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Impact of Length of Residence in the United States on Risk of Diabetes and Hypertension in Resettled Refugees

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Abstract

The relationship between resettlement and development of chronic disease has yet to be elucidated in refugees. We aimed to assess the relationship between length of residence in the US and development of diabetes and hypertension utilizing multivariable logistic regression models in a sample of former refugee patients seeking primary care services. Multivariable logistic regression models adjusting for age, gender, and country of origin showed significantly increasing odds of type 2 diabetes (OR 1.12, 95% CI 1.03–1.22, p < 0.01) and hypertension (OR 1.07, 95% CI 1.00–1.14) with increasing length of stay in the US for resettled refugee adults. A significant proportion of diabetes (26.7%) and hypertension (36.9%) diagnoses were made within one year of arrival, highlighting the critical role of focusing diagnosis and prevention of chronic disease in newly resettled refugees, and continuing this focus throughout follow-up as these patients acculturate to their new homeland.

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Acknowledgements

Thank you to Ibrahima Bah and Anjalene Whittier for their invaluable technical assistance, and clinic staff for all of their dedication and hard work in this project.

Funding

This research was supported by the University of Rochester CTSA award number TL1 RR024135 and TL1 TR000096 from the National Center for Advancing Translational Sciences of the National Institutes of Health. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

  1. Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, USA

    Natalia Golub

  2. Department of Public Health Sciences, University of Rochester Medical Center, 265 Crittenden Blvd., Rochester, NY, 14642, USA

    Christopher Seplaki, Kelly Thevenet-Morrison & Diana Fernandez

  3. Department of Family Medicine, University of Rochester, 777 South Clinton Ave, Rochester, NY, 14620, USA

    Douglas Stockman

  4. Department of Clinical Sciences, Temple University School of Medicine, 3440 Broad Street, Kresge Bldg. Suite 211, Philadelphia, PA, 19140, USA

    Susan Fisher

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  1. Natalia Golub
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Correspondence to Natalia Golub.

Ethics declarations

Conflict of interest

Natalia Golub, Christopher Seplaki, Douglas Stockman, Kelly Thevenet-Morrison, Diana Fernandez and Susan Fisher declares that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

A waiver of informed consent was obtained as the study consisted of chart review of existing records.

Appendix

Appendix

Classification of Length of Stay (LOS) in the US

Information available on LOS in paper charts and the EMR ranged from exact date of arrival, to year of arrival, to no mention of arrival date, but presence of PPD and County Health Department (CHD) vaccination dates, or the date that an individual was registered as a patient at the clinic. The following criteria were used to determine arrival date:

  1. i)

    If exact date was present: entered exact date (MM/DD/YYYY).

  2. ii)

    If month/year of arrival was present: entered 1/month/year.

  3. iii)

    If year of arrival was present: entered 7/1/year.

  4. iv)

    If there was no arrival date, but PPD/CHD vaccine dates were present, and it was documented in the chart that the patient had arrived recently: entered PPD date if PPD clearly done at CHD, or entered first CHD vaccine date.

  5. v)

    If there was no arrival date, but family members had arrival date, the family member’s arrival date was used with caution (at least vaccines/PPD date had to be close to this date, and there was no note that the individual arrived before or after rest of family).

  6. vi)

    If family members who clearly arrived together had slightly differing arrival dates (ex 2/12/05 2/7/05 2/10/05), the earliest date was used.

  7. vii)

    If chart stated “arrived to US X months ago”, if X ≤ 12 months, then exactly X months was subtracted from the date the statement was made. Ex: “arrived to US 5 months ago on 7/23/12”. LOS was entered as 2/23/12. If X > 12 months, then subtract X, but put the 1st of that month. Ex “arrived 2 years ago on 5/15/02” was entered as 5/1/00.

  8. viii)

    If only the date of first clinic visit or registration at clinic was available (but it was clear that person arrived recently, for example “new refugee” or “just arrived”), then entered 1/month/year of clinic registration or clinic visit.

Classification of Diabetes and Hypertension

Type 2 diabetes mellitus diagnosis was defined as health-care provider generated diagnosis (‘adult onset diabetes mellitus’, ‘AODM’, ‘diabetes mellitus type II’, ‘type 2 diabetes’, or ‘DM’, or ‘diabetes’) documented in the paper record. Cases of type 1 diabetes mellitus were clearly documented in the medical records, and were excluded from the study. In the EMR, type 2 diabetes mellitus diagnosis was defined as follows: (1) any patient with ICD-9 codes for type 2 diabetes in their problem list (250.00 = Type II Diabetes Mellitus—Uncomplicated, Controlled; 250.02 = Diabetes Mellitus Poorly Controlled). (2) Some patients with type 2 diabetes did not have the diagnosis their problem list in the EMR. To identify these patients, their hemoglobin A1C, fasting, and random blood glucose values recorded during the time of the EMR were examined using SAS (statistical analysis software). The study investigator manually reviewed EMR records of all patients with a hemoglobin A1C value of ≥6.5, random glucose value of ≥200, or a fasting glucose value of ≥126, in order to determine if they had a diagnosis of diabetes in the EMR that was not documented in their problem list.

A patient was defined as having hypertension if a health-care provider documented in their paper chart: (i) ‘hypertension’ or ‘HTN’, AND patient was put on a blood pressure lowering medication, (ii) ‘high blood pressure’ AND patient was put on a blood pressure lowering medication, (iii) ‘HTN’, and recommended a blood pressure lowering medication, but patient refused medication, (iv) hypertension diagnosis from a previous healthcare provider. This definition was based on variability of how hypertension diagnosis was recorded in paper charts and difficulty in determining whether a hypertension diagnosis was made, as opposed to a notation of hypertension present at a particular clinic visit. This categorization errs on the side of having false negatives. In the EMR, diagnosis of hypertension was defined as: (1) ICD-9 codes: 401.1 = benign essential hypertension; 401.9 = hypertension. (2) Some patients with hypertension did not have the diagnosis in their problem list in the EMR. If an individual had three or more consecutive blood pressures in the hypertensive range, had three or more hypertensive readings in a period of less than a year, blood pressure greater than 160/100 on any visit, or had consistent hypertensive readings throughout their follow-up, their EMR chart notes were examined manually for a hypertension diagnosis. Date of diagnosis for diabetes and hypertension was recorded as initial date when healthcare provider made diagnosis, or estimate of date of diagnosis for patients who were diagnosed prior to their care at the clinic.

Classification of Traumatic Experiences Relating to Life as a Refugee Prior to Resettlement

Collecting trauma information was not an aim of the study and was added later as an exploratory survey, as it became evident that some patients had trauma experiences recorded in their charts. Given the documented effects of trauma on the hypothalamic–pituitary–adrenal axis, and the postulated effects that this may have in chronic disease development, it was an opportunity to examine whether trauma experiences were associated with outcomes in the former refugee sample. Information on trauma experiences was extracted from the first few clinic visits a patient had, as this is usually when this information was discussed. Also, when electronic medical records were reviewed just for the purpose of extracting demographic information, it was not feasible to look through all patient notes for information about trauma experiences; notes from the first few clinic visits were examined.

Traumatic experiences defined as relating to life as a refugee were: rape, assault, beating, family members and/or friends killed, witnessing people raped, beaten and/or killed, torture, being a “walking boy” (male from Sudan who was part of the thousands of boys who were separated from their entire family and walked long distances and spent years in refugee camps), war injuries, participation in combat, and/or loved ones left behind. Information on trauma was collected in a subset of the study sample, as this was an exploratory aim added to the study as it became evident that some patients had records of traumatic experiences in their charts.

Individuals who did not have information on trauma in the initial visits were coded as not having experienced trauma. It is likely that the prevalence of trauma experienced by individuals in the sample was underestimated (Table 7).

Table 7 Basic demographic characteristics of adults in diabetes analysis
Full size table

Assessment of Whether Multi-level Modeling was Necessary for Analysis of Length of Stay and Diabetes

A multilevel logistic intercept-only model (PROC GLIMMIX in SAS) was utilized to determine whether multilevel modeling should be conducted to take into account the effect of family membership on odds of diabetes. The intraclass correlation coefficient (ICC) from the intercept-only model was 5.2%, which means that 5% of the variability in odds of diabetes was accounted for by family membership. The Chi square test for covariance parameters showed that the family-level variance of the intercept was not significant (p = 0.3668). As such, a random coefficient analysis (RCA) logistic model was not necessary to characterize the relationship between years in US and type 2 diabetes (Table 8).

Table 8 Basic demographic characteristics of adults in hypertension analysis
Full size table

Assessment of Whether Multi-level Modeling was Necessary for Analysis of Length of Stay and Hypertension

A multilevel logistic intercept-only model (PROC GLIMMIX in SAS) was utilized to determine whether multilevel modeling should be utilized to take into account the effect of family membership on odds of hypertension. The intraclass correlation coefficient (ICC) from the intercept-only model was 15.7%, meaning that almost 16% of the variability in odds of hypertension was accounted for by family membership. In addition, the Chi square test for covariance parameters showed that the family-level variance of the intercept was statistically significant (p = 0.04). Based on this, a multilevel logistic model with a random intercept for family membership was utilized to characterize the relationship between years in US and hypertension. However, multivariable logistic analyses were also conducted, because while the family variable appeared to significantly contribute to variance in odds of hypertension, the effect estimates between a multilevel model and a standard logistic model were very similar. In the multi-level model, length of stay in US was not significant at the α = 0.05 level, while in the logistic model it was. This is due to the fact that the multivariable logistic model does not account for the clustering of hypertension within families, which leads to a lower p-value for the association between years in the US and odds of hypertension. In contrast, the multi-level model with a random intercept takes the clustering of hypertension within families into account (Table 9).

Table 9 Demographic characteristics of individuals with and without diabetes and hypertension
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Golub, N., Seplaki, C., Stockman, D. et al. Impact of Length of Residence in the United States on Risk of Diabetes and Hypertension in Resettled Refugees. J Immigrant Minority Health 20, 296–306 (2018). https://doi.org/10.1007/s10903-017-0636-y

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