Inequities in Healthcare Seeking Among Migrants and Residents of Climate Affected Coastal Area of Bangladesh

Inequities in Healthcare Seeking Among Migrants and Residents of Climate Affected Coastal Area of Bangladesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Inequities in Healthcare Seeking Among Migrants and Residents of Climate Affected Coastal Area of Bangladesh Mosammat Ivylata Khanam, Abdul Khalek, Syed Manzoor Ahmed Hanifi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1234008/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Climate change induced sea level rise, increased soil salinity and poor socio-economic condition have triggered migration around the world. So far, the impact of migration on health and healthcare utilization have not been studied adequately especially in Bangladesh. Our study examines impact of migration on healthcare seeking behavior of people living in low-lying coastal areas of Bangladesh. Methods: We analyzed data from Chakaria, Health and Demographic Surveillance System, 2017-18. Univariate analysis and regression analysis were conducted to assess the determinants of migration. Finally, we assessed differences in health care seeking behavior between migrants in low-lying coastal areas and plain areas. Stata version 14 was used for analysis. Results: In terms of determinants of migration, adults, males, educated, unemployed and poor were significantly more like to migrate compared to children, females, people with little education, those engaged in agriculture or other occupations and wealthier households. For pregnancy related services healthcare utilization was significantly lower in coastal areas compared to the plains. In terms of utilization of maternal and child health services, service use was 2-28 % lower among residents of low-lying coastal regions and 13-70% lower among coastal migrants compared to those living in plains. Conclusion: Our findings suggest that in terms of maternal health services there was geographic inequity in service utilization favoring the residents of plains and being a migrant significantly increases the geographical disadvantage in terms of maternal health services. The existing inequity indicates the necessity of immediate action to address the health and healthcare utilization of residents of coastal area with special attention to the migrants in Bangladesh. Migration healthcare seeking behavior coastal area climate change Bangladesh Introduction Bangladesh has been recognized as one of the most vulnerable countries to natural disasters aggravated by global climate change due to its unique geographical location [ 1 ]. Bangladesh currently is ranked sixth on Global Climate Risk Index in 2017 for climate vulnerability [ 2 ]. Within Bangladesh, densely populated country the coastal communities have experienced highest number of natural disasters than any other regions of Bangladesh [ 3 , 4 ]. In addition to the prevailing population density, poverty and weak infrastructure, population of coastal areas have suffered from frequent natural disasters such as floods, cyclone, storm and saline intrusion of soil and water due to proximity to the ocean, low elevation and river bank erosion, which has impact on their livelihood and health [ 1 , 5 ]. In global literature and research conducted in Bangladesh, researchers have reported that the climate change has adversely affected land and water leading to food and water insecurity and therefore, reduced health and wellbeing [ 6 – 8 ]. In terms of health indicators coastal population tend to suffer from greater burden of communicable and non-communicable diseases such as hypertension and cardiovascular diseases (CVD) [ 9 , 10 ]. Researchers have associated the rise in CVDs, still birth and miscarriage to higher consumption of salt among coastal population [ 10 – 12 ]. Also, poor coastal people find difficulties in accessing necessary primary healthcare services particularly maternal and child healthcare (MCH) because of the sub-standard healthcare delivery system (availability, accessibility, utilization, adequate and effective coverage) and limited skilled healthcare providers compared to other parts of the country [ 13 , 14 ]. Further, the adverse impact of climate change on livelihood has led to impoverishment and migration [ 15 , 16 ]. In Bangladesh about 2.5 million coastal residents are displaced each year due to natural disasters [ 17 ]. Researchers have studied the impacts of climate change on health, resources, human settlement, as well as adaptation to climate change such as migration in Bangladesh [ 3 , 18 – 23 ]. It is projected by 2050, around 140,000 coastal residents will be forced to migrate within the district and about 60,000 outside the district to increased soil salinity [ 24 ]. Based on some projections the number of Bangladeshis displaced by impacts of climate change could reach 13.3 million by 2050 [ 25 ]. In previous studies of migration pattern in Bangladesh researchers described migration as a coping strategy to deal with poverty with mostly male members and sometimes families migrating out of their area in search of better employment opportunities [ 26 ]. Migration is less likely among women, children, elderly and disabled population [ 27 ]. When the reason for not migrating was explored, researchers found that attachment to property and power, homeland and familial responsibilities were important factors for people to stay in their area of residence [ 28 ]. Migration have shown to impact the health of migrants adversely [ 29 , 30 ]. Little is known about the impact of migration on health service utilization specially if migrant settle in climate affected coastal communities. In the current study we have utilized the strength of existing data from Health and Demographic Surveillance System (HDSS) to investigate the impact of migration on healthcare service utilization geographically diverse coastal region of Bangladesh. The findings will provide an insight about strengthening the health systems for migrants in Bangladesh. Methods This secondary data analysis was conducted by using Chakaria HDSS data from 2017-2018. Chakaria HDSS is one of the field sites of International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) established in 1999 that collects data regarding socio-demographic and health indicators such as birth, death, marriage, education, occupation, health and health services utilization in regular intervals in Chakaria [ 31 ]. Chakaria is subdistrict under Cox’s Bazar district located in the South-East of Bangladesh. Chakaria is geographically diverse and contains hilly areas in the east, coastal low-lying areas in the west as well as plain land. For our analysis we used information from mothers and children (12-23 months of age) who were residents of plains and the low-lying coastal regions of Chakaria. In terms of migration we exclude any migration that happened within the same area and migration due to marriage for our analysis. In terms of healthcare utilization, we considered maternal and child care services such as antenatal care, postnatal care, trained assistance during delivery (skilled birth attendant, doctor or nurses) among recently delivered mothers and immunization (12-23) month old children. To explore the association between healthcare seeking behavior and contextual variables, Chi-square test was performed. A logistic regression analysis was carried out to assess the factors that are significantly associated with migration. To identify the inequity in health service utilization we used the service utilization rates of plain area as reference and compared this with service utilization rates in coastal residents and coastal migrants to assess inequities. Data analysis was done using Stata version 14. Results Table 1: Socio-demographic characteristics of migrant and non-migrant population in coastal area and plains of Chakaria in 2018 Variables Low-lying coastal area Plain area Migrants (n=709) Non-migrants (n=15,234) Migrants (2,320) Non-migrants (49,704) n (%) n (%) n (%) n (%) Age <15 y 254 (35.8) 5,576 (36.6) 893 (38.5) 17,630 (35.5) (15-49) y 418 (59.0) 7,762 (51.0) 1,309 (56.4) 25,229 (50.7) 50+ y 37 (5.2) 1,896 (12.4) 118 (5.1) 6,845 (13.8) Sex Male 382 (53.9) 7,726 (50.7) 1,222 (52.7) 24,855 (50.0) Female 327 (46.1) 7,508 (49.3) 1,098 (47.3) 24,849 (50.0) Years of schooling No education 408 (57.5) 9,135 (60.0) 979 (42.2) 22,019 (44.3) 0-5 years 175 (24.7) 3,664 (24.1) 665 (28.7) 14,837 (29.8) 6-10 years 75 (10.6) 1,816 (11.9) 505 (21.7) 10,021 (20.2) 10+ 51 (7.2) 619 (4.1) 171 (7.4) 2,827 (5.7) HH occupation Agriculture 58 (8.2) 2,211 (14.5) 257 (11.1) 9,703 (19.5) Day laborer 329 (46.4) 6,668 (43.8) 739 (31.8) 15,208 (30.6) Others 189 (26.7) 3,717 (24.4) 777 (33.5) 16,112 (32.4) Unemployed 133 (18.7) 2,638 (17.3) 547 (23.6) 8,681 (17.5) Wealth quantile Lowest 272 (38.4) 4,802 (31.5) 597 (25.7) 8,609 (17.3) Middle 270 (38.1) 6,387 (42.0) 745 (32.1) 18,577 (37.4) Highest 167 (23.5) 4,045 (26.5) 978 (42.2) 22,518 (45.3) In terms of socio-demographic characteristics, most of the migrants and non-migrants of both areas were adults (Table 1). Majority people (both migrants and local) from both areas had no institutional education and were engaged as day laborer compare to other professions. Wealth quantile of migrant people from both areas were comparatively lower than local people. Table 2: Sociodemographic determinants of migration during 2018 in rural Chakaria Socio-demographic characteristics Migration AOR (95% CI) P value Geographical location Low-lying coastal Reference Plain 1.04 (0.99-1.08) 0.067 Age <15 years Reference (15-49) years 1.09 (1.01-1.17) 0.025 50+ years 0.37 (0.31-0.44) 0.000 Sex Female Reference Male 1.14 (1.06-1.23) 0.000 Years of schooling No education Reference 0-5 years 1.02 (0.93-1.11) 0.633 6-10 years 1.11(1.00-1.24) 0.037 10+ 1.54 (1.31-1.81) 0.000 Occupation Agriculture Reference Day laborer 1.54 (1.35-1.766) 0.000 Others 1.74 (1.52-1.99) 0.000 Unemployed 2.27 (1.98-2.60) 0.000 Wealth quintile Highest Reference Middle 1.04 (0.94-1.14) 0.386 Lowest 1.69 (1.52-1.87) 0.000 In terms of geographical location, people from the plains were more likely to migrate compared to those living in the coastal area although this association was not significant (Table 2). Compared to people aged less than 15 years, migration rate was significantly higher among 15-49 age group (OR:1.09, 95% CI:1.01-1.17) and lower among people older than 50 years of age (OR:0.37, 95% CI: 0.31-0.44). Males were significantly more likely to migrate (OR:1.14, 95% CI:1.06-1.23) compared to females. Compared to those with little or no education those with higher education (OR:1.54, 95% CI:1.31-1.81) were significantly more likely to migrate. Compared to those engaged in agriculture, those in other occupations (OR:1.74, 95% CI:1.52-1.99) were significantly more likely to migrate with unemployed people (OR:2.27, 95% CI:1.98-2.60) having the highest likelihood of migration. In terms of household wealth quintile, compared people from wealthier households, those from poorest households (OR:1.69, 95% CI:1.52-1.87) were significantly more likely to migrate. Table 3: Healthcare utilization based on area of residence during 2017 and 2018 Variables Chakaria [recently delivered mothers, n=3218; (12-23) months old children, n=4188] P-value Plain area, n (%) Coastal area, n (%) ANC (received at least 4 visit) 673 (20.9) 153 (4.7) 0.000 Delivery by SBA 1067 (33.2) 242 (7.5) 0.000 PNC (received at least 1 visit) 1328 (41.3) 326 (10.1) 0.000 Child immunization 2791 (66.6) 963 (23.0) 0.16 In a subsection of population, pregnant women and mothers of young children we explored the utilization of primary health care services such as antenatal care (ANC), delivery attendance by skilled birth attendants (SBA), postnatal care (PNC) and child immunization. We found that maternal service utilization was significantly higher among residents of plains compared to those living in coastal areas. However, in terms of utilization of immunization services there was no significant differences between residents of plain and low-lying coastal area (Table 3). Table 4: Inequities of healthcare utilization based on area of residence and migration status in rural Chakaria (2017 & 2018) Variables Plain non-migrants % Coastal non-migrants % Coastal migrants % % change Plain non-migrants vs. Coastal non-migrants Plain non-migrants vs. Coastal migrants ANC (received at least 4 visits) 27.5 19.8 9.7 28.0 64.7 Delivery by SBA 43.5 31.5 13.0 27.6 70.1 PNC (received at least 1 visit) 54.4 42.3 22.6 22.2 58.5 Child immunization (12-23) months 89.5 91.3 77.5 2.0 13.4 Assuming that the health service utilization of the residents of the plains were the benchmark for what is expected in Chakaria, we compared the rates of health service utilization among residents of plains with that of the coast and the migrants living in coastal area. Compared to residents of plains, utilization of different maternal and child health care services among coastal residents were 2-28% lower (Table 4). Compared to residents of plains, utilization of different maternal and child health care services among coastal migrants were 13-70% lower. Discussion Although studies on impact of climate change human lives, both health and non-health aspects, and migration as a coping mechanism has been studied around the world, less is known about utilization of health services among migrants especially in climate vulnerable coastal areas. Our study is the first to use strength of longitudinal data on migration and health care utilization to study the determinants of migration in climate vulnerable coastal area of Bangladesh and assess how intersection of coastal residence and migration create inequities in health care utilization. The findings from the study will be applicable to 2.5 million residents of several coastal regions of Bangladesh who are displaced every year due to frequent natural disasters and similar populations in low resource settings [ 17 ]. Further the insights will help to inform policies and programs to reduce inequities in health service utilization among migrants who live in coastal areas. According to our study findings, adults (≥15 years) were more likely to migrate compared to other age groups and more males migrated compared to females. Previous researches also reported that mostly adults were migrated in search of employment opportunities and in our cultural setting gender role assigned to men as breadwinners made them more likely to move in search of opportunities [ 32 , 33 ]. On the other hand, we also observed that compared to those with little or no education, those with better educational status were more likely to migrate. Several studies suggested the similar findings which may indicate that education provide more job opportunities to people which in turn encourages migration [ 34 – 37 ]. In our study, we found that people engaged in agricultural professions were less likely to migrate compared to people in other professions. In previous studies, researchers explained that those engaged with agriculture often owned land and had better social status and power in the context of rural Bangladesh which probably discourages migration [ 38 , 39 ]. Our study findings also demonstrated that members from poorest households were more likely to migrate than wealthier households. Similar findings were reported in several studies where researchers explained that the poor with limited resources are very vulnerable to natural disasters and lacking the resources to recover, often resort to migration for better employment opportunities [ 40 , 41 ]. In this study, we found only 4.7%, 7.5% and 10.1% women of coastal areas accessed maternal healthcare services in terms of ANC, assisted delivery by SBA and PNC services respectively, significantly lower compared to those living in plains and the national average [ 42 ]. Previous research conducted in geographically adverse rural areas of Bangladesh and other countries also reported the similar findings [ 43 – 49 ]. Researchers discussed a number of barriers of healthcare utilization at individual, community and health systems-level. At individual-level, lack of education, knowledge and awareness, poverty, limited mobility and autonomy of women; at community-level, difficulties in accessing health facilities due to lack of road and transport, cultural barriers related to using health facilities, and tradition of using local untrained traditional birth attendants; and at health systems-level, limited and expensive services and disrespectful behavior of health workers towards poor were associated with low service utilization [ 46 , 49 , 50 ]. The low service utilization was also apparent for child immunization, a primary care service with 95% national coverage [ 51 ] in the study population. The rate of child immunization coverage was only 23% among children of coastal areas, compared to that of the residents of the plains (67%). Previous studies conducted in rural or hard to reach areas of Bangladesh and other developing countries reported the lower childhood immunization rate than national average [ 52 – 58 ]. Although in Bangladesh the vaccination services are free of charge, Factors such as geographical and social inequalities, poor income, transportation cost, fragile communication system, limited knowledge and awareness about the benefit of vaccination, fear, stigma and lack of autonomy in decision making among mothers, have been associated with low rates of immunization among children [ 59 – 67 ]. Our research indicated that the intersection of living in disadvantaged areas and migration compounded the inequities in healthcare utilization. Our study findings showed that people who migrate in geographically vulnerable low-lying coastal areas have lower service utilization than residents. Researchers have described inequities in healthcare utilization among migrants compared to residents of the areas [ 68 ] [ 69 ]. Several factors such as higher mobility, financial hardship, being poorly informed about health risk and available health facilities, high healthcare cost relative to income have shown to adversely affect the use of healthcare services among the migrants [ 46 , 70 – 72 ]. Bangladesh has made remarkable progress in achieving several health indicators as indicated by the achievement of Millennium Development Goals (MDG 4 and 5) related to maternal mortality and child mortality [ 73 ]. However, given that Bangladesh is one of the countries most affected by climate change and contain 2.5 million climate migrants, it is imperative that health of the migrant population is given priority [ 1 , 17 ]. To achieve Sustainable Development Goal (SDG10) related to reducing inequality in the population, it is important that gaps in service provision are addressed and barriers to healthcare seeking among vulnerable and hard to reach populations such as the migrants, are removed [ 74 ]. While climate migrants use migration as an adaptation strategy it is important that health systems have the provision to ensure that primary care is accessible and available to the migrants while they make efforts to overcome poverty. Multi-sector partnership, coordinated efforts, migration-friendly policies and strategies are needed to develop the migration-sensitive health systems. The significant strength of this paper was that we analyzed the longitudinal data from multiple years which allowed us to look at trends over time and have sufficient numbers to look at determinants. This study also had few limitations that should be acknowledged. This study was cross sectional in nature, so it does not permit any cause and effect relationship to be inferred. As the analysis was done based on existing data we could not add any questions to refine our inquiry. Conclusion In terms of determinants of migration adults, males, educated, poor and unemployed were more likely to migrate than the others. In terms of utilization of maternal and child health care services, residents of coastal areas had lower health healthcare utilization rates than those who lived in adjacent plain area. The disadvantages of living in coastal area were more pronounces among the migrants. The insights from this study would be useful for creating a migration -sensitive health systems in the future. Abbreviations ANC: Antenatal Care; CVD: Cardiovascular diseases; HDSS: Health and Demographic Surveillance System; MCH: Maternal and Child Healthcare; MDG: Millennium Development Goal; PNC: Postnatal Care; SBA: Skilled Birth Attendant; SDG: Sustainable Development Goal Declarations Ethics approval and consent to participate: We conducted a secondary analysis of Chakaria HDSS data. Chakaria HDSS survey protocol (ACT-00230) was approved by Research Review Committee (RRC) and Ethical Review Committee (ERC) of International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) and written informed consent was obtained from all the participants before participating in the study. The original Chakaria HDSS data collection with all methods were carried out in accordance with national ethical guidelines and regulations (which conforms to the declaration of Helsinki). Consent for publication: Not applicable Availability of data and materials: Data we used in this manuscript are not publicly available due to the data policy of icddr,b. Data are available upon reasonable request for researchers from Armana Ahmed ( [email protected] ), Head of Research Administration of icddr,b, and as per the data access policy of icddr,b. Competing interests: None declared. Funding: This study was funded by Swedish International Development Cooperation Agency (SIDA). No other specific grant from any funding agency or from any other non-profit sectors was received for this research. Author’s contribution: Mosammat Ivylata Khanam: Conceptualization, data curation, analysis and interpretation of data, writing original draft, Abdul Khalek: Analysis and interpretation of data, Sabrina Rasheed: Conceptualization and writing original draft. SR provided guidance with respect to the whole research, including methodological guidance. All authors critically reviewed, edited and approved the final manuscript Acknowledgements: icddr,b acknowledges with gratitude the commitment of SIDA to its research efforts. We would also like to thank Dr. Daniel D Reidpath and the whole team of Chakaria HDSS of icddr,b for their guidance and assistance in the data analysis. icddr,b is grateful to the Governments of Bangladesh, Canada, Sweden and the UK for providing core/unrestricted support. 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Oleribe O, Kumar V, Awosika-Olumo A, Taylor-Robinson SD: Individual and socioeconomic factors associated with childhood immunization coverage in Nigeria . The Pan African Medical Journal 2017, 26 . Clouston S, Kidman R, Palermo T: Social inequalities in vaccination uptake among children aged 0–59 months living in Madagascar: An analysis of Demographic and Health Survey data from 2008 to 2009 . Vaccine 2014, 32 (28):3533-3539. Rahman L, Biswas H, Hossain T, Khan AM, Khan IA: Study on reasons of dropout of EPI vaccination in selected slum area of Dhaka city, Bangladesh . South East Asia Journal of Public Health 2012, 2 (1):64-67. Kumar P, Ranjan A, Kumar D, Pandey S, Singh C, Agarwal N: Factors associated with Immunisation coverage in children of migrant brick kiln workers in selected districts of Bihar, India . Indian Journal of Community Health 2020, 32 (1):91-96. Antai D: Inequitable childhood immunization uptake in Nigeria: a multilevel analysis of individual and contextual determinants . BMC infectious diseases 2009, 9 (1):1-10. Uddin MJ, Koehlmoos TP, Saha NC, Khan IA: Child immunization coverage in rural hard-to-reach areas of Bangladesh . Vaccine 2010, 28 (5):1221-1225. Uddin MJ, Shamsuzzaman M, Horng L, Labrique A, Vasudevan L, Zeller K, Chowdhury M, Larson CP, Bishai D, Alam N: Use of mobile phones for improving vaccination coverage among children living in rural hard-to-reach areas and urban streets of Bangladesh . Vaccine 2016, 34 (2):276-283. Goodwin-Gill GS, McAdam J: UNHCR and climate change, disasters and displacement . The United Nations Refugee Agency (UNHCR), Geneva, Switzerland 2017. Kusuma YS, Kumari R, Pandav CS, Gupta SK: Migration and immunization: determinants of childhood immunization uptake among socioeconomically disadvantaged migrants in Delhi, India . Tropical Medicine & International Health 2010, 15 (11):1326-1332. Mutton D, Haque CE: Human vulnerability, dislocation and resettlement: adaptation processes of river‐bank erosion‐induced displacees in Bangladesh . Disasters 2004, 28 (1):41-62. Sikder R, Xiaoying J: Climate change impact and agriculture of Bangladesh . Journal of Environment and Earth Science 2014, 4 (1):35-40. Haque MR, Parr N, Muhidin S: Parents' healthcare-seeking behavior for their children among the climate-related displaced population of rural Bangladesh . Social Science & Medicine 2019, 226 :9-20. Chowdhury S, Banu L, Chowdhury T, Rubayet S, Khatoon S: Achieving millennium development goals 4 and 5 in Bangladesh . BJOG: An International Journal of Obstetrics & Gynaecology 2011, 118 :36-46. Tulloch O, Machingura F, Melamed C: Health, migration and the 2030 Agenda for Sustainable Development . 2016. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1234008","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":81035323,"identity":"5ceb8b9c-d1a5-4e63-8ed1-4c281dff7149","order_by":0,"name":"Mosammat Ivylata Khanam","email":"data:image/png;base64,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","orcid":"","institution":"International Centre for Diarrhoeal Disease Research","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Mosammat","middleName":"Ivylata","lastName":"Khanam","suffix":""},{"id":81035324,"identity":"7ac66e4d-421d-4494-8c98-1b8266d2553d","order_by":1,"name":"Abdul Khalek","email":"","orcid":"","institution":"International Centre for Diarrhoeal Disease Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Abdul","middleName":"","lastName":"Khalek","suffix":""},{"id":81035325,"identity":"bc674e5b-f21a-44ed-8b38-f897d95a900d","order_by":2,"name":"Syed Manzoor Ahmed Hanifi","email":"","orcid":"","institution":"International Centre for Diarrhoeal Disease Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Syed","middleName":"Manzoor Ahmed","lastName":"Hanifi","suffix":""},{"id":81035326,"identity":"ee44e10c-c13a-4a32-a0e7-d3241df70ae3","order_by":3,"name":"Sabrina Rasheed","email":"","orcid":"","institution":"International Centre for Diarrhoeal Disease Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sabrina","middleName":"","lastName":"Rasheed","suffix":""}],"badges":[],"createdAt":"2022-01-06 05:44:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1234008/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1234008/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":19849728,"identity":"98960869-fc08-4f16-8989-7236a8b85bd8","added_by":"auto","created_at":"2022-04-01 07:44:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1719100,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1234008/v1/04158876-36f2-492d-833b-ece9a9980880.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eInequities in Healthcare Seeking Among Migrants and Residents of Climate Affected Coastal Area of Bangladesh\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBangladesh has been recognized as one of the most vulnerable countries to natural disasters aggravated by global climate change due to its unique geographical location [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Bangladesh currently is ranked sixth on Global Climate Risk Index in 2017 for climate vulnerability [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Within Bangladesh, densely populated country the coastal communities have experienced highest number of natural disasters than any other regions of Bangladesh [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition to the prevailing population density, poverty and weak infrastructure, population of coastal areas have suffered from frequent natural disasters such as floods, cyclone, storm and saline intrusion of soil and water due to proximity to the ocean, low elevation and river bank erosion, which has impact on their livelihood and health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In global literature and research conducted in Bangladesh, researchers have reported that the climate change has adversely affected land and water leading to food and water insecurity and therefore, reduced health and wellbeing [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In terms of health indicators coastal population tend to suffer from greater burden of communicable and non-communicable diseases such as hypertension and cardiovascular diseases (CVD) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Researchers have associated the rise in CVDs, still birth and miscarriage to higher consumption of salt among coastal population [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Also, poor coastal people find difficulties in accessing necessary primary healthcare services particularly maternal and child healthcare (MCH) because of the sub-standard healthcare delivery system (availability, accessibility, utilization, adequate and effective coverage) and limited skilled healthcare providers compared to other parts of the country [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Further, the adverse impact of climate change on livelihood has led to impoverishment and migration [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Bangladesh about 2.5 million coastal residents are displaced each year due to natural disasters [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Researchers have studied the impacts of climate change on health, resources, human settlement, as well as adaptation to climate change such as migration in Bangladesh [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. It is projected by 2050, around 140,000 coastal residents will be forced to migrate within the district and about 60,000 outside the district to increased soil salinity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Based on some projections the number of Bangladeshis displaced by impacts of climate change could reach 13.3 million by 2050 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In previous studies of migration pattern in Bangladesh researchers described migration as a coping strategy to deal with poverty with mostly male members and sometimes families migrating out of their area in search of better employment opportunities [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Migration is less likely among women, children, elderly and disabled population [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. When the reason for not migrating was explored, researchers found that attachment to property and power, homeland and familial responsibilities were important factors for people to stay in their area of residence [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Migration have shown to impact the health of migrants adversely [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Little is known about the impact of migration on health service utilization specially if migrant settle in climate affected coastal communities. In the current study we have utilized the strength of existing data from Health and Demographic Surveillance System (HDSS) to investigate the impact of migration on healthcare service utilization geographically diverse coastal region of Bangladesh. The findings will provide an insight about strengthening the health systems for migrants in Bangladesh.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis secondary data analysis was conducted by using Chakaria HDSS data from 2017-2018. Chakaria HDSS is one of the field sites of International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) established in 1999 that collects data regarding socio-demographic and health indicators such as birth, death, marriage, education, occupation, health and health services utilization in regular intervals in Chakaria [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Chakaria is subdistrict under Cox\u0026rsquo;s Bazar district located in the South-East of Bangladesh. Chakaria is geographically diverse and contains hilly areas in the east, coastal low-lying areas in the west as well as plain land.\u003c/p\u003e \u003cp\u003eFor our analysis we used information from mothers and children (12-23 months of age) who were residents of plains and the low-lying coastal regions of Chakaria. In terms of migration we exclude any migration that happened within the same area and migration due to marriage for our analysis. In terms of healthcare utilization, we considered maternal and child care services such as antenatal care, postnatal care, trained assistance during delivery (skilled birth attendant, doctor or nurses) among recently delivered mothers and immunization (12-23) month old children.\u003c/p\u003e \u003cp\u003eTo explore the association between healthcare seeking behavior and contextual variables, Chi-square test was performed. A logistic regression analysis was carried out to assess the factors that are significantly associated with migration. To identify the inequity in health service utilization we used the service utilization rates of plain area as reference and compared this with service utilization rates in coastal residents and coastal migrants to assess inequities. Data analysis was done using Stata version 14.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Socio-demographic characteristics of migrant and non-migrant population in coastal area and plains of Chakaria in 2018\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" valign=\"top\" width=\"28.84310618066561%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"35.02377179080824%\"\u003e\n \u003cp\u003eLow-lying coastal area\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"36.13312202852615%\"\u003e\n \u003cp\u003ePlain area\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.826280623608017%\"\u003e\n \u003cp\u003eMigrants\u003c/p\u003e\n \u003cp\u003e(n=709)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.394209354120267%\"\u003e\n \u003cp\u003eNon-migrants\u003c/p\u003e\n \u003cp\u003e(n=15,234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.053452115812917%\"\u003e\n \u003cp\u003eMigrants\u003c/p\u003e\n \u003cp\u003e(2,320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.7260579064588%\"\u003e\n \u003cp\u003eNon-migrants\u003c/p\u003e\n \u003cp\u003e(49,704)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.826280623608017%\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.394209354120267%\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.053452115812917%\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.7260579064588%\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"13.47068145800317%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.37242472266244%\"\u003e\n \u003cp\u003e\u0026lt;15 y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.530903328050712%\"\u003e\n \u003cp\u003e254 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.492868462757528%\"\u003e\n \u003cp\u003e5,576 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.11568938193344%\"\u003e\n \u003cp\u003e893 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.01743264659271%\"\u003e\n \u003cp\u003e17,630 (35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.765567765567766%\"\u003e\n \u003cp\u003e(15-49) y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.94871794871795%\"\u003e\n \u003cp\u003e418 (59.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.52747252747253%\"\u003e\n \u003cp\u003e7,762 (51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.78021978021978%\"\u003e\n \u003cp\u003e1,309 (56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.978021978021978%\"\u003e\n \u003cp\u003e25,229 (50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.765567765567766%\"\u003e\n \u003cp\u003e50+ y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.94871794871795%\"\u003e\n \u003cp\u003e37 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.52747252747253%\"\u003e\n \u003cp\u003e1,896 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.78021978021978%\"\u003e\n \u003cp\u003e118 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.978021978021978%\"\u003e\n \u003cp\u003e6,845 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"13.47068145800317%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.37242472266244%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.530903328050712%\"\u003e\n \u003cp\u003e382 (53.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.492868462757528%\"\u003e\n \u003cp\u003e7,726 (50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.11568938193344%\"\u003e\n \u003cp\u003e1,222 (52.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.01743264659271%\"\u003e\n \u003cp\u003e24,855 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.765567765567766%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.94871794871795%\"\u003e\n \u003cp\u003e327 (46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.52747252747253%\"\u003e\n \u003cp\u003e7,508 (49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.78021978021978%\"\u003e\n \u003cp\u003e1,098 (47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.978021978021978%\"\u003e\n \u003cp\u003e24,849 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" width=\"13.47068145800317%\"\u003e\n \u003cp\u003eYears of schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.37242472266244%\"\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.530903328050712%\"\u003e\n \u003cp\u003e408 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.492868462757528%\"\u003e\n \u003cp\u003e9,135 (60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.11568938193344%\"\u003e\n \u003cp\u003e979 (42.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.01743264659271%\"\u003e\n \u003cp\u003e22,019 (44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.765567765567766%\"\u003e\n \u003cp\u003e0-5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.94871794871795%\"\u003e\n \u003cp\u003e175 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.52747252747253%\"\u003e\n \u003cp\u003e3,664 (24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.78021978021978%\"\u003e\n \u003cp\u003e665 (28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.978021978021978%\"\u003e\n \u003cp\u003e14,837 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.765567765567766%\"\u003e\n \u003cp\u003e6-10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.94871794871795%\"\u003e\n \u003cp\u003e75 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.52747252747253%\"\u003e\n \u003cp\u003e1,816 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.78021978021978%\"\u003e\n \u003cp\u003e505 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.978021978021978%\"\u003e\n \u003cp\u003e10,021 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"17.765567765567766%\"\u003e\n \u003cp\u003e10+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.94871794871795%\"\u003e\n \u003cp\u003e51 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.52747252747253%\"\u003e\n \u003cp\u003e619 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.78021978021978%\"\u003e\n \u003cp\u003e171 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.978021978021978%\"\u003e\n \u003cp\u003e2,827 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" width=\"13.47068145800317%\"\u003e\n \u003cp\u003eHH occupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.37242472266244%\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.530903328050712%\"\u003e\n \u003cp\u003e58 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.492868462757528%\"\u003e\n \u003cp\u003e2,211 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.11568938193344%\"\u003e\n \u003cp\u003e257 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.01743264659271%\"\u003e\n \u003cp\u003e9,703 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.765567765567766%\"\u003e\n \u003cp\u003eDay laborer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.94871794871795%\"\u003e\n \u003cp\u003e329 (46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.52747252747253%\"\u003e\n \u003cp\u003e6,668 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.78021978021978%\"\u003e\n \u003cp\u003e739 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.978021978021978%\"\u003e\n \u003cp\u003e15,208 (30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.765567765567766%\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.94871794871795%\"\u003e\n \u003cp\u003e189 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.52747252747253%\"\u003e\n \u003cp\u003e3,717 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.78021978021978%\"\u003e\n \u003cp\u003e777 (33.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.978021978021978%\"\u003e\n \u003cp\u003e16,112 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.765567765567766%\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.94871794871795%\"\u003e\n \u003cp\u003e133 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.52747252747253%\"\u003e\n \u003cp\u003e2,638 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.78021978021978%\"\u003e\n \u003cp\u003e547 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.978021978021978%\"\u003e\n \u003cp\u003e8,681 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"13.47068145800317%\"\u003e\n \u003cp\u003eWealth quantile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.37242472266244%\"\u003e\n \u003cp\u003eLowest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.530903328050712%\"\u003e\n \u003cp\u003e272 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.492868462757528%\"\u003e\n \u003cp\u003e4,802 (31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.11568938193344%\"\u003e\n \u003cp\u003e597 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.01743264659271%\"\u003e\n \u003cp\u003e8,609 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.765567765567766%\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.94871794871795%\"\u003e\n \u003cp\u003e270 (38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.52747252747253%\"\u003e\n \u003cp\u003e6,387 (42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.78021978021978%\"\u003e\n \u003cp\u003e745 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.978021978021978%\"\u003e\n \u003cp\u003e18,577 (37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.765567765567766%\"\u003e\n \u003cp\u003eHighest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.94871794871795%\"\u003e\n \u003cp\u003e167 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.52747252747253%\"\u003e\n \u003cp\u003e4,045 (26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.78021978021978%\"\u003e\n \u003cp\u003e978 (42.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.978021978021978%\"\u003e\n \u003cp\u003e22,518 (45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIn terms of socio-demographic characteristics, most of the migrants and non-migrants of both areas were adults (Table 1). Majority people (both migrants and local) from both areas had no institutional education and were engaged as day laborer compare to other professions. Wealth quantile of migrant people from both areas were comparatively lower than local people.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Sociodemographic determinants of migration during 2018 in rural Chakaria\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" width=\"49.51456310679612%\"\u003e\n \u003cp\u003eSocio-demographic characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"50.48543689320388%\"\u003e\n \u003cp\u003eMigration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"63.46153846153846%\"\u003e\n \u003cp\u003eAOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"36.53846153846154%\"\u003e\n \u003cp\u003eP value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"26.21359223300971%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGeographical location\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.300970873786408%\"\u003e\n \u003cp\u003eLow-lying coastal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"32.03883495145631%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"18.446601941747574%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.57894736842105%\"\u003e\n \u003cp\u003ePlain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"43.421052631578945%\"\u003e\n \u003cp\u003e1.04 (0.99-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.067\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"26.21359223300971%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.300970873786408%\"\u003e\n \u003cp\u003e\u0026lt;15 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"32.03883495145631%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"18.446601941747574%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.57894736842105%\"\u003e\n \u003cp\u003e(15-49) years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"43.421052631578945%\"\u003e\n \u003cp\u003e1.09 (1.01-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.025\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.57894736842105%\"\u003e\n \u003cp\u003e50+ years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"43.421052631578945%\"\u003e\n \u003cp\u003e0.37 (0.31-0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"26.21359223300971%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.300970873786408%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"32.03883495145631%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"18.446601941747574%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.57894736842105%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"43.421052631578945%\"\u003e\n \u003cp\u003e1.14 (1.06-1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" width=\"26.21359223300971%\"\u003e\n \u003cp\u003eYears of schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.300970873786408%\"\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"32.03883495145631%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"18.446601941747574%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.57894736842105%\"\u003e\n \u003cp\u003e0-5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"43.421052631578945%\"\u003e\n \u003cp\u003e1.02 (0.93-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.633\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.57894736842105%\"\u003e\n \u003cp\u003e6-10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"43.421052631578945%\"\u003e\n \u003cp\u003e1.11(1.00-1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.037\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.57894736842105%\"\u003e\n \u003cp\u003e10+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"43.421052631578945%\"\u003e\n \u003cp\u003e1.54 (1.31-1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" width=\"26.21359223300971%\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.300970873786408%\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"32.03883495145631%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"18.446601941747574%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.57894736842105%\"\u003e\n \u003cp\u003eDay laborer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"43.421052631578945%\"\u003e\n \u003cp\u003e1.54 (1.35-1.766)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.57894736842105%\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"43.421052631578945%\"\u003e\n \u003cp\u003e1.74 (1.52-1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.57894736842105%\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"43.421052631578945%\"\u003e\n \u003cp\u003e2.27 (1.98-2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"26.21359223300971%\"\u003e\n \u003cp\u003eWealth quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.300970873786408%\"\u003e\n \u003cp\u003eHighest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"32.03883495145631%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"18.446601941747574%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.57894736842105%\"\u003e\n \u003cp\u003eMiddle\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"43.421052631578945%\"\u003e\n \u003cp\u003e1.04 (0.94-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.57894736842105%\"\u003e\n \u003cp\u003eLowest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.1945%;\" valign=\"top\" width=\"43.421052631578945%\"\u003e\n \u003cp\u003e1.69 (1.52-1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4165%;\" valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn terms of geographical location, people from the plains were more likely to migrate compared to those living in the coastal area although this association was not significant (Table 2). Compared to people aged less than 15 years, migration rate was significantly higher among 15-49 age group (OR:1.09, 95% CI:1.01-1.17) and lower among people older than 50 years of age (OR:0.37, 95% CI: 0.31-0.44). Males were significantly more likely to migrate (OR:1.14, 95% CI:1.06-1.23) compared to females. Compared to those with little or no education those with higher education (OR:1.54, 95% CI:1.31-1.81) were significantly more likely to migrate. Compared to those engaged in agriculture, those in other occupations (OR:1.74, 95% CI:1.52-1.99) were significantly more likely to migrate with unemployed people (OR:2.27, 95% CI:1.98-2.60) having the highest likelihood of migration. In terms of household wealth quintile, compared people from wealthier households, those from poorest households (OR:1.69, 95% CI:1.52-1.87) were significantly more likely to migrate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Healthcare utilization based on area of residence during 2017 and 2018\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"27.884615384615383%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60.241%;\" valign=\"top\" width=\"53.84615384615385%\"\u003e\n \u003cp\u003eChakaria\u003c/p\u003e\n \u003cp\u003e[recently delivered mothers, n=3218;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(12-23) months old children, n=4188]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12.2203%;\" valign=\"top\" width=\"18.26923076923077%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3907%;\" valign=\"top\" width=\"48.214285714285715%\"\u003e\n \u003cp\u003ePlain area, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3666%;\" valign=\"top\" width=\"51.785714285714285%\"\u003e\n \u003cp\u003eCoastal area, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.884615384615383%\"\u003e\n \u003cp\u003eANC\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(received at least 4 visit)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3907%;\" width=\"25.96153846153846%\"\u003e\n \u003cp\u003e673 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3666%;\" width=\"27.884615384615383%\"\u003e\n \u003cp\u003e153 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2203%;\" width=\"18.26923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.884615384615383%\"\u003e\n \u003cp\u003eDelivery by SBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3907%;\" width=\"25.96153846153846%\"\u003e\n \u003cp\u003e1067 (33.2) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3666%;\" width=\"27.884615384615383%\"\u003e\n \u003cp\u003e242 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2203%;\" width=\"18.26923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.884615384615383%\"\u003e\n \u003cp\u003ePNC\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(received at least 1 visit)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3907%;\" width=\"25.96153846153846%\"\u003e\n \u003cp\u003e1328 (41.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3666%;\" width=\"27.884615384615383%\"\u003e\n \u003cp\u003e326 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2203%;\" width=\"18.26923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.884615384615383%\"\u003e\n \u003cp\u003eChild immunization\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3907%;\" width=\"25.96153846153846%\"\u003e\n \u003cp\u003e2791 (66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3666%;\" width=\"27.884615384615383%\"\u003e\n \u003cp\u003e963 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2203%;\" width=\"18.26923076923077%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIn a subsection of population, pregnant women and mothers of young children we explored the utilization of primary health care services such as antenatal care (ANC), delivery attendance by skilled birth attendants (SBA), postnatal care (PNC) and child immunization. We found that maternal service utilization was significantly higher among residents of plains compared to those living in coastal areas. However, in terms of utilization of immunization services there was no significant differences between residents of plain and low-lying coastal area (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Inequities of healthcare utilization based on area of residence and migration status in rural Chakaria (2017 \u0026amp; 2018)\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"28.30188679245283%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"11.320754716981131%\"\u003e\n \u003cp\u003ePlain non-migrants\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"12.264150943396226%\"\u003e\n \u003cp\u003eCoastal non-migrants\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"13.20754716981132%\"\u003e\n \u003cp\u003eCoastal migrants\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"34.905660377358494%\"\u003e\n \u003cp\u003e% change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.8262%;\" valign=\"top\" width=\"51.351351351351354%\"\u003e\n \u003cp\u003ePlain non-migrants vs. Coastal non-migrants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.105%;\" valign=\"top\" width=\"48.648648648648646%\"\u003e\n \u003cp\u003ePlain non-migrants vs. Coastal migrants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.30188679245283%\"\u003e\n \u003cp\u003eANC\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(received at least 4 visits)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003e27.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.264150943396226%\"\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.8262%;\" width=\"17.92452830188679%\"\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.105%;\" width=\"16.9811320754717%\"\u003e\n \u003cp\u003e64.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.30188679245283%\"\u003e\n \u003cp\u003eDelivery by SBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003e43.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.264150943396226%\"\u003e\n \u003cp\u003e31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.8262%;\" valign=\"bottom\" width=\"17.92452830188679%\"\u003e\n \u003cp\u003e27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.105%;\" valign=\"bottom\" width=\"16.9811320754717%\"\u003e\n \u003cp\u003e70.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.30188679245283%\"\u003e\n \u003cp\u003ePNC\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(received at least 1 visit)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e54.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.264150943396226%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e42.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.8262%;\" valign=\"bottom\" width=\"17.92452830188679%\"\u003e\n \u003cp\u003e22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.105%;\" valign=\"bottom\" width=\"16.9811320754717%\"\u003e\n \u003cp\u003e58.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.30188679245283%\"\u003e\n \u003cp\u003eChild immunization\u003c/p\u003e\n \u003cp\u003e(12-23) months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e89.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.264150943396226%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e91.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.8262%;\" valign=\"bottom\" width=\"17.92452830188679%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.105%;\" valign=\"bottom\" width=\"16.9811320754717%\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAssuming that the health service utilization of the residents of the plains were the benchmark for what is expected in Chakaria, we compared the rates of health service utilization among residents of plains with that of the coast and the migrants living in coastal area. Compared to residents of plains, utilization of different maternal and child health care services among coastal residents were 2-28% lower (Table 4). Compared to residents of plains, utilization of different maternal and child health care services among coastal migrants were 13-70% lower.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough studies on impact of climate change human lives, both health and non-health aspects, and migration as a coping mechanism has been studied around the world, less is known about utilization of health services among migrants especially in climate vulnerable coastal areas. Our study is the first to use strength of longitudinal data on migration and health care utilization to study the determinants of migration in climate vulnerable coastal area of Bangladesh and assess how intersection of coastal residence and migration create inequities in health care utilization. The findings from the study will be applicable to 2.5 million residents of several coastal regions of Bangladesh who are displaced every year due to frequent natural disasters and similar populations in low resource settings [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. Further the insights will help to inform policies and programs to reduce inequities in health service utilization among migrants who live in coastal areas.\u003c/p\u003e\n\u003cp\u003eAccording to our study findings, adults (\u0026ge;15 years) were more likely to migrate compared to other age groups and more males migrated compared to females. Previous researches also reported that mostly adults were migrated in search of employment opportunities and in our cultural setting gender role assigned to men as breadwinners made them more likely to move in search of opportunities [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. On the other hand, we also observed that compared to those with little or no education, those with better educational status were more likely to migrate. Several studies suggested the similar findings which may indicate that education provide more job opportunities to people which in turn encourages migration [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. In our study, we found that people engaged in agricultural professions were less likely to migrate compared to people in other professions. In previous studies, researchers explained that those engaged with agriculture often owned land and had better social status and power in the context of rural Bangladesh which probably discourages migration [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]. Our study findings also demonstrated that members from poorest households were more likely to migrate than wealthier households. Similar findings were reported in several studies where researchers explained that the poor with limited resources are very vulnerable to natural disasters and lacking the resources to recover, often resort to migration for better employment opportunities [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn this study, we found only 4.7%, 7.5% and 10.1% women of coastal areas accessed maternal healthcare services in terms of ANC, assisted delivery by SBA and PNC services respectively, significantly lower compared to those living in plains and the national average [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. Previous research conducted in geographically adverse rural areas of Bangladesh and other countries also reported the similar findings [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. Researchers discussed a number of barriers of healthcare utilization at individual, community and health systems-level. At individual-level, lack of education, knowledge and awareness, poverty, limited mobility and autonomy of women; at community-level, difficulties in accessing health facilities due to lack of road and transport, cultural barriers related to using health facilities, and tradition of using local untrained traditional birth attendants; and at health systems-level, limited and expensive services and disrespectful behavior of health workers towards poor were associated with low service utilization [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]. The low service utilization was also apparent for child immunization, a primary care service with 95% national coverage [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e] in the study population. The rate of child immunization coverage was only 23% among children of coastal areas, compared to that of the residents of the plains (67%). Previous studies conducted in rural or hard to reach areas of Bangladesh and other developing countries reported the lower childhood immunization rate than national average [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]. Although in Bangladesh the vaccination services are free of charge, Factors such as geographical and social inequalities, poor income, transportation cost, fragile communication system, limited knowledge and awareness about the benefit of vaccination, fear, stigma and lack of autonomy in decision making among mothers, have been associated with low rates of immunization among children [\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eOur research indicated that the intersection of living in disadvantaged areas and migration compounded the inequities in healthcare utilization. Our study findings showed that people who migrate in geographically vulnerable low-lying coastal areas have lower service utilization than residents. Researchers have described inequities in healthcare utilization among migrants compared to residents of the areas [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e] [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]. Several factors such as higher mobility, financial hardship, being poorly informed about health risk and available health facilities, high healthcare cost relative to income have shown to adversely affect the use of healthcare services among the migrants [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eBangladesh has made remarkable progress in achieving several health indicators as indicated by the achievement of Millennium Development Goals (MDG 4 and 5) related to maternal mortality and child mortality [\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e]. However, given that Bangladesh is one of the countries most affected by climate change and contain 2.5 million climate migrants, it is imperative that health of the migrant population is given priority [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. To achieve Sustainable Development Goal (SDG10) related to reducing inequality in the population, it is important that gaps in service provision are addressed and barriers to healthcare seeking among vulnerable and hard to reach populations such as the migrants, are removed [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]. While climate migrants use migration as an adaptation strategy it is important that health systems have the provision to ensure that primary care is accessible and available to the migrants while they make efforts to overcome poverty. Multi-sector partnership, coordinated efforts, migration-friendly policies and strategies are needed to develop the migration-sensitive health systems.\u003c/p\u003e\n\u003cp\u003eThe significant strength of this paper was that we analyzed the longitudinal data from multiple years which allowed us to look at trends over time and have sufficient numbers to look at determinants. This study also had few limitations that should be acknowledged. This study was cross sectional in nature, so it does not permit any cause and effect relationship to be inferred. As the analysis was done based on existing data we could not add any questions to refine our inquiry.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn terms of determinants of migration adults, males, educated, poor and unemployed were more likely to migrate than the others. In terms of utilization of maternal and child health care services, residents of coastal areas had lower health healthcare utilization rates than those who lived in adjacent plain area. The disadvantages of living in coastal area were more pronounces among the migrants. The insights from this study would be useful for creating a migration -sensitive health systems in the future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eANC: Antenatal Care; CVD: Cardiovascular diseases; HDSS: Health and Demographic Surveillance System; MCH: Maternal and Child Healthcare; MDG: Millennium Development Goal; PNC: Postnatal Care; SBA: Skilled Birth Attendant; SDG: Sustainable Development Goal\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a secondary analysis of Chakaria HDSS data. Chakaria HDSS survey protocol (ACT-00230) was approved by Research Review Committee (RRC) and Ethical Review Committee (ERC) of International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) and written informed consent was obtained from all the participants before participating in the study. The original Chakaria HDSS data collection with all methods were carried out in accordance with national ethical guidelines and regulations (which conforms to the declaration of Helsinki).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData we used in this manuscript are not publicly available due to the data policy of icddr,b. Data are available upon reasonable request for researchers from Armana Ahmed (\u003ca href=\"mailto:[email protected]\" target=\"_blank\"\[email protected]\u003c/a\u003e), Head of Research Administration of icddr,b, and as per the data access policy of icddr,b.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Swedish International Development Cooperation Agency (SIDA). No other specific grant from any funding agency or from any other non-profit sectors was received for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMosammat Ivylata Khanam: Conceptualization, data curation, analysis and interpretation of data, writing original draft, Abdul Khalek: Analysis and interpretation of data, Sabrina Rasheed: Conceptualization and writing original draft. SR provided guidance with respect to the whole research, including methodological guidance. All authors critically reviewed, edited and approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eicddr,b acknowledges with gratitude the commitment of SIDA to its research efforts. We would also like to thank Dr. Daniel D Reidpath and the whole team of Chakaria HDSS of icddr,b for their guidance and assistance in the data analysis. icddr,b is grateful to the Governments of Bangladesh, Canada, Sweden and the UK for providing core/unrestricted support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRahman S, Rahman MA: \u003cstrong\u003eClimate extremes and challenges to infrastructure development in coastal cities in Bangladesh\u003c/strong\u003e. \u003cem\u003eWeather and Climate Extremes\u0026nbsp;\u003c/em\u003e2015, \u003cstrong\u003e7\u003c/strong\u003e:96-108.\u003c/li\u003e\n \u003cli\u003eKreft S, Eckstein D, Melchior I: \u003cstrong\u003eGlobal climate risk index 2017: who suffers most from extreme weather events\u003c/strong\u003e. \u003cem\u003eWeather-related loss events in\u0026nbsp;\u003c/em\u003e2015.\u003c/li\u003e\n \u003cli\u003eKabir R, Khan HT, Ball E, Caldwell K: \u003cstrong\u003eClimate Change Impact: The experience of the coastal areas of Bangladesh affected by Cyclones Sidr and Aila\u003c/strong\u003e. \u003cem\u003eJournal of environmental and public health\u0026nbsp;\u003c/em\u003e2016, \u003cstrong\u003e2016\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eLeridon H: \u003cstrong\u003eWorld population outlook: Explosion or implosion?\u003c/strong\u003e \u003cem\u003ePopulation Societies\u0026nbsp;\u003c/em\u003e2020(1):1-4.\u003c/li\u003e\n \u003cli\u003eSarwar MGM: \u003cstrong\u003eImpacts of sea level rise on the coastal zone of Bangladesh\u003c/strong\u003e. \u003cem\u003eSee\u0026nbsp;\u003c/em\u003e\u003ca href=\"http://static\"\u003e\u003cem\u003ehttp://static\u003c/em\u003e\u003c/a\u003e\u003cem\u003e\u0026nbsp;weadapt org/placemarks/files/225/golam_sarwar pdf\u0026nbsp;\u003c/em\u003e2005.\u003c/li\u003e\n \u003cli\u003eGitz V, Meybeck A, Lipper L, Young CD, Braatz S: \u003cstrong\u003eClimate change and food security: risks and responses\u003c/strong\u003e. \u003cem\u003eFood and Agriculture Organization of the United Nations (FAO) Report\u0026nbsp;\u003c/em\u003e2016, \u003cstrong\u003e110\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eShah T, Lele U: \u003cstrong\u003eClimate change, food and water security in South Asia: Critical issues and cooperative strategies in an age of increased risk and uncertainty\u003c/strong\u003e. 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Gynaecology\u0026nbsp;\u003c/em\u003e2011, \u003cstrong\u003e118\u003c/strong\u003e:36-46.\u003c/li\u003e\n \u003cli\u003eTulloch O, Machingura F, Melamed C: \u003cstrong\u003eHealth, migration and the 2030 Agenda for Sustainable Development\u003c/strong\u003e. 2016.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Migration, healthcare seeking behavior, coastal area, climate change, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-1234008/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1234008/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eClimate change induced sea level rise, increased soil salinity and poor socio-economic condition have triggered migration around the world. So far, the impact of migration on health and healthcare utilization have not been studied adequately especially in Bangladesh. Our study examines impact of migration on healthcare seeking behavior of people living in low-lying coastal areas of Bangladesh. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eWe analyzed data from Chakaria, Health and Demographic Surveillance System, 2017-18. Univariate analysis and regression analysis were conducted to assess the determinants of migration. Finally, we assessed differences in health care seeking behavior between migrants in low-lying coastal areas and plain areas. Stata version 14 was used for analysis. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eIn terms of determinants of migration, adults, males, educated, unemployed and poor were significantly more like to migrate compared to children, females, people with little education, those engaged in agriculture or other occupations and wealthier households. For pregnancy related services healthcare utilization was significantly lower in coastal areas compared to the plains. In terms of utilization of maternal and child health services, service use was 2-28 % lower among residents of low-lying coastal regions and 13-70% lower among coastal migrants compared to those living in plains. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOur findings suggest that in terms of maternal health services there was geographic inequity in service utilization favoring the residents of plains and being a migrant significantly increases the geographical disadvantage in terms of maternal health services. The existing inequity indicates the necessity of immediate action to address the health and healthcare utilization of residents of coastal area with special attention to the migrants in Bangladesh.\u003c/p\u003e","manuscriptTitle":"Inequities in Healthcare Seeking Among Migrants and Residents of Climate Affected Coastal Area of Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-02-04 21:04:09","doi":"10.21203/rs.3.rs-1234008/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"542c10c9-fdb4-429e-b0bd-641cb0a253f2","owner":[],"postedDate":"February 4th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-04-01T07:44:31+00:00","versionOfRecord":[],"versionCreatedAt":"2022-02-04 21:04:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1234008","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1234008","identity":"rs-1234008","version":["v1"]},"buildId":"iFTdqyg4nuje_uAy1AHro","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}