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Health Selection, Migration, and HIV Infection in Malawi

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Demography

Abstract

Despite its importance in studies of migrant health, selectivity of migrants—also known as migration health selection—has seldom been examined in sub-Saharan Africa (SSA). This neglect is problematic because several features of the context in which migration occurs in SSA—very high levels of HIV, in particular—differ from contextual features in regions that have been studied more thoroughly. To address this important gap, we use longitudinal panel data from Malawi to examine whether migrants differ from nonmigrants in pre-migration health, assessed via SF-12 measures of mental and physical health. In addition to overall health selection, we focus on three more-specific factors that may affect the relationship between migration and health: (1) whether migration health selection differs by destination (rural-rural, rural-town, and rural-urban), (2) whether HIV infection moderates the relationship between migration and health, and (3) whether circular migrants differ in pre-migration health status. We find evidence of the healthy migrant phenomenon in Malawi, where physically healthier individuals are more likely to move. This relationship varies by migration destination, with healthier rural migrants moving to urban and other rural areas. We also find interactions between HIV-infected status and health: HIV-infected women moving to cities are physically healthier than their nonmigrant counterparts.

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Notes

  1. Nauman et al. (2015) referred to this phenomenon as the “midnight train effect” instead of “salmon bias” because it is often the persons least fit for the trip in the first place who return to origin, whereas for salmon, those who make it back to origin are the most fit.

  2. Between 1998 and 2004, the MLSFH was known as the Malawi Diffusion and Ideational Change Project (MDICP).

  3. Most migrants moving internationally were MLSFH respondents from the central region, Mchinji, which boarders on Zambia, and moved a relatively short distance across the border.

  4. Although the MHM study traced migrants who were interviewed in MLSFH 2004 and migrated afterward, the MLSFH did not include SF-12 scores until 2006. Therefore, these migrants are not included in the analysis here.

  5. We also examined an alternative measure of return migration, living outside the current residence for six months or more since age 15. Analysis of this measure of return migration yielded results that were not substantively different.

  6. We also include a quadratic measure of age to test for a nonlinear relationship with future migration.

  7. We also examined whether individuals who migrated previously have different health and are more likely to move again. To do so, we ran regressions similar to those in Step 1, but ran them separately for each year (2006 and 2008) instead of using pooled random-effects regressions, and included measures of previous migration (lived outside the district for one month or more in the past year, and lived outside the current residence for six months or more since age 15). We also included interactions between these previous migration measures and both mental and physical health, which test whether those who previously migrated and have different health are more likely to move again. The results (Tables 7 and 8 in the appendix) show that previous migrants do not have greater odds of moving again (for either measure of migration), and the interactions between previous migration and health are not statistically significant in any of the models.

  8. The MLSFH survey that included information on return migration (staying outside the district for one month or more in the past year) was administered separately from the HIV test and measure of SF-12 score. As a result, fewer respondents answered the question on previous migration, and our overall sample size for this analysis is reduced (n = 2,840). We compare the characteristics of individuals in the full sample compared with the sample of return migration (Table 9 in the appendix) and find no statistically significant differences in SF-12 health status or HIV infection, although there are differences in gender and age.

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Acknowledgments

The authors thank Invest in Knowledge Initiative, which collected data for the MHM and MLSFH. We are also very grateful to the reviewers for their very insightful and helpful comments. This research was funded by National Institute of Child Health and Development (NICHD) (MHM Grant No. R21HD071471-01; MLSFH Grant Nos. R03 HD05 8976, R21 HD050652, R01 HD044228, R01 HD053781, and R24 HD-044964).

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Authors and Affiliations

  1. Department of Global Community Health and Behavioral Sciences, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street Suite 2210, New Orleans, LA, 70112, USA

    Philip Anglewicz & Mark VanLandingham

  2. Malawi College of Medicine, John Chiphangwi Learning Resource Centre, University of Malawi, 3rd Floor, Private Bag 360, Chichiri Blantyre 3, Malawi

    Lucinda Manda-Taylor

  3. Department of Sociology and Population Studies Center, University of Pennsylvania, 3718 Locust Walk, Philadelphia, PA, 19104-6299, USA

    Hans-Peter Kohler

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Correspondence to Philip Anglewicz.

Appendix

Appendix

Fig. 2
figure 2

Predictive margins of the probability of migration at SF-12 PCS of 20–70, by gender

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Table 7 Logistic regression results for previous migration, measured as lived outside the district for one or more months in past year
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Table 8 Logistic regression results for previous migration, measured as lived outside the current residence for six or more months since age 15
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Table 9 Pre-migration background characteristics for individuals in the return migration sample compared with the full sample: MLSFH men and women, 2006 and 2008
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Anglewicz, P., VanLandingham, M., Manda-Taylor, L. et al. Health Selection, Migration, and HIV Infection in Malawi. Demography 55, 979–1007 (2018). https://doi.org/10.1007/s13524-018-0668-5

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