Migrant health penalty: evidence of higher mortality risk among internal migrants in sub-Saharan Africa

Figures & data

Figure 1. Map of HDSS sites

Table 1. HDSS site characteristics

Site	Country	Start and end of HDSS (from 1 January to 31 December)	Settlement type	Person-years at risk (exposure)	Probability of child death: 5q0	Probability of adult death: 45q15	In-migration rate	Out-migration rate	HIV prevalence as % in adults aged 15–49 (2010) [46]	Gross cell product in US$ at purchase power parity exchange rates (2005) [47]
Nanoro	Burkina Faso	2009–2014	Rural	324,630	0.06	0.20	85.70	126.54	1.0	1.015
Nouna	Burkina Faso	1998–2015	Mostly rural	1 228,023	0.11	0.26	75.72	106.14	0.8	0.182
Ouagadougou	Burkina Faso	2009–2015	Urban	554,584	0.05	0.18	100.54	141.86	1.7	2.132
Taabo	Cote d’Ivoire	2009–2016	Mostly rural	316,338	0.10	0.27	101.76	148.39	3.9	0.944
Gilgel Gibe	Ethiopia	2006–2015	Mostly rural	500,458	0.09	0.24	38.59	58.06	0.5	1.237
Kilte Awulaelo	Ethiopia	2010–2014	Mostly rural	325,211	0.04	0.11	24.72	73.91	1.2	0.553
Kersa	Ethiopia	2008–2016	Mostly rural	625,960	0.10	0.29	10.21	29.60	1.0	0.740
Harar Urban	Ethiopia	2012–2016	Urban	173,915	0.01	0.19	58.26	116.94	1.0	0.740
Dabat	Ethiopia	2009–2015	Mostly rural	343,874	0.03	0.20	32.48	62.94	1.2	0.590
Arba Minch	Ethiopia	2010–2015	Mostly rural	406,654	0.04	0.16	43.00	59.42	1.1	2.029
Navrongo	Ghana	1994–2014	Mostly rural	1 552,935	0.12	0.38	111.37	109.36	1.4	0.111
Kintampo	Ghana	2006–2014	Mostly rural	1 133,005	0.06	0.26	97.62	124.22	1.6	0.275
Dodowa	Ghana	2006–2011	Mostly rural	637,472	0.03	0.29	93.80	131.77	2.3	1.222
Farafenni	Gambia	1990–2015	Mostly rural	773,793	0.09	0.26	81.95	106.89	1.8	1.306
Nairobi	Kenya	2003–2015	Urban	770,322	0.07	0.33	199.37	270.16	6.2	2.502
Kombewa	Kenya	2011–2015	Rural	649,740	0.06	0.30	76.11	93.83	4.4	4.386
Karonga	Malawi	2003–2016	Mostly rural	472,001	0.06	0.30	78.03	104.25	9.0	0.360
Chokwe	Mozambique	2010–2015	Mostly rural	461,903	0.07	0.40	66.76	123.63	25.8	0.473
Nahuche	Nigeria	2011–2014	Rural	526,069	0.27	0.26	7.90	24.40	1.4	1.996
Bandafassi	Senegal	1990–2016	Rural	300,114	0.18	0.29	35.51	23.01	1.4	0.073
Mlomp	Senegal	1990–2016	Rural	210,522	0.08	0.22	76.14	77.42	1.6	0.559
Niakhar	Senegal	1990–2016	Rural	880,616	0.12	0.22	56.61	49.83	0.4	1.306
Ifakara Rural	Tanzania	1997–2014	Rural	1 502,326	0.10	0.28	102.47	158.80	5.3	0.476
Rufiji	Tanzania	1999–2014	Mostly rural	1 229,392	0.09	0.28	74.62	115.20	4.6	1.176
Magu	Tanzania	1994–2013	Rural	446,678	0.10	0.37	167.17	211.72	5.8	0.816
Iganga/Mayuge	Uganda	2005–2015	Mostly rural	681,290	0.09	0.23	92.07	127.37	4.8	3.017
Agincourt	South Africa	1993–2016	Rural	1 950,892	0.05	0.38	61.18	90.29	22.3	1.070
Dimamo	South Africa	1996–2016	Rural	362,059	0.02	0.33	31.04	40.13	12.9	3.400
AHRI	South Africa	2000–2016	Mostly rural	1 142,236	0.06	0.54	84.83	122.35	26.1	0.334

Figure 2. (a, b) In-migration patterns by sex and settlement type¹

¹In-migration patterns differ significantly by sex (HR = 1.13, 95% CI 1.12–1.13) and settlement type (HR = 0.48, 95% CI 0.48–0.49).

Figure 3. (a, b) Mortality patterns by sex and period

Figure 4. (a–d) Effect of migration status by duration since migration by age and sex: rural HDSS sites

Figure 5. (a ,b) Effect of migration status by duration since migration by age (15–8) and sex: urban HDSS sites

Institute for Health Metrics and Evaluation. 2019. Local burden of disease. GHDx. [Internet]. [ cited 2019 Sept]. Available from: http://ghdx.healthdata.org/lbd-data

 Google Scholar

Nordhaus W, Azam Q, Corderi D, et al. The G-Econ database on gridded output: methods and data [Internet]. Yale University. 2006 [ cited 2019 Apr]. Available from: https://gecon.yale.edu/data-and-documentation-g-econ-project

 Google Scholar