[Skip to Content]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Figure 1.  Refugees to Denmark by Year
Refugees to Denmark by Year

Source: Authors’ calculations using data from the United Nations High Commissioner for Refugees (http://popstats.unhcr.org).

Figure 2.  Percentage of Immigrants From Countries Sending Refugees to Denmark by Municipality
Percentage of Immigrants From Countries Sending Refugees to Denmark by Municipality

Percentage of immigrants shown in the year before (A) and the year after (B) the dispersal policy was in effect (1985 vs 1999).

Source: Authors’ calculations using data from Statistics Denmark.

Figure 3.  Association of Neighborhood Disadvantage With Cardiovascular Outcomes, N = 49 305
Association of Neighborhood Disadvantage With Cardiovascular Outcomes, N = 49 305

The study population includes all adult immigrants (aged 18 years and older) who arrived in Denmark from countries sending refugees from 1986 to 1998. Diagnoses were extracted from register data using medication and physician diagnosis codes. A continuous variable representing a composite index of socioeconomic disadvantage was created for each neighborhood by year using principal component analysis to combine 8 neighborhood-level sociodemographic variables (eTable 1 in the Supplement). Analyses involved multivariable linear regressions, with covariates including family size, sex, marital status, region of origin, year of arrival, and fixed effects for municipality. Coefficients represent the change in risk (in percentage points) per unit of the disadvantage index, with 95% CIs in parentheses for both risk factors and end points. MI indicates myocardial infarction.

Table.  Sociodemographic characteristics of Refugees Arriving in Denmark During 1986-1998, by Neighborhood Disadvantage Level on Arrival
Sociodemographic characteristics of Refugees Arriving in Denmark During 1986-1998, by Neighborhood Disadvantage Level on Arrival
1.
Sangalang  CC, Vang  C.  Intergenerational trauma in refugee families: a systematic review.   J Immigr Minor Health. 2017;19(3):745-754. doi:10.1007/s10903-016-0499-7 PubMedGoogle ScholarCrossref
2.
Shishehgar  S, Gholizadeh  L, DiGiacomo  M, Green  A, Davidson  PM.  Health and socio-cultural experiences of refugee women: an integrative review.   J Immigr Minor Health. 2017;19(4):959-973. doi:10.1007/s10903-016-0379-1 PubMedGoogle ScholarCrossref
3.
Markides  KS, Rote  S. Immigrant health paradox. In: Scott  RA, Kosslyn  SM, eds.  Emerging Trends in the Social and Behavioral Sciences. John Wiley & Sons; 2015. doi:10.1002/9781118900772.etrds0174
4.
Inhorn  MC, Serour  GI.  Islam, medicine, and Arab-Muslim refugee health in America after 9/11.   Lancet. 2011;378(9794):935-943. doi:10.1016/S0140-6736(11)61041-6 PubMedGoogle ScholarCrossref
5.
Connor  P.  Explaining the refugee gap: economic outcomes of refugees versus other immigrants.   J Refug Stud. 2010;23(3):377-397. doi:10.1093/jrs/feq025 Google ScholarCrossref
6.
Sikorski  N.  Understanding the Algorithm Meant to Help Refugees Get Jobs Fast. News Deeply; 2018.
7.
Ellis  BH, MacDonald  HZ, Lincoln  AK, Cabral  HJ.  Mental health of Somali adolescent refugees: the role of trauma, stress, and perceived discrimination.   J Consult Clin Psychol. 2008;76(2):184-193. doi:10.1037/0022-006X.76.2.184 PubMedGoogle ScholarCrossref
8.
Fox  PG, Cowell  JM, Montgomery  AC.  Southeast Asian refugee children: violence experience and depression.   Int J Psychiatr Nurs Res. 1999;5(2):589-600.PubMedGoogle Scholar
9.
Geltman  PL, Grant-Knight  W, Mehta  SD,  et al.  The “lost boys of Sudan”: functional and behavioral health of unaccompanied refugee minors re-settled in the United States.   Arch Pediatr Adolesc Med. 2005;159(6):585-591. doi:10.1001/archpedi.159.6.585 PubMedGoogle ScholarCrossref
10.
Marshall  GN, Schell  TL, Wong  EC,  et al.  Diabetes and cardiovascular disease risk in Cambodian refugees.   J Immigr Minor Health. 2016;18(1):110-117. doi:10.1007/s10903-014-0142-4 PubMedGoogle ScholarCrossref
11.
Barnes  DM, Harrison  C, Heneghan  R.  Health risk and promotion behaviors in refugee populations.   J Health Care Poor Underserved. 2004;15(3):347-356. doi:10.1353/hpu.2004.0034 PubMedGoogle ScholarCrossref
12.
Kinzie  JD, Riley  C, McFarland  B,  et al.  High prevalence rates of diabetes and hypertension among refugee psychiatric patients.   J Nerv Ment Dis. 2008;196(2):108-112. doi:10.1097/NMD.0b013e318162aa51 PubMedGoogle ScholarCrossref
13.
DesMeules  M, Gold  J, McDermott  S,  et al.  Disparities in mortality patterns among Canadian immigrants and refugees, 1980-1998: results of a national cohort study.   J Immigr Health. 2005;7(4):221-232. doi:10.1007/s10903-005-5118-y PubMedGoogle ScholarCrossref
14.
Bo  A, Zinckernagel  L, Krasnik  A, Petersen  JH, Norredam  M.  Coronary heart disease incidence among non-Western immigrants compared to Danish-born people: effect of country of birth, migrant status, and income.   Eur J Prev Cardiol. 2015;22(10):1281-1289. doi:10.1177/2047487314551538 PubMedGoogle ScholarCrossref
15.
Dimsdale  JE.  Psychological stress and cardiovascular disease.   J Am Coll Cardiol. 2008;51(13):1237-1246. doi:10.1016/j.jacc.2007.12.024 PubMedGoogle ScholarCrossref
16.
Clark  AM, DesMeules  M, Luo  W, Duncan  AS, Wielgosz  A.  Socioeconomic status and cardiovascular disease: risks and implications for care.   Nat Rev Cardiol. 2009;6(11):712-722. doi:10.1038/nrcardio.2009.163 PubMedGoogle ScholarCrossref
17.
Hamad  R, Penko  J, Kazi  DS,  et al.  Association of low socioeconomic status with premature coronary heart disease in US adults.   JAMA Cardiol. Published online May 27, 2020. doi:10.1001/jamacardio.2020.1458 PubMedGoogle Scholar
18.
Fazel  M, Reed  RV, Panter-Brick  C, Stein  A.  Mental health of displaced and refugee children resettled in high-income countries: risk and protective factors.   Lancet. 2012;379(9812):266-282. doi:10.1016/S0140-6736(11)60051-2 PubMedGoogle ScholarCrossref
19.
Edberg  M, Cleary  S, Vyas  A.  A trajectory model for understanding and assessing health disparities in immigrant/refugee communities.   J Immigr Minor Health. 2011;13(3):576-584. doi:10.1007/s10903-010-9337-5 PubMedGoogle ScholarCrossref
20.
Rundle  A, Neckerman  KM, Freeman  L,  et al.  Neighborhood food environment and walkability predict obesity in New York City.   Environ Health Perspect. 2009;117(3):442-447. doi:10.1289/ehp.11590 PubMedGoogle ScholarCrossref
21.
Sundquist  K, Eriksson  U, Kawakami  N, Skog  L, Ohlsson  H, Arvidsson  D.  Neighborhood walkability, physical activity, and walking behavior: the Swedish Neighborhood and Physical Activity (SNAP) study.   Soc Sci Med. 2011;72(8):1266-1273. doi:10.1016/j.socscimed.2011.03.004 PubMedGoogle ScholarCrossref
22.
Sundquist  K, Eriksson  U, Mezuk  B, Ohlsson  H.  Neighborhood walkability, deprivation and incidence of type 2 diabetes: a population-based study on 512,061 Swedish adults.   Health Place. 2015;31:24-30. doi:10.1016/j.healthplace.2014.10.011 PubMedGoogle ScholarCrossref
23.
Boardman  JD, Finch  BK, Ellison  CG, Williams  DR, Jackson  JS.  Neighborhood disadvantage, stress, and drug use among adults.   J Health Soc Behav. 2001;42(2):151-165. doi:10.2307/3090175 PubMedGoogle ScholarCrossref
24.
Giurgescu  C, Misra  DP, Sealy-Jefferson  S,  et al.  The impact of neighborhood quality, perceived stress, and social support on depressive symptoms during pregnancy in African American women.   Soc Sci Med. 2015;130(0):172-180. doi:10.1016/j.socscimed.2015.02.006 PubMedGoogle ScholarCrossref
25.
Hill  TD, Angel  RJ.  Neighborhood disorder, psychological distress, and heavy drinking.   Soc Sci Med. 2005;61(5):965-975. doi:10.1016/j.socscimed.2004.12.027 PubMedGoogle ScholarCrossref
26.
Kasarda  JD.  Inner-city concentrated poverty and neighborhood distress: 1970 to 1990.   Hous Policy Debate. 1993;4(3):253-302. doi:10.1080/10511482.1993.9521135 Google ScholarCrossref
27.
Damm  AP, Dustmann  C.  Does growing up in a high crime neighborhood affect youth criminal behavior?   Am Econ Rev. 2014;104(6):1806-1832. doi:10.1257/aer.104.6.1806 Google ScholarCrossref
28.
Damm  AP.  The Danish Dispersal Policy on Refugee Immigrants 1986-1998: A Natural Experiment? Aarhus School of Business, Department of Economics; 2005.
29.
Damm  AP.  Determinants of recent immigrants’ location choices: quasi-experimental evidence.   J Popul Econ. 2009;22(1):145-174. doi:10.1007/s00148-007-0148-5 Google ScholarCrossref
30.
Damm  AP.  Ethnic enclaves and immigrant labor market outcomes: quasi-experimental evidence.   J Labor Econ. 2009;27(2):281-314. doi:10.1086/599336 Google ScholarCrossref
31.
Damm  AP, Rosholm  M.  Employment effects of spatial dispersal of refugees.   Rev Econ Househ. 2010;8(1):105-146. doi:10.1007/s11150-009-9067-4 Google ScholarCrossref
32.
Ludvigsson  JF, Håberg  SE, Knudsen  GP,  et al.  Ethical aspects of registry-based research in the Nordic countries.   Clin Epidemiol. 2015;7:491-508. doi:10.2147/CLEP.S90589 PubMedGoogle ScholarCrossref
33.
Schmidt  M, Schmidt  SAJ, Adelborg  K,  et al.  The Danish health care system and epidemiological research: from health care contacts to database records.   Clin Epidemiol. 2019;11:563-591. doi:10.2147/CLEP.S179083 PubMedGoogle ScholarCrossref
34.
Pottegård  A, Schmidt  SAJ, Wallach-Kildemoes  H, Sørensen  HT, Hallas  J, Schmidt  M.  Data resource profile: the Danish national prescription registry.   Int J Epidemiol. 2017;46(3):798-798f.PubMedGoogle Scholar
35.
Schmidt  M, Schmidt  SAJ, Sandegaard  JL, Ehrenstein  V, Pedersen  L, Sørensen  HT.  The Danish National Patient Registry: a review of content, data quality, and research potential.   Clin Epidemiol. 2015;7:449-490. doi:10.2147/CLEP.S91125 PubMedGoogle ScholarCrossref
36.
Thygesen  LC, Ersbøll  AK.  Danish population-based registers for public health and health-related welfare research: introduction to the supplement.   Scand J Public Health. 2011;39(7 suppl):8-10. doi:10.1177/1403494811409654PubMedGoogle Scholar
37.
Hernán  MA, Hernández-Díaz  S, Robins  JM.  A structural approach to selection bias.   Epidemiology. 2004;15(5):615-625. doi:10.1097/01.ede.0000135174.63482.43 PubMedGoogle ScholarCrossref
38.
Greenland  S, Robins  JM.  Identifiability, exchangeability and confounding revisited.   Epidemiol Perspect Innov. 2009;6(1):4. doi:10.1186/1742-5573-6-4 PubMedGoogle ScholarCrossref
39.
Ludwig  J, Duncan  GJ, Gennetian  LA,  et al.  Long-term neighborhood effects on low-income families: evidence from Moving to Opportunity.   Am Econ Rev. 2013;103(3):226-231. doi:10.1257/aer.103.3.226 Google ScholarCrossref
40.
Hirano  K, Imbens  GW, Rubin  DB, Zhou  X-H.  Assessing the effect of an influenza vaccine in an encouragement design.   Biostatistics. 2000;1(1):69-88. doi:10.1093/biostatistics/1.1.69 PubMedGoogle ScholarCrossref
41.
Messer  LC, Laraia  BA, Kaufman  JS,  et al.  The development of a standardized neighborhood deprivation index.   J Urban Health. 2006;83(6):1041-1062. doi:10.1007/s11524-006-9094-x PubMedGoogle ScholarCrossref
42.
Havard  S, Deguen  S, Bodin  J, Louis  K, Laurent  O, Bard  D.  A small-area index of socioeconomic deprivation to capture health inequalities in France.   Soc Sci Med. 2008;67(12):2007-2016. doi:10.1016/j.socscimed.2008.09.031 PubMedGoogle ScholarCrossref
43.
Huie  SAB.  The concept of neighborhood in health and mortality research.   Sociol Spectr. 2001;21(3):341-358. doi:10.1080/027321701300202028 Google ScholarCrossref
44.
Meijer  M, Röhl  J, Bloomfield  K, Grittner  U.  Do neighborhoods affect individual mortality? a systematic review and meta-analysis of multilevel studies.   Soc Sci Med. 2012;74(8):1204-1212. doi:10.1016/j.socscimed.2011.11.034 PubMedGoogle ScholarCrossref
45.
Meijer  M, Engholm  G, Grittner  U, Bloomfield  K.  A socioeconomic deprivation index for small areas in Denmark.   Scand J Public Health. 2013;41(6):560-569. doi:10.1177/1403494813483937 PubMedGoogle ScholarCrossref
46.
Meijer  M, Kejs  AM, Stock  C, Bloomfield  K, Ejstrud  B, Schlattmann  P.  Population density, socioeconomic environment and all-cause mortality: a multilevel survival analysis of 2.7 million individuals in Denmark.   Health Place. 2012;18(2):391-399. doi:10.1016/j.healthplace.2011.12.001 PubMedGoogle ScholarCrossref
47.
Buys  DR, Howard  VJ, McClure  LA,  et al.  Association between neighborhood disadvantage and hypertension prevalence, awareness, treatment, and control in older adults: results from the University of Alabama at Birmingham Study of Aging.   Am J Public Health. 2015;105(6):1181-1188. doi:10.2105/AJPH.2014.302048 PubMedGoogle ScholarCrossref
48.
Carlsson  AC, Li  X, Holzmann  MJ,  et al.  Neighborhood socioeconomic status at the age of 40 years and ischemic stroke before the age of 50 years: a nationwide cohort study from Sweden.   Int J Stroke. 2017;12(8):815-826. doi:10.1177/1747493017702663 PubMedGoogle ScholarCrossref
49.
Cubbin  C, Sundquist  K, Ahlén  H, Johansson  S-E, Winkleby  MA, Sundquist  J.  Neighborhood deprivation and cardiovascular disease risk factors: protective and harmful effects.   Scand J Public Health. 2006;34(3):228-237. doi:10.1080/14034940500327935 PubMedGoogle Scholar
50.
Halonen  JI, Stenholm  S, Pentti  J,  et al.  Childhood psychosocial adversity and adult neighborhood disadvantage as predictors of cardiovascular disease: a cohort study.   Circulation. 2015;132(5):371-379. doi:10.1161/CIRCULATIONAHA.115.015392 PubMedGoogle ScholarCrossref
51.
Kaiser  P, Diez Roux  AV, Mujahid  M,  et al.  Neighborhood environments and incident hypertension in the multi-ethnic study of atherosclerosis.   Am J Epidemiol. 2016;183(11):988-997. doi:10.1093/aje/kwv296 PubMedGoogle ScholarCrossref
52.
Kershaw  KN, Diez Roux  AV, Burgard  SA, Lisabeth  LD, Mujahid  MS, Schulz  AJ.  Metropolitan-level racial residential segregation and black-white disparities in hypertension.   Am J Epidemiol. 2011;174(5):537-545. doi:10.1093/aje/kwr116 PubMedGoogle ScholarCrossref
53.
White  JS, Hamad  R, Li  X,  et al.  Long-term effects of neighbourhood deprivation on diabetes risk: quasi-experimental evidence from a refugee dispersal policy in Sweden.   Lancet Diabetes Endocrinol. 2016;4(6):517-524. doi:10.1016/S2213-8587(16)30009-2 PubMedGoogle ScholarCrossref
54.
Kershaw  KN, Osypuk  TL, Do  DP, De Chavez  PJ, Diez Roux  AV.  Neighborhood-level racial/ethnic residential segregation and incident cardiovascular disease: the multi-ethnic study of atherosclerosis.   Circulation. 2015;131(2):141-148. doi:10.1161/CIRCULATIONAHA.114.011345 PubMedGoogle ScholarCrossref
55.
Hamad  R, Modrek  S, Kubo  J, Goldstein  BA, Cullen  MR.  Using “big data” to capture overall health status: properties and predictive value of a claims-based health risk score.   PLoS One. 2015;10(5):e0126054. doi:10.1371/journal.pone.0126054 PubMedGoogle Scholar
56.
Scherrer  JF, Virgo  KS, Zeringue  A,  et al.  Depression increases risk of incident myocardial infarction among Veterans Administration patients with rheumatoid arthritis.   Gen Hosp Psychiatry. 2009;31(4):353-359. doi:10.1016/j.genhosppsych.2009.04.001 PubMedGoogle ScholarCrossref
57.
Chetty  R, Hendren  N, Katz  LF.  The effects of exposure to better neighborhoods on children: new evidence from the moving to opportunity experiment.   Am Econ Rev. 2016;106(4):855-902. doi:10.1257/aer.20150572 PubMedGoogle ScholarCrossref
58.
Guyatt  GH, Osoba  D, Wu  AW, Wyrwich  KW, Norman  GR; Clinical Significance Consensus Meeting Group.  Methods to explain the clinical significance of health status measures.   Mayo Clin Proc. 2002;77(4):371-383. doi:10.4065/77.4.371 PubMedGoogle ScholarCrossref
59.
Diez Roux  AV, Merkin  SS, Arnett  D,  et al.  Neighborhood of residence and incidence of coronary heart disease.   N Engl J Med. 2001;345(2):99-106. doi:10.1056/NEJM200107123450205 PubMedGoogle ScholarCrossref
60.
Diez-Roux  AV, Nieto  FJ, Muntaner  C,  et al.  Neighborhood environments and coronary heart disease: a multilevel analysis.   Am J Epidemiol. 1997;146(1):48-63. doi:10.1093/oxfordjournals.aje.a009191 PubMedGoogle ScholarCrossref
61.
Sundquist  K, Theobald  H, Yang  M, Li  X, Johansson  S-E, Sundquist  J.  Neighborhood violent crime and unemployment increase the risk of coronary heart disease: a multilevel study in an urban setting.   Soc Sci Med. 2006;62(8):2061-2071. doi:10.1016/j.socscimed.2005.08.051 PubMedGoogle ScholarCrossref
62.
Sundquist  K, Winkleby  M, Ahlén  H, Johansson  S-E.  Neighborhood socioeconomic environment and incidence of coronary heart disease: a follow-up study of 25,319 women and men in Sweden.   Am J Epidemiol. 2004;159(7):655-662. doi:10.1093/aje/kwh096 PubMedGoogle ScholarCrossref
63.
Starks  MA, Schmicker  RH, Peterson  ED,  et al; Resuscitation Outcomes Consortium (ROC).  Association of neighborhood demographics with out-of-hospital cardiac arrest treatment and outcomes: where you live may matter.   JAMA Cardiol. 2017;2(10):1110-1118. doi:10.1001/jamacardio.2017.2671 PubMedGoogle ScholarCrossref
64.
Documét  PI, Sharma  RK.  Latinos’ health care access: financial and cultural barriers.   J Immigr Health. 2004;6(1):5-13. doi:10.1023/B:JOIH.0000014638.87569.2e PubMedGoogle ScholarCrossref
65.
Kirby  JB, Kaneda  T.  Neighborhood socioeconomic disadvantage and access to health care.   J Health Soc Behav. 2005;46(1):15-31. doi:10.1177/002214650504600103 PubMedGoogle ScholarCrossref
66.
Zuvekas  SH, Taliaferro  GS.  Pathways to access: health insurance, the health care delivery system, and racial/ethnic disparities, 1996-1999.   Health Aff (Millwood). 2003;22(2):139-153. doi:10.1377/hlthaff.22.2.139 PubMedGoogle ScholarCrossref
67.
Raphael  E, White  J, Li  X,  et al.  Neighborhood deprivation and mental health among immigrants to Sweden.   Epidemiology. 2020;31(3):e25-e27. doi:10.1097/EDE.0000000000001160PubMedGoogle Scholar
68.
Ben-Shlomo  Y, Kuh  D.  A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives.   Int J Epidemiol. 2002;31(2):285-293. doi:10.1093/ije/31.2.285 PubMedGoogle ScholarCrossref
69.
Menec  VH, Shooshtari  S, Nowicki  S, Fournier  S.  Does the relationship between neighborhood socioeconomic status and health outcomes persist into very old age? a population-based study.   J Aging Health. 2010;22(1):27-47. doi:10.1177/0898264309349029 PubMedGoogle ScholarCrossref
70.
Hamano  T, Kawakami  N, Li  X, Sundquist  K.  Neighbourhood environment and stroke: a follow-up study in Sweden.   PLoS One. 2013;8(2):e56680. doi:10.1371/journal.pone.0056680 PubMedGoogle Scholar
71.
Howard  VJ, McClure  LA, Kleindorfer  DO,  et al.  Neighborhood socioeconomic index and stroke incidence in a national cohort of blacks and whites.   Neurology. 2016;87(22):2340-2347. doi:10.1212/WNL.0000000000003299 PubMedGoogle ScholarCrossref
72.
Organisation for Economic Co-operation and Development.  International Migration Outlook. OECD Publishing; 2018.
73.
Bansak  K, Ferwerda  J, Hainmueller  J,  et al.  Improving refugee integration through data-driven algorithmic assignment.   Science. 2018;359(6373):325-329. doi:10.1126/science.aao4408 PubMedGoogle ScholarCrossref
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
    Original Investigation
    Global Health
    August 21, 2020

    Association of Neighborhood Disadvantage With Cardiovascular Risk Factors and Events Among Refugees in Denmark

    Author Affiliations
    • 1Philip R. Lee Institute for Health Policy Studies, University of California School of Medicine, San Francisco
    • 2Department of Family & Community Medicine, University of California School of Medicine, San Francisco
    • 3Department of Clinical Epidemiology, Aarhus University, Denmark
    • 4Center for Population Health Science, Stanford University, Stanford, California
    • 5Department of Cardiology, Aarhus University Hospital, Denmark
    • 6Department of Epidemiology & Biostatistics, University of California School of Medicine, San Francisco
    JAMA Netw Open. 2020;3(8):e2014196. doi:10.1001/jamanetworkopen.2020.14196
    Key Points español 中文 (chinese)

    Question  Is there an association of neighborhood socioeconomic disadvantage with the development of cardiovascular risk factors, myocardial infarction, and stroke among refugees?

    Findings  In this quasi-experimental cohort study, 49 305 refugees who were assigned to more disadvantaged neighborhoods across Denmark were at increased risk of developing hypertension, hyperlipidemia, diabetes, and myocardial infarction over subsequent decades. No associations were found for stroke.

    Meaning  Neighborhood characteristics may be associated with long-term cardiovascular risk among refugees.

    Abstract

    Importance  Refugees are among the most disadvantaged individuals in society, and they often have elevated risks of cardiovascular risk factors and events. Evidence is limited regarding factors that may worsen cardiovascular health among this vulnerable group.

    Objective  To test the hypothesis that refugee placement in socioeconomically disadvantaged neighborhoods is associated with increased cardiovascular risk.

    Design, Setting, and Participants  The study population of this quasi-experimental, registry-based cohort study included 49 305 adults 18 years and older who came to Denmark as refugees from other countries during the years of Denmark’s refugee dispersal policy from 1986 to 1998. Refugees were dispersed to neighborhoods with varying degrees of socioeconomic disadvantage in an arbitrary manner conditional on observed characteristics. The association of neighborhood disadvantage on arrival with several cardiovascular outcomes in subsequent decades was evaluated using regression models that adjusted for individual, family, and municipal characteristics. Health outcomes were abstracted from the inpatient register, outpatient specialty clinic register, and prescription drug register through 2016. Data analysis was conducted from May 2018 to July 2019.

    Exposures  A composite index of neighborhood disadvantage was constructed using 8 neighborhood-level socioeconomic characteristics derived from Danish population register data.

    Main Outcomes and Measures  Primary study outcomes included hypertension, hyperlipidemia, type 2 diabetes, myocardial infarction, and stroke. Before data analysis commenced, it was hypothesized that higher levels of neighborhood disadvantage were associated with an increased risk of cardiovascular risk factors and events.

    Results  A total of 49 305 participants were included (median [interquartile range] age, 30.5 [24.9-39.8] years; 43.3% women). Participant region of origin included 6318 from Africa (12.8%), 7253 from Asia (14.7%), 3446 from Eastern Europe (7.0%), 5416 from Iraq (11.0%), 6206 from Iran (12.6%), 5558 from Palestine (via Lebanon, Israel, Occupied Palestinian Territories; 11.3%), and 15 108 from Yugoslavia (30.6%). Adjusted models revealed an association between placement in disadvantaged neighborhoods and increased risk of hypertension (0.71 [95% CI, 0.30-1.13] percentage points per unit of disadvantage index; P < .01), hyperlipidemia (0.44 [95% CI, 0.06-0.83] percentage points; P = .01), diabetes (0.45 [95% CI, 0.09-0.81] percentage points; P = .01), and myocardial infarction (0.14 [95% CI, 0.03-0.25] percentage points; P = .01). No association was found for stroke. Individuals who arrived in Denmark before age 35 years had an increased risk of hyperlipidemia (1.16 [95% CI, 0.41-1.92] percentage points; P < .01), and there were no differences by sex.

    Conclusions and Relevance  In this quasi-experimental cohort study, neighborhood disadvantage was associated with increased cardiovascular risk in a relatively young population of refugees. Neighborhood characteristics may be an important consideration when refugees are placed by resettlement agencies and host countries. Future work should examine additional health outcomes as well as potential mediating pathways to target future interventions (eg, neighborhood ease of walking, employment opportunities).

    Introduction

    Refugees are among the most vulnerable individuals in society, often surviving violence, war, and other trauma before relocating to an unfamiliar host country.1,2 Although some immigrants have better health than native-born citizens,3 refugees are uniquely at risk due to the often involuntary and sudden nature of their displacement and the adversity faced in their home countries.4,5 In many countries, population-level studies of refugee health are notoriously difficult to conduct due to data constraints. For example, the United States collects minimal data on refugees only during the 90 days after their arrival.6 Most literature is therefore limited to a few small cross-sectional studies of refugees from a single country or region or to the few refugees who happen to be interviewed in national surveys.5,7-9

    A few studies have demonstrated that rates of cardiovascular disease (CVD) and related risk factors among some refugees are increased compared with those of native-born populations.10-14 In the general population, CVD is more common among individuals of lower socioeconomic status and is associated with chronic stress,15-17 making it a particularly relevant outcome among refugees.

    Previous studies have investigated factors associated with health among refugees, although most focused on mental health as an outcome and on individual risk factors, such as sex or exposure to violence.18 Fewer studies focused on neighborhood factors,18 which theory suggests are key determinants of immigrant health.19 For example, socioeconomically disadvantaged neighborhoods often have limited walkability or availability of nutritious food, thereby affecting health behaviors associated with CVD.20-22 Alternatively, reduced employment opportunities or greater neighborhood crime rates may increase stress, which in turn is associated with increased CVD.23-27 Neighborhood socioeconomic characteristics may be particularly salient for refugees, who often have limited control over their placement and limited financial means.

    This study took advantage of a unique natural experiment in which refugees to Denmark were assigned to neighborhoods with different levels of socioeconomic disadvantage.28 From 1986 to 1998, the Danish government dispersed tens of thousands of incoming refugees across the country in an arbitrary fashion, conditional on observed characteristics, to avoid overcrowding in major cities.27-31 We employed unique population-level data spanning 3 decades from Denmark’s population and health registers. We tested the hypothesis that neighborhood socioeconomic disadvantage is associated with increased risk of CVD risk factors, myocardial infarction, and stroke across the life course among resettled refugees.

    Methods
    Ethics Approval

    This quasi-experimental, registry-based cohort study was approved by the Danish Data Protection Agency (record number 2015-57). Registry-based studies do not require ethical board approval in Denmark,32 and this study did not include any personally identifiable information on study participants. As a result, participant consent was not obtained.

    Data

    The cohort was created by linking several Danish national registers.33 Sociodemographic data were drawn from administrative registers of the total population (January 1, 1986, to December 31, 2016), whereas outcomes were ascertained from inpatient (January 1, 1986, to December 31, 2016), outpatient specialty clinic (January 1, 1994, to December 31, 2016), and prescription drug (January 1, 1995, to December 31, 2016) registers.34-36 Danish registers do not include diagnostic information on primary care encounters. The registers include 8.1 million individuals cumulatively during the study period. Data analysis was conducted from May 2018 to July 2019. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

    Study Population

    The term immigrants refers to foreign-born individuals, whereas refugees are a unique subset of immigrants fleeing persecution and entitled to specific protections under international law. The study population included adult immigrants (18 years and older) arriving in Denmark from refugee-sending countries during the 1986 to 1998 period, which represents the years of the government dispersal policy (N = 49 305; eFigure 1 in the Supplement). We excluded individuals reuniting with family in Denmark, because they were not subject to the dispersal policy.

    Danish Dispersal Policy

    We leveraged a unique natural experiment in which incoming refugees were assigned to neighborhoods with varying levels of disadvantage throughout the country. Denmark implemented this refugee dispersal policy from 1986 to 1998.28 Because of the surge in arrivals to Denmark during this period (Figure 1), the policy’s goal was to ease labor market conditions in heavily settled areas and to promote better integration of refugees. As a result of the policy, refugees were spread more evenly across the country (Figure 2).

    Refugee placement officers only had knowledge of the following characteristics that were available on refugees’ applications: sex, birthdate, marital status, family size, and nationality.28 Officers otherwise had no direct interaction with families. Therefore, any possible confounding would have arisen only from observed characteristics in the refugees' applications, reducing the chances of confounding by unmeasured factors (ie, guaranteeing conditional exchangeability).37,38 Thus, initial neighborhood assignment within a municipality was arbitrary, conditional on these demographic characteristics. These characteristics are available in the register data, and we adjusted for them during data analysis. Studies have identified greater geographic dispersal of refugees during the policy period (as in Figure 2), no evidence of sorting by ethnic group, and covariate balance by neighborhood type.27-30 It is therefore unlikely that unobserved factors influenced neighborhood assignment. As a result, this study attempts to address the challenges of selective migration present in existing studies on neighborhood outcomes.

    Although register data do not include information on the neighborhood to which refugees were assigned, official reports document that more than 90% of incoming refugees agreed to placement in their assigned municipality, indicating strong adherence to the placement program.28 Those who refused placement were responsible for finding their own place of residence, which may have resulted in some residual bias in our estimates. Nevertheless, this represents a high level of compliance relative to the only prior relevant assignment study to our knowledge, the US Moving to Opportunity experiment, in which only half of participants adhered to their assignment.39 As refugees reuniting with family were not subject to the dispersal policy, we excluded these individuals using family structure information available in the registers. For the remaining refugees, we assumed that the observed neighborhood of residence on arrival was the neighborhood to which refugees were assigned. For those who moved within 1 year of arrival, we assumed that the first place of residence was temporary housing—similar to prior work based on how the policy was implemented28,29—and instead used the second place of residence as the assigned neighborhood.

    After assignment to an initial residence, the government offered language and training courses and social welfare support lasting approximately 18 months. There were no relocation restrictions if a family later decided to move, and receipt of welfare was not conditional on remaining in the assigned residence. Therefore, our study design is akin to a randomized encouragement design in which participants are randomly encouraged to be exposed to a given neighborhood.40 The approach provides a lower bound on the health outcomes associated with neighborhood disadvantage. The design is similar to that used for the Moving to Opportunity experiment.39 Later relocations represent mediating pathways through which disadvantage in the initially assigned neighborhood may be associated with later cardiovascular health.

    Variables
    Exposure

    For each individual, the primary exposure was a measure of socioeconomic disadvantage in the initial neighborhood assigned on arrival to Denmark. We created a composite disadvantage index for each neighborhood by year, using principal component analysis to combine 8 neighborhood-level sociodemographic variables examined in prior research that represent different theoretical constructs capturing disadvantage: median income, family poverty, income inequality, unemployment rate, crime rate, foreign-born, welfare participation, and education.41,42 Additional details are available in the eAppendix and eTables 1 and 2 in the Supplement.

    Small geographic units were used to define neighborhoods, as these align better than larger areas with residents’ definitions of neighborhoods.43 Using larger areas can also mask neighborhood effects.44 The Danish census bureau has geocoded all individuals to historically meaningful neighborhoods known as parishes. Parishes are nested within the larger geographic unit of municipality and each contain a mean of approximately 2000 individuals. During the study period, there were 2159 parishes nested within 271 municipalities, and they have been used previously to define neighborhoods.45,46

    Outcomes

    We examined hypertension, hyperlipidemia, type 2 diabetes, myocardial infarction (MI), and stroke, which represent 3 CVD risk factors and 2 clinical end points that have been plausibly linked to neighborhood disadvantage previously.47-54 For MI and stroke, individuals were considered to have these diagnoses if they were assigned relevant International Classification of Diseases, Eighth Revision (ICD-8) and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes, based on physician discharge diagnoses in inpatient settings. For hypertension, hyperlipidemia, and diabetes, we used ICD-8 and ICD-10 codes from outpatient specialty clinics and inpatient settings, as well as Anatomic Therapeutic Chemical medication codes from prescriptions of relevant medications (eg, statins for hyperlipidemia). To capture incident rather than prevalent cases, we excluded diagnoses within 2 years after arriving in Denmark.55,56 We also extracted the date on which diagnoses were first identified, which we used in Cox models.

    Covariates

    We adjusted for the characteristics available to placement officers who implemented the dispersal policy: sex, age and age-squared, marital status, family size, and region of origin. We included indicator variables for year of arrival to adjust for secular trends. Finally, we included indicator variables (ie, fixed effects) for municipality, which accounted for all time-invariant characteristics of municipalities. This allowed us to estimate associations with neighborhood disadvantage based on the difference in outcomes for refugees placed in the same municipality who were assigned to a high- vs low-disadvantage neighborhood.

    Statistical Analysis
    Primary Analysis

    We first examined characteristics of individuals in the study population, splitting the neighborhood disadvantage index at the median (for the purposes of this descriptive analysis only) and comparing individuals assigned to high- vs low-disadvantage neighborhoods. We also compared the prevalence of each outcome among the refugee population with the prevalence among an age- and sex-matched population of native-born Danes.

    Next, we regressed each outcome on the continuous measure of neighborhood disadvantage. In primary analyses, these were linear regressions, and we confirmed a linear association between the disadvantage index and each outcome through a graphical spline analysis. The models were first unadjusted and then adjusted for the covariates listed previously. All tests were 2-sided, and P < .05 was considered statistically significant; 95% CIs indicated precision of effect estimates. Robust SEs were clustered by municipality to account for correlated observations.

    Secondary Analyses

    Linear models do not consider the date of diagnosis and therefore do not capture the time to event, which may be important for these outcomes. To account for this possible bias, we next used Cox models to estimate the association of neighborhood disadvantage with time-to-diagnosis for each outcome. These models were first unadjusted and then adjusted for the covariates mentioned previously. They incorporated shared frailty at the municipality level to account for correlated observations. See the eAppendix in the Supplement for additional details.

    Finally, we assessed whether associations differed by sex and by age on arrival, as men and women may experience neighborhood disadvantage differently, and individuals exposed at younger ages experience longer exposure to a neighborhood after arrival.57 We carried out an analysis in which we included an interaction term between neighborhood disadvantage and sex and another with an interaction term between neighborhood disadvantage and age younger than 35 years on arrival.

    Results
    Cohort Characteristics

    A total of 49 305 participants were analyzed (median [interquartile range] age, 30.5 [24.9-39.8] years; 43.3% women) (Table). More than half (28 629 [58.1%]) were married, and approximately one-fifth had completed secondary education (14.4%) or more (5.8%). Participant region of origin included 6318 from Africa (12.8%), 7253 from Asia (14.7%), 3446 from Eastern Europe (7.0%), 5416 from Iraq (11.0%), 6206 from Iran (12.6%), 5558 from Palestine (via Lebanon, Israel, Occupied Palestinian Territories; 11.3%), and 15 108 from Yugoslavia (30.6%). Characteristics were roughly balanced across high- and low-disadvantage neighborhoods, except for the characteristics that were available to placement officers and may have influenced neighborhood assignment (eg, region of origin) and those correlated with such characteristics (eg, educational attainment). The median follow-up time was 16.0 (interquartile range, 3.7-20.3) years.

    During the follow-up period, 37.2% of refugees were diagnosed with hypertension, 25.2% with hyperlipidemia, 16.0% with diabetes, 3.3% with MI, and 2.8% with stroke. Rates of all conditions except stroke were higher among the refugee population compared with a population of native-born Danes matched by age and sex (hypertension, 37.2% vs 35.3%; hyperlipidemia, 25.2% vs 18.7%; diabetes, 16.0% vs 7.1%; MI, 3.3% vs 2.7%; stroke 2.8% vs 3.6%) (eTable 3 in the Supplement).

    Association of Neighborhood Disadvantage With Cardiovascular Risk Factors and Events

    In unadjusted models (Figure 3), greater neighborhood disadvantage was associated with a greater risk of hypertension (1.13 [95% CI, 0.44-1.83] percentage points per unit of deprivation index; P = .001), hyperlipidemia (0.75 [95% CI, 0.25-1.25] percentage points; P = .003), diabetes (0.14 [95% CI, 0.03-0.25] percentage points; P = .01), and MI (0.18 [95% CI, 0.07-0.30] percentage points; P = .002) but had no association with stroke.

    In adjusted models, greater neighborhood disadvantage was associated with increased risk of hypertension (0.71 [95% CI, 0.30-1.13] percentage points; P < .01), hyperlipidemia (0.44 [95% CI, 0.06-0.83] percentage points; P = .01), diabetes (0.45 [95% CI, 0.09-0.81] percentage points; P = .01), and MI (0.14 [95% CI, 0.03-0.25] percentage points; P = .01), but not with stroke.

    In Cox models, greater neighborhood disadvantage was associated with increased risk of diabetes (adjusted hazard ratio, 1.03; 95% CI, 1.01-1.06; P = .04) (eFigure 2 in the Supplement). However, we were unable to rule out the null hypothesis of no association for other outcomes.

    Subgroup Analyses

    Differences in the association between neighborhood disadvantage and each outcome were similar for men and women (eTable 4 in the Supplement). Increased neighborhood disadvantage was associated with a greater likelihood of hyperlipidemia among refugees who arrived before age 35 years compared with those who arrived at older ages (1.16 [95% CI, 0.41-1.92] percentage points; P < .01) (eTable 4 in the Supplement). For other outcomes, there were no differences in the association of neighborhood disadvantage among those who arrived in Denmark at younger or older ages.

    Discussion

    This quasi-experimental cohort study leveraged a unique natural experiment and rich Danish register data to provide among the first rigorous estimates of the association of neighborhood disadvantage with cardiovascular risk factors, MI, and stroke among refugees. Study results suggest that refugees who were assigned to more disadvantaged neighborhoods were more likely to develop hypertension, hyperlipidemia, diabetes, and MI in subsequent decades. Effect sizes were small, representing a 2% increase from baseline rates for each condition; although not necessarily clinically meaningful, these rates signify more meaningful outcomes when considering changes in distribution at a population level.58 We found no association of neighborhood disadvantage with risk of stroke, although results suggest that younger individuals were more likely than older individuals to develop hyperlipidemia in association with living in a more disadvantaged neighborhood.

    Our findings are consistent with prior evidence from numerous observational studies that suggest that neighborhood characteristics are associated with CVD and its risk factors.47-51,59-63 Although many previous studies were limited by potential reverse association or confounding due to possible selection of unhealthy individuals (ie, those with higher CVD risk at baseline) into more disadvantaged neighborhoods, our use of a natural experiment reduced these sources of bias. One prior study that took advantage of a similar natural experiment in Sweden found that assignment to a more disadvantaged neighborhood was associated with higher rates of diabetes among refugees in later decades of life.53 This finding strengthens the argument that neighborhood socioeconomic circumstances matter for CVD risk.

    Numerous mechanisms may underlie our findings. For example, disadvantaged neighborhoods may constrain employment and economic opportunities, thereby reducing income and the ability to purchase nutritious food.61 In addition, more disadvantaged neighborhoods often have more restricted food environments and walkability, thereby affecting health behaviors related to CVD.20-22 These neighborhoods also may have poorer access to primary care and other health care resources, resulting in lower-quality prevention and treatment of health conditions. Notably, health care access in Denmark is more equitably available nationwide relative to the United States.64-66 In addition, reduced income and greater neighborhood crime may increase chronic stress levels, which have been associated with increased rates of CVD.23-26 However, 1 prior study found that neighborhood disadvantage was not associated with refugee mental health in the context of a similar refugee dispersal policy in Sweden.67

    Our results also suggest that, for hyperlipidemia, these pathways are more salient for refugees who arrive at younger ages. This is consistent with the literature on sensitive periods earlier in life that might constitute windows in which social exposures are particularly important.57,68 Future studies are needed to identify additional individual- and neighborhood-level characteristics that may place individuals at greater risk.

    Previous work also has demonstrated that neighborhood disadvantage is associated with an increased risk of stroke,48,69-71 which we did not confirm in this study. Although it is possible that prior studies suffered from confounding and that there is, in fact, no effect of neighborhood disadvantage on this outcome, our study population may have been too young and this outcome too uncommon to replicate previous findings.

    Limitations

    Our study has several limitations. First, the study population included only refugees to Denmark, so the results may not generalize to refugees in other settings or to nonrefugees. Also, our study is unique due to the presence of the dispersal policy that created a natural experiment, and the outcomes of neighborhood disadvantage may differ when individuals self-select their place of residence. In addition, the dispersal policy that we examined was implemented during the 1986 to 1998 period, and the role of neighborhood disadvantage may differ based on other contemporaneous factors, such as economic conditions. Future studies should seek to identify more recent natural experiments. Additionally, the Danish register data include information on neighborhood of residence rather than neighborhood assigned. Although official reports document adherence of over 90% to the dispersal policy, there may nevertheless be some bias in resulting estimates. One limitation of Cox models is possible differential left-truncation bias, as various registers were established at different times. If differences in start date are associated with levels of neighborhood disadvantage and dates of diagnosis, Cox models could be biased. Finally, our study does not elucidate the specific socioeconomic aspects of neighborhoods that may underlie our findings, because we used a composite measure of neighborhood disadvantage. Future studies could examine how different aspects of neighborhoods—eg, unemployment, crime, walkability—may interact in order to develop targeted community interventions.

    Conclusions

    Given the surge in refugee migration to high-income countries in recent years,72 evidence is needed to inform governmental policies to optimize the economic and health trajectories of this vulnerable group. Such evidence can inform the development of tools to guide placement of refugees by host countries. Available tools currently do not include neighborhood-level characteristics.73

    The findings of this quasi-experimental, registry-based cohort study suggest that neighborhoods where refugees are placed may have an association with risk of several CVD outcomes. Future studies should examine whether results are stronger after additional years of follow-up and aging of the cohort and should include additional outcomes. Additional studies could assess whether other subgroups (eg, those defined by region of origin or psychological resiliency) are particularly susceptible to neighborhood circumstances.

    Back to top
    Article Information

    Accepted for Publication: June 9, 2020.

    Published: August 21, 2020. doi:10.1001/jamanetworkopen.2020.14196

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

    Corresponding Author: Rita Hamad, MD, PhD, Philip R. Lee Institute for Health Policy Studies, University of California School of Medicine, San Francisco, 995 Potrero Ave, Bldg 80, Ward 83, San Francisco, California, 94110 (rita.hamad@ucsf.edu).

    Author Contributions: Ms Öztürk and Dr Foverskov had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Hamad, Pedersen, Sørensen, White.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Hamad.

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

    Statistical analysis: Öztürk, Foverskov, Pedersen, White.

    Obtained funding: Hamad, Pedersen, Sørensen.

    Administrative, technical, or material support: Sørensen.

    Supervision: Hamad, Pedersen, Sørensen, Bøtker.

    Conflict of Interest Disclosures: Dr Hamad reported receiving grants from the National Institutes of Health outside the submitted work. Dr Sørensen reported that the Department of Clinical Epidemiology of Aarhus University Hospital receives funding for other studies from companies in the form of research grants. None of these studies have any relation to the present study. No other disclosures were reported.

    Funding/Support: This work was supported by grant UL1 TR001085 from the Stanford Clinical and Translational Science Award to Spectrum (Drs Hamad and White), grant R01 AG063385 from the National Institute on Aging (Dr Hamad), and grant K08 HL132106 from the National Heart, Lung, and Blood Institute (Dr Hamad).

    Role of the Funder/Sponsor: The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

    Additional Contributions: We thank Jan P. Vandenbroucke, PhD, for his critical reading of the manuscript and Nicolaj Mühlbach, PhD, for his review of the analytic code. There was no financial compensation for these contributions.

    References
    1.
    Sangalang  CC, Vang  C.  Intergenerational trauma in refugee families: a systematic review.   J Immigr Minor Health. 2017;19(3):745-754. doi:10.1007/s10903-016-0499-7 PubMedGoogle ScholarCrossref
    2.
    Shishehgar  S, Gholizadeh  L, DiGiacomo  M, Green  A, Davidson  PM.  Health and socio-cultural experiences of refugee women: an integrative review.   J Immigr Minor Health. 2017;19(4):959-973. doi:10.1007/s10903-016-0379-1 PubMedGoogle ScholarCrossref
    3.
    Markides  KS, Rote  S. Immigrant health paradox. In: Scott  RA, Kosslyn  SM, eds.  Emerging Trends in the Social and Behavioral Sciences. John Wiley & Sons; 2015. doi:10.1002/9781118900772.etrds0174
    4.
    Inhorn  MC, Serour  GI.  Islam, medicine, and Arab-Muslim refugee health in America after 9/11.   Lancet. 2011;378(9794):935-943. doi:10.1016/S0140-6736(11)61041-6 PubMedGoogle ScholarCrossref
    5.
    Connor  P.  Explaining the refugee gap: economic outcomes of refugees versus other immigrants.   J Refug Stud. 2010;23(3):377-397. doi:10.1093/jrs/feq025 Google ScholarCrossref
    6.
    Sikorski  N.  Understanding the Algorithm Meant to Help Refugees Get Jobs Fast. News Deeply; 2018.
    7.
    Ellis  BH, MacDonald  HZ, Lincoln  AK, Cabral  HJ.  Mental health of Somali adolescent refugees: the role of trauma, stress, and perceived discrimination.   J Consult Clin Psychol. 2008;76(2):184-193. doi:10.1037/0022-006X.76.2.184 PubMedGoogle ScholarCrossref
    8.
    Fox  PG, Cowell  JM, Montgomery  AC.  Southeast Asian refugee children: violence experience and depression.   Int J Psychiatr Nurs Res. 1999;5(2):589-600.PubMedGoogle Scholar
    9.
    Geltman  PL, Grant-Knight  W, Mehta  SD,  et al.  The “lost boys of Sudan”: functional and behavioral health of unaccompanied refugee minors re-settled in the United States.   Arch Pediatr Adolesc Med. 2005;159(6):585-591. doi:10.1001/archpedi.159.6.585 PubMedGoogle ScholarCrossref
    10.
    Marshall  GN, Schell  TL, Wong  EC,  et al.  Diabetes and cardiovascular disease risk in Cambodian refugees.   J Immigr Minor Health. 2016;18(1):110-117. doi:10.1007/s10903-014-0142-4 PubMedGoogle ScholarCrossref
    11.
    Barnes  DM, Harrison  C, Heneghan  R.  Health risk and promotion behaviors in refugee populations.   J Health Care Poor Underserved. 2004;15(3):347-356. doi:10.1353/hpu.2004.0034 PubMedGoogle ScholarCrossref
    12.
    Kinzie  JD, Riley  C, McFarland  B,  et al.  High prevalence rates of diabetes and hypertension among refugee psychiatric patients.   J Nerv Ment Dis. 2008;196(2):108-112. doi:10.1097/NMD.0b013e318162aa51 PubMedGoogle ScholarCrossref
    13.
    DesMeules  M, Gold  J, McDermott  S,  et al.  Disparities in mortality patterns among Canadian immigrants and refugees, 1980-1998: results of a national cohort study.   J Immigr Health. 2005;7(4):221-232. doi:10.1007/s10903-005-5118-y PubMedGoogle ScholarCrossref
    14.
    Bo  A, Zinckernagel  L, Krasnik  A, Petersen  JH, Norredam  M.  Coronary heart disease incidence among non-Western immigrants compared to Danish-born people: effect of country of birth, migrant status, and income.   Eur J Prev Cardiol. 2015;22(10):1281-1289. doi:10.1177/2047487314551538 PubMedGoogle ScholarCrossref
    15.
    Dimsdale  JE.  Psychological stress and cardiovascular disease.   J Am Coll Cardiol. 2008;51(13):1237-1246. doi:10.1016/j.jacc.2007.12.024 PubMedGoogle ScholarCrossref
    16.
    Clark  AM, DesMeules  M, Luo  W, Duncan  AS, Wielgosz  A.  Socioeconomic status and cardiovascular disease: risks and implications for care.   Nat Rev Cardiol. 2009;6(11):712-722. doi:10.1038/nrcardio.2009.163 PubMedGoogle ScholarCrossref
    17.
    Hamad  R, Penko  J, Kazi  DS,  et al.  Association of low socioeconomic status with premature coronary heart disease in US adults.   JAMA Cardiol. Published online May 27, 2020. doi:10.1001/jamacardio.2020.1458 PubMedGoogle Scholar
    18.
    Fazel  M, Reed  RV, Panter-Brick  C, Stein  A.  Mental health of displaced and refugee children resettled in high-income countries: risk and protective factors.   Lancet. 2012;379(9812):266-282. doi:10.1016/S0140-6736(11)60051-2 PubMedGoogle ScholarCrossref
    19.
    Edberg  M, Cleary  S, Vyas  A.  A trajectory model for understanding and assessing health disparities in immigrant/refugee communities.   J Immigr Minor Health. 2011;13(3):576-584. doi:10.1007/s10903-010-9337-5 PubMedGoogle ScholarCrossref
    20.
    Rundle  A, Neckerman  KM, Freeman  L,  et al.  Neighborhood food environment and walkability predict obesity in New York City.   Environ Health Perspect. 2009;117(3):442-447. doi:10.1289/ehp.11590 PubMedGoogle ScholarCrossref
    21.
    Sundquist  K, Eriksson  U, Kawakami  N, Skog  L, Ohlsson  H, Arvidsson  D.  Neighborhood walkability, physical activity, and walking behavior: the Swedish Neighborhood and Physical Activity (SNAP) study.   Soc Sci Med. 2011;72(8):1266-1273. doi:10.1016/j.socscimed.2011.03.004 PubMedGoogle ScholarCrossref
    22.
    Sundquist  K, Eriksson  U, Mezuk  B, Ohlsson  H.  Neighborhood walkability, deprivation and incidence of type 2 diabetes: a population-based study on 512,061 Swedish adults.   Health Place. 2015;31:24-30. doi:10.1016/j.healthplace.2014.10.011 PubMedGoogle ScholarCrossref
    23.
    Boardman  JD, Finch  BK, Ellison  CG, Williams  DR, Jackson  JS.  Neighborhood disadvantage, stress, and drug use among adults.   J Health Soc Behav. 2001;42(2):151-165. doi:10.2307/3090175 PubMedGoogle ScholarCrossref
    24.
    Giurgescu  C, Misra  DP, Sealy-Jefferson  S,  et al.  The impact of neighborhood quality, perceived stress, and social support on depressive symptoms during pregnancy in African American women.   Soc Sci Med. 2015;130(0):172-180. doi:10.1016/j.socscimed.2015.02.006 PubMedGoogle ScholarCrossref
    25.
    Hill  TD, Angel  RJ.  Neighborhood disorder, psychological distress, and heavy drinking.   Soc Sci Med. 2005;61(5):965-975. doi:10.1016/j.socscimed.2004.12.027 PubMedGoogle ScholarCrossref
    26.
    Kasarda  JD.  Inner-city concentrated poverty and neighborhood distress: 1970 to 1990.   Hous Policy Debate. 1993;4(3):253-302. doi:10.1080/10511482.1993.9521135 Google ScholarCrossref
    27.
    Damm  AP, Dustmann  C.  Does growing up in a high crime neighborhood affect youth criminal behavior?   Am Econ Rev. 2014;104(6):1806-1832. doi:10.1257/aer.104.6.1806 Google ScholarCrossref
    28.
    Damm  AP.  The Danish Dispersal Policy on Refugee Immigrants 1986-1998: A Natural Experiment? Aarhus School of Business, Department of Economics; 2005.
    29.
    Damm  AP.  Determinants of recent immigrants’ location choices: quasi-experimental evidence.   J Popul Econ. 2009;22(1):145-174. doi:10.1007/s00148-007-0148-5 Google ScholarCrossref
    30.
    Damm  AP.  Ethnic enclaves and immigrant labor market outcomes: quasi-experimental evidence.   J Labor Econ. 2009;27(2):281-314. doi:10.1086/599336 Google ScholarCrossref
    31.
    Damm  AP, Rosholm  M.  Employment effects of spatial dispersal of refugees.   Rev Econ Househ. 2010;8(1):105-146. doi:10.1007/s11150-009-9067-4 Google ScholarCrossref
    32.
    Ludvigsson  JF, Håberg  SE, Knudsen  GP,  et al.  Ethical aspects of registry-based research in the Nordic countries.   Clin Epidemiol. 2015;7:491-508. doi:10.2147/CLEP.S90589 PubMedGoogle ScholarCrossref
    33.
    Schmidt  M, Schmidt  SAJ, Adelborg  K,  et al.  The Danish health care system and epidemiological research: from health care contacts to database records.   Clin Epidemiol. 2019;11:563-591. doi:10.2147/CLEP.S179083 PubMedGoogle ScholarCrossref
    34.
    Pottegård  A, Schmidt  SAJ, Wallach-Kildemoes  H, Sørensen  HT, Hallas  J, Schmidt  M.  Data resource profile: the Danish national prescription registry.   Int J Epidemiol. 2017;46(3):798-798f.PubMedGoogle Scholar
    35.
    Schmidt  M, Schmidt  SAJ, Sandegaard  JL, Ehrenstein  V, Pedersen  L, Sørensen  HT.  The Danish National Patient Registry: a review of content, data quality, and research potential.   Clin Epidemiol. 2015;7:449-490. doi:10.2147/CLEP.S91125 PubMedGoogle ScholarCrossref
    36.
    Thygesen  LC, Ersbøll  AK.  Danish population-based registers for public health and health-related welfare research: introduction to the supplement.   Scand J Public Health. 2011;39(7 suppl):8-10. doi:10.1177/1403494811409654PubMedGoogle Scholar
    37.
    Hernán  MA, Hernández-Díaz  S, Robins  JM.  A structural approach to selection bias.   Epidemiology. 2004;15(5):615-625. doi:10.1097/01.ede.0000135174.63482.43 PubMedGoogle ScholarCrossref
    38.
    Greenland  S, Robins  JM.  Identifiability, exchangeability and confounding revisited.   Epidemiol Perspect Innov. 2009;6(1):4. doi:10.1186/1742-5573-6-4 PubMedGoogle ScholarCrossref
    39.
    Ludwig  J, Duncan  GJ, Gennetian  LA,  et al.  Long-term neighborhood effects on low-income families: evidence from Moving to Opportunity.   Am Econ Rev. 2013;103(3):226-231. doi:10.1257/aer.103.3.226 Google ScholarCrossref
    40.
    Hirano  K, Imbens  GW, Rubin  DB, Zhou  X-H.  Assessing the effect of an influenza vaccine in an encouragement design.   Biostatistics. 2000;1(1):69-88. doi:10.1093/biostatistics/1.1.69 PubMedGoogle ScholarCrossref
    41.
    Messer  LC, Laraia  BA, Kaufman  JS,  et al.  The development of a standardized neighborhood deprivation index.   J Urban Health. 2006;83(6):1041-1062. doi:10.1007/s11524-006-9094-x PubMedGoogle ScholarCrossref
    42.
    Havard  S, Deguen  S, Bodin  J, Louis  K, Laurent  O, Bard  D.  A small-area index of socioeconomic deprivation to capture health inequalities in France.   Soc Sci Med. 2008;67(12):2007-2016. doi:10.1016/j.socscimed.2008.09.031 PubMedGoogle ScholarCrossref
    43.
    Huie  SAB.  The concept of neighborhood in health and mortality research.   Sociol Spectr. 2001;21(3):341-358. doi:10.1080/027321701300202028 Google ScholarCrossref
    44.
    Meijer  M, Röhl  J, Bloomfield  K, Grittner  U.  Do neighborhoods affect individual mortality? a systematic review and meta-analysis of multilevel studies.   Soc Sci Med. 2012;74(8):1204-1212. doi:10.1016/j.socscimed.2011.11.034 PubMedGoogle ScholarCrossref
    45.
    Meijer  M, Engholm  G, Grittner  U, Bloomfield  K.  A socioeconomic deprivation index for small areas in Denmark.   Scand J Public Health. 2013;41(6):560-569. doi:10.1177/1403494813483937 PubMedGoogle ScholarCrossref
    46.
    Meijer  M, Kejs  AM, Stock  C, Bloomfield  K, Ejstrud  B, Schlattmann  P.  Population density, socioeconomic environment and all-cause mortality: a multilevel survival analysis of 2.7 million individuals in Denmark.   Health Place. 2012;18(2):391-399. doi:10.1016/j.healthplace.2011.12.001 PubMedGoogle ScholarCrossref
    47.
    Buys  DR, Howard  VJ, McClure  LA,  et al.  Association between neighborhood disadvantage and hypertension prevalence, awareness, treatment, and control in older adults: results from the University of Alabama at Birmingham Study of Aging.   Am J Public Health. 2015;105(6):1181-1188. doi:10.2105/AJPH.2014.302048 PubMedGoogle ScholarCrossref
    48.
    Carlsson  AC, Li  X, Holzmann  MJ,  et al.  Neighborhood socioeconomic status at the age of 40 years and ischemic stroke before the age of 50 years: a nationwide cohort study from Sweden.   Int J Stroke. 2017;12(8):815-826. doi:10.1177/1747493017702663 PubMedGoogle ScholarCrossref
    49.
    Cubbin  C, Sundquist  K, Ahlén  H, Johansson  S-E, Winkleby  MA, Sundquist  J.  Neighborhood deprivation and cardiovascular disease risk factors: protective and harmful effects.   Scand J Public Health. 2006;34(3):228-237. doi:10.1080/14034940500327935 PubMedGoogle Scholar
    50.
    Halonen  JI, Stenholm  S, Pentti  J,  et al.  Childhood psychosocial adversity and adult neighborhood disadvantage as predictors of cardiovascular disease: a cohort study.   Circulation. 2015;132(5):371-379. doi:10.1161/CIRCULATIONAHA.115.015392 PubMedGoogle ScholarCrossref
    51.
    Kaiser  P, Diez Roux  AV, Mujahid  M,  et al.  Neighborhood environments and incident hypertension in the multi-ethnic study of atherosclerosis.   Am J Epidemiol. 2016;183(11):988-997. doi:10.1093/aje/kwv296 PubMedGoogle ScholarCrossref
    52.
    Kershaw  KN, Diez Roux  AV, Burgard  SA, Lisabeth  LD, Mujahid  MS, Schulz  AJ.  Metropolitan-level racial residential segregation and black-white disparities in hypertension.   Am J Epidemiol. 2011;174(5):537-545. doi:10.1093/aje/kwr116 PubMedGoogle ScholarCrossref
    53.
    White  JS, Hamad  R, Li  X,  et al.  Long-term effects of neighbourhood deprivation on diabetes risk: quasi-experimental evidence from a refugee dispersal policy in Sweden.   Lancet Diabetes Endocrinol. 2016;4(6):517-524. doi:10.1016/S2213-8587(16)30009-2 PubMedGoogle ScholarCrossref
    54.
    Kershaw  KN, Osypuk  TL, Do  DP, De Chavez  PJ, Diez Roux  AV.  Neighborhood-level racial/ethnic residential segregation and incident cardiovascular disease: the multi-ethnic study of atherosclerosis.   Circulation. 2015;131(2):141-148. doi:10.1161/CIRCULATIONAHA.114.011345 PubMedGoogle ScholarCrossref
    55.
    Hamad  R, Modrek  S, Kubo  J, Goldstein  BA, Cullen  MR.  Using “big data” to capture overall health status: properties and predictive value of a claims-based health risk score.   PLoS One. 2015;10(5):e0126054. doi:10.1371/journal.pone.0126054 PubMedGoogle Scholar
    56.
    Scherrer  JF, Virgo  KS, Zeringue  A,  et al.  Depression increases risk of incident myocardial infarction among Veterans Administration patients with rheumatoid arthritis.   Gen Hosp Psychiatry. 2009;31(4):353-359. doi:10.1016/j.genhosppsych.2009.04.001 PubMedGoogle ScholarCrossref
    57.
    Chetty  R, Hendren  N, Katz  LF.  The effects of exposure to better neighborhoods on children: new evidence from the moving to opportunity experiment.   Am Econ Rev. 2016;106(4):855-902. doi:10.1257/aer.20150572 PubMedGoogle ScholarCrossref
    58.
    Guyatt  GH, Osoba  D, Wu  AW, Wyrwich  KW, Norman  GR; Clinical Significance Consensus Meeting Group.  Methods to explain the clinical significance of health status measures.   Mayo Clin Proc. 2002;77(4):371-383. doi:10.4065/77.4.371 PubMedGoogle ScholarCrossref
    59.
    Diez Roux  AV, Merkin  SS, Arnett  D,  et al.  Neighborhood of residence and incidence of coronary heart disease.   N Engl J Med. 2001;345(2):99-106. doi:10.1056/NEJM200107123450205 PubMedGoogle ScholarCrossref
    60.
    Diez-Roux  AV, Nieto  FJ, Muntaner  C,  et al.  Neighborhood environments and coronary heart disease: a multilevel analysis.   Am J Epidemiol. 1997;146(1):48-63. doi:10.1093/oxfordjournals.aje.a009191 PubMedGoogle ScholarCrossref
    61.
    Sundquist  K, Theobald  H, Yang  M, Li  X, Johansson  S-E, Sundquist  J.  Neighborhood violent crime and unemployment increase the risk of coronary heart disease: a multilevel study in an urban setting.   Soc Sci Med. 2006;62(8):2061-2071. doi:10.1016/j.socscimed.2005.08.051 PubMedGoogle ScholarCrossref
    62.
    Sundquist  K, Winkleby  M, Ahlén  H, Johansson  S-E.  Neighborhood socioeconomic environment and incidence of coronary heart disease: a follow-up study of 25,319 women and men in Sweden.   Am J Epidemiol. 2004;159(7):655-662. doi:10.1093/aje/kwh096 PubMedGoogle ScholarCrossref
    63.
    Starks  MA, Schmicker  RH, Peterson  ED,  et al; Resuscitation Outcomes Consortium (ROC).  Association of neighborhood demographics with out-of-hospital cardiac arrest treatment and outcomes: where you live may matter.   JAMA Cardiol. 2017;2(10):1110-1118. doi:10.1001/jamacardio.2017.2671 PubMedGoogle ScholarCrossref
    64.
    Documét  PI, Sharma  RK.  Latinos’ health care access: financial and cultural barriers.   J Immigr Health. 2004;6(1):5-13. doi:10.1023/B:JOIH.0000014638.87569.2e PubMedGoogle ScholarCrossref
    65.
    Kirby  JB, Kaneda  T.  Neighborhood socioeconomic disadvantage and access to health care.   J Health Soc Behav. 2005;46(1):15-31. doi:10.1177/002214650504600103 PubMedGoogle ScholarCrossref
    66.
    Zuvekas  SH, Taliaferro  GS.  Pathways to access: health insurance, the health care delivery system, and racial/ethnic disparities, 1996-1999.   Health Aff (Millwood). 2003;22(2):139-153. doi:10.1377/hlthaff.22.2.139 PubMedGoogle ScholarCrossref
    67.
    Raphael  E, White  J, Li  X,  et al.  Neighborhood deprivation and mental health among immigrants to Sweden.   Epidemiology. 2020;31(3):e25-e27. doi:10.1097/EDE.0000000000001160PubMedGoogle Scholar
    68.
    Ben-Shlomo  Y, Kuh  D.  A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives.   Int J Epidemiol. 2002;31(2):285-293. doi:10.1093/ije/31.2.285 PubMedGoogle ScholarCrossref
    69.
    Menec  VH, Shooshtari  S, Nowicki  S, Fournier  S.  Does the relationship between neighborhood socioeconomic status and health outcomes persist into very old age? a population-based study.   J Aging Health. 2010;22(1):27-47. doi:10.1177/0898264309349029 PubMedGoogle ScholarCrossref
    70.
    Hamano  T, Kawakami  N, Li  X, Sundquist  K.  Neighbourhood environment and stroke: a follow-up study in Sweden.   PLoS One. 2013;8(2):e56680. doi:10.1371/journal.pone.0056680 PubMedGoogle Scholar
    71.
    Howard  VJ, McClure  LA, Kleindorfer  DO,  et al.  Neighborhood socioeconomic index and stroke incidence in a national cohort of blacks and whites.   Neurology. 2016;87(22):2340-2347. doi:10.1212/WNL.0000000000003299 PubMedGoogle ScholarCrossref
    72.
    Organisation for Economic Co-operation and Development.  International Migration Outlook. OECD Publishing; 2018.
    73.
    Bansak  K, Ferwerda  J, Hainmueller  J,  et al.  Improving refugee integration through data-driven algorithmic assignment.   Science. 2018;359(6373):325-329. doi:10.1126/science.aao4408 PubMedGoogle ScholarCrossref
    ×