
    20.
    Nguyen  T, Lurie  M, Gomez  M, Reddy  A, Pandya  K, Medvesky  M.  The National Asthma Survey, New York State: association of the home environment with current asthma status.   Public Health Rep. 2010;125(6):877-887. doi:10.1177/003335491012500615PubMedGoogle ScholarCrossref
    21.
    Fire Department of New York City. EMS Incident Dispatch Data. NYC Open Data. Published 2020. Accessed May 21, 2020. https://data.cityofnewyork.us/Public-Safety/EMS-Incident-Dispatch-Data/76xm-jjuj
    22.
    Alexander  M. Reviving EMS: restructuring emergency medical services in New York City. Published November 25, 2018. Accessed October 15, 2019. https://cbcny.org/research/reviving-ems
    23.
    von Elm  E, Altman  DG, Egger  M, Pocock  SJ, Gøtzsche  PC, Vandenbroucke  JP; STROBE Initiative.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.   Ann Intern Med. 2007;147(8):573-577. doi:10.7326/0003-4819-147-8-200710160-00010PubMedGoogle ScholarCrossref
    24.
    Priority Dispatch. Discover ProQA. Priority Dispatch Corp. Accessed May 21, 2020. https://prioritydispatch.net/discover_proqa/
    25.
    US Census Bureau. American Community Survey (ACS). Accessed May 21, 2020. https://www.census.gov/programs-surveys/acs
    26.
    US Environmental Protection Agency. AirData website. Published 2020. Accessed May 21, 2020. https://aqs.epa.gov/aqsweb/airdata/download_files.html
    27.
    Mirabelli  MC, Beavers  SF, Chatterjee  AB, Moorman  JE.  Age at asthma onset and subsequent asthma outcomes among adults with active asthma.   Respir Med. 2013;107(12):1829-1836. doi:10.1016/j.rmed.2013.09.022PubMedGoogle ScholarCrossref
    28.
    Guarnieri  M, Balmes  JR.  Outdoor air pollution and asthma.   Lancet. 2014;383(9928):1581-1592. doi:10.1016/S0140-6736(14)60617-6PubMedGoogle ScholarCrossref
    29.
    Alhanti  BA, Chang  HH, Winquist  A, Mulholland  JA, Darrow  LA, Sarnat  SE.  Ambient air pollution and emergency department visits for asthma: a multi-city assessment of effect modification by age.   J Expo Sci Environ Epidemiol. 2016;26(2):180-188. doi:10.1038/jes.2015.57PubMedGoogle ScholarCrossref
    30.
    Eisner  MD, Katz  PP, Yelin  EH, Shiboski  SC, Blanc  PD.  Risk factors for hospitalization among adults with asthma: the influence of sociodemographic factors and asthma severity.   Respir Res. 2001;2(1):53-60. doi:10.1186/rr37PubMedGoogle Scholar
    31.
    Thakur  N, Oh  SS, Nguyen  EA,  et al.  Socioeconomic status and childhood asthma in urban minority youths. The GALA II and SAGE II studies.   Am J Respir Crit Care Med. 2013;188(10):1202-1209. doi:10.1164/rccm.201306-1016OCPubMedGoogle ScholarCrossref
    32.
    Linden  A.  A comprehensive set of postestimation measures to enrich interrupted time-series analysis.   Stata J. 2017;17(1):73-88. doi:10.1177/1536867X1701700105Google ScholarCrossref
    33.
    Linden  A.  Conducting interrupted time-series analysis for single- and multiple-group comparisons.   Stata J. Published online June 1, 2015. doi:10.1177/1536867X1501500208Google Scholar
    34.
    Cumby  RE, Huizinga  J.  Testing the autocorrelation structure of disturbances in ordinary least squares and instrumental variables regressions.   Econometrica. 1992;60(1):185-195. doi:10.2307/2951684Google ScholarCrossref
    35.
    OpenStreetMap. Accessed June 9, 2020. https://www.openstreetmap.org/
    36.
    Nawar  EW, Niska  RW, Xu  J.  National Hospital Ambulatory Medical Care Survey: 2005 emergency department summary.   Adv Data. 2007;(386):1-32.PubMedGoogle Scholar
    37.
    US Department of Health Human Services. National Hospital Ambulatory Medical Care Survey: 2016 Emergency Department Summary Tables. Published online 2016:38. Accessed October 27, 2020. https://www.cdc.gov/nchs/data/nhamcs/web_tables/2016_ed_web_tables.pdf
    38.
    Guilbert  TW, Garris  C, Jhingran  P,  et al.  Asthma that is not well-controlled is associated with increased healthcare utilization and decreased quality of life.   J Asthma. 2011;48(2):126-132. doi:10.3109/02770903.2010.535879PubMedGoogle ScholarCrossref
    39.
    Bel  EH.  Clinical Practice. Mild asthma.   N Engl J Med. 2013;369(6):549-557. doi:10.1056/NEJMcp1214826PubMedGoogle ScholarCrossref
    40.
    Cloutier  MM, Hall  CB, Wakefield  DB, Bailit  H.  Use of asthma guidelines by primary care providers to reduce hospitalizations and emergency department visits in poor, minority, urban children.   J Pediatr. 2005;146(5):591-597. doi:10.1016/j.jpeds.2004.12.017PubMedGoogle ScholarCrossref
    41.
    Schatz  M, Rachelefsky  G, Krishnan  JA.  Follow-up after acute asthma episodes: what improves future outcomes?   Proc Am Thorac Soc. 2009;6(4):386-393. doi:10.1513/pats.P09ST6PubMedGoogle ScholarCrossref
    42.
    Park  HJ, Byun  MK, Kim  HJ,  et al.  Regular follow-up visits reduce the risk for asthma exacerbation requiring admission in Korean adults with asthma.   Allergy Asthma Clin Immunol. 2018;14(1):29. doi:10.1186/s13223-018-0250-0PubMedGoogle ScholarCrossref
    43.
    Hill  J, Arrotta  N, Villa-Roel  C, Dennett  L, Rowe  BH.  Factors associated with relapse in adult patients discharged from the emergency department following acute asthma: a systematic review.   BMJ Open Respir Res. 2017;4(1):e000169. doi:10.1136/bmjresp-2016-000169PubMedGoogle Scholar
    44.
    Al-Jahdali  H, Ahmed  A, Al-Harbi  A,  et al.  Improper inhaler technique is associated with poor asthma control and frequent emergency department visits.   Allergy Asthma Clin Immunol. 2013;9(1):8. doi:10.1186/1710-1492-9-8PubMedGoogle ScholarCrossref
    45.
    Al-Muhsen  S, Horanieh  N, Dulgom  S,  et al.  Poor asthma education and medication compliance are associated with increased emergency department visits by asthmatic children.   Ann Thorac Med. 2015;10(2):123-131. doi:10.4103/1817-1737.150735PubMedGoogle ScholarCrossref
    46.
    US Centers for Disease Control and Prevention. Do you have an Asthma Action Plan? Published April 13, 2020. Accessed June 4, 2020. https://www.cdc.gov/asthma/actionplan.html
    47.
    Coffman  JM, Cabana  MD, Halpin  HA, Yelin  EH.  Effects of asthma education on children’s use of acute care services: a meta-analysis.   Pediatrics. 2008;121(3):575-586. doi:10.1542/peds.2007-0113PubMedGoogle ScholarCrossref
    48.
    McGovern  CM, Redmond  M, Arcoleo  K, Stukus  DR.  A missed primary care appointment correlates with a subsequent emergency department visit among children with asthma.   J Asthma. 2017;54(9):977-982. doi:10.1080/02770903.2017.1283697PubMedGoogle ScholarCrossref
    49.
    Dolan  E, Prezant  D, Thomson  K, Thottam  B, Napoli  A, McPherson  A. FDNY’s Computerized Triage Software. FDNY Pro. Published 2017. Accessed August 25, 2019. https://www.fdnypro.org/fdny-computerized-triage-software/
    ×