Health and well-being of male international migrants and non-migrants in Bangladesh: A cross-sectional follow-up study.

BACKGROUND: Temporary labor migration is an increasingly important mode of migration that generates substantial remittance flows, but raises important concerns for migrant well-being. The migration and health literature has seen a growing call for longitudinal, binational surveys that compare migrants to relevant non-migrant counterfactual groups in the sending country, in order to answer the basic question "Is migration good for health?" This study compares the health of male international migrants, internal migrants, and non-migrants using a unique representative panel survey of the Matlab subdistrict of Bangladesh. METHODS AND
FINDINGS: A cohort of 5,072 respondents born 1958-1992 were interviewed in 1996-1997, and reinterviewed in 2012-2014. Extensive migrant follow-up yielded a 92% reinterview rate. We explored health and income outcomes for respondents who at the time of the follow-up interview were current international migrants (n = 790), returned international migrants (n = 209), internal migrants (n = 1,260), and non-migrants (n = 2,037). Compared to non-migrants, current international migrants were younger (mean 32.9 years versus 35.8 years), had more schooling (7.6 years versus 5.8 years), and were more likely to have an international migrant father (9.7% versus 4.0%) or brother (49.1% versus 30.3%). We estimated multivariate ordinary least squares and logistic regression models controlling for a wide range of control variables measured as far back as 1982. Results show that current international migrants had substantially better health status on factors that likely relate to self-selection such as grip strength and self-rated health. Current international migrants had no excess risk of injury in the past 12 months compared to non-migrants (adjusted mean risk = 6.0% versus 9.3%, p = 0.084). Compared to non-migrants, current international migrants had roughly twice the risk of overweight/obesity (adjusted mean risk = 51.7% versus 23.3%, p < 0.001), obesity (6.9% versus 3.4%, p = 0.012), and stage 1 or higher hypertension (13.0% versus 7.0%, p = 0.014). Compared to internal migrants, current international migrants had significantly higher levels of overweight/obesity (adjusted mean risk = 51.7% versus 37.7%, p < 0.001). Current international migrants showed above average levels of depressive symptoms on a 12-item standardized short-form Center for Epidemiologic Studies Depression Scale (+0.220 SD, 95% CI 0.098-0.342), significantly higher than internal migrants (-0.028 SD, 95% CI -0.111, 0.055; p < 0.001). Depressive symptoms differed significantly from those reported by non-migrants when restricting to items on negative emotions (international migrant score = 0.254 SD, non-migrant score = 0.056 SD, p = 0.004). Key limitations include the descriptive nature of the analysis, the use of both in-person and phone survey data for international migrants, the long recall period for occupational and mental health risk measures, and the coverage of a single out-migration area of origin.
CONCLUSIONS: In this study, we observed that international migrants had comparable or lower injury and mortality risks compared to respondents remaining in Bangladesh, due in part to the high risks present in Bangladesh. International migrants also showed higher levels of self-rated health and physical strength, reflective of positive self-selection into migration. They had substantially higher risks of overweight/obesity, hypertension, and depression. Negative health impacts may reflect the effects of both harsh migration conditions and assimilation into host population conditions. Our results suggest the need for bilateral cooperation to improve the health of guest workers.

Introduction

Temporary labor migration is an increasingly important mode of migration that generates substantial remittance flows, but has important consequences for migrant well-being [1,2]. Up to one-third of the world’s migrant workers are thought to work on temporary visas, meaning about 50–60 million workers in 2018 [1,3]. Advocates and the popular press have raised concerns about mortality, injury, mental health, and life course health risks facing these workers, especially those living and working in the nations of the Gulf Cooperation Council (GCC) [4,5]. However, guest workers typically migrate from highly disadvantaged sending areas where health risks may be equally high or higher [6]. To date, no large-scale survey to our knowledge has systematically documented health outcomes among a large, representative cohort of guest workers, or compared them to outcomes among non-migrant counterfactuals. This study compares the health of international migrants, internal migrants, and non-migrants using a unique representative panel survey of the Matlab subdistrict of Bangladesh, an area characterized by unusually high rates of migration to labor-importing countries in the Persian Gulf and Southeast Asia.

In recent years, studies of migrant well-being have recognized the importance of comparing migrants to relevant non-migrant counterfactual groups, in order to answer the basic question “Is migration good for your health?” along with a host of questions relating to specific health conditions, risk factors, and structural mechanisms [7-10]. Due to data limitations, most studies comparing the health of emigrants to that of non-migrants take the innovative but limited approach of pooling or matching separate datasets from sending and receiving countries [8,11,12]. Given the methodological challenges facing these studies and the continued struggle to account for self-selection across context, these studies have yet to give rise to a systematic or generalizable set of conclusions regarding the health consequences of migration for migrants [13,14]. There has thus been a growing call for binational panel surveys that can measure the conditions, pre- and post-migration, affecting a set of self-selected international migrants who are drawn from a large representative sample of an origin population, in which non-migrants and internal migrants serve as comparison groups [15]. Yet existing binational migrant surveys are limited by small [16,17] or purposive migrant samples [14,18].

Evidence on the health of migrants to the Persian Gulf region is limited due to the difficulty of collecting data in the receiving countries. Studies of occupational injuries noted rates of injury and fatality above those experienced by host populations, but did not compare rates to those in the countries of origin or in high-risk occupations in other countries [19-21]. A recent systematic review of all studies related to migrant mental health in the Persian Gulf identified fewer than a dozen articles published between 2002 and 2010 [22]. These limited studies offer quantitative evidence of a high rate of suicide [23-26] and psychiatric symptoms [27,28]. A small number of studies have used depression scales to quantify burdens and covariates of mental illness among migrants [29,30]. Even fewer studies have addressed general health or metabolic health, though these tend to identify high burdens among migrants [31,32]. None of these studies was longitudinal or included a non-migrant reference group. A larger body of studies documents the existence of social and occupational conditions that may give rise to health risk, including legal vulnerability [33-36], occupational abuse [37-39], and indebtedness [40].

Using 2 rounds of survey data from the Matlab Health and Socioeconomic Survey (MHSS), we compare the health and livelihoods of temporary international migrants to those of non-migrants and internal migrants interviewed in 2012–2014 who were drawn from the same representative sample initially interviewed in 1996–1997. We look at effects on injury, general health, hypertension, mental health, and risk factors such as smoking and obesity.

Methods

The study used 2 rounds of panel survey data from the MHSS, with the first round, MHSS1, conducted in 1996–1997 and the second round, MHSS2, conducted in 2012–2014. The rural Matlab subdistrict is the site of the Matlab Health and Demographic Surveillance System (MHDSS), the longest-running vital registration system in the Global South, instituted by icddr,b (International Centre for Diarrhoeal Disease Research, Bangladesh). MHDSS provided prospective records from of all birth, death, marriage, and migration episodes in a 141-village study area. MHDSS offers numerous advantages for the collection of high-quality health survey data, including precise age data for individuals born since 1974, prospective observation of prior demographic events including migration and mortality, and long-term baseline data from censuses conducted in 1974 and 1982. In 1996–1997, MHSS1 was conducted by icddr,b, RAND, University of Pennsylvania, and partner institutions, with funding from the National Institute on Aging and the National Institute of Child Health and Human Development. MHSS1 collected detailed health, social, demographic, and economic data for 2,637 MHDSS households, a 7% random sample of the study area. MHSS1 included data on family migration history, household assets, kinship, remittances, schooling, and cognition at baseline.

Conducted from 2012 to 2014, MHSS2 followed all individuals from MHSS1. Protection of human participants during fieldwork and data analysis was ensured under icddr,b Ethical Review Committee Protocol #PR-10005. Fieldwork included an unusually long and intensive migrant follow-up effort [41]. A migrant’s last known location was identified first through the electronic records in MHDSS, and then through intensive migrant tracking conducted in the last known household and with other kin. Migrant interviews were carried out in 4 phases: (1) as part of the regular fieldwork, (2) through a rapid response method aimed at maximizing familial connections to quickly complete interviews with migrants, (3) through interviews with returning international migrants and migrants in faraway domestic locations during the Muslim Eid festivals, and (4) through a phone survey that captured a subset of key survey questions [41].

The analysis was carried out with support from the Research & Empirical Analysis of Labor Migration (REALM) program administered by New York University Abu Dhabi. The analysis met all requirements of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines, as shown in S1 STROBE Checklist. The analysis plan (included as S1 Text) identified the (1) statistical methodology, (2) coding scheme for the migration status treatment variable, and (3) control variables, but it did not name the specific dependent variables. Analysis followed the exact guidelines of the protocol, with the exception of restricting the sample to a narrower age range in order to focus on age groups with a non-trivial risk of current international migration. Analysis focused on 5,072 males born 1958 to 1992, who were age 20–56 years at the time of MHSS2. Fig 1 reports survival and interview follow-up status stratified by the respondent’s last known location. Just over half were living in Matlab or the surrounding Chandpur/Comilla districts (including never-migrants and returned international migrants; 54%, 2,727 cases), 28% were outside these districts (internal migrants; 1,400 cases), and 19% were outside the country (international migrants; 945 cases). The proportion dying over the ≥16-year follow-up period was 2.6%. Death was more likely among respondents who were non-migrants at the time of survey (3.7%) compared to internal migrants (1.9%) or current international migrants (0.4%).

Fig 1

MHSS1 to MHSS2 follow-up and sample inclusions.

The follow-up rate was unusually high for a 16- to 18-year follow-up, with 92% of survivors interviewed and high rates of follow-up for non-migrants (94%), internal migrants (89%), and international migrants (90%). The resulting interview sample of 4,545 respondents was 54% non-migrant, 27% internal migrant, and 19% international migrant. Of the 4,545 eligible respondents, 240 (5% of total) were excluded due to missing variables in 1 of the 2 rounds, yielding a sample of 4,296 respondents. Missing data were spread across a wide range of variables, primarily income and baseline variables.

Statistical estimates of health and economic outcomes at MHSS2 were based on weighted ordinary least squares regression (for continuous variables) and weighted logistic regression (for binominal variables) controlling for migration status at MHSS2, time-invariant individual characteristics (year of birth, religion), current individual characteristics measured in MHSS2, life course individual characteristics in MHSS1, parental schooling, sibling composition, father’s and brother’s migration history, and baseline characteristics measured in 1982. We report our estimates of the association of migration status with health and economic variables in terms of the marginal predicted values (for continuous variables) or marginal predicted probabilities (for binomial variables). Along with predicted values/probabilities and confidence intervals, we report statistical tests of difference in which current international migrants are the reference category, thereby comparing international migrants to all 3 counterfactual comparison groups: non-migrants, internal migrants, and returned international migrants. Coefficient estimates for all control variables are reported in S1 Table for the model specifications shown in the main tables. We also estimated 2-stage propensity score models in which baseline variables were used to predict selection into international migration. Because this approach did not alter the results, we report only the more intuitive single-stage estimates.

Dependent variables

We explored how migration impacts a broad range of health outcomes. For all respondents, including phone survey respondents, we tested models of self-rated health, injury and morbidity, overweight/obesity risks based on height/weight (observed for in-person interviews, self-reported for phone interviews), and a shortened 12-question version of the Center for Epidemiologic Studies Depression Scale (CES-D). Because there is no validated diagnostic cutoff for depression using this abridged scale, we simply estimated models for standardized depressive symptom scores. Because items relating to positive emotions (e.g., happiness, hopefulness) had shown lower item–overall score correlations, particularly for migrants, we also tested models separately for the 4 items relating to positive emotions and the 8 items relating to negative emotions (e.g., sadness, loneliness).

We explored differences in BMI, and followed the Asian reference standards [42] to further model overweight/obese (BMI ≥ 23 kg/m2), obese (BMI ≥ 27.5 kg/m2), and underweight (BMI < 18.0 kg/m2). Height and weight were directly observed for in-person respondents and self-reported in the international migrant phone survey. We observed minimal differences in BMI for international migrants between in-person and phone surveys (0.47 kg/m2, or 0.14 SD). We reestimated BMI models excluding or adjusting phone survey respondents, with no qualitative change in the results. For respondents interviewed in person (including one-third of international migrants), we also conducted physical examinations and measured whether a respondent had stage 1 or higher hypertension (defined as systolic blood pressure > 130 mm Hg or diastolic blood pressure > 80 mm Hg per the 2017 ACC/AHA guidelines [43]) and tested mean grip strength across 2 trials (measured in kilograms-force [kgf]).

We measured annual income and hours worked directly using a recall matrix that asked respondents to report work across a wide range of job types (e.g., salaried employment, daily work, agricultural work).

Independent variable

The migration status measure compared current international migrants, returned international migrants, current internal migrants, and non-migrants. Migration status was identified based on current place of residence. Among respondents in Bangladesh, returned international migrants were identified based on a self-report of whether they had lived abroad in the past 5 years. Internal migrants were identified based on area of residence outside Chandpur/Comilla districts, with the majority moving for work to major cities such as Dhaka and Chittagong, and others moving to nearby towns. All remaining respondents were classified as non-migrants. For simplicity of presentation, we assigned respondents to mutually exclusive migration status categories, with returned international migrant status superseding current residential status. The analytic sample includes 4,296 respondents, with 790 living internationally at the time of follow-up (referred to as “current international migrants”), 2,037 non-migrants, 1,260 internal migrants, and 209 returned international migrants. Of the 790 current international migrants, 515 were interviewed by phone and 275 in person.

The statistical estimates reported in the main tables come from regressions that also controlled for age, education, religion, height, father and mother’s schooling (measured directly or reported by respondent), sibling composition, whether the respondent’s father and any brother ever lived abroad (based on MHDSS tracking), and 1996–1997 household assets (taken from MHSS1 as the sum value of assets across all productive and non-productive types). Religion was coded as 1 if the respondent was Hindu (9.5% of respondents) and 0 if the respondent was Muslim. Years of schooling were collected via survey, with individuals who never attended school or attended only Koranic recitation (Maktab) coded with 0 completed years. S1 Table also reports additional models that introduced controls for 1982 baseline variables (from the 1982 MHDSS census). Unreported models also controlled for (1) MHSS2 month of interview effects, (2) birth year fixed effects, and (3) village fixed effects. Each of these specifications yielded models that were comparable to the reported models.

Results

Bivariate analysis

Table 1 provides some background on countries of destination and living/working conditions for migrants in those destinations. Respondents primarily lived in Saudi Arabia (26%), United Arab Emirates (26%), other GCC countries (19%), and Singapore or Malaysia (22%), with another 4% scattered in other guest worker destinations (e.g., Libya, Lebanon, Maldives) and the remaining 3% in Europe or North America. Table 1 reports the unique living and working conditions affecting migrants. The vast majority of migrants across all destinations (93%) had legal documentation, typically in the form of a 3-year temporary work permit. An even higher proportion had used a manpower agency to arrange their migration (97%). Few migrants were in possession of their own passports (17%), with the lowest share in Saudi Arabia (10%) and United Arab Emirates (10%) and the highest share in other destination countries (42%). Only a small percentage could read and write the local language, ranging from just 1% in other GCC to countries to 17% in other destination countries. About half of respondents lived in company housing, ranging from 38% in other GCC countries to 56% in Singapore/Malaysia.

Table 1

Selected migrant living/working characteristics for international migrants, by destination country.

Characteristic	Saudi Arabia(n = 202)	United Arab Emirates(n = 204)	Other GCC country(n = 153)	Singapore or Malaysia(n = 171)	Other(n = 60)	Total(n = 790)
Has legal documentation	92%	93%	92%	94%	92%	93%
Used manpower agent	97%	97%	98%	98%	93%	97%
Keeps passport	10%	10%	18%	19%	42%	17%
Reads/writes local language	7%	2%	1%	13%	17%	6%
Lives in company housing	47%	55%	38%	56%	49%	49%

Source: MHSS2 (2012–2014).

GCC, Gulf Cooperation Council.

Fig 2 summarizes variation in health outcomes by migration status from bivariate analysis. Current international migrants were significantly less likely than all groups to report fair/poor self-rated health (4% compared to 19% for non-migrants and 12% for internal migrants and returned international migrants). They also reported significantly lower levels of injury in the past year (5% versus 6%–9% for other groups), current smoking (30% versus 36%–39% for other groups), and poor grip strength (13% versus 23%–38% for other groups). Current international migrants showed substantially higher burdens of overweight/obese status (60%) in comparison to returned international migrants (46%), internal migrants (36%), and, especially, non-migrants (22%). Similar patterns emerge for obesity, though at much lower levels (10% for current international migrants versus 3%–7% for other groups). Current international migrants also had higher levels of hypertension (14% versus 8%–11% for other groups).

Fig 2

Prevalence of key health outcomes, by migration status—Unadjusted.

For poor grip strength (<36 kilograms-force [kgf]) and stage 1 or higher hypertension, current international migrants include only those who were interviewed in person (n = 275).

Table 2 compares the baseline characteristics of migration status groups. On most characteristics, current international and internal migrants were comparable, making internal migrants a relevant reference group. Current international migrants were younger than non-migrants (mean 33 versus 36 years), though internal migrants were even younger (mean 31 years). Current international migrants also had higher levels of schooling than non-migrants (mean 7.6 versus 5.8 years), but they were similar to internal migrants (mean 7.9 years). Current and returned international migrants were about 2 cm taller on average than non-migrants and 1 cm taller than internal migrants. While sibling composition was broadly similar across all categories, international migrants had preexisting familial socioeconomic advantages. International migrants had significantly higher levels of mother’s and father’s schooling relative to non-migrants, though significantly lower than those of internal migrants. International migrants were much more likely to have a father who had lived abroad or a brother who had lived abroad, though even 30% of non-migrants had an international migrant brother. Finally, both current and returned international migrants came from households whose 1996–1997 household assets were about 30% higher than those of both non-migrant and internal migrant households.

Table 2

Baseline characteristics of respondents, by migration status.

Characteristic	Non-migrant n = 2,037	Internal migrant n = 1,260	Returned international migrant n = 209	Current international migrant n = 790	Total n = 4,296
Age (years)	35.8 (10.4)	30.8 (8.2)	34.4 (7.2)	32.9 (6.7)	33.8 (9.3)
Years of schooling	5.8 (4.6)	7.9 (4.6)	7.7 (3.8)	7.6 (3.3)	6.8 (4.5)
Religion Hindu	13.3%	7.6%	4.3%	4.0%	9.5%
Height (cm)	162.9 (6.4)	163.8 (6.0)	164.5 (6.0)	164.7 (5.0)	163.6 (6.1)
Younger brothers	1.0 (1.1)	0.9 (1.0)	1.0 (1.0)	1.1 (1.1)	1.0 (1.1)
Older brothers	1.0 (1.2)	1.1 (1.3)	1.2 (1.4)	1.2 (1.3)	1.1 (1.3)
Younger sisters	1.0 (1.1)	0.9 (1.0)	0.9 (1.0)	0.9 (1.0)	1.0 (1.1)
Older sisters	0.9 (1.1)	0.9 (1.1)	1.1 (1.2)	1.0 (1.2)	0.9 (1.1)
Father’s years of schooling	3.0 (3.7)	3.8 (4.2)	3.6 (3.9)	3.3 (3.7)	3.3 (3.9)
Mother’s years schooling	1.1 (2.2)	1.9 (2.8)	1.5 (2.2)	1.5 (2.4)	1.5 (2.5)
Father lived abroad	4.0%	5.9%	6.7%	9.7%	5.8%
Brother lived abroad	30.3%	21.2%	51.2%	49.1%	32.1%
Household assets, 1996–1997 (US dollars)	4,858 (7,962)	4,813 (7,432)	6,228 (7,110)	6,397 (8,579)	5,150 (7,878)

Data are given as mean (SD) or percent. Source: MHSS1 (1996–1997), MHSS2 (2012–2014), Matlab Health and Demographic Surveillance System (1982–2014).

Multivariate analysis: Employment

We estimated differences in earnings, hours worked, and hourly wages, shown in Table 3. In terms of annual earnings, current international migrants earned about 4 times more than non-migrants (mean $5,032 versus $1,235, p < 0.001) and nearly 3 times more than internal migrants (mean $5,032 versus $1,813, p < 0.001). Work effort was high for all groups, but current international migrants worked substantially more hours per week than non-migrants (mean 66.8 versus 48.6), though only about 10% more than internal migrants (mean 60.8). Returned international migrants earned comparable (though slightly higher) wages than non-migrants on similar hours worked.

Table 3

Estimates of earnings, hours, and hourly wages—covariate-adjusted ordinary least squares regression models.

Measure	Non-migrant	Internal migrant	Returned international migrant	Current international migrant	n
Annual income	$1,235***	$1,813***	$1,500***	$5,032	4,296
	($1,036–$1,407)	($1,607–$2,019)	($1,081–$1,919)	($4,654–$5,411)
Hours worked per week	48.6***	60.8***	48.5***	66.8	4,296
	(47.0–50.2)	(59.0–62.7)	(43.4–53.6)	(64.9–68.8)
Hourly wage	$0.51***	$0.64***	$0.64***	$1.49	4,047
	($0.45–0.57)	($0.56–$0.71)	($0.48–$0.79)	($1.38–$1.60)

Data are mean (95% CI). Marginal predictions from regressions controlling for age, education, religion, height, parental schooling, sibling composition, father’s international migration, brother’s international migration, and 1996–1997 household assets. Statistical test of difference from current international migrant

***p < 0.001.

Multivariate analysis: Physical health

Table 4 evaluates the association between migration status and health measures after controlling for baseline variables. After controlling for other factors, current international migrants remained significantly less likely to report fair/poor self-rated health status (6.7%) than non-migrants (16.6%, p < 0.001), internal migrants (12.8%, p = 0.012), and returned international migrants (13.8%, p = 0.30). Current international migrants were less likely than non-migrants, though not significantly so, to have experienced an injury in the past 12 months (6.0% versus 9.3%, p = 0.084). We found no significant differences between current international migrants and other groups in the likelihood of having a work-related injury, and, after controlling for covariates, migrant advantages in smoking were no longer significant.

Table 4

Estimates of health outcomes by migration status from covariate-adjusted logistic regression models.

Measure	Non-migrant	Internal migrant	Returned international migrant	Current international migrant	n
Self-reported health measures
Fair/poor self-rated health	16.6%**	12.8%*	13.8%*	6.7%	4,296
	(14.4%–18.7%)	(10.1%–15.6%)	(7.6%–20.0%)	(3.6%–9.9%)
Injury	9.3%+	5.1%	8.7%	6.0%	4,296
	(7.6%–11.0%)	(3.4%–6.8%)	(3.5%–13.8%)	(3.3%–8.8%)
Work-related injury	5.2%	3.5%	6.2%	5.3%	4,296
	(4.0%–6.5%)	(2.0%–5.0%)	(1.4%–10.9%)	(2.5%–8.0%)
Current smoker	37.6%	37.7%	37.9%	32.7%	4,296
	(34.6%–40.5%)	(33.7%–41.7%)	(29.2%–46.5%)	(27.7%–37.7%)
Body mass index	20.9***	22.0***	22.7+	23.3	4,296
	(20.7–21.0)	(21.7–22.2)	(22.0–23.3)	(23.0–23.6)
Overweight or obese	23.3%***	37.7%**	47.0%	51.7%	4,296
	(20.7%–26.0%)	(33.8%–41.6%)	(37.5%–56.5%)	(46.5%–56.9%)
Obese	3.4%*	5.1%	5.0%	6.9%	4,296
	(2.2%–4.7%)	(3.3%–7.0%)	(2.0%–8.1%)	(4.5%–9.2%)
Underweight	17.5%***	11.2%***	7.8%**	2.2%	4,296
	(15.2%–19.8%)	(8.9%–13.4%)	(2.7%–12.8%)	(0.7%–3.7%)
Objective health measures (in-person interviews only)
Mean grip strength (kgf)	38.6***	39.6***	40.0*	41.8	3,754
	(38.3–39.0)	(39.1–40.2)	(38.8–41.3)	(40.7–42.8)
Hypertension—stage 1 or higher	7.0%*	12.6%	8.4%	13.0%	3,760
	(5.6%–8.4%)	(9.8%–15.5%)	(3.8%–12.9%)	(7.4%–18.6%)

Data are mean (95% CI). Marginal predictions from regressions controlling for age, education, religion, height, parental schooling, sibling composition, father’s international migration, brother’s international migration, and 1996–1997 household assets. Statistical test of difference from current international migrant

***p < 0.001

**p < 0.01

*p < 0.05

+p < 0.10.

kgf, kilograms-force.

The introduction of controls also diminished the magnitude and significance of the association of migration with overweight/obese (BMI ≥ 23.0 kg/m2) and obese (BMI ≥ 27.5 kg/m2) status, yet effects remained significant. After adjustments, mean BMI for current international migrants was 23.3 kg/m2 (95% CI 23.0–23.6), with highly significant differences from non-migrants (mean 20.9 kg/m2, 95% CI 20.7–21.0, difference = 2.4 kg/m2, p < 0.001) and internal migrants (mean 22.0 kg/m2, 95% CI 21.7–22.2, difference = 1.3 kg/m2, p < 0.001). BMI for returned international migrants was between that of current international migrants and internal migrants (mean 22.7 kg/m2, 95% CI 22.0–23.3), with the difference from current international migrants significant only at the 10% level. International migrants remained more than twice as likely as non-migrants to be overweight/obese (51.7% versus 23.3%, p < 0.001) or obese (6.9% versus 3.4%, p = 0.012) than non-migrants. International migrants were also significantly more likely to be overweight/obese than internal migrants (51.7% versus 37.7%), though differences in obesity were not significant. Underweight status also varied considerably across the groups, with very few current international migrants observed as underweight (mean 2.2%, 95% CI 0.7%–3.7%), a rate significantly lower than those of all other groups.

Objective health measures were not included in the phone survey, and so analysis of these variables includes only the 275 current international migrants interviewed during the Eid festivals. In spite of this small sample size, we observed significant differences. Current international migrants achieved significantly higher levels of grip strength (mean 41.8 kgf, 95% CI 40.7–42.8), with a 0.42 SD advantage over non-migrants (mean 38.6 kgf, 95% CI 38.3–39.0, p < 0.001) and a 0.30 SD advantage over internal migrants (mean 39.6 kgf, 95% CI 39.1–40.2, p < 0.001). Current international migrants were significantly more likely to have stage 1 or higher hypertension than non-migrants (13.0% versus 7.0%, p = 0.014), but they were no more likely than internal migrants.

Multivariate analysis: Mental health

We estimated the association of migration status with mental health, measured by a 12-item shortened CES-D questionnaire. Fig 3 reports the kernel density estimates of aggregate depressive symptom scores by migrant status and mode of interview. These results suggest that the timing of Eid festival interviews may have biased the scores. Because these in-person interviews with international migrants were conducted at the one time in a 3-year period in which respondents were reunited with family and friends, overall CES-D scores were much lower than for any other group, including international phone survey respondents. We therefore excluded Eid festival interviews from analysis of mental health.

Fig 3

Kernel density estimate of Center for Epidemiologic Studies Depression Scale (CES-D) scores by migrant status and mode of interview.

Kernel density estimates were constructed using the Stata kdensity function with Epanechnikov kernel and bandwidth of 1. For this analysis, migration status was based strictly on current residence, with returned international migrants included in the non-migrant or internal migrant groups, as appropriate. Sample sizes were as follows: non-migrant, 2,190; internal migrant, 1,315; current international migrant interviewed in person, 275; current international migrant interviewed by phone, 515.

Table 5 reports the covariate-adjusted standardized depressive symptom scores by migrant status, using only the phone survey respondents to represent current international migrants. Using all 12 items, current international migrants had an adjusted mean score of 0.22 SD above the mean, with higher values indicating higher levels of depressive symptoms. Internal migrants showed significantly lower scores than international migrants (adjusted mean score −0.028 SD, p < 0.001), with non-migrants having a marginally significant advantage over international migrants (adjusted mean score 0.099 SD, p = 0.090). Looking only at the 4 items relating to positive emotions, there was little variation by migrant status, with current international migrants having an adjusted mean score of +0.098 SD and only internal migrants showing a marginally significant difference from international migrants (adjusted mean score −0.028 SD, p = 0.092). Variations by migrant status were highly significant for items relating to negative emotions, however. International migrants had an adjusted mean score of +0.254 SD All other groups scored significantly lower than current international migrants, with internal migrants performing best (adjusted mean score −0.020 SD, p < 0.001). Non-migrants, internal migrants, and returned international migrants did not significantly differ from one another.

Table 5

Estimates of standardized depressive symptom scores by migration status—covariate-adjusted logistic regression models.

Status	Non-migrant	Internal migrant	Returned international migrant	Current international migrant#	n
All items (12)	0.099+	−0.028***	0.058	0.220	4,019
	(0.043, 0.156)	(−0.111, 0.055)	(−0.121, 0.236)	(0.098, 0.342)
Positive emotion items (4)	0.111	−0.027+	0.105	0.098	4,019
	(0.055, 0.168)	(−0.109, 0.055)	(−0.103, 0.313)	(−0.023, 0.219)
Negative emotion items (8)	0.056**	−0.020***	−0.003*	0.254	4,019
	(−0.003, 0.115)	(−0.099, 0.060)	(−0.236, 0.230)	(0.138, 0.370)

Data are adjusted mean score (SD) (95% CI). Marginal predictions from regressions controlling for age, education, religion, height, parental schooling, sibling composition, father’s international migration, brother’s international migration, and 1996–1997 household assets. Statistical test of difference from current international migrant

***p < 0.001

**p < 0.01

*p < 0.05

+p < 0.10.

#Excludes Eid festival survey respondents.

Discussion

In this study, we observed that international migrants had comparable or lower injury and mortality risks compared to respondents remaining in Bangladesh, due in part to the high risks present in Bangladesh. International migrants also showed higher levels of self-rated health and physical strength, reflective of positive self-selection into migration. They had substantially higher body mass, higher risk of overweight/obesity, moderately higher risk of hypertension, and greater depressive symptoms.

Guest workers represent both a highly vulnerable population and critical yet understudied participants in the machinery of global development. Up to one-third of the world’s international migrants are on temporary work visas. Several of the world’s largest economies, including most of the destination countries included in this study, are highly dependent on guest workers. The mechanisms under which guest workers are recruited, employed, and often rejected are rightly debated on legal and ethical grounds [6]. Much of the narrative surrounding guest worker rights has been based on anecdotal and thinly sourced reports of widespread injury and fatality [37-39], alongside qualitative studies and non-representative studies pointing to other risks. By following a large sample of guest workers from within a larger prospective cohort study of migrants, internal migrants, and non-migrants, our study is the first that can adequately shed light on the health consequences of migration for guest workers working in the Persian Gulf and Asia.

Our results find little support for the most extreme concerns about injury and mortality. The most potent concern relates to the possibility that guest workers may face a substantial excess risk of mortality, whether in comparison to the those remaining in the sending country or to a global standard for working age populations. Mortality represents an important health outcome, and could also lead the results on non-mortality health outcomes in this paper to be biased, as the most disadvantaged guest workers are missing from the data because they died. Our results appear to reject such concerns. Our attrition analysis yielded only 4 deaths among 945 individuals whose last known location was outside Bangladesh. Yet it remains possible, however unlikely, that respondents were injured abroad only to die in Bangladesh. We therefore took advantage of the prospective migration data from the MHDSS to conduct a survival analysis of the annual risk of mortality before and after individuals’ first recorded episode abroad. We found that the annual hazard of mortality in the years following international migration was about 75% lower in years spent abroad than in years spent in Bangladesh, and comparable to average annual death probabilities for typical host country populations.

Relatedly, we found no evidence of excess injury risk among international migrants, and in fact found a modest advantage relative to non-migrants. But this does not mean that injury risks were low among international migrants, but rather that the risks of injury are extraordinarily high in Matlab and throughout Bangladesh [44]. We found no significant differences in terms of workplace injuries. Given the wide economic and infrastructural disparities between guest worker destinations and major Bangladeshi cities like Dhaka, we might expect substantially fewer injuries among international migrants, and so the lack of a migrant advantage in this case may still raise concern.

Yet a much clearer picture of the health burdens affecting guest workers emerges when we consider chronic disease and associated risk factors. International migrants show numerous health advantages that largely reflect the substantial force of self-selection into the physically taxing process of migration. International migrants not only come from socioeconomically advantaged households in terms of wealth and schooling, they also have substantial advantages in terms of stature and strength. Based on reports for the full sample, international workers on average are about 2 cm taller than non-migrants, have grip strength 0.4 SD higher, and are half as likely to report fair/poor self-rated health. They are also less likely to smoke, though this advantage was diminished by control variables. Each of these tendencies has been documented in studies of migrant selection using paired or matched samples from sending and host countries [8,11], but such studies always carry the possibility of measurement or sampling bias across distinct samples. Our analysis based on a population follow-up study confirms evidence of positive self-selection from earlier studies.

In spite of these obvious health advantages, international migrants also bear markers of health disadvantage in terms of dietary and metabolic risk, and to a lesser extent mental health. Guest workers have a mean BMI that is 2.3 kg/m2 higher than that of their non-migrant peers (a 0.7 SD difference) and 1.3 kg/m2 higher than that of internal migrants. As a result, guest workers are twice as likely as non-migrants or internal migrants to be obese according to the Asian standard cutoff of BMI ≥ 27.5 kg/m2. The 7% adjusted obesity prevalence among international migrants is still relatively low in comparison to host populations or to international migrants in other societies. Yet we observe a far more substantial attributable burden of overweight/obesity among international migrants, who have a 52% adjusted risk of being overweight or obese compared to just 23% among non-migrants and 38% among internal migrants. Given the secular trend towards rising metabolic risk in Bangladesh [45] and the high susceptibility to diabetes and cardiovascular disease among South Asian populations [46], these differences may point to serious health risk. This concern is further reinforced by the fact that international migrants were nearly twice as likely to experience stage 1 or higher hypertension as non-migrants, though we note that internal migrants had equally high hypertension burdens. We also observed significantly higher depressive symptom scores among international migrants, particularly when focusing on those CES-D items that address negative emotions. While it is salutary that burdens of hypertension and mental illness appear to diminish substantially when migrants return home permanently or for festivals, they nonetheless raise cause for concern that should be addressed in long-term follow-up studies of health and mortality.

This initial analysis carries a number of limitations, some of which can be addressed in future work. First, this study is merely descriptive. Future work based on a follow-up survey currently in the field will tie variations in health to variations in the conditions of entry into host countries (social networks, debt, living conditions), to occupational risk exposures (measured by frequency and protection), and experiences of abuse. Second, while our data provide valuable controls for baseline characteristics dating back decades, the long gap between baseline and follow-up surveys and the fact that many respondents were very young at baseline does not allow us to directly measure a change in any single health indicator associated with migration. Third, the use of a combination of in-person interviews during Eid festivals and phone surveys for international migrants created important gaps in the availability of objective health measures for phone respondents, in the need to use a shortened 12-item CES-D module, and in the validity of mental health data for festival respondents. This also raises concerns about the validity and comparability of measures drawn from distinct survey modes, though a methodological companion paper demonstrates the high level of comparability of income, height, and weight reports [47]. Finally, the study would benefit from more salient and time-sensitive measures of injury, abuse, and mental distress. Many of the most salient risks affecting guest workers can occur on a very rapid temporal scale, whether we are speaking of daily variations in heat stress, episodes of abuse, or visa issues. In the future we hope to link real-time measures of risk exposure with ecological momentary analysis of mental distress using a mobile data collection platform.

Finally, we note that temporary guest worker migration is a diverse process, containing multiple migrant streams that are stratified by industry, recruitment pathway, and exposure to risk. Matlab has a very well established migration history and well-developed recruitment mechanisms, and so results might look different in source populations with lower stocks of migration-specific social capital.

In spite of these limitations, this study raises critical policy concerns with respect to the health of guest workers and points the way to a conceptual model of guest worker health, and immigrant health more broadly. Guest worker migration continues to be popular because, in spite of the risks, we find that migrants earn roughly 4 times as much annually as non-migrants, and 3 times as much as internal migrants. In the process, their health may be compromised by the physical burdens of their work and by the psychological burdens of separation from family and home society. Migrant health may also be influenced by the complex mix of assimilation into local dietary and health-seeking behavior patterns but exclusion from key societal and health system functions. In spite of these burdens, our findings do not support the notion that guest worker migration should be eliminated or curtailed simply on the basis of health risks. Our qualitative interviews with migrants revealed that migrants are drawn to migration, in spite of the well-understood risks, by the opportunity to secure their own livelihoods, improve their family security, and also explore a new society and culture.

Both the countries of arrival and departure can respond to these findings by introducing measures to reduce the intensity of work-related stressors, societal adjustment issues, and family separation. These can include better working and living conditions and shorter working hours, as well as more flexible family leave arrangements. The Global Compact for Safe, Orderly and Regular Migration offers a template for pursuing these goals on a multilateral basis [48]. Implementation of these goals will require further binational monitoring of migrant health and well-being along with interventions and policy analysis aimed at identifying best practices for protecting migrant health.

Conclusion

This descriptive analysis of the health and well-being of current and returned international guest workers from a rural area of Bangladesh provides rare evidence on a population that is both understudied and at risk. The comparison of migrants to non-migrant and internal migrant comparators, combined with longitudinal data going back many years, allows us to approach an answer to the question of whether guest worker migration is bad for health, and in what ways. Contrary to the popular narrative of a population saddled with acute health risks relating to injury and mortality, our results point to a more subtle set of emerging health risks that nonetheless raise concern. International migrants show significantly better health on measures of long-term health such as grip strength, reflecting self-selection in the highly competitive process of migration. Yet in spite of these advantages, migrants have significantly higher risks of overweight, obesity, hypertension, and depression than both non-migrant and internal migrant comparators. While returned international migrants in some cases look more similar to non-migrants, international migrants nonetheless accumulate years of exposure to chronic disease and mental health risk. The new Global Compact for Safe, Orderly and Regular Migration offers a potential mechanism for addressing these burdens through bilateral cooperation.

STROBE statement.

(DOC)

Click here for additional data file.

Full model specifications for all dependent variables.

(DOCX)

Click here for additional data file.

Proposal submitted to Research & Empirical Analysis of Labor Migration (REALM).

(DOCX)

Click here for additional data file.

18 Dec 2019

Dear Dr. Kuhn,

Thank you very much for submitting your manuscript "Is being a guest worker bad for your health? Lessons from a large-scale binational survey of Bangladeshi migrants and non-migrants" (PMEDICINE-D-19-03799) for consideration at PLOS Medicine for our upcoming special issue on refugee and migrant health.

Your paper was discussed among the editorial team and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to invite you to submit a revised version that fully addresses the reviewers' and editors' comments. You will appreciate that we cannot make a decision about publication until we have seen the revised manuscript and your response, and we expect to seek re-review by one or more of the reviewers.

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Requests from the editors:

Please finalize the arrangements for data deposition.

Regarding data access, you state "No: some restrictions will apply", and we ask you to detail these restrictions.

We ask you to restructure your title to accord with journal style (non declarative, with a study descriptor). We suggest: "Health and wellbeing of international migrants and non-migrants in Bangladesh: a cross-sectional study".

Please combine the "methods" and "findings" subsections of your abstract; and add a new final sentence to the new combined subsection summarizing the study's main limitations.

In the abstract, please detail study participants along with summary demographic details. Please also provide quantitative estimates for the main study outcomes, along with 95% CI and p values where appropriate.

Please begin the "conclusions" subsection of your abstract with "In this study, we observed that ..." or similar, and condense the subsection.

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We ask you to trim the introduction section of your main text. Around 500 words is usually sufficient; additional material on previous literature can perhaps be relocated to the discussion section.

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Early in the methods section of your main text, please state whether the study had a protocol or prospective analysis plan, and if so attach the document(s) as a supplementary file (referred to in the text). Please highlight analyses that were not prespecified.

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Please format reference call-outs as follows: "... in 2018 [1,4].".

We ask that you revisit the reference list to ensure that all citations meet journal style. Reference 41 may need additional access information to be included.

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Comments from the reviewers:

*** Reviewer #1:

I confine my remarks to statistical aspects of this paper. The general approach is fine, but I have a few issues to resolve before I can recommend publication.

NOTE: Adding page and line numbers would have made it easier to reference my comments

General comment: Why not use some sort of matching (e.g. by propensity score)? I'm not saying it's required, but was it considered? If so, why was it rejected?

Abstract:

It's logistic regression, not logistics

There should only be one reference group.

Introduction:

"larger representative sample ..." representative of what?

Methods

The formula can probably be deleted. As written, it is incorrect in that some fo the betas are vectors, while the formula implies they are scalars. You would need one beta for each variable, not for groups of variables

Independent variables (e.g. BMI) should not be categorized. In *Regression Modeling Strategies* Frank Harrell lists 11 problems that this gives rise to and summarizes "nothing could be more disastrous". I wrote a blog post illustrating some of these, graphically: https://medium.com/@peterflom/what-happens-when-we-categorize-an-independent-variable-in-regression-77d4c5862b6c

When listing all the IVs, they need to be given operational definitions. That is how were they measured? e.g. what were the choices for "religion"?

Why were fair and poor health lumped together? This is probably a mistake.

Table 5: Give mean BMI and grip strength for each group. Don't categorize.

Peter Flom

*** Reviewer #2:

This study represents important advances in research of migrant health. There are very few studies that are able to compare migrants, non-migrants and returned migrants from the same community; this study does so using very high quality longitudinal data from a long-standing Demographic Surveillance System. The focus of the study is on migration from South Asia to the Middle East - one of the very established routes for labour migration, but also a very dangerous one for migrants, entailing brutal work conditions, lack of access to services, servitude to an employer, and high risks of death and injury.

The methods and data are appropriate. I have a few questions relating to models and interpretation.

1. The paper highlights higher risks of depression among migrants, but these seem to be non-significant in Table 6.

2. With respect to the models, it would be useful to first estimate an interaction model, in which all respondents are pooled and migration status is interacted, for example with sadness items. This would allow the authors to directly estimate the effect of migrations status. It would also indicate where stratified analyses are needed.

3. When we think about poor health of guest workers, we generally think about injuries and harm directly resulting from the labour migration. This paper does not find evidence of such problems, but instead finds evidence of overweight and hypertension (though this is also not significant). These are different types of health risks, and are not directly hazards of the work migrants are doing, but likely rather a bi-product of integration into the host society. It would be informative if the article also indicated the levels of overweight in the host country - do migrants just look like the population into which they are integrating? If so, this should be discussed as a component of integration foremost, and less so as a hazard of migration.

4. The interpretation in the abstract goes further than that in the paper in describing the depletion of migrants, and seems to go beyond the findings of the paper.

5. It seems there is something interesting going on with internal migrants. Either in this paper or another, it will be an interesting to examine.

*** Reviewer #3:

This study benefits from well described migrant vs non-migrant groups with adequate follow-up over time. That is a unique and major strength of the study. Below some feedback to constructively contribute to improve this work.

MAJOR COMMENTS

1. Methods: "Along with predicted values/probabilities and confidence intervals, we report statistical tests of difference in which current international migrants are the reference category, thereby comparing international migrants to all three counterfactual comparisons groups of non-migrants, internal migrants, and returned migrants." Returned migrants are not described in Table 1. The reason I mention this is because returned migrants can be internal or international returning migrants, so clarification is needed. Later on, it reads "Among respondents in Bangladesh, returned international migrants were identified based on a self-report of whether they had lived abroad in the past five years..." Best to clarify throughout the text.

2. Table 1 would be better to be transformed into a Flowchart, to comply with STROBE recommendations, and in that flowchart add how many people were evaluated by what method (in-person or telephone) and how much of the actual data was measured vs self-reported.

3. Depression. In methods the authors say that "Because there is no validated diagnostic cutoff for depression using this abridged scale, we estimated models for standardized depression scores." What were those scores? How do they compare or related to the existing literature? Are they comparable to other definitions of depression and the cut-offs used? Possibly, given these differences, the authors are better off not labelling this as depression (which has a gold standard definition) but as depressive symptoms. All of this should be clear in methods and also described as a limitation.

4. Hypertension. Why do the authors used 130/80 mm Hg as the cut offs? Explain in the manuscript. Is that cut off applicable to Bangladesh population?

5. On mortality. When the authors say, in the Discussion, that "This does not mean that injury risks were low among international migrants, but rather that the risks of injury are extraordinarily high in Matlab and throughout Bangladesh (41)", this reviewer wonders if a similar reasoning could be applied to the mortality indicator. Also, on the mortality, is is something that would require longer follow-up periods to observe differences? What if the accumulated risk factors —higher obesity, hypertension and depression scores among international migrants— would turn into higher mortality later on? Some reflection on this would be ideal.

MINOR COMMENTS

6. Introduction and discussion. It could benefit from more recent bibliography. See at the end some suggested references for the authors to consider.

7. Some clarification of the type of location of internal migration and how much similar were they to Matlab? This is important to understand, a bit, the context of the internal migrants.

8. On the reference group. See comment #1 and contrast it with this sentence from Findings: "Table 3 compares the baseline characteristics of migration status groups. On most characteristics, international and internal migrants were comparable, making internal migrants a relevant reference group." Avoid confusions.

9. Typo: mich. "International migrants were mich more likely to have a father who had lived abroad."

10. Consider moving Fig 1 (bivariate analysis) to supplementary material and transform some of the tables in graphs for a better communication of the estimates presented.

SUGGESTED REFERENCES

1: Dominguez K, Penman-Aguilar A, Chang MH, Moonesinghe R, Castellanos T, Rodriguez-Lainz A, Schieber R; Centers for Disease Control and Prevention (CDC).

Vital signs: leading causes of death, prevalence of diseases and risk factors, and use of health services among Hispanics in the United States - 2009-2013.

MMWR Morb Mortal Wkly Rep. 2015 May 8;64(17):469-78. Erratum in: MMWR Morb Mortal Wkly Rep. 2015 Oct 16;64(40):1153. PubMed PMID: 25950254; PubMed Central PMCID: PMC4584552.

2: Abubakar I, Aldridge RW, Devakumar D, Orcutt M, Burns R, Barreto ML, Dhavan P, Fouad FM, Groce N, Guo Y, Hargreaves S, Knipper M, Miranda JJ, Madise N, Kumar B, Mosca D, McGovern T, Rubenstein L, Sammonds P, Sawyer SM, Sheikh K, Tollman S, Spiegel P, Zimmerman C; UCL-Lancet Commission on Migration and Health.

The UCL-Lancet Commission on Migration and Health: the health of a world on the move.

Lancet. 2018 Dec 15;392(10164):2606-2654. doi: 10.1016/S0140-6736(18)32114-7. Epub 2018 Dec 5. Review. PubMed PMID: 30528486.

3: Aldridge RW, Nellums LB, Bartlett S, Barr AL, Patel P, Burns R, Hargreaves S, Miranda JJ, Tollman S, Friedland JS, Abubakar I.

Global patterns of mortality in international migrants: a systematic review and meta-analysis.

Lancet. 2018 Dec 15;392(10164):2553-2566. doi: 10.1016/S0140-6736(18)32781-8. Epub 2018 Dec 5. PubMed PMID: 30528484; PubMed Central PMCID: PMC6294735.

4: Hargreaves S, Rustage K, Nellums LB, McAlpine A, Pocock N, Devakumar D, Aldridge RW, Abubakar I, Kristensen KL, Himmels JW, Friedland JS, Zimmerman C.

Occupational health outcomes among international migrant workers: a systematic review and meta-analysis.

Lancet Glob Health. 2019 Jul;7(7):e872-e882. doi: 10.1016/S2214-109X(19)30204-9. Epub 2019 May 20. PubMed PMID: 31122905; PubMed Central PMCID: PMC6565984.

5: Juárez SP, Honkaniemi H, Dunlavy AC, Aldridge RW, Barreto ML, Katikireddi SV, Rostila M.

Effects of non-health-targeted policies on migrant health: a systematic review and meta-analysis.

Lancet Glob Health. 2019 Apr;7(4):e420-e435. doi: 10.1016/S2214-109X(18)30560-6. Epub 2019 Mar 6. PubMed PMID: 30852188; PubMed Central PMCID: PMC6418177.

6: Lu Y, Qin L.

Healthy migrant and salmon bias hypotheses: a study of health and internal migration in China.

Soc Sci Med. 2014 Feb;102:41-8. doi:10.1016/j.socscimed.2013.11.040. Epub 2013 Nov 28. PubMed PMID: 24565140.

7: Puschmann P, Donrovich R, Matthijs K.

Salmon Bias or Red Herring? : Comparing Adult Mortality Risks (Ages 30-90) between Natives and Internal Migrants: Stayers, Returnees and Movers in Rotterdam, the Netherlands, 1850-1940.

Hum Nat. 2017 Dec;28(4):481-499. doi: 10.1007/s12110-017-9303-1. PubMed PMID: 29043501; PubMed Central PMCID: PMC5662680.

8: Dodd W, Humphries S, Patel K, Majowicz S, Little M, Dewey C.

Determinants of internal migrant health and the healthy migrant effect in South India: a mixed methods study.

BMC Int Health Hum Rights. 2017 Sep 12;17(1):23. doi:10.1186/s12914-017-0132-4. PubMed PMID: 28899374; PubMed Central PMCID: PMC5596496.

9: Hayes L, White M, McNally RJQ, Unwin N, Tran A, Bhopal R.

Do cardiometabolic, behavioural and socioeconomic factors explain the 'healthy migrant effect' in the UK? Linked mortality follow-up of South Asians compared with white Europeans in the Newcastle Heart Project.

J Epidemiol Community Health. 2017 Jul 25. pii:jech-2017-209348. doi: 10.1136/jech-2017-209348. [Epub ahead of print] PubMed PMID: 28743730.

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

13 Jan 2020

Submitted filename: Response to editor and reviewer comments.docx

Click here for additional data file.

6 Feb 2020

Dear Dr. Kuhn,

Thank you very much for re-submitting your manuscript "Health and wellbeing of international migrants and non-migrants in Bangladesh: a cross-sectional followup study" (PMEDICINE-D-19-03799R1) for consideration at PLOS Medicine for our upcoming special issue on refugee and migrant health.

I have discussed the paper with editorial colleagues and the guest editors for the special issue, and it was also seen again by two reviewers. I am pleased to tell you that, provided the remaining editorial and production issues are dealt with, we expect to be able to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

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Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

Please let me know if you have any questions. Otherwise, we look forward to receiving the revised manuscript shortly.

Sincerely,

Richard Turner, PhD

Senior Editor, PLOS Medicine

rturner@plos.org

------------------------------------------------------------

Requests from Editors:

We ask you to finalize the arrangements for data deposition.

Please add "male", or similar, to the title.

At line 40, please make that "... a single out-migration area of origin.".

At line 46, we suggest substituting "ill health" for "weathering".

Rather than "current international migrants" which to our understanding does not reflect the "current" situation (in the abstract and elsewhere), we suggest rephrasing to "at follow-up, international migrants ..." or similar.

In the abstract and elsewhere, please quote exact p values or p< 0.001.

We ask you to trim the "author summary" section. Generally, we expect the three subsections to contain 2-3 points, each consisting of 1-2 short sentences. Background information and information on earnings, for example, can be substantially reduced.

Please refer to the attached STROBE checklist in the methods section of your main text.

At line 475, please revisit the phrase "temporary guest worker is a diverse process" to improve readability.

At line 493, we suggest rewording to "Both countries of departure and arrival ..." or similar.

At line 507, we suggest removing "your" (from "your health") as you are referring to migrant health.

Please adopt the style "follow-up" throughout the text.

Please trim the journal name for reference 42 to "Lancet".

Comments from Reviewers:

*** Reviewer #1:

The authors have addressed my concerns and I now recommend publiication

Peter Flom

*** Reviewer #2:

The authors have been responsive to reviewers and have engaged very actively with the comments received - this has further improved an excellent paper.

My one substantial comment:

Are there differences in health-relevant characteristics among who goes to the various destinations? As you summarize the findings, are there differences in experiences of guest workers and their health outcomes depending on where they go that should be mentioned?

Other thoughts for your consideration:

When you say "also examined observed health measures", be clearer that you actually measured hypertension. Would be important to note when you are talking about self-reported vs. directly-measured indicators. For example, was BMI measured for some people and self-reported for others? If so, there could be systematic differences to consider.

In terms of BMI, you focus on overweight/obese, but was there also underweight, eg. among non-migrants?

It is really nice that you are able to compare some measures for in-person interviews vs. phone interviews, and interesting to see how these differ. Thoughts on why?

The figures are a bit blurry.

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

27 Feb 2020

Dear Prof. Kuhn,

On behalf of my colleagues and the academic editor, Dr. Paul Spiegel, I am delighted to inform you that your manuscript entitled "Health and wellbeing of male international migrants and non-migrants in Bangladesh: a cross-sectional followup study" (PMEDICINE-D-19-03799R2) has been accepted for publication in PLOS Medicine.

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