Social ecological determinants of the mental distress among Syrian refugees in Lebanon and Turkey: A transnational perspective.

OBJECTIVE: Refugees are frequently shown to have worse mental health outcomes than non-displaced populations. This fact is commonly attributed to traumatic pre-displacement experiences. While important, the focus on trauma risks overlooking the role socioeconomic living-conditions in different arrival and transit contexts can play in determining refugees' mental distress. Building on the ecological model of refugee distress, we investigate how social ecological conditions relate to the mental distress of Syrians in Lebanon and Turkey. Both countries present important spaces of arrival and transit for millions of displaced Syrians, each with a specific historical, political, social and economic context. METHODS: The empirical analysis is based on data gathered in early 2021 in face-to-face surveys among displaced Syrians in Lebanon (N = 1127) and Turkey (N = 1364). Individual mental distress is evaluated using the Patient Health Questionnaire (PHQ-8) score as the dependent variable in a multivariate regression analysis. RESULTS: Social ecological factors do not only differ in their extent of deprivation between Lebanon and Turkey. They also differ in their relationship with individual mental health outcomes. In Lebanon, limited access to the health care system and having family in the same city are major risk factors for elevated mental distress, whereas in Turkey, these are low education, poverty, unemployment as well as employment as day laborer. Discrimination and social isolation emerge as relevant predictors in both countries. CONCLUSION: Based on this analysis, we argue that a context-specific understanding of mental distress amidst the social ecology refugees face in countries of refuge and transit is necessary. This approach needs to be pursued to provide adequate support and alleviate refugees' mental distress both, in the country of first refuge and after possible onward migration. In addition to clinical implications, the study particularly highlights the important role anti-discrimination and social inclusion policies could play in promoting refugee mental health.

Using machine learning to impute legal status of immigrants in the National Health Interview Survey.

We describe a novel machine learning method of imputing legal status for immigrants using nationally representative survey data from the Survey of Income and Program Participation (SIPP) and the National Health Interview Survey (NHIS). K-nearest Neighbor (KNN) classifier and Random Forest (RF) Algorithm machine learning were described as novel imputation methods compared to established regression-based imputation. After validating the imputation methods using sensitivity, specificity, positive predictive value (PPV) and accuracy statistics, the Random Forest Algorithm was more accurate in identifying undocumented immigrants and minimized bias in both socio-demographic variables included in the imputation, and unobserved health variables relative to regression-based imputation and KNN.•We developed a new machine learning method of imputing legal status for immigrants that can be used with nationally representative, publicly available data.•Our findings indicate that using machine learning to impute legal status of immigrants, specifically the Random Forest Algorithm, was more accurate in identifying undocumented immigrants and minimized bias relative to other imputation methods.

A healthy migrant effect? Estimating health outcomes of the undocumented immigrant population in the United States using machine learning.

This paper investigated whether the commonly observed immigrant health advantage persists among undocumented immigrants in the U.S. and provides nationally representative evidence on the health of this vulnerable population. Data were derived from pooled cross-sections of the National Health Interview Survey (NHIS, 2000-2018). The legal status of foreign-born NHIS respondents is imputed using a non-parametric machine learning model built based on information from the 2004, 2008 and 2014 cohorts of the Survey of Income and Program Participation (SIPP). Multivariate logistic regression analysis indicated that, despite exposure to numerous additional risk factors, the undocumented population experienced a more pronounced Healthy Migrant Effect, with lower odds of reporting fair or poor self-rated health, any physician-diagnosed chronic conditions or being obese. The observed patterns in undocumented health outcomes may be related to the additional challenges and exclusionary policies associated with undocumented migration that could in turn lead to a more pronounced selection of healthy and resilient individuals.

Establishing a baseline for surgical care in Mongolia: a situational analysis using the six indicators from the Lancet Commission on Global Surgery.

OBJECTIVES: To inform national planning, six indicators posed by the Lancet Commission on Global Surgery were collected for the Mongolian surgical system. This situational analysis shows one lower middle-income country's ability to collect the indicators aided by a well-developed health information system. DESIGN: An 11-year retrospective analysis of the Mongolian surgical system using data from the Health Development Center, National Statistics Office and Household Socio-Economic Survey. Access estimates were based on travel time to capable hospitals. Provider density, surgical volume and postoperative mortality were calculated at national and regional levels. Protection against impoverishing and catastrophic expenditures was assessed against standard out-of-pocket expenditure at government hospitals for individual operations. SETTING: Mongolia's 81 public hospitals with surgical capability, including tertiary, secondary and primary/secondary facilities. PARTICIPANTS: All operative patients in Mongolia's public hospitals, 2006-2016. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes were national-level results of the indicators. Secondary outcomes include regional access; surgeons, anaesthesiologists and obstetricians (SAO) density; surgical volume; and perioperative mortality. RESULTS: In 2016, 80.1% of the population had 2-hour access to essential surgery, including 60% of those outside the capital. SAO density was 47.4/100 000 population. A coding change increased surgical volume to 5784/100 000 population, and in-hospital mortality decreased from 0.27% to 0.14%. All households were financially protected from caesarean section. Appendectomy carried 99.4% and 98.4% protection, external femur fixation carried 75.4% and 50.7% protection from impoverishing and catastrophic expenditures, respectively. Laparoscopic cholecystectomy carried 42.9% protection from both. CONCLUSIONS: Mongolia meets national benchmarks for access, provider density, surgical volume and postoperative mortality with notable limitations. Significant disparities exist between regions. Unequal access may be efficiently addressed by strengthening or building key district hospitals in population-dense areas. Increased financial protections are needed for operations involving hardware or technology. Ongoing monitoring and evaluation will support the development of context-specific interventions to improve surgical care in Mongolia.