Home Business & Economics The Muslim Ban and Preventive Care for Children of Middle Eastern Ancestry
Article
Licensed
Unlicensed Requires Authentication

The Muslim Ban and Preventive Care for Children of Middle Eastern Ancestry

  • Shooshan Danagoulian , Owen Fleming , Daniel Grossman and David Slusky
Published/Copyright: May 26, 2025
Forum for Health Economics and Policy
From the journal Forum for Health Economics and Policy Volume 27 Issue 2

Abstract

Individuals of Middle Eastern and North African (MENA) ancestry in the US have been the targets of anti-immigrant policies, counterterrorism operations, and vitriolic political rhetoric. Yet, lack of data identifying MENA individuals has prevented systematic evaluation of the impact of these policies and rhetoric on MENA communities’ wellbeing, including investment in health capital. We begin to address this gap in knowledge by focusing on the travel ban from majority Muslim countries implemented at the start of the first Trump administration. Using a large, longitudinal medical records database we evaluate the impact of this policy on preventive care use among MENA children in the US, finding decreased well-visits, and associated vaccinations among MENA children. Documenting MENA health outcomes following changes in official US policy is paramount for understanding the full consequences of policies that target underrepresented groups.

Keywords: MENA; Muslim ban; disparities; preventive care
JEL Classification: I14; I18; H12; J15
Corresponding author: Shooshan Danagoulian, Wayne State University, Detroit, MI, USA, E-mail:

Appendix A: Data

A.1 Racial and Ethnic Composition of Patients

Figure A1: Individual counts and composition over time. (a) Count. (b) Share. Source: COVID-19 Research Database, 2013–2019. Notes: Each bar/point represents the number or share of unique patients with an encounter within each year by race and ethnicity. Asian, Hispanic, and MENA individual patient counts are based on the secondary y-axis, while all patient and White patient counts are based on the primary y-axis.
Figure A1:

Individual counts and composition over time. (a) Count. (b) Share. Source: COVID-19 Research Database, 2013–2019. Notes: Each bar/point represents the number or share of unique patients with an encounter within each year by race and ethnicity. Asian, Hispanic, and MENA individual patient counts are based on the secondary y-axis, while all patient and White patient counts are based on the primary y-axis.

A.2 State Composition

The data does not represent a balanced panel of either individuals nor states. This raises the concern that as states enter and exit the panel, the composition of individuals tracked over time changes, creating omitted variable bias. In this appendix, we explore the impact of the changing composition of states.

Figure A1 shows the share of sample by state over the years of panel. We see that the early years of the panel (2004–2008) are dominated by Washington and Massachusetts. By 2009, these states have a smaller share, though they continue to have an outsize share relative to others. In 2013–2014 observations from Ohio begin to dominate the sample, remaining a steady 20%–23 % of the sample through the end of the panel period.

Therefore, recognizing significant compositional changes attributable to states drifting in and out of the sample, we limit our sample to 2012–2019 so that the composition of states is constant over time, as described above.

Figure A2: State shares in samples across years. Source: COVID-19 Research Database, HealthJump and AnalyticIQ 2004–2019.
Figure A2:

State shares in samples across years. Source: COVID-19 Research Database, HealthJump and AnalyticIQ 2004–2019.

Figure A3: Length in range of birth year by race and ethnicity of patient. (a) White. (b) Asian. (c) Hispanic. (d) MENA. Source: COVID-19 Research Database, 2013–2019. Notes: Each bar/point represents the share of unique patients whose age range has the length specified on the horizontal axis.
Figure A3:

Length in range of birth year by race and ethnicity of patient. (a) White. (b) Asian. (c) Hispanic. (d) MENA. Source: COVID-19 Research Database, 2013–2019. Notes: Each bar/point represents the share of unique patients whose age range has the length specified on the horizontal axis.

Appendix B: Adjusted Time Trends, all Groups

Figure B1: Adjusted time trend by race/ethnicity. (a) On schedule on vaccines. (b) Total count of vaccines. (c) Total count of lead tests. Source: COVID-19 Research Database, 2013–2019. Notes: Outcomes are well-visits (monthly occurrence and total), vaccines (whether on schedule and total), and lead tests (total) in each month. Annual trends adjusted with fixed effects for years in panel, state, year by month, state by year, and birth year. Averages have been added to adjusted trends for ease of interpretation.
Figure B1:

Adjusted time trend by race/ethnicity. (a) On schedule on vaccines. (b) Total count of vaccines. (c) Total count of lead tests. Source: COVID-19 Research Database, 2013–2019. Notes: Outcomes are well-visits (monthly occurrence and total), vaccines (whether on schedule and total), and lead tests (total) in each month. Annual trends adjusted with fixed effects for years in panel, state, year by month, state by year, and birth year. Averages have been added to adjusted trends for ease of interpretation.

Figure B2: Quarterly and monthly event studies for well-visits. (a) Quarterly frequency. (b) Monthly frequency. Source: COVID-19 Research Database, 2013–2019. Notes: Each point is estimate of difference between MENA and non-Hispanic White child relative to 2016. Outcomes are well-visits (monthly occurrence and total) in each month and each quarter. Annual trends adjusted with fixed effects for years in panel, state, year by month, state by year, and birth year. Standard errors clustered at birth year level. 95 % confidence intervals around estimate.
Figure B2:

Quarterly and monthly event studies for well-visits. (a) Quarterly frequency. (b) Monthly frequency. Source: COVID-19 Research Database, 2013–2019. Notes: Each point is estimate of difference between MENA and non-Hispanic White child relative to 2016. Outcomes are well-visits (monthly occurrence and total) in each month and each quarter. Annual trends adjusted with fixed effects for years in panel, state, year by month, state by year, and birth year. Standard errors clustered at birth year level. 95 % confidence intervals around estimate.

Figure B3: Event study without de-trending of data with MENA pre-trend indicated. (a) Any well-visits in month. (b) Total count of well-visits. (c) On schedule on vaccines. (d) Total count of vaccines. (e) Total count of lead tests. Source: COVID-19 Research Database, 2013–2019. Notes: Each point is estimate of difference between MENA and non-Hispanic White child relative to 2016. Outcomes are well-visits (monthly occurrence and total), vaccines (whether on schedule and total), and lead tests (total) in each month. Annual trends adjusted with fixed effects for years in panel, state, year by month, state by year, and birth year. Standard errors clustered at birth year level. 95 % confidence intervals around estimate.
Figure B3:

Event study without de-trending of data with MENA pre-trend indicated. (a) Any well-visits in month. (b) Total count of well-visits. (c) On schedule on vaccines. (d) Total count of vaccines. (e) Total count of lead tests. Source: COVID-19 Research Database, 2013–2019. Notes: Each point is estimate of difference between MENA and non-Hispanic White child relative to 2016. Outcomes are well-visits (monthly occurrence and total), vaccines (whether on schedule and total), and lead tests (total) in each month. Annual trends adjusted with fixed effects for years in panel, state, year by month, state by year, and birth year. Standard errors clustered at birth year level. 95 % confidence intervals around estimate.

Appendix C: Additional Regression Results, all Groups

Table C1:

Summary statistics.

(1) (2)
Before After
Panel A: Well visits
% with any in month
 MENA 0.0891 0.0582
 Hispanic 0.0943 0.0525
 Asian 0.1041 0.0726
 Non-Hispanic White 0.0804 0.0536
Total count
 MENA 2.6974 3.4809
 Hispanic 2.1172 2.6776
 Asian 2.4374 3.1139
 Non-Hispanic White 2.4606 2.8253
Panel B: Vaccines
% on schedule
 MENA 0.2080 0.2952
 Hispanic 0.3625 0.4390
 Asian 0.2553 0.3289
 Non-Hispanic White 0.2215 0.2893
Total count
 MENA 13.4856 17.7584
 Hispanic 13.3218 16.3998
 Asian 14.2955 18.0316
 Non-Hispanic White 19.5097 24.9682
Panel C: Blood lead testing
Total count
 MENA 0.0076 0.0048
 Hispanic 0.0021 0.0024
 Asian 0.0037 0.0035
 Non-Hispanic White 0.0204 0.0227
Panel D: Demographics
Female
 MENA 0.4493 0.4904
 Hispanic 0.5448 0.5646
 Asian 0.5102 0.5329
 Non-Hispanic White 0.5120 0.5347
Medicaid
 MENA 0.2368 0.2036
 Hispanic 0.5834 0.5451
 Asian 0.1832 0.1395
 Non-Hispanic White 0.1726 0.1405
Panel E: Age (% of individuals)
Age 0–7
 MENA 0.0045 0.0024
 Hispanic 0.0153 0.0133
 Asian 0.0130 0.0061
 Non-Hispanic White 0.0140 0.0078
Age 8–12
 MENA 0.3707 0.0287
 Hispanic 0.3129 0.0479
 Asian 0.4028 0.0421
 Non-Hispanic White 0.3850 0.0581
Age 13–17
 MENA 0.9592 0.9928
 Hispanic 0.9504 0.9790
 Asian 0.9476 0.9921
 Non-Hispanic White 0.9430 0.9873
n (Children) 65,182 43,493
N (Tracked Months) 2,050,337 948,907

  1. Source: COVID-19 Research Database, 2013–2019. Notes: In Panels A and B the total count of well-visits and vaccines is calculated based on the cumulative number of these at the end of the pre- and post-period respectively. In Panel E, each category of age reflect the share of individual children in each. As children age they are present in more than one category. This motivates the large proportion of children in the 13–17 age group, and that the age do not sum to 1. For vaccines, the monthly indicator is whether child is compliant with AAP recommended schedule.

Table C2:

Two-way fixed effects, all groups.

(1) (2) (3)
Well-visits Vaccines Lead test
Any in month Total count On schedule Total count Total count
ASIAN −0.00001 0.0002 −0.0004 −0.0225 0.0001
(0.0013) (0.0373) (0.0126) (0.4320) (0.0021)
MENA 0.0002 −0.0036 −0.0006 −0.0361 0.0001
(0.0013) (0.0533) (0.0154) (0.3675) (0.0046)
HISPANIC 0.0001 −0.0184 0.0005 0.0303 −0.0001
(0.0007) (0.0232) (0.0060) (0.4288) (0.0023)
ASIAN * POST 0.0009 −0.0159 −0.1054*** −4.1374*** −0.0011
(0.0014) (0.0390) (0.0192) (0.7825) (0.0013)
MENA * POST −0.0083*** −0.0964 −0.0283 −1.6976** −0.0041
(0.0028) (0.0661) (0.0204) (0.7276) (0.0039)
HISPANIC * POST −0.0021 0.0754*** 0.001 0.171 0.0037
(0.0013) (0.0267) (0.0056) (1.2153) (0.0022)
Dep. var. mean 0.041 2.025 0.2808 14.6871 0.0055
State FE YES YES YES YES YES
Month * year FE YES YES YES YES YES
State * year FE YES YES YES YES YES
Demographics YES YES YES YES YES
N 2,999,244 2,999,245 2,999,244 2,999,244 2,999,246

  1. Source: COVID-19 Research Database, 2013–2019. Notes: Outcomes are well-visits (monthly occurrence and total), vaccines (whether on schedule and total), and lead tests (total) in each month. Annual trends adjusted with fixed effects for years in panel, state, year by month, state by year, and birth year. Standard errors clustered at birth year level. ***p < 0.01 **p < 0.05 *p < 0.1.

Table C3:

Two-way fixed effects estimates stratified by gender.

(1) (2) (3)
Well-visits Vaccines Lead test
Any in month Total count On schedule Total count Total count
Panel A: Female
MENA −0.0007 0.0373 0.0082 −0.2927 −0.0025
(0.0011) (0.0609) (0.0234) (0.6020) (0.0047)
MENA * POST −0.0059 −0.0809 −0.0489 −2.2960** −0.0047
(0.0040) (0.0669) (0.0307) (1.0832) (0.0049)
Dep. var. mean 0.0425 2.061 0.2653 13.9532 0.0051
N 1,402,068 1,402,068 1,402,068 1,402,068 1,402,068
Panel B: Male
MENA 0.0012 −0.056 −0.0099 0.2177 0.0038
(0.0018) (0.0855) (0.0262) (0.6543) (0.0073)
MENA * POST −0.0110*** −0.116 −0.0097 −1.1858 −0.0041
(0.0027) (0.1063) (0.0236) (1.9627) (0.0067)
Dep. var. mean 0.0398 1.9935 0.2943 15.3312 0.0059
N 1,597,176 1,597,176 1,597,176 1,597,176 1,597,176
State FE YES YES YES YES YES
Month * year FE YES YES YES YES YES
State * year FE YES YES YES YES YES
Demographics YES YES YES YES YES

  1. Source: COVID-19 Research Database, 2013–2019. Notes: Outcomes are well-visits (monthly occurrence and total), vaccines (whether on schedule and total), and lead tests (total) in each month. Annual trends adjusted with fixed effects for years in panel, state, year by month, state by year, and birth year. Standard errors clustered at birth year level. ***p < 0.01 **p < 0.05 *p < 0.1.

Appendix D: Travel to Middle Eastern Countries

Figure D1: Travel to Middle Eastern Countries from the United States. Source: US Department of Homeland Security Advanced Passenger Information System (APIS – formerly DHS I-92) 2013–2019. Notes: Obtained from US Department of Commerce National Travel and Tourism Office report “Market Profile: US Travelers to: Middle East”. All airlines are required to electronically submit passenger data on flights arriving and departing from the US. Leisure includes passenger counts who report primary trip purpose as leisure/vacation/holiday or visiting friends/relatives.
Figure D1:

Travel to Middle Eastern Countries from the United States. Source: US Department of Homeland Security Advanced Passenger Information System (APIS – formerly DHS I-92) 2013–2019. Notes: Obtained from US Department of Commerce National Travel and Tourism Office report “Market Profile: US Travelers to: Middle East”. All airlines are required to electronically submit passenger data on flights arriving and departing from the US. Leisure includes passenger counts who report primary trip purpose as leisure/vacation/holiday or visiting friends/relatives.

Appendix E: Sensitivity of Main Results to State Inclusion

Figure E1: Leave-one-out analysis of states (main estimate in black; estimate excluding New York in red; estimate excluding Ohio in blue). (a) On schedule on vaccines. (b) Total count of vaccines. (c) Any well-visits in month. (d) Total count of well-visits. (e) Total count of lead tests. Source: COVID-19 Research Database, 2013–2019. Notes: Each point is estimate of difference between MENA and non-Hispanic White child relative to 2016. Outcomes are vaccines (whether on schedule and total), well-visits (monthly occurrence and total), and lead tests (total) in each month. Annual trends adjusted with fixed effects for years in panel, state, year by month, state by year, and birth year. The following estimates are highlighted: main estimate in black; estimate excluding New York in red; estimate excluding Ohio in blue. Standard errors clustered at birth year level. 95 % confidence intervals around estimate.
Figure E1:

Leave-one-out analysis of states (main estimate in black; estimate excluding New York in red; estimate excluding Ohio in blue). (a) On schedule on vaccines. (b) Total count of vaccines. (c) Any well-visits in month. (d) Total count of well-visits. (e) Total count of lead tests. Source: COVID-19 Research Database, 2013–2019. Notes: Each point is estimate of difference between MENA and non-Hispanic White child relative to 2016. Outcomes are vaccines (whether on schedule and total), well-visits (monthly occurrence and total), and lead tests (total) in each month. Annual trends adjusted with fixed effects for years in panel, state, year by month, state by year, and birth year. The following estimates are highlighted: main estimate in black; estimate excluding New York in red; estimate excluding Ohio in blue. Standard errors clustered at birth year level. 95 % confidence intervals around estimate.

Appendix F: Alternative Sample Definitions

Table F1:

Alternative assumptions about child age.

(1) (2) (3)
Well-visits Vaccines Lead test
Any in month Total count On schedule Total count Total count
Panel A: Highest possible birth year
MENA 0.0004 −0.0091 −0.0009 −0.0825 0.0001
(0.0013) (0.0878) (0.0207) (1.0052) (0.0045)
MENA * POST −0.0070*** −0.0125 −0.0196 −1.5635 −0.0018
(0.0018) (0.0783) (0.0271) (1.3327) (0.0030)
Dep. var. mean 0.0471 2.2854 0.198 10.9652 0.0081
N 3,511,811 3,511,811 3,511,811 3,511,811 3,511,811
Panel B: Lowest possible birth year
MENA 0.0004 0.0061 −0.0007 −0.0331 0.0001
(0.0013) (0.0469) (0.0188) (0.5622) (0.0039)
MENA * POST −0.0041* 0.0593 −0.0395* −2.3932** −0.0006
(0.0021) (0.0775) (0.0228) (0.9622) (0.0023)
Dep. var. mean 0.0638 2.5022 0.2161 12.0289 0.0069
N 2,259,576 2,259,576 2,259,576 2,259,576 2,259,576
Panel C: All births after 1996
MENA 0.0009 −0.0218 −0.0011 −0.0683 0.00003
(0.0017) (0.0661) (0.0174) (0.5610) (0.0031)
MENA * POST −0.0053** −0.2012*** −0.0241 −2.0880*** 0.0005
(0.0021) (0.0633) (0.0142) (0.6544) (0.0027)
Dep. var. mean 0.0369 1.6534 0.1872 9.953 0.0056
N 6,472,755 6,472,755 6,472,755 6,472,755 6,472,755
State FE YES YES YES YES YES
Month * year FE YES YES YES YES YES
State * year FE YES YES YES YES YES
Demographics YES YES YES YES YES

  1. Source: COVID-19 Research Database, 2013–2019. Notes: Outcomes are well-visits (monthly occurrence and total), vaccines (whether on schedule and total), and lead tests (total) in each month. In Panel A, the birth year is set at the maximum of the range; Panel B, the birth year is set at the minimum of the range; Panel C includes all births, whether or not the age range can be narrowed down further. Annual trends adjusted with fixed effects for years in panel, state, year by month, state by year, and birth year. Standard errors clustered at birth year level. ***p < 0.01 **p < 0.05 *p < 0.1.

References

Abadie, Alberto, Susan Athey, Guido W. Imbens, and Jeffrey M. Wooldridge. 2022. “When Should You Adjust Standard Errors for Clustering?*.” Quarterly Journal of Economics 138 (1): 1–35. https://doi.org/10.1093/qje/qjac038.Search in Google Scholar

Abdulrahim, S., S. A. James, R. Yamout, and W. Baker. 2012. “Discrimination and Psychological Distress: Does Whiteness Matter for Arab Americans?” Social Science & Medicine 75: 2116–23. https://doi.org/10.1016/j.socscimed.2012.07.030.Search in Google Scholar

Abuelezam, N. N. 2020. “Health Equity during COVID-19: The Case of Arab Americans.” American Journal of Preventive Medicine 59: 455–7. https://doi.org/10.1016/j.amepre.2020.06.004.Search in Google Scholar

Abuelezam, N. N., A. M. El-Sayed, and S. Galea. 2018. “The Health of Arab Americans in the United States: An Updated Comprehensive Literature Review.” Frontiers in Public Health 6: 262. https://doi.org/10.3389/fpubh.2018.00262.Search in Google Scholar

Alsan, M., and C. S. Yang. 2022. “Fear and the Safety Net: Evidence from Secure Communities.” The Review of Economics and Statistics: 1–45. https://doi.org/10.1162/rest_a_01250.Search in Google Scholar

Amuedo-Dorantes, C., E. Arenas-Arroyo, and A. Sevilla. 2018. “Immigration Enforcement and Economic Resources of Children with Likely Unauthorized Parents.” Journal of Public Economics 158: 63–78. https://doi.org/10.1016/j.jpubeco.2017.12.004.Search in Google Scholar

Amuedo-Dorantes, Catalina, Brandyn Churchill, and Song Yang. 2022. “Immigration Enforcement and Infant Health.” American Journal of Health Economics 8 (3): 323–58. https://doi.org/10.1086/718510.Search in Google Scholar

Angier, H., M. Hoopes, M. Marino, N. Huguet, E. A. Jacobs, J. Heintzman, H. Holderness, C. M. Hood, and J. E. DeVoe. 2017. “Uninsured Primary Care Visit Disparities under the Affordable Care Act.” The Annals of Family Medicine 15: 434–42. https://doi.org/10.1370/afm.2125.Search in Google Scholar

Antman, F., B. Duncan, and S. J. Trejo. 2016. “Ethnic Attrition and the Observed Health of Later-Generation Mexican Americans.” The American Economic Review 106 (5): 467–71. https://doi.org/10.1257/aer.p20161111.Search in Google Scholar

Attanasio, O., R. Bernal, M. Giannola, and M. Nores. 2020. “Child Development in the Early Years: Parental Investment and the Changing Dynamics of Different Dimensions.” Working Paper Series.10.3386/w27812Search in Google Scholar

Bakalian, A., and M. Bozorgmehr. 2005. “Muslim American Mobilization.” Diaspora: A Journal of Transnational Studies 14: 7–43. https://doi.org/10.1353/dsp.0.0004.Search in Google Scholar

Baselgia, E., and I. Z. Martinez. 2024. “Mobility Responses to Special Tax Regimes for the Super-Rich: Evidence from Switzerland.” The Economic Journal ueae101. https://doi.org/10.1093/ej/ueae101.Search in Google Scholar

Bradley, C. J., D. Neumark, and L. S. Walker. 2018. “The Effect of Primary Care Visits on Other Health Care Utilization: A Randomized Controlled Trial of Cash Incentives Offered to Low Income, Uninsured Adults in Virginia.” Journal of Health Economics 62: 121–33. https://doi.org/10.1016/j.jhealeco.2018.07.006.Search in Google Scholar

Buettgens, M., F. Blavin, and C. Pan. 2021. “The Affordable Care Act Reduced Income Inequality in the US: Study Examines the ACA and Income Inequality.” Health Affairs 40: 121–9. https://doi.org/10.1377/hlthaff.2019.00931.Search in Google Scholar

Butikofer, A., K. V. Løken, and K. G. Salvanes. 2015. “Long-term Consequences of Access to Well-Child Visits.” NHH Dept. of Economics Discussion Paper, No. 29.10.2139/ssrn.2719404Search in Google Scholar

Calvo, R. 2016. “Health Literacy and Quality of Care Among Latino Immigrants in the United States.” Health & Social Work 41: e44–e51. https://doi.org/10.1093/hsw/hlv076.Search in Google Scholar

Chen, J., S. Liu, and X. Zhang. 2025. “Resources Coupled with Executive Authority: Implications of Relocating Government Administrative Headquarters for Local Economic Development.” World Development 185: 106798. https://doi.org/10.1016/j.worlddev.2024.106798.Search in Google Scholar

Cunha, F., and J. Heckman. 2007. “The Technology of Skill Formation.” The American Economic Review 97: 31–47. https://doi.org/10.1257/aer.97.2.31.Search in Google Scholar

Cunha, F., and J. J. Heckman. 2009. “The Economics and Psychology of Inequality and Human Development.” Journal of the European Economic Association 7: 320–64. https://doi.org/10.1162/jeea.2009.7.2-3.320.Search in Google Scholar

Cunningham, S. A., J. D. Ruben, and K. V. Narayan. 2008. “Health of Foreign-Born People in the United States: A Review.” Health & Place 14: 623–35. https://doi.org/10.1016/j.healthplace.2007.12.002.Search in Google Scholar

Derose, K. P., and D. W. Baker. 2000. “Limited English Proficiency and Latinos’ Use of Physician Services.” Medical Care Research and Review 57: 76–91. https://doi.org/10.1177/107755870005700105.Search in Google Scholar

Diamond, L., K. Izquierdo, D. Canfield, K. Matsoukas, and F. Gany. 2019. “A Systematic Review of the Impact of Patient–Physician Non-English Language Concordance on Quality of Care and Outcomes.” Journal of General Internal Medicine 34: 1591–606. https://doi.org/10.1007/s11606-019-04847-5.Search in Google Scholar

El Reda, D. K., V. Grigorescu, S. F. Posner, and A. Davis-Harrier. 2007. “Lower Rates of Preterm Birth in Women of Arab Ancestry: An Epidemiologic paradox—Michigan, 1993–2002.” Maternal and Child Health Journal 11: 622–7. https://doi.org/10.1007/s10995-007-0199-y.Search in Google Scholar

El-Sayed, A. M., and S. Galea. 2009. “The Health of Arab-Americans Living in the United States: A Systematic Review of the Literature.” BMC Public Health 9: 272. https://doi.org/10.1186/1471-2458-9-272.Search in Google Scholar

Goodman-Bacon, A. 2018. “Difference-in-Differences with Variation in Treatment Timing.” Working Paper Series.10.3386/w25018Search in Google Scholar

Hobbs, W., and N. Lajevardi. 2019. “Effects of Divisive Political Campaigns on the Day-To-Day Segregation of Arab and Muslim Americans.” American Political Science Review 113: 270–6. https://doi.org/10.1017/S0003055418000801.Search in Google Scholar

Howell, S., and A. Shryock. 2003. “Cracking Down on Diaspora: Arab Detroit and America’s “War on Terror”.” Anthropological Quarterly 76: 443–62. https://doi.org/10.1353/anq.2003.0040.Search in Google Scholar

Jang, Y., and M. T. Kim. 2019. “Limited English Proficiency and Health Service Use in Asian Americans.” Journal of Immigrant and Minority Health 21: 264–70. https://doi.org/10.1007/s10903-018-0763-0.Search in Google Scholar

Karger, E., and A. Rajan. 2021. “Heterogeneity in the Marginal Propensity to Consume: Evidence from Covid-19 Stimulus Payments.” FRB of Chicago Working Paper No. 2020-15.10.2139/ssrn.3612828Search in Google Scholar

Lauderdale, D. S. 2006. “Birth Outcomes for Arabic-named Women in California before and after September 11.” Demography 43: 185–201. https://doi.org/10.1353/dem.2006.0008.Search in Google Scholar

Lee, J. S., E. J. Pérez-Stable, S. E. Gregorich, M. H. Crawford, A. Green, J. Livaudais-Toman, and L. S. Karliner. 2017. “Increased Access to Professional Interpreters in the Hospital Improves Informed Consent for Patients with Limited English Proficiency.” Journal of General Internal Medicine 32: 863–70. https://doi.org/10.1007/s11606-017-3983-4.Search in Google Scholar

Liang, Y., X. Peng, and M. A. Sun. 2024. “Long-term Impacts of Growth and Development Monitoring: Evidence from Routine Health Examinations in Early Childhood.” CESifo Working Paper No. 10912.10.2139/ssrn.4711269Search in Google Scholar

Mahmoudi, E., and G. A. Jensen. 2012. “Diverging Racial and Ethnic Disparities in Access to Physician Care: Comparing 2000 and 2007.” Medical Care 50: 327–34. https://doi.org/10.1097/mlr.0b013e318245a111.Search in Google Scholar

Marks, R., P. Jacobs, and A. Coritz. 2023. Lebanese, Iranian and Egyptian Populations Represented Nearly Half of the MENA Population in 2020 Census Census.Gov. https://www.census.gov/library/stories/2023/09/2020-census-dhc-a-mena-population.html (accessed April 29, 24).Search in Google Scholar

Marthey, D., H. Rochford, and E. Andreyeva. 2024. “Examining the Impact of Medicaid Payments for Immediate Postpartum Long‐acting Reversible Contraception on the Mental Health of Low‐income Mothers.” Health Services Research 59 (3): e14281. https://doi.org/10.1111/1475-6773.14281.Search in Google Scholar

Medina, P. C., V. Mittal, and M. Page. 2021. “The Effect of Stock Ownership on Individual Spending and Loyalty.” NBER Working Paper No. 28479.10.3386/w28479Search in Google Scholar

Mishra, S., and J. Lokaneeta. 2021. “Combatting Suspicion, Creating Trust: The Interface of Muslim Communities and Law Enforcement in the United States after 9/11.” Polity 53: 288–314. https://doi.org/10.1086/713704.Search in Google Scholar

Müller, K., and C. Schwarz. 2023. “From Hashtag to Hate Crime: Twitter and Antiminority Sentiment.” American Economic Journal: Applied Economics 15: 270–312. https://doi.org/10.1257/app.20210211.Search in Google Scholar

Tom, J. O., C. W. Tseng, J. Davis, C. Solomon, C. A. Zhou, and R. Mangione-Smith. 2010. “Missed Well-Child Care Visits, Low Continuity of Care, and Risk of Ambulatory Care-Sensitive Hospitalizations in Young Children.” Archives of Pediatrics and Adolescent Medicine 164 (11): 1052–8. https://doi.org/10.1001/archpediatrics.2010.201.Search in Google Scholar

Tom, J. O., R. Mangione-Smith, D. C. Grossman, C. Solomon, and C. W. Tseng. 2013. “Well-child Care Visits and Risk of Ambulatory Care-Sensitive Hospitalizations.” American Journal of Managed Care 19 (5): 354–60.Search in Google Scholar

Ukert, Benjamin, and Theodoros V. Giannouchos. 2023. “Association of the Affordable Care Act with Racial and Ethnic Disparities in Uninsured Emergency Department Utilization.” BMC Health Services Research 23 (1): 1302. https://doi.org/10.1186/s12913-023-10168-5.Search in Google Scholar

Vargas, E. D., and M. A. Pirog. 2016. “Mixed‐status Families and WIC Uptake: The Effects of Risk of Deportation on Program Use.” Social Science Quarterly 97: 555–72. https://doi.org/10.1111/ssqu.12286.Search in Google Scholar

Vargas, E. D., and V. D. Ybarra. 2017. “US Citizen Children of Undocumented Parents: The Link between State Immigration Policy and the Health of Latino Children.” Journal of Immigrant and Minority Health 19: 913–20. https://doi.org/10.1007/s10903-016-0463-6.Search in Google Scholar

Vega, W. A., M. A. Rodriguez, and E. Gruskin. 2009. “Health Disparities in the Latino Population.” Epidemiologic Reviews 31: 99–112. https://doi.org/10.1093/epirev/mxp008.Search in Google Scholar

Vu, H. 2024. “I Wish I Were Born in Another Time: Unintended Consequences of Immigration Enforcement on Birth Outcomes.” Health Economics 33: 345–62. https://doi.org/10.1002/hec.4775.Search in Google Scholar

Wang, J. S.-H., and N. Kaushal. 2019. “Health and Mental Health Effects of Local Immigration Enforcement.” International Migration Review 53: 970–1001. https://doi.org/10.1177/0197918318791978.Search in Google Scholar

Watson, T. 2014. “Inside the Refrigerator: Immigration Enforcement and Chilling Effects in Medicaid Participation.” American Economic Journal: Economic Policy 6: 313–38. https://doi.org/10.1257/pol.6.3.313.Search in Google Scholar
Received: 2024-12-17
Accepted: 2025-03-27
Published Online: 2025-05-26

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Articles
  3. COVID-19 Biopharmaceutical Innovation and Industry Appropriation
  4. The Muslim Ban and Preventive Care for Children of Middle Eastern Ancestry
https://doi.org/10.1515/fhep-2024-0052
Keywords for this article
MENA; Muslim ban; disparities; preventive care

Articles in the same Issue

  1. Frontmatter
  2. Articles
  3. COVID-19 Biopharmaceutical Innovation and Industry Appropriation
  4. The Muslim Ban and Preventive Care for Children of Middle Eastern Ancestry
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 15.12.2025 from https://www.degruyterbrill.com/document/doi/10.1515/fhep-2024-0052/html
Scroll to top button