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Cadernos de Saúde Pública

Print version ISSN 0102-311X

Cad. Saúde Pública vol.30 no.3 Rio de Janeiro Mar. 2014

http://dx.doi.org/10.1590/0102-311X00005913 

ARTICLE

Migration among individuals with leprosy: a population-based study in Central Brazil

Migração entre pessoas com hanseníase: estudo de base populacional no Centro-Oeste do Brasil

La migración entre personas con lepra: un estudio basado en la población en el centro de Brasil

Christine Murto 1   2  

Liana Ariza 3  

Carlos Henrique Alencar 3  

Olga André Chichava 3  

Alexcian Rodrigues Oliveira 3  

Charles Kaplan 4  

Luciana Ferreira Marques da Silva 5  

Jorg Heukelbach 3   6  

1Swiss Tropical and Public Health Institute, Basel, Switzerland.

2University of Basel, Basel, Switzerland.

3Faculdade de Medicina, Universidade Federal do Ceará, Fortaleza, Brasil.

4Hamovitch Center for Science in the Human Services, University of Southern California School of Social Work, Los Angeles, U.S.A.

5Secretaria da Saúde de Tocantins, Palmas, Brasil.

6James Cook University, Anton Breinl Centre for Public Health and Tropical Medicine, Townsville, Australia.

ABSTRACT

This study investigates social and clinical factors associated with migration among individuals affected by leprosy. A cross-sectional study was conducted among those newly diagnosed with leprosy (2006-2008), in 79 endemic municipalities in the state of Tocantins, Brazil (N = 1,074). In total, 76.2% were born in a municipality different from their current residence. In the five years before diagnosis 16.7% migrated, and 3.6% migrated after leprosy diagnosis. Findings reflect aspects associated with historical rural-urban population movement in Brazil. Indicators of poverty were prominent among before-diagnosis migrants but not after-diagnosis migrants. Migration after diagnosis was associated with prior migration. The association of multibacillary leprosy with migration indicates healthcare access may be an obstacle to early diagnosis among before-diagnosis migrants, which may also be related to the high mobility of this group.

Key words: Internal Migration; Leprosy; Poverty

RESUMO

Este estudo investiga fatores sociais e clínicos associados à migração entre pessoas afetadas pela hanseníase. Estudo transversal entre recém- diagnosticados com hanseníase (2006-2008), em 79 municípios endêmicos do Estado de Tocantins, Brasil (N = 1.074). No total, 76,2% nasceram em município diferente de sua residência atual. Nos cinco anos antes do diagnóstico, 16,7% migraram, e 3,6% migraram após o diagnóstico da hanseníase. Resultados refletem aspectos associados com o movimento histórico da população rural-urbana no Brasil. Indicadores de pobreza foram proeminentes antes do diagnóstico de migrantes. A migração após o diagnóstico foi associada com migração anterior. A associação da forma multibacilar com migração indica que o acesso à saúde pode ser um obstáculo para o diagnóstico precoce de migrantes, o que pode também estar relacionado com a elevada mobilidade desse grupo.

Palavras-Chave: Migração Interna; Hanseníase; Pobreza

RESUMEN

Este estudio investiga los factores sociales y clínicos asociados con la migración entre las personas afectadas por lepra. Un estudio transversal se llevó a cabo entre las personas recién diagnosticadas con lepra (2006-2008), en 79 municipios endémicos en el estado de Tocantins, Brasil (N = 1,074). En total, el 76,2% nacieron en otro municipio diferente a su residencia actual. En los cinco años antes del diagnóstico el 16,7% emigró, y el 3,6% migró después del diagnóstico de lepra. Los resultados reflejan aspectos relacionados con el movimiento histórico de la población rural-urbana en Brasil. Los indicadores de pobreza fueron sobresalientes entre el grupo de migrantes antes del diagnóstico. La migración tras el diagnóstico se asoció a una migración anterior. La asociación de lepra multibacilar con migración indica que el acceso a la atención médica puede ser un obstáculo para el diagnóstico temprano en el grupo de migrantes antes de la migración.

Palabras-clave: Migración Interna; Lepra; Pobreza

Background

Leprosy remains a public health problem in endemic pockets among several countries throughout the world, including Brazil. Migration has been identified as one of the social determinants that can influence health and risk for neglected tropical diseases (NTDs) 1 , 2, and is considered a possible factor in leprosy susceptibility and distribution in Brazil 3 , 4. Other neglected diseases have also been associated with population movement, including leishmaniasis 5 , 6 schistosomiasis 7 , 8 , 9, and Chagas disease 10. Migration can increase the risk of NTD transmission and susceptibility, as non-immune migrants move into areas of NTD endemicity, and infected migrants may return to non-endemic areas through circular migration or permanent movement 5 , 7.

There are many reasons for migration: employment opportunities and access to better infrastructure, such as healthcare and education, can attract migrants from other areas 11 , 12; while the socioeconomic environment, including poor job opportunities and low wages 12 , 13 , 14 , 15 influence the decision to migrate from the place of origin. This is especially reflected in rural to urban population movement. In Brazil, migration has historically been stimulated by strong disparities between poor rural areas in the northeast of the country and large urban centers, a pattern typical of migration flow throughout Latin America 16. Recently, there has been a shift in migration dynamics toward rural in-migra- tion 17 resulting from opportunities in civil development projects and agricultural expansion. National policies and regional economic disparities and conditions can influence the direction and duration of migration 5, and temporary or circular patterns 18 , 19 , 20.

A complex relationship exists where low socioeconomic status and poor education influence job skills and employment options, creating urgency for movement, particularly to urban areas creating uncontrolled growth around city perimeters. Poverty and biological vulnerability converge in crowded and substandard housing in areas lacking basic sanitary conditions, access to clean water and other utilities, factors that are also associated with leprosy transmission 4 , 21 , 22. These crowded living conditions that include close proximity to individuals with leprosy, particularly multibacillary leprosy, increase the risk of infection in comparison to other social contacts 22 , 23 , 24. In Brazil, household contact monitoring is part of the national leprosy surveillance strategy, as is monitoring leprosy among children as an indicator of ongoing active transmission 25.

Understanding leprosy transmission dynamics is important for insight into how population movement complicates disease control 5 , 7. As World Health Organization (WHO) strategies increasingly move toward greater control and elimination of NTDs, a focused examination of factors associated with migration in those affected by the disease is necessary to better integrate interventions aimed at disease control and elimination. This study has the goal of supporting the Brazil Ministry of Health Leprosy Control Programs in providing services for migrating populations. The study was designed with the objective of identifying demographic, socioeconomic, health-service related and clinical factors associated with migration before and after diagnosis with leprosy in an affected population.

Methods

Study design

This cross-sectional study was designed as operational research to provide evidence for improvement to the national and state leprosy control programs. All municipalities included are located in a major endemic cluster identified by the Brazilian Ministry of Health as high-risk areas for leprosy 26.

Study area and population

Tocantins, the newest Brazilian state located in the north region, is a leprosy hyperendemic area with the highest incidence in Brazil – 88.54/100,000 inhabitants in 2009 (Health Informatics Department. http://tabnet.datasus.gov.br/cgi/deftohtm.exe?idb2011/d0206.def, accessed on 10/Apr/2011). With one of the fastest growing agriculture-based economies, Tocantins attracts labor migration with more than one third of the population coming from a different state and more than one half born in different municipalities 27 , 28.

The target population included all new leprosy cases diagnosed between 2006 and 2008, who were living in endemic municipalities. Individuals living outside of the cluster, those with mental illness or other characteristics that hindered interviews were excluded. Relapsed leprosy cases and those who died after diagnosis were also not included.

Data collection

Municipality Health Secretariats were informed by the Tocantins State Health Department about the study and field visits were coordinated for data collection. The study population was identified through the database of the National Information System for Notifiable Diseases (SINAN). Patients were invited through Community Health Agents to participate in the study and to be interviewed at the local health care center. Home visits accompanied by local Community Health Agents were performed when individuals did not present at the health care center.

Data collection was conducted between September and December 2009. Clinical data were collected from patients’ charts. All other variables, including information on migration, were investigated by interview using structured questionnaires. Data collection forms were composed of six groups of variables, and information on migration itself: (1) Socio-demographics (sex, age, marital status, education, employment); (2) Housing/Economic variables (household density, household income, area of residence, utility access; (3) Disease-related variables (clinical form of the disease, operational classification, grade of disability at diagnosis); (4) Health services variables (visits by community health worker, access to health services); (5) Migration variables (length of time at residence, migration before and after diagnosis); and (6) Attitudes and reported practices regarding leprosy and its cure. For detailed information on migration, study participants were asked for the municipality and state of their birth, where they had lived during the five years prior to diagnosis, and whether they had moved after diagnosis.

Data analysis

Bivariate analysis using Fisher’s exact test was conducted whereby socio-demographic, economic, clinical, health service-related and attitudes/practices variables were compared between migrants with leprosy and non-migrant residents with leprosy (Table 1). These variables were investigated for their association with three different (migration) outcome variables: (1) migration after birth, defined as municipality of birth different from current municipality of residence; (2) migration during five years prior to leprosy diagnosis; and (3) migration after diagnosis. Migration after birth provided a baseline for any lifetime migration, while migration before diagnosis was limited to the average five year latency period for leprosy onset, which is also the current standard in the Brazilian Census survey and reduces recall bias in the survey. As migrant multi-stage migration was also considered, we allowed for non-exclusivity between the three migration outcomes being investigated in the bivariate analysis.

Table 1  - Bivariate analysis of factors associated with migration before and after leprosy diagnosis *. 

      After birth migration Migration before diagnosis After diagnosis migration
Total ** [n = 1,050] Positive n (%) OR (95%CI) p- value Total [n = 1,071] Positive n (%) OR (95% CI) p- value Total ** [n = 1,062] Positive n (%) OR (95%CI) p- value
     
Socio-demographic variables                        
  Sex                        
    Male 545 413 (75.8) 0.95 (0.71-1.28) 0.77 553 102 (18.4) 1.30 (0.93-1.82) 0.12 548 28 (5.1) 2.71 (1.26-6.32) 0.007 ***
    Female 505 387 (76.6) Reference 518 77 (14.9) Reference 541 10 (2.0) Reference
  Age groups (years)                        
    0-14 82 40 (48.8) Reference   79 8 (10.1) Reference   80 2 (2.5) Reference  
    15-29 236 135 (57.2) 1.4 (0.82-2.4) 0.20 239 52 (21.8) 2.46 (1.09-6.30) 0.20 237 10 (4.2) 1.72 (0.35-16.44) 0.74
    30-44 261 194 (74.3) 3.04 (1.76-5.42) < 0.0001 *** 269 64 (23.8) 2.77 (1.24 – 7.00) 0.01 *** 267 12 (4.5) 1.84 (0.40-17.21) 0.54
    45-59 254 224 (88.2) 7.84 (4.23-14.54) < 0.0001 *** 257 30 (11.7) 1.17 (0.50-3.10) 0.84 254 12 (4.7) 1.93 (0.42-18.13) 0.53
    ≥ 60 217 207 (95.4) 21.74 (9.62-51.95) < 0.0001 * 227 25 (11.0) 1.10 (0.45-2.95) 1.00 224 2 (1.0) 0.35 (0.03-4.94) 0.28
  Education                        
    Illiterate/Never attended school 231 210 (90.9) 3.86 (2.38-6.53) < 0.0001 *** 240 37 (15.4) 0.88 (0.58-1.32) 0.56 236 5 (2.1) 0.52 (0.16-1.36) 0.23
    Attended school any time 815 588 (72.2) Reference 827 142 (17.2) Reference 822 33 (4.0) Reference
  Work status                        
    Employed 453 346 (76.4) Reference   458 79 (17.3) Reference   455 17 (3.7) Reference  
    Unemployed 155 122 (78.7) 1.14 (0.72-1.84) 0.58 162 28 (17.3) 1.00 (0.60-1.64) 1.00 161 10 (6.2) 1.71 (0.68-4.04) 0.19
    Part-time 55 43 (78.2) 1.11 (0.55-2.40) 0.87 55 15 (27.3) 1.80 (0.88-3.52) 0.09 54 4 (7.4) 2.06 (0.48-6.65) 0.26
    Retired/Pensioner 170 160 (94.1) 4.95 (2.50-10.88) < 0.0001 *** 178 22 (12.4) 0.67 (0.39-1.14) 0.08 174 1 (0.6) 0.15 (0.0-0.97) 0.03 ***
    Student/Housewife/Others 217 129 (59.5) 0.45 (0.32-0.65) < 0.0001 * 218 35 (16.1) 0.92 (0.58-1.44) 0.74 218 6 (2.8) 0.73 (0.23-1.97) 0.65
Socio-demographic variables                        
  Farm worker (any time in life)                        
    Yes 413 351 (85.0) 2.38 (1.71-3.33) < 0.0001 *** 427 74 (17.3) 1.09 (0.77-1.53) 0.68 423 13 (3.1) 0.80 (0.37-1.66) 0.61
    No 629 443 (70.4) Reference 636 103 (16.2) Reference 632 24 (3.8) Reference
Housing- and economic-related variables                        
  Household monthly income #                        
    ≥ R$ 465,00 736 566 (76.9) Reference 0.42 298 52 (17.4) 1.06 (0.73-1.53) 0.78 299 12 (4.0) 1.14 (0.52-2.40) 0.72
    < R$ 465,00 (≈ US$ 270) 289 215 (74.4) 0.87 (0.63-1.21) 750 124 (16.5) Reference 741 26 (3.5) Reference
  Residence area                        
    Rural/Settlement 252 194 (77.0) 1.06 (0.75-1.51) 0.80 256 53 (20.7) 1.43 (0.98-2.06) 0.06 256 12 (4.7) 1.47 (0.67-3.08) 0.33
    Urban 797 605 (75.9) Reference 814 126 (15.5) Reference 805 26 (3.2) Reference
  Electricity                        
    No 64 42 (65.6) 0.57 (0.33-1.03) 0.049 *** 64 18 (28.1) 2.05 (1.09-3.72) 0.02 *** 65 3 (4.6) 1.33 (0.25-4.40) 0.42
    Yes 985 757 (76.9) Reference 1006 161 (16.0) Reference 996 35 (3.5) Reference
  Public waste collection                        
    No 291 221 (76.0) 0.98 (0.71-1.37) 0.93 297 64 (21.6) 1.57 (1.11-2.23) 0.01 * 295 14 (4.8) 1.54 (0.73-3.15) 0.20
    Yes 758 578 (76.2) Reference 773 115 (14.9) Reference 766 24 (3.1) Reference
  Public sewer system                        
    No 120 86 (71.7) 0.76 (0.49-1.20) 0.21 123 27 (21.0) 1.47 (0.89-2.37) 0.12 122 4 (3.3) 0.90 (0.23-2.60) 1.00
    Yes 929 714 (76.9) Reference 947 152 (16.1) Reference 939 34 (3.6) Reference
  Public water supply                        
    No 194 150 (77.3) 1.08 (0.74-1.60) 0.71 197 43 (21.8) 1.52 (1.01-2.25) 0.44 196 9 (4.6) 1.39 (0.57-3.08) 0.40
    Yes 856 650 (75.9) Reference 874 136 (15.6) Reference 866 29 (3.4) Reference
  Housing- and economic-related variables                          
  Brick/Adobe house construction                        
    No 192 147 (76.6) 1.03 (0.70-1.52) 0.93 197 44 (22.34) 1.57 (1.05-2.33) 0.03 *** 194 6 (3.1) 0.83 (0.28-2.06) 0.83
    Yes 858 653 (76.1) Reference 874 135 (15.5) Reference 868 32 (3.7) Reference
  Number of rooms/household                        
    1-2 67 50 (74.6) 0.91 (0.50-1.71) 0.77 67 11 (16.4) 0.97 (0.45-1.93) 1.00 66 1 (1.5) 0.40 (0.0-2.44) 0.51
    > 2 980 749 (76.4) Reference 1,001 168 (16.8) Reference 993 37 (3.7) Reference
  Living alone                          
    1 person 56 52 (92.9) 4.28 (1.55 – 16.44) 0.002* 58 10 (17.2) 1.04 (0.46 – 2.13) 0.86 58 (6.9) 2.11 (0.52-6.23) 0.15
    > 1 person 993 747 (75.2) Reference 1,012 169 (16.7) Reference 1,003 34 (3.4) Reference
Disease-related variables at diagnosis                            
  Clinical form                          
    Tuberculoid 182 133 (73.1) 1.04 (0.68-1.61) 0.92 185 29 (15.7) 1.00 (0.58-1.68) 1.0 185 10 (5.4) 1.81 (0.66-4.93) 0.24
    Borderline 247 194 (78.5) 1.41 (0.94-2.12) 0.10 255 45(17.7) 1.15 (0.72-1.82) 0.58 255 9 (3.5) 1.16 (0.41-3.22) 0.82
    Lepromatous 92 79 (85.9) 2.33 (1.21-4.80) 0.01 *** 94 19 (20.2) 1.36 (0.71-2.51) 0.35 94 2 (2.1) 0.69 (0.07-3.31) 1.00
    Indeterminate 324 234 (72.2) Reference   331 52 (15.7) Reference   326 10 (3.1) Reference  
  Operational classification                          
    Multibacillary 416 335 (80.5) 1.54 (1.12-2.11) 0.006 *** 426 79 (18.5) 1.37 (0.96-1.95) 0.07 424 12 (2.8) 0.74 (0.33-1.58) 0.48
    Paucibacillary 572 417 (72.9) Reference 583 83 (14.2) Reference 579 22 (3.8) Reference
  Disability grade at diagnosis                          
    Disability grade II 29 26 (89.7) 2.98 (0.90-15.56) 0.08 30 5 (16.7) 1.20 (0.35-3.28) 0.79 30 0 (0.0) - 1.00
    Disability grade 0 or I 703 523 (74.4) Reference 719 103 (14.3) Reference 717 22 (3.1) Reference
                         
  Time from symptom onset and sought diagnosis (months)                        
    > 6 271 214 (79.0) 1.24 (0.87-1.78) 0.24 276 51 (18.5) 1.16 (0.78-1.69) 0.45 274 11 (4.0) 1.07 (0.47-2.30) 0.85
    ≤ 6 661 497 (75.2) Reference 671 110 (16.4) Reference 667 25 (3.8) Reference
Health service-related variables                        
  Regular home community health worker visit (≤ 1 months)                        
    No 338 267 (79.0) 1.26 (0.91-1.75) 0.16 345 59 (17.1) 1.04 (0.73-1.48) 0.86 343 15 (4.4) 1.38 (0.66-2.80) 0.38
    Yes 712 533 (74.9) Reference   726 120 (16.5) Reference 719 23 (3.2) Reference
  Time to reach the health care centre (minutes)                        
    > 30 181 137 (75.7) 0.98 (0.66-1.46) 0.92 407 64 (15.7) 0.87 (0.61-1.23) 0.45 189 8 (4.2) 1.21 (0.47-2.77) 0.67
    ≤ 30 850 647 (76.1) Reference 647 114 (17.6) Reference 856 30 (3.5) Reference
  Difficulty reaching health care center                        
    Yes 201 158 (78.6) 1.19 (0.81-1.77) 0.41 209 37 (17.7) 1.07 (0.70-1.61) 0.76 207 11 (5.3) 1.70 (0.74-3.60) 0.15
    No 835 631 (75.6) Reference 848 142 (16.8) Reference 841 27 (3.2) Reference
Migration                        
  Migrant after diagnosis                        
    Yes - - - - 38 22 (57.9) 7.87 (3.83- 16.38) < 0.0001 *** - - - -
    No - - - 1,022 152 (14.9) Reference - - -
  Migrant 5-years prior to diagnosis                        
    Yes - - - - - - - - 174 22 (12.6) 7.87 (3.83-16.38) < 0.0001 ***
    No - - - - - - 886 16 (1.8) Reference
  Health service-related variables                          
  Time at residence (years)                        
    0-5 470 349 (74.3) 0.79 (0.56-1.11) 0.18 476 146 (30.7) 25.22 (11.06-70.63) < 0.0001 *** 469 33 (7.0) 8.70 (2.69-44.64) < 0.0001 ***
    6-10 237 183 (77.2) 0.93 (0.61-1.41) 0.76 245 27 (11.0) 7.06 (2.79-21.2) < 0.0001 *** 243 2 (0.8) 0.95 (0.8-8.40) 1.00
    ≥ 11 340 267 (78.5) Reference   348 6 (1.7) Reference   348 3 (0.9) Reference  
Practices and attitudes                        
  Sought other health service prior to diagnosis                          
    Yes - - - - 181 36 (19.9) 1.29 (0.83-1.96) 0.23 179 10 (5.6) 1.80 (0.76-3.90) 0.12
    No - - - 886 143 (16.1) Reference 879 28 (3.2) Reference
  Hide leprosy diagnosis due of fear of prejudice                          
    Yes - - - - - - - - 1,039 38 (3.7) - 1.00
    No - - - - - - 20 0 (0.0) Reference
  Different behavior from others after diagnosis                          
    Yes - - - - - - - - 157 3 (1.9) 0.48 (0.09-1.55) 0.35
    No - - - - - - 898 35 (3.9) Reference

Odds ratios and their respective 95% confidence intervals (95%CI) were calculated. Theoretically meaningful confounders (age, income, gender and education) were investigated in the bivariate analysis by determining their association (p < 0.05) with the three migration variables. Only age was a potential confounder. Income was not associated with the three migration outcomes and education was no longer significant among birth migrants after controlling for age. As internal migration is equally distributed between males and females in Brazil 29, and the sample is also equally distributed between males and females, gender was not believed to present confounding. Additionally, only one of the migration outcomes in the bivariate analysis was significantly associated with gender.

A separate multivariate logistic regression analysis was conducted for each variable found to be significant in bivariate analysis with a p-value < 0.05 controlling for age. Adjusted odds ratios for the association of migration before diagnosis and after diagnosis migration outcomes compared to non-migrant residents were calculated.

Data were entered twice, using Epi Info software version 3.5.1 (Centers for Disease Control and Prevention, Atlanta, USA) and cross-checked for entry-related errors. Shapiro-Wilk test and histograms were used to assess normality. Data analysis was conducted using Stata version 11 (Stata Corp., College Station, USA).

Ethics

The study was approved by the Ethics Research Committee of the Federal University of Ceará (Fortaleza, Brazil) and by the Ethics Research Committee of Lutheran University of Palmas (Palmas, Brazil). Permission to perform the study was also obtained by the Tocantins State Health Secretariat, the State Leprosy Control Program and the municipalities involved. Informed written consent was obtained from study participants after explaining the objectives of the study. To avoid any harm, strict confidentiality was kept, and the diagnosis was not revealed to others, including family members. Interviews were conducted in private. In the case of minors, consent was obtained from a guardian.

Results

The sample was selected from 2,160 individuals diagnosed with leprosy between 2006 and 2008. A total of 1,074 individuals from 79 municipalities were included in the analysis. One municipality did not diagnose any cases of leprosy during the study period, and three municipalities had few cases (n = 12) which were not included due to non-consent or because they could not be located. Of those who were not interviewed, 11 were not confirmed leprosy cases, were unable to attend due to illness/hospitalization, inebriation or incarceration (n = 15), could not be located at the given address (n = 35), were not known at the healthcare center (n = 23), lived in a remote area (n = 23), moved after diagnosis (n = 269), or were otherwise not at home/working/traveling (n = 469). Despite multiple attempts, some did not attend the scheduled interviews (n = 210) and 31 refused to participate. These individuals were excluded from the study.

Of the total 1,074 individuals, 555 (51.7%) were males and 519 (48.3%) females, ranging in age from 5 to 98 years of age (mean = 41.8 year; standard deviation: 19.01). There were 82 children under 15 (7.6%). Nearly half of the individuals (514; 47.9%) were working at least part time/ in temporary employment, 162 (15.1%) were unemployed, 178 (16.6%) retired and 230 (21.4%) engaged otherwise, most notably as students 127 (11.8%) or housewives 78 (7.3%). About one in five (n = 240, 22.4%) was illiterate and 190 (17.8%) completed a high school education or more. The mean monthly household income was R$ 757 (≈ US$ 440), and nearly one-third (n = 299, 28.5%) were living on less than the minimum monthly wage.

Overall, 426 (42.1%) were classified with multibacillary leprosy at the time of diagnosis, the majority having Grade 0 disability at diagnosis (n = 566, 75.4%), followed by Grade I (n = 155, 20.6%) and Grade II (n = 30, 4.0%). The clinical form of diagnosis was primarily indeterminate (n = 332, 38.3%), followed by borderline (n = 255, 29.5%), tuberculoid (n = 185, 21.4%) and lepromatous leprosy (n = 94, 10.9%).

In terms of migration, 800 (76.2%) individuals interviewed migrated at some point in time after birth; 179 (16.7%) were migrants in the five years prior to diagnosis; and 38 (3.6%) migrated after diagnosis. Children also were among those migrating, and comprised 4.5% of those migrating before diagnosis (n = 8) and 8.5% after diagnosis (n = 19). In total, nearly one fifth (n = 199, 18.6%) of those interviewed lived in a different municipality or state five years prior and/or after diagnosis. Migration in the endemic cluster in Tocantins (43.9%, n = 76) and migration residence in other states (45.1%, n = 78) comprised the majority of population movement before diagnosis. Only 17.3% of migrants resided in non-endemic municipalities in Tocantins during the five years prior to diagnosis. After diagnosis, 73.7% moved within Tocantins, 57.9% to endemic areas of the state. 26% of those who migrated after diagnosis moved to other states.

Factors associated with migration in the five years before diagnosis

In bivariate analysis age (30-44), poverty, and residence of 10 years or less were associated with migration before diagnosis with leprosy (Table 1).

Logistic regression, controlling for age, identified poverty and clinical variables associated with migration before diagnosis with leprosy. The migrants were more likely to lack access to electricity, public water, and waste management, all indicators of poverty in Brazil. Migrants were also significantly less likely to live in a brick home compared to non-migrant residents, with significantly less time living in their current place of residence (10 years or less). Migrants before diagnosis were also more likely to have multibacillary form of leprosy compared to non-migrant residents with leprosy (Table 2).

Table 2  - Adjusted odds ratios (OR) of factors significantly associated with before diagnosis migration compared to non-migrant residents with leprosy, controlling for age. 

    Before diagnosis migration
Adjusted OR (95%CI) p-value
Socio-demographic variables    
  No public water 1.65 (1.12-2.43) 0.012 *
  No trash service 1.70 (1.2-2.41) 0.003 *
  Living in a non-brick home 1.57 (1.01-2.32) 0.022 *
  Diagnosis multibacillary 1.55 (1.09-2.19) 0.014 *
Migration variables    
  Years at current residence 0-5 years 23.38 (10.1-54.09) < 0.0001 *
  Years at current residence 6-10 years 6.77 (2.73-16.75) < 0.0001 *

Factors associated with migration after diagnosis

After diagnosis, residence in the current household of five years or less and before diagnosis migration was associated with migration (Table 1).

Migration after diagnosis was associated with key demographic factors after adjusting for age (Table 3). Males were more likely to migrate than females. Also, residence at current household of five years or less and before diagnosis migration was significantly associated with migration.

Table 3  - Adjusted odds ratios (OR) of factors significantly associated with after diagnosis migration compared to non-migrant residents with leprosy, controlling for age. 

    After diagnosis migration
Adjusted OR (95% CI) p-value
Socio-demographic variables    
  Male sex 2.87 (1.38-5.99) 0.005 *
Migration variables    
  Before diagnosis migration 7.74 (3.89-15.37) < 0.0001 *
  Years at current residence 0-5 years 8.69 (2.57-29.32) < 0.0001 *

Discussion

Migration can complicate disease control when infected and susceptible people move between endemic and non-endemic areas. Environmental and social factors can influence migration, while health outcomes can be affected by the conditions at locations where movements take place.

In this study, many socio-demographic, clinical, health service and migration variables were investigated. After adjusting for age, a confounding factor for leprosy and migration, key demographics, poverty, factors associated with migration, and multibacillary form of leprosy remained significant for those who migrated before leprosy diagnosis, while only factors related to migration remained associated after diagnosis. Contrary to our expectations, migrant accounts of health service access and stigma did not appear to be associated with migration, although advanced disease expression indicated a delay in diagnosis.

A culture of migration was observed among those affected by leprosy in Tocantins, with more than three-quarters having migrated at some stage in their lives and nearly one-fifth within the last five years. We also found that after diagnosis migration was significantly associated with prior migration, consistent with findings in other studies 30. Migration can additionally place resident populations at risk, and in Brazil migration has been considered as a possible explanation for diseases, such as leishmaniasis, schistosomiasis and Chagas disease, that have moved into previously non-endemic areas 5 , 7 , 8 , 9 , 10. We found that much of the migration in the five years prior to diagnosis was within the endemic cluster in Tocantins and also other states, primarily neighboring Maranhão and Pará. From Maranhão, migration was largely from Imperatriz, while in Pará State, Conceição do Araguaia and São Geraldo were principal sites of prior residence. These three municipalities are located in hyperendemic areas for leprosy 26. Considerably fewer migrants resided in municipalities in Tocantins outside of the endemic cluster during the five years prior to diagnosis. The majority of after diagnosis migrants moved to other endemic areas in Tocantins. Presence of leprosy among children who migrated highlights active transmission in these regions.

Key demographics

Migration is most often associated with the movement of young adults, typically males between the ages of 20 and 35, who migrate for employment 14 , 19 , 30 , 31. We found that migration of leprosy-affected individuals was significantly associated with being male after diagnosis, and overall, migrants were slightly older than the younger age-set typical of migration globally. This age pattern is consistent with population movement in Brazil 17. Migration increased with age and dropped only slightly among those aged 60 or older. Migration of the older age groups may be the result of historical population movements in the Northeast region from rural areas to urban centers due to industrialization 32 and periods of severe drought 33. This trend has continued into recent decades and may be a factor hindering disease control 8 , 34. This historic population movement has contributed to poor sanitation and overcrowding in areas of uncontrolled urbanization in Brazil.

Nearly half of those with leprosy were employed regardless of whether they were migrants or non-migrant residents. This indicates that stigma as a result of leprosy does not appear to be a significant factor for securing employment. In a previous study, stigma was also found to be an insignificant factor in changing residence 35 and was a minor issue in therapy interruption 36.

Poverty

NTDs are known to be associated with low socioeconomic status, often resulting in poor health 2. While migration typically provides an opportunity to lift individuals out of poverty over time 20, the initial decision to migrate is often a strategy to mitigate poverty, and migration also supplements income at critical moments 16 , 18 , 20 , 30. Unfortunately, these decisions can have further repercussions, negatively affecting health as result of poor housing, sanitation and other socio-environmental conditions 2 closely associated with poverty.

While low household income was not specifically associated with migration among leprosy-affected individuals in Tocantins, indirect indicators of poverty were associated with migration in this study. This was particularly relevant for those who migrated prior to diagnosis compared to non-migrant residents with leprosy. Absence of trash collection and access to public water, or not living in a house made of brick were all associated with those who migrated in the five years before diagnosis. Previous studies have found that non-migrants typically have a higher socioeconomic status than migrants 37 , 38. Thus, migrants and non-migrant residents with leprosy might be exposed to low socioeconomic levels and poor living standards differentially.

Migration after diagnosis had no association with indicators of poverty. Socioeconomic factors influence the initial decision to migrate, and these variables may change once migration has taken place 30. Although our study only considered socioeconomic variables and utility access after leprosy diagnosis, access to better amenities, such as electricity, has been associated with a reduction in further migration 19.

Migration, leprosy and healthcare access

In Brazil, the most prominent form of leprosy is borderline (41.5%), followed by lepromatous (23.2%), tuberculoid (19.6%) and indeterminate (15.6%) leprosy 39. A quarter of leprosy cases in Brazil in 2010 were classified as multibacillary 40, which includes midborderline, borderline lepromatous, and lepromatous forms of leprosy. In Tocantins, before diagnosis migration was associated with the more severe multibacillary classification. Multibacillary has a high risk of transmission 23 , 41, while paucibacillary forms have a low transmission risk among those in close contact with individuals with leprosy 42. The odds of multibacillary among before diagnosis migrants were 1.5 times higher than non-migrant residents with leprosy. Access to early diagnosis may in fact be a consideration for this group.

While poor access to health services has been found to be a motivating factor for migration 11, our findings show minimal after diagnosis migration. This is perhaps a response to the need to maintain treatment at the place of diagnosis, within primary health care. Another study of the same population found that lifestyle changes (home ownership, family, better living/neighborhood conditions) were the primary reasons for changing residences, with less than 5% moving for the purpose of seeking diagnosis or treatment 35. The decentralization of health services for leprosy diagnosis and treatment to community health centers throughout Brazil has likely played an important role in this regard.

There was no significant difference in the time from symptom onset to diagnosis among migrants in Tocantins compared to non-migrants. While there is some speculation that migrants are less likely to use health facilities 43, other research has shown that availability of health services even among the displaced, has contributed to improved health 44. Health services were sought by up to a fifth of those that migrated prior to diagnosis and by a quarter of those who migrated after diagnosis, yet migrants did not have significantly more difficulty in accessing health centers or community health workers than non-migrants. Despite this positive information, the prevalence of advanced multibacillary leprosy among those who migrated in the five years prior to diagnosis suggests a delay in diagnosis or poor knowledge of symptoms associated with leprosy. Given the progressive evolution of multibacillary leprosy, lack of access to health care and poor attention to infection could occur over multiple movements.

Limitations

Like many other cross-sectional studies, our study is subject to several limitations. First, the cross-sectional design made causal and temporal relationships difficult to establish. Migration may cause certain behaviors/characteristics, and also be caused by these same variables. For this reason, we focused on associations rather than causes in the analysis and discussion of data.

Despite being a population-based study in a hyperendemic area, we only included in-migrants to municipalities. Anyone moving outside of the cluster during the defined study period was excluded, which limited additional knowledge in regards to after diagnosis migration.

This study was performed in 79 municipalities with a broad geographical range. While this increases the representativeness of our findings, approximately 50% of the population could not be reached. Many individuals were not encountered even after multiple home visits or did not attend scheduled interviews. Some individuals moved to another city outside the cluster. Incomplete patients’ charts and subsequent missing data hampered analysis in some cases. Non-participation bias may have played a role. We aimed at reducing bias by rigorously planning field visits and integrating local primary health care professionals and the State and Municipal Leprosy Control Programs during field work for the present study.

A final limitation is that socioeconomic data were collected after migration. Other researchers have noted the difficulty in differentiating between migrant and non-migrant households because socioeconomic factors may influence the decision to migrate, and these same variables may change once migration has taken place 31. In addition, current economic conditions do not account for latency in leprosy, which can manifest up to decades after exposure.

Conclusions

This is the first major systematic study exploring migration in leprosy-affected individuals. In this population in a highly endemic area, factors associated with poverty were associated with migration.

Attention to reaching possibly infected and highly mobile populations in Brazil should be a focus to prevent further transmission of the disease and development of disabilities among those infected. This is particularly important in endemic states, with high in- and between- municipality migration, such as Tocantins. Attention to low-income rural areas should take into account difficulties with transportation. Ease of healthcare access provides the opportunity to reduce disability and increase leprosy control.

Newly emerging trends of circular migration provide an opportunity to investigate these patterns and their relationship to disease transmission and migration flow between community of origin and destination and should be considered for future studies.

Acknowledgments

We thank Adriana Cavalcante Ferreira, Suen de Oliveira Santos, the Health Secretariat of Tocantins, the Municipality Health Secretariats and Primary Health Care Centers. We are also grateful to all patients that kindly agreed to participate in the study. Special thanks to Professor Marcel Tanner, Director of the Swiss Tropical and Public Health Institute (SwissTPH) for insightful review and guidance. This publication is part of the MAPATOPI study (an interdisciplinary project providing evidence for improving the Brazilian leprosy control program), co-financed by CNPq and DECIT.

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Recebido: 16 de Janeiro de 2013; Revisado: 13 de Agosto de 2013; Aceito: 26 de Agosto de 2013

Correspondence: J. Heukelbach. Departamento de Saúde Comunitária, Faculdade de Medicina, Universidade Federal do Ceará. Rua Professor Costa Mendes 1608, 5 o andar, Fortaleza, CE 60430-140, Brasil. heukelbach@web.de

Contributors

C. Murto contributed with analysis and interpretation of data; drafting the article; revision of the article for important intellectual content; and final approval of the version to be published. L. Ariza contributed to the conception and design, acquisition of data; analysis and interpretation of data; revision of the article for important intellectual content; and final approval of the version to be published. C. H. Alencar contributed to the conception and design, acquisition of data; revision of the article for important intellectual content; and final approval of the version to be published. O. A. Chichava contributed to conception and design, acquisition of data; revision of the article for important intellectual content; and final approval of the version to be published. A. R. Oliveira contributed to conception and design, data entry; revision of the article for important intellectual content; and final approval of the version to be published. C. Kaplan contributed to the conception, data analysis, and writing of the paper. L. F. M. Silva contributed to conception and design; revision of the article for important intellectual content; and final approval of the version to be published. J. Heukelbach contributed to conception and design, acquisition of data; revision of the article for important intellectual content; and final approval of the version to be published.

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