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Longitudinal Analysis of the Relationship between Migration and Health Status Study of Adult Population of Indonesia Salut Muhidin, Dominic Brown & Martin.

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Presentation on theme: "Longitudinal Analysis of the Relationship between Migration and Health Status Study of Adult Population of Indonesia Salut Muhidin, Dominic Brown & Martin."— Presentation transcript:

1 Longitudinal Analysis of the Relationship between Migration and Health Status Study of Adult Population of Indonesia Salut Muhidin, Dominic Brown & Martin Bell 4 th International Conference on Population Geographies 12 July 2007, Hong Kong

2 What’s New? Some studies have been done on the link between migration (M) & health (H). Among others: UK (Bentham 1988; Boyle et al. 1999 & 2001; Dorling 1998) USA (Findley 1988; Kington et al. 1998) NL (Verheij et al. 1998) Australia (Larson et al. 2005) The studies are applied in the context of developed countries. YET, it is still little known in the developing countries. One of its main reason is data limitation. The ideal design for testing the M-H relationship requires life histories data, with appropriate information on background characteristics at different points in the life cycle Fact: Indonesia has now a longitudinal data which cover information on migration and health. IFLS The contribution here: –Investigating the relationship M-H in the context of a developing country –Using the available longitudinal data, i.e. IFLS

3 Research Question Is there any relationship between migration and health in the context of Indonesia? Q1 Do migrants differ from non migrants in terms of health and socioeconomic status? Q2 Does the probability of migration depend upon the health status accounting for socioeconomic variables? HealthMigration

4 Side 1: Migration Determinant of Migration –It is strongly related to particular personal traits and some important life events: e.g. education, marriage and separation, job related, and retired (elderly). Age regularities in migration (Rogers and Castro, 1980) Dimension Migration : –Time: Permanent - temporary (Intention to stay) –Distance: short - long –Geographic: Internal and international (urban-rural)

5 Side 2: Health Health has multi dimensions –It has been linked to many factors: physical, mental, and social well-being, genotype and phenotype, gender and place of residence. Health measures: –General Health Status (GHS) –Physical ability (ADL) –Chronic illness –Mental Health, or –Health related behaviors, etc.

6 Data Source Indonesia Family Life Survey (IFLS) –Longitudinal survey 3 waves: 1993, 1997, and 2000 –Organizer RAND, University of Indonesia and Gadjah Mada University –Coverage 13 provinces (83% population of Indonesia) 7,224 HH (Base in 1993) 6,820 HH (94% were re-interviewed) 33,081 people (Base in 1993)

7 Data Structure IFLS-1IFLS-3 Stayed or Moved away Health Status 1997 Health Status 2000 All respondents Re-tracked respondents Health Status 1993 IFLS-2 Stayed or Moved away Migration History

8 Data Structure IFLS 1993 N=33,081 Age 15< 11,451 Age 15+ 21,630 Health info 12,985 Migration info 21,630 Health 93 Migration 93-97 N = 12,985 IFLS 1997 (MH93) N=12,985 Died (454) No traced (165) Traced 12,366 Health info 11,495 Migration info 12,366 Health 97 Migration 97-00 N = 11,495

9 Variable: Migration Definition of Migration It is based on the status of leaving (staying) or changing their usual residence as recorded at the baseline (previous) survey  Current Migration –IFLS2 = Migration 1993-1997 –IFLS3 = Migration 1997-2000 Type of Migration Short Distant (inter village and district) and Long Distant Information on migration characteristics (age, destination and reasons) of those who have moved was also collected.

10 Variable: Health Status General Health Status (GHS): Self reported GHS was generated from a question “In general, how is your health at this time?” The answers were: (a) Very healthy, (b) somewhat healthy and (c) somewhat unhealthy or (d) unhealthy. Activity of Daily Living (ADL): Reported & observed ADL was constructed by using nine questions if the respondent could do (was capable of) certain daily activities. The answers were three possibilities: “easily”, “with difficulty”, and “unable to do”. It includes three functions: (1) mobility (to walk 5 kilometers; to bow, squat, and kneel; to stand up from sitting in a chair or from sitting on the floor), (2) personal care (i.e. to dress and to go to the bathroom without help); (3) home occupation (i.e. to carry a heavy load; to sweep; and to draw a pail of water).

11 Results Proportion of Migration Current Note: GHS (General Health Status) and ADL (Activity of Daily Living) GHS ADL

12 Current

13

14 Models Model 1  Selectivity What is the probability of migration with respect to the health status (does migrant has better health?).  Migration(93-97) = f (Health 93)  Migration(97-00) = f (Health 97) Model 2  State Dependency What is the probability of migration with respect to the current and previous health status.  Migration(97-00) = f (Health 93, Health 97)  Migration(97-00) = f (Health 97) among Healthy Pop.93 Logit Regression Model: the dependent variable is (1) Migration or (0) No migration

15 Health StatusShort-MoveDistant-MoveAll Move GHS-93 + (OR=1.013) - (0.995) - (0.986) ADL-93 + (OR=1.160) + (1.599)*** + (1.294)*** Model 1A: Selectivity Migration(93-97) = f (Health 93) Health StatusShort-MoveDistant-MoveAll Move GHS-93 - (0.952) - (0.930) - (0.949) ADL-93 - (0.960) + (1.075) - (0.980) Without Control Variable With Control Variables Yet: significances are washed out by covariates

16 Health StatusAll-MoveShort-MoveDistant-Move GHS-97 + (OR=1.113)** + (1.195)** + (1. 814) ADL-97 + (OR=1.421)*** + (1.151)*** + (1.021)*** Model 1B: Selectivity Migration(97-00) = f (Health 97) Health StatusAll-MoveShort-MoveDistant-Move GHS-97 - (0.996) + (1. 036) - (0.914) ADL-97 - (0.991) - (0.885) + (1.243)*** Without Control Variable With Control Variables Yet: significances are washed out by covariates

17 Health StatusAll-MoveShort-MoveDistant Move GHS-93 - (OR=0.491)*** - (0.552)*** - (0.440)*** GHS-97 + (OR=1.160)*** + (1.195)*** + (1.082) ADL-93 - (OR=0.474)*** - (0.537)*** - (0.424)*** ADL-97 + (OR=1.547)*** + (1.319)*** + (2.029)*** Model 2A: Dependency Migration(97-00) = f (Health 93, Health 97)

18 Health Statusall-MoveShort-MoveDistant-Move GHS-97 + (OR=1.152) + (1.206) + (1.001) ADL-97 + (OR=1.308)*** + (1.109) + (1.743)*** Model 2B: Dependency Migration(97-00) = f (Health 97) among Healthy 93

19 Age & Sex Education Covariates Age Groups: 15-19, 20-24….60+ Sex: Male (1) Female (0) Education: Primary, Secondary, Tertiary Employment: Working (1) Expenditure: 21-40%, 41-60%, 61-80%, 81-100% Marital Status: Union (1) Birth Place: Urban (1) Current Residence: Java-Bali (1)

20 Conclusion Longitudinal data (IFLS survey) offers the possibility –To assess the relationship Health – Migration in Indonesia –To evaluate the selectivity & dependency In the context of Indonesia: –The relationship between Health and Migration tends to be positive –People with good health status (ADL in particular) are more likely to be positively associated with migration (Mig 97-00 in particular) –YET, the significances are often washed out by other socio- economic covariates. Age Separation: Young & Older Data: Focus on IFLS2 & IFLS3 Health Measurement

21 Discussion Measurement of Health Measurement of Migration  Different Result? More Questions: –Health Changes: Improved, Deteriorated, Stable “Does health improve or deteriorate with migration?” –Changes in socio-economic variables: employment status, marital status, & income –Relationship Migration  Health Status

22 Thank You…


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