Association between Female Migration and Perinatal Mortality in Nigeria: An analysis of 2018 Nigeria Demographic and Health Survey

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Agboola, Sunday T. Tuwatimi, Lalita Kumari Sah, Rajeeb Kumar Sah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7479175/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 : Perinatal mortality remains a critical public health concern in Nigeria, serving as a key indicator of maternal and child health outcomes. Despite efforts to reduce perinatal deaths, rates remain high, reflecting persistent healthcare disparities and socio-economic inequalities. Understanding the role of female migration in shaping perinatal mortality is crucial, as migration influences healthcare access, social support systems, and economic stability. However, research on migration and perinatal mortality in Nigeria remains limited. This study explores how different female migration patterns impact perinatal mortality, providing insights into potential policy and healthcare interventions. Methods : This study utilized data from the 2018 Nigeria Demographic and Health Survey (NDHS), analyzing a weighted sample of 33,083 women aged 15-49 years. Female migration was categorized into six groups: rural-rural, rural-urban, urban-rural, urban-urban migrants, rural non-migrants, and urban non-migrants. Various socio-demographic factors were considered as potential confounders. Data analysis involved univariate, bivariate, and multivariate statistical techniques to examine associations between migration patterns and perinatal mortality. Results : The prevalence of perinatal mortality in Nigeria was 40.28%, highlighting a significant public health burden. Among the study population, 45.41% were internal migrants. Rural-urban migrants had a lower likelihood of perinatal mortality than rural-rural migrants, likely due to better healthcare access and socio-economic opportunities in urban areas. Increasing maternal age was protective, while easier healthcare access reduced perinatal mortality risk. However, attending more than four antenatal care visits was linked to higher odds of perinatal mortality, suggesting healthcare quality concerns. Muslim women faced higher perinatal mortality risk than Christians, possibly due to cultural practices like early marriage. Conclusions : Female migration patterns play a significant role in perinatal mortality outcomes in Nigeria. Improving healthcare accessibility, particularly in rural areas, and addressing cultural and systemic healthcare challenges are essential for reducing perinatal mortality. Targeted policy interventions focusing on maternal education, healthcare quality, and culturally sensitive health programs are crucial for mitigating perinatal mortality among migrant and non-migrant populations. Perinatal Mortality Female Migration Nigeria Demographic and Health Survey (NDHS) Maternal and Child Health and Socio-Demographic Factors Background Perinatal mortality, a significant global public health concern, profoundly impacts maternal and child health. The World Health Organization (WHO) defines perinatal mortality as the sum of stillbirths and early neonatal deaths per 1,000 total births, commencing at 22 completed weeks (154 days) of gestation ( 1 ). Studies indicate that perinatal mortality is influenced by the quality of healthcare received by pregnant women and their infants ( 2 – 4 ). Beyond statistics, it has serious economic, psychological, and social consequences for mothers and families ( 5 ). The United Nations' Sustainable Development Goal 3 (SDG 3) aims to ensure healthy lives and promote well-being for all ages. However, the global burden of perinatal mortality remains high, with approximately 2 million stillbirths and 1.8 million early neonatal deaths recorded in 2019 ( 6 ). This burden is particularly pronounced in low- and middle-income countries (LMICs), notably sub-Saharan Africa and South Asia ( 7 ). Nigeria, like many LMICs, faces significant challenges in reducing perinatal mortality. Data from the 2018 Nigeria Demographic and Health Survey (NDHS) highlight a concerning trend: while infant and under-five mortality rates have declined, perinatal mortality has increased over the same period ( 8 ). Studies conducted within Nigerian health facilities also report consistently high perinatal mortality rates ( 9 – 11 ), posing a major obstacle to achieving SDG 3.2 targets. Understanding the factors driving this trend is critical. One emerging factor is population mobility, or migration, which significantly influences livelihoods, economic opportunities, and healthcare access ( 12 , 13 ). Migration, particularly rural-urban movement, has played a key role in Nigeria’s urbanization, driven by limited employment opportunities in rural areas and the lure of higher incomes in cities ( 14 ). Women, in particular, migrate frequently within Nigeria, seeking improved opportunities ( 15 ). However, while previous research has linked migration patterns to child mortality, its impact on perinatal mortality remains underexplored. This study aims to fill this gap by investigating how female migration patterns influence perinatal mortality in Nigeria. By doing so, it contributes to a deeper understanding of this critical public health issue and informs targeted maternal and child health interventions. Methods Study Design This study utilizes secondary data from the 2018 Nigeria Demographic and Health Survey (NDHS) to examine the relationship between female migration patterns and perinatal mortality in Nigeria. The NDHS survey follows a descriptive research design, specifically a sample survey approach, aimed at providing a comprehensive understanding of naturally occurring phenomena. Data were systematically collected from a nationally representative sample using structured questionnaires. Sample Size and Data Source The study's weighted sample size consists of 33,083 women of reproductive age (15–49 years) who participated in the NDHS 2018. These women were drawn from all 36 states and the Federal Capital Territory (FCT) of Abuja, covering Nigeria's six geopolitical zones. The NDHS dataset is a robust source for demographic, socioeconomic, and health-related information. Sampling Method The NDHS survey employed a two-stage stratified sampling design. In the first stage, enumeration areas (EAs) were selected using probability proportional to size (PPS). In the second stage, households were systematically selected within each EA, ensuring adequate national representation. Variable Measurement Dependent Variable Perinatal Mortality (PM): Defined as the sum of stillbirths (fetal deaths) and early neonatal deaths (ENND)—the latter referring to live newborns who die within the first seven completed days of life. The Perinatal Mortality Rate (PMR) is calculated as: Additional Independent Variables (Confounders) Key covariates include age, access to healthcare, current employment status, ethnicity, exposure to mass media, education level, occupation, region, religion, wealth status, and internet usage. Data Analysis Statistical analysis was performed using STATA version 12.0, following a three-step approach: 1. Univariate Analysis: ○ Descriptive statistics (frequencies, percentages, tables) were used to summarize socio-demographic characteristics. 2. Bivariate Analysis: ○ Chi-square (χ²) tests were conducted to assess relationships between perinatal mortality and migration status, alongside other socio-demographic variables. 3. Multivariate Analysis: ○ Binary logistic regression was employed to estimate odds ratios (ORs), examining the association between perinatal mortality and independent variables while controlling for potential confounders. Results Univariate Analysis Characteristics of the Study Population The Table 1 below presents the socio-demographic characteristics of women aged 15-49 and perinatal mortality. The age distribution of the respondents highlights that the majority of women fell into the 35-39 years age group, accounting for 22.28% of the sample. The next most prevalent age group was 45-49 years, comprising 20.12% of the respondents. Following closely were women aged 40-44 years and 30-34 years, representing 19.50% and 18.66% respectively. Women in the age groups of 25-29 years and 20-24 years accounted for 13.46% and 5.19% respectively, while those between 15-19 years were the least represented, making up only 0.79% of the sample. Regarding access to healthcare facilities, a significant proportion of women reported that it was not a problem, constituting 77.26% of the respondents. In contrast, 22.74% of the women indicated that accessing healthcare was a significant challenge for them. The distribution of respondents based on the number of antenatal care visits revealed that the majority of women (97.89%) had less than four visits, while only a small proportion (2.11%) reported having four or more visits. Similarly, the data indicates that a higher percentage of women in the study were currently employed compared to those who were not working, accounting for 73.92% and 26.08% respectively. In terms of ethnicity, Hausa/Fulani women were the most represented group, constituting 56.82% of the sample. This was followed by women from other ethnic groups and the Igbo ethnic group, comprising 27.00% and 8.28% respectively. The Yoruba ethnic group had the lowest representation, accounting for 7.90% of the respondents. Regarding exposure to mass media, a majority of women reported being exposed, representing 58.45% of the sample. In contrast, 41.55% of women indicated that they were not exposed to mass media. The distribution of respondents based on educational attainment revealed that the highest proportion of women had no formal education, accounting for 56.78% of the sample. Following this, women with secondary education and primary education levels were reported at 18.94% and 18.68% respectively. The lowest reported educational level was women with a higher level of education, representing 5.59% of the respondents. When considering the place of delivery, the majority of women reported giving birth outside of a hospital setting, constituting 61.02% of the respondents. In contrast, 38.98% of women stated that they delivered in a hospital. In terms of residential location, a higher percentage of women resided in rural areas compared to those residing in urban areas, accounting for 65.64% and 34.36% respectively. Examining the regional distribution of women, it was found that a higher percentage of women resided in the North-west and North-east regions, constituting 47.38% and 19.95% respectively. Following this, women residing in the North-central and South-west regions accounted for 10.43% and 8.84% respectively. Women residing in the South-south region were reported at 7.08%, while the lowest reported region was the South-east, representing 6.32% of the respondents. In terms of religious affiliation, the majority of women identified as Muslims, accounting for 72.85% of the sample. Christian women constituted 26.73% of the respondents, while a small percentage of women identified as traditionalists, representing 0.42% of the sample. Regarding internet usage, a higher percentage of women reported never using the internet, representing 93.52% of the sample. In contrast, 6.48% of women reported using the internet. Considering wealth status, the majority of women were classified as poor, accounting for 52.49% of the respondents. Rich women represented 27.91% of the sample, while those classified as middle wealth status were reported at 19.69%. Lastly, when examining the experience of perinatal mortality, a higher percentage of women reported not having experienced perinatal mortality, representing 72.16% of the sample. In contrast, 27.84% of women reported having experienced perinatal mortality. Bivariate Analysis Table 2 presents the distribution of women in Nigeria based on their migration status and different migration patterns. Examining the migration status of women, the majority, accounting for 54.59% of the weighted sample, reported themselves as non-migrants. On the other hand, 45.41% of women reported that they are migrants, totaling approximately 15,024 women. Among the 15,024 women who identified themselves as migrants, the distribution of various migration patterns was as follows: rural-rural migration pattern was the most frequently reported, accounting for 44.40% of the migrant population. The second most common migration pattern was urban-urban, reported by 28.23% of the migrant population. Urban-rural migration pattern was reported by 17.23% of the migrants, while rural-urban migration pattern was the least reported, accounting for 10.14% of the migrant population. Table 3 provides an analysis of the socio-demographic factors associated with perinatal mortality among women in Nigeria. The results indicate significant associations between women's age and perinatal mortality (χ2=101.56, P<0.001). Women between the ages of 35-39 years and 30-34 years appear to be at higher risk of perinatal mortality in Nigeria, accounting for 23.46% and 20.76% respectively. This is followed by women aged 40-44 years and 45-49 years, with rates of 18.21% and 17.39% respectively. Women aged 25-29 years and 20-24 years have lower rates of perinatal mortality at 14.21% and 5.14% respectively, while women aged 15-19 years have the lowest reported rate at 0.83%. Ethnicity is significantly associated with perinatal mortality (χ2=72.05, P<0.001). Women from the Hausa/Fulani ethnic group tend to be at higher risk of perinatal mortality compared to women from other ethnic groups, with rates of 53.49% and 28.01% respectively. Women from the Igbo ethnic group have a rate of 9.55%, while women from the Yoruba ethnic group have the lowest reported rate at 8.95%. Exposure to mass media is also significantly associated with perinatal mortality (χ2=88.52, P<0.001). Women who are not exposed to mass media tend to be at higher risk of perinatal mortality compared to women who are exposed, with rates of 62.61% and 37.39% respectively. The level of education of women is significantly associated with perinatal mortality (χ2=147.13, P<0.001). Women with no formal education have a higher risk of perinatal mortality at 52.37%. Similarly, women with secondary and primary education levels have higher rates of perinatal mortality (21.88% and 18.75%) compared to women with higher education levels (7.00%). Place of residence is also associated with perinatal mortality (χ2=69.38, P<0.001). Women residing in rural areas tend to be at higher risk of perinatal death compared to women residing in urban areas, with rates of 62.1% and 37.87% respectively. The region of women in Nigeria is significantly associated with perinatal mortality (χ2=115.49, P<0.001). Women from the North-west and North-east regions have higher rates of perinatal mortality at 42.90% and 21.19% respectively. Women from the North-central and South-west regions have rates of 10.69% and 9.86% respectively, while women from the South-south region have a rate of 8.15%. The South-east region has the lowest reported rate of perinatal mortality at 7.214%. Religion is associated with perinatal mortality (χ2=82.59, P<0.05). Muslim women face higher risks (69.42%), while Christian women have a higher risk than other religious groups (30.26% vs. 0.32%). Internet usage is significantly associated with perinatal mortality (χ2=120.89, P<0.001). Non-users face higher risks (91.12%) compared to users (8.88%). Wealth status is significantly associated with perinatal mortality (χ2=161.98, P<0.001). Poor women face higher risks (47.91%), and rich women have higher risks than middle wealth status (32.84% vs. 19.25%). These findings highlight the importance of considering socio-demographic factors in understanding perinatal mortality trends and developing targeted interventions to reduce perinatal mortality rates in Nigeria. Table 4 below shows the chi-square analysis to examine the association between female migration streams and migration status in relation to perinatal mortality among women in Nigeria. The chi-square statistic (χ2) for migration pattern is 17.65, indicating a statistically significant association between migration pattern and perinatal mortality. This suggests that the likelihood of perinatal mortality varies significantly based on the specific migration pattern. Also, the chi-square statistic (χ2) for migration status is 28.57, indicating a statistically significant association between migration status and perinatal mortality. This suggests that there is a significant difference in perinatal mortality rates between migrants and non-migrants. Table 5 presents the binary logistic regression models analyzing the association between various female migration patterns, migration status, socio-demographic characteristics, and perinatal mortality among women in Nigeria. The significance levels are indicated by p-values of <0.05, 0.01, and 0.001. Two models were employed: Model 1, which was unadjusted, focused on the relationship between female migration pattern, migration status, and perinatal mortality in Nigeria. Model 2, adjusted for socio-demographic characteristics, aimed to determine the relationship between these factors and perinatal mortality. In Model 1, the results indicate that women who migrated from rural to urban areas were 1.03 times more likely to be at risk of perinatal mortality compared to those who migrated from rural to rural places (OR=1.03, CI=0.76-1.40). Similarly, women who migrated from urban to rural areas had a 1.06 times higher risk of perinatal mortality compared to rural-rural migrants (OR=1.06, CI=0.87-1.30). Additionally, women who migrated from urban to urban areas were 1.19 times more likely to face perinatal mortality risk than rural-rural migrants in Nigeria (OR=1.19, CI=0.99-1.44). Conversely, women who did not migrate had a 0.87 times lower likelihood of perinatal mortality risk compared to migrants (OR=0.87, CI=0.79-0.97). In Model 2, after adjusting for migration status and socio-demographic characteristics, a significant relationship between these factors and perinatal mortality was observed. Women who migrated from rural to urban areas were 0.98 times less likely to experience perinatal death than rural-rural migrants (OR=0.99, CI=0.63-1.53). On the other hand, urban-rural migrants had a 1.07 times higher likelihood of perinatal death compared to rural-rural migrants (OR=1.07, CI=0.85-1.33). Similarly, women who migrated from urban to urban areas had a 1.07 times higher risk of perinatal death than rural-rural migrants in Nigeria (OR=1.07, CI=0.82-1.39). Furthermore, non-migrant women were 0.95 times less likely to face perinatal mortality compared to migrant women (OR=0.95, CI=0.85-1.05). Furthermore, the analysis revealed additional factors associated with perinatal mortality. Women who reported that access to healthcare is not a significant problem were 0.86 times less likely to be at risk of perinatal death compared to those who perceived it as a major issue (OR=0.86, CI=0.75-0.98). Moreover, women who had more than four antenatal care visits had a 1.62 times higher likelihood of perinatal death compared to those with less than four visits (OR=1.62, CI=0.96-2.74). Age was also found to be a contributing factor. Women between the ages of 20-24 years and 25-29 years were 0.88 times and 0.90 times less likely to experience perinatal death, respectively, compared to women aged 15-19 years (OR=0.88, CI=0.61-1.27; and OR=0.90, CI=0.63-1.31). Similarly, women in the age groups 30-34 years and 35-39 years had a lower risk of perinatal mortality, with odds ratios of 0.98 (CI=0.68-1.41) and 0.89 (CI=0.61-1.30), respectively, compared to the reference group of women aged 15-19 years. Additionally, women aged 40-44 years and 45-49 years had a decreased likelihood of perinatal mortality, with odds ratios of 0.76 (CI=0.53-1.12) and 0.70 (CI=0.47-1.04), respectively, compared to women aged 15-19 years. Lastly, women who stated that access to health facilities was not a significant problem were 0.72 times less likely to face perinatal death compared to those who identified it as a major barrier (OR=0.72**, CI=0.60-0.87). Analysis of the working status of women revealed noteworthy findings. Women who are currently employed were found to be 1.08 times more likely to be at risk of perinatal mortality compared to those who are not working (OR=1.08, CI=0.93-1.25). Additionally, women belonging to the Igbo and Other ethnic groups exhibited a decreased risk of perinatal mortality, with odds ratios of 0.90 (CI=0.60-1.36) and 0.92 (CI=0.75-1.56), respectively, in comparison to women from the Hausa/Fulani ethnic group. Conversely, women from the Yoruba ethnic group had a slightly higher likelihood of perinatal death, with an odds ratio of 1.04 (CI=0.70-1.56), compared to women from the Hausa/Fulani ethnic group. Furthermore, women who reported no exposure to mass media were 1.13 times more likely to be at risk of perinatal mortality than women who were exposed to mass media (OR=1.13, CI=0.99-1.30). Additionally, women who delivered in a hospital setting had a 1.15 times higher likelihood of perinatal mortality than women who did not give birth in a hospital (OR=1.15, CI=1.00-1.32). On the other hand, women residing in rural areas had a decreased risk of perinatal mortality, with an odds ratio of 0.80 (CI=0.71-0.92), compared to women residing in urban areas. An examination of the regional distribution of women revealed significant insights. Women from the North-East region were found to be 1.27 times more likely to be at risk of perinatal mortality compared to those from the North-Central region (OR=1.27, CI=0.97-1.67). Conversely, women from the North-West and South-East regions exhibited a decreased risk of perinatal mortality, with odds ratios of 0.97 (CI=0.72-1.31) and 0.97 (CI=0.50-1.20), respectively, in comparison to women from the North-Central region. Similarly, women from the South-South and South-West regions had lower odds of perinatal mortality, with odds ratios of 0.93 (CI=0.70-1.28) and 0.81 (CI=0.54-1.22), respectively, compared to women from the North-Central region. Regarding religious affiliation, Muslim and traditional women displayed reduced likelihoods of perinatal mortality, with odds ratios of 0.86 (CI=0.71-1.05) and 0.78 (CI=0.29-2.15), respectively, in comparison to women from the Christian religion. Additionally, women with primary and higher levels of education were less likely to experience perinatal mortality, with odds ratios of 0.93 (CI=0.73-1.18) and 0.97 (CI=0.67-1.41), respectively, compared to women with no formal education. Conversely, women with a secondary level of education exhibited a slightly higher likelihood of perinatal mortality, with an odds ratio of 1.06 (CI=0.82-1.38), compared to those with no formal education. Furthermore, women who reported internet usage were 1.23 times more likely to be at risk of perinatal mortality than those who had never used the internet (OR=1.23, CI=0.91-1.68). Lastly, women with middle and rich wealth statuses demonstrated lower odds of perinatal mortality, with odds ratios of 0.98 (CI=0.78-1.26) and 1.36 (CI=1.05-1.77), respectively, compared to women from poor wealth status. Discussion The study conducted a thorough examination of female migration patterns and their association with perinatal mortality in Nigeria. The research aimed to shed light on the prevalence of perinatal mortality among migrant women, the proportion of women who are migrants, and the potential link between female migration and perinatal mortality in Nigeria. The findings revealed several key insights that are vital for understanding the complex relationship between migration, socio-demographic factors, and perinatal mortality. Prevalence of Perinatal Mortality One of the most striking findings of this study is the high prevalence of perinatal mortality in Nigeria, which stands at 27.84%. This alarming rate underscores the urgency of addressing perinatal mortality as a major public health concern. Perinatal mortality is not only a significant indicator of maternal and child health but also a substantial contributor to under-five mortality rates. This finding aligns with previous research by ( 16 ) in Sub-Saharan Africa, which highlighted that many countries in the region face challenges related to inadequate healthcare services, conflict, and weak healthcare systems, resulting in a surge in perinatal mortality. Conflict, in particular, has been identified as a barrier to facility-based deliveries and has led to the destruction of health infrastructure and a shortage of skilled healthcare personnel, thereby increasing the risk of perinatal mortality due to unassisted births and a lack of timely emergency perinatal services ( 16 ). Migration Patterns and Perinatal Mortality The study's analysis of migration patterns revealed important associations with perinatal mortality. Women who migrated from urban to urban areas were found to be 0.78 times less likely to experience perinatal death compared to those who migrated from rural to rural locations. This result suggests that urban areas may offer better access to healthcare facilities and services, contributing to improved perinatal outcomes. The migrant adaptation theory also plays a role, as migrants tend to adopt urban lifestyles and healthcare-seeking behaviors over time, similar to urban non-migrants ( 17 ). Conversely, women who followed migration patterns from urban to rural or urban to urban areas exhibited a higher risk of perinatal death. Urban-rural and urban-urban migration were associated with 1.01 times and 1.07 times higher odds of perinatal mortality than rural-rural migration, indicating that migration can disrupt social networks and support systems, leading to reduced access to healthcare and potentially contributing to perinatal mortality. Urban non-migrant women had a lower risk of perinatal mortality compared to women who had a rural to rural migration pattern, emphasizing the importance of social networks and support for pregnant women. Socio-Demographic Factors and Perinatal Mortality The study also uncovered several socio-demographic factors associated with perinatal mortality. Notably, women who reported that access to healthcare was not a significant problem had a lower risk of perinatal death. This finding underscores the importance of improving healthcare access, especially in rural areas where access may be limited ( 18 , 19 ). Impact of Maternal Age The study observed a significant association between maternal age and perinatal mortality. As women get older from the age group 20–24 years to 45–49 years, they are more likely at increased risk of perinatal death compared to younger mothers ( 15 – 19 ). This finding aligns with the work of Ezeh and colleagues, who also noted that increasing maternal age is associated with better perinatal outcomes ( 20 ). A possible explanation for this could be related to medical risk factors, such as antepartum hemorrhage, hypertension, operative vaginal delivery and gestational diabetes which were previously reported to be associated with advanced maternal age ( 21 ). Role of Education and Media Exposure Education emerged as a crucial determinant of perinatal mortality, with women having secondary and higher levels of education exhibiting a lower risk of perinatal death. This finding is consistent with the research conducted by ( 22 ), which emphasized the importance of maternal education in child survival. The study also highlighted that women who are not exposed to mass media are more at risk of perinatal deaths than women who are exposed to mass media. The finding of this study is dissimilar with existing studies of Girma and colleagues, who emphasized that media plays a significant role in disseminating vital healthcare information and promoting positive health behaviors ( 23 ). The differences observed in this study could be due to the nature of the data that was used in understanding the association between perinatal death and media exposure. Wealth Status The study uncovered that women from middle and rich wealth statuses experience lower risk of perinatal mortality as compared to women who are poor as their economic factor. These findings are in agreement with Akombi and Renzaho, who noted that perinatal mortality was highest in Nigeria among west African countries and second highest in Sub-Saharan Africa due to the high level of poverty experienced in the country ( 16 ). Similarly, Mmusi-Phetoe (2016), poverty is an underlying factor for poor birth outcomes among women ( 24 ). Hence, it is important that poverty being a multidimensional phenomenon be addressed to curb the undesired risk of perinatal mortality. Religion and Its Influence The study found that women of Muslim and traditional religions had different perinatal mortality risk profiles compared to Christian women. This observation may be related to cultural practices, such as early marriage in the northern part of Nigeria among Muslim communities which have allowed children not matured enough to either take care of themselves nor the pregnancy they have as noted by Envuladu and colleagues ( 25 ). These findings underscore the need for culturally sensitive interventions to address perinatal mortality disparities across religious groups. Implications and Policy Recommendations The outcomes of this study hold crucial implications for healthcare policies in Nigeria, specifically in addressing the prevalent issue of perinatal mortality. Policymakers are urged to consider a multifaceted approach to tackle this challenge effectively. Foremost among the recommendations is the promotion of maternal education, emphasizing the need to encourage girls' education and enhance women's literacy. This approach is seen as pivotal in empowering women with the knowledge and decision-making capabilities essential for optimal maternal and child health outcomes. In addition, the study suggests the intensification of health education programs and media campaigns, focusing on disseminating critical information related to maternal and child health practices. Key aspects include emphasizing the significance of antenatal care, facility-based delivery, and postnatal care. The importance of community-based support systems is highlighted, especially for pregnant women, with a particular focus on providing emotional and social support, particularly for migrants who may lack access to their original support structures. Furthermore, the study emphasises the need to address regional disparities in perinatal mortality rates. Tailored interventions are recommended, particularly for vulnerable regions like the North-East, necessitating targeted efforts to improve healthcare infrastructure and outcomes. Lastly, strategies aimed at reducing wealth-related disparities in perinatal mortality should be a priority, ensuring that economically disadvantaged women have equitable access to healthcare services through targeted interventions. Limitations Firstly, the retrospective nature of the survey may introduce recall bias, as respondents might not accurately remember past events or experiences. Additionally, migration status was derived from the available variables in the dataset, which could potentially introduce measurement error. The cross-sectional design of the study also limits the ability to establish causality between variables, as it only provides a snapshot at a specific point in time. Furthermore, some variables of interest were not collected in the NDHS dataset, which constrains the scope of analysis and may limit the depth of the findings. Further Research Further research is needed to explore the unexpected findings related to hospital delivery and antenatal care visits. Qualitative studies and in-depth investigations may provide insights into the reasons behind these associations. Conclusion This study provides critical insights into the relationship between female migration patterns and perinatal mortality in Nigeria. Our findings highlight the significant role of migration status in shaping maternal and newborn health outcomes. Specifically, rural-urban migrants were found to have lower perinatal mortality rates than rural-rural migrants, underscoring the potential benefits of urban healthcare access and socio-economic opportunities. However, disruptions in social support systems among urban-rural and urban-urban migrants were linked to an increased risk of perinatal mortality, emphasizing the complexities of migration and health. Beyond migration, the study identifies key socio-demographic factors influencing perinatal mortality. Limited healthcare access, lower maternal education, poverty, and lack of media exposure were strongly associated with higher perinatal mortality rates. Additionally, cultural and religious influences, particularly early marriage and restricted healthcare autonomy among certain groups, further exacerbated risks. These findings reinforce the urgent need for targeted interventions that address not only healthcare accessibility but also education, economic empowerment, and culturally sensitive maternal health programs. Abbreviations ● CI Confidence Interval ● DHS Demographic and Health Survey ● EA Enumeration Area ● ENND Early Neonatal Death ● FCT Federal Capital Territory ● LMICs Low–and Middle–Income Countries ● NDHS Nigeria Demographic and Health Survey ● OR Odds Ratio ● PPS Probability Proportional to Size ● PM Perinatal Mortality ● PMR Perinatal Mortality Rate ● SDG Sustainable Development Goal ● WHO World Health Organization Declarations The data used in this study were obtained from the 2018 Nigeria Demographic and Health Survey (NDHS), a publicly available dataset. Ethical approval for the survey was granted by the National Health Research Ethics Committee of Nigeria and the ICF Institutional Review Board. Written informed consent was obtained from all adult participants, and for participants under 18 years of age, consent was provided by their parent(s) or legal guardian(s) prior to participation. Ethics Approval and Consent to Participate This study was based on secondary data analysis of the 2018 Nigeria Demographic and Health Survey (NDHS), which was publicly available and de-identified. Ethical approval for the original NDHS survey was obtained from the National Health Research Ethics Committee of Nigeria (NHREC) and the Institutional Review Board of ICF International. Written informed consent was obtained from all participants by the NDHS survey team before data collection. Given that this study utilized secondary data with no direct involvement of human participants, additional ethical approval was not required. Also, since the study is not a Clinical trial. Consent for Publication Not applicable. This study does not include identifiable individual participant data. Availability of Data and Materials The dataset used in this study is publicly available through the Demographic and Health Surveys (DHS) Program and can be accessed upon request via the MEASURE DHS website: https://www.dhsprogram.com/data/available-datasets.cfm. The STATA DO-File for all analyses performed in this study can be assessed here Competing Interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency, commercial, or not-for-profit sectors. Authors' Contributions ● Opeyemi Agboola & Sunday Tuwatimi: Conceptualized the study, performed data analysis, and wrote the manuscript. ● Opeyemi Agboola, Sunday Tuwatimi & Lalita Kumari Sah: Contributed to study design, data interpretation, and manuscript revision. ● Rajeeb Kumar Sah & Lalita Kumari Sah: Provided critical feedback, reviewed statistical methods, and revised the final manuscript. All authors read and approved the final manuscript. Acknowledgements The authors appreciate the DHS Program for granting access to the 2018 NDHS dataset. References World Health Organization. Neonatal and Perinatal Mortality: Country, Regional and Global Estimates. Geneva: WHO; 2006. World Health Organization. Neonatal and perinatal mortality: country, regional, and global estimates. Geneva: WHO; 2006. doi:10.2471/BLT.15.168047. Leon B, Suswardany D, Michener K, Tarigan I, Suhaemi A, Siregar A. Utility of local health registers in measuring perinatal mortality: A case study in rural Indonesia. BMC Pregnancy Childbirth. 2011;11(20). Lawn J, Shibuya K, Stein C. No cry at birth: Global estimates of intrapartum stillbirths and intrapartum-related neonatal deaths. Bull World Health Organ. 2005;83:409–17. Gausia K, Moran AC, Ali M, Ryder D, Fisher C, Koblinsky M. Psychological and social consequences among mothers suffering from perinatal loss: perspective from a low-income country. BMC Public Health. 2011;11:1-9. United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). A Neglected Tragedy: The global burden of stillbirths. 2020. Lawn JE, Yakoob MY, Haws RA, Soomro T, Darmstadt GL, Bhutta ZA. 3.2 million stillbirths: epidemiology and overview of the evidence review. BMC Pregnancy Childbirth. 2009;9:1-17. National Population Commission. Nigeria Demographic and Health Survey 2018. NPC, ICF; 2019. Oji S, Odimegwu C. Perinatal mortality in Nigeria: do place of delivery and delivery assistants matter? Open Demogr J. 2011;4:110. Adimora GN, Odetunde IO. Perinatal mortality in University of Nigeria Teaching Hospital (UNTH) Enugu at the end of the last millennium. Niger J Clin Pract. 2007;10:19–23. Suleiman MB, Mokuolu OA. Perinatal mortality in a Northwestern Nigerian city: A wake-up call. Front Pediatr. 2014;2:105. United Nations. Human Development Report 2009: Overcoming Barriers: Human Mobility and Development. New York: United Nations Development Program; 2009. World Bank. World Development Report 2009: Reshaping Economic Geography. Washington, DC: The World Bank; 2009. World Bank. From Oil to Cities: Nigeria’s Next Transformation. Directions in Development. Washington, DC: World Bank; 2016. doi:10.1596/978-1-4648-0792-3. Goldin I, Reinert KA. Globalization for development: trade, finance, aid, migration, and policy. Washington, DC: World Bank Publications; 2006. Akombi BJ, Renzaho AM. Perinatal mortality in sub-Saharan Africa: a meta-analysis of demographic and health surveys. Ann Glob Health. 2019;85(1). Robert S, De-jong GF, Stokes CS. The effect of female migration on infant and child survival in Uganda. Popul Res Policy Rev. 2002;21:403–31. De Haas H. The impact of international migration on social and economic development in Moroccan sending regions: A review of the empirical literature. IMI Working Paper No. 3. Oxford: International Migration Institute, University of Oxford; 2007. Berhan Y, Berhan A. Commentary: Reasons for persistently high maternal and perinatal mortalities in Ethiopia: Part III—Perspective of the "three delays" model. Ethiop J Health Sci. 2014;24 Suppl(0 Suppl):137–48. PMID: 25489188; PMCID: PMC4249213. Ezeh OK, Uche-Nwachi EO, Abada UD, Agho KE. Community- and proximate-level factors associated with perinatal mortality in Nigeria: evidence from a nationwide household survey. BMC Public Health. 2019;19:1-9. Bhandari S, Raja EA, Shetty A, Bhattacharya S. Maternal and perinatal consequences of antepartum haemorrhage of unknown origin. BJOG. 2014;121(1):44-52. Issaka AI, Agho KE, Renzaho AM. Correction: The impact of internal migration on under-five mortality in 27 sub-Saharan African countries. PLoS One. 2017;12(2):e0171766. Girma D, Abita Z, Fetene G, Birie B. Individual and community-level factors of perinatal mortality in the high-mortality regions of Ethiopia: a multilevel mixed-effect analysis. BMC Public Health. 2022;22(1):247. Mmusi-Phetoe RM. Social factors determining maternal and neonatal mortality in South Africa: A qualitative study. Curationis. 2016;39(1):1–8. doi:10.4102/curationis.v39i1.1571. Envuladu E, Umaru R, Iorapuu N, Osagie I, Okoh E, Zoakah A. Determinants and effect of girl child marriage: A cross-sectional study of school girls in Plateau State, Nigeria. Int J Med Biomed Res. 2016;5(3):122-9. Tables Table 1: Distribution of Respondents by Socio-Demographic Characteristics and perinatal mortality. Characteristics Weighted Frequency (n= 33,083) Percent (100%) Age 15-19 20-24 25-29 30-34 35-39 40-44 45-49 260 1,717 4 ,453 6,172 7,371 6,452 6,657 0.79 5.19 13.46 18.66 22.28 19.50 20.12 Access/ distance to health facility Big problem Not a big problem 9,178 23,905 22.74 77.26 Antenatal care visits Less than 4 4 and above 32,385 698 97.89 2.11 Currently working No Yes 8,628 24,455 26.08 73.92 Ethnicity Hausa/Fulani Igbo Yoruba Others 18,797 2,741 2,614 8,931 56.82 8.28 7.90 27.00 Exposure to mass media Not exposed Exposed 13,731 19,352 41.55 58.45 Level of education No education Primary Secondary Higher 18,788 6,179 6,267 1,848 56.79 18.68 18.94 5.59 Place of delivery Not in hospital Hospital 20,187 12,896 61.02 38.98 Place of residence Urban Rural 11,366 21,717 34.36 65.64 Region North central North east North west South east South south South west 3,450 6,599 15,674 2,092 2,341 2,926 10.43 19.95 47.38 6.32 7.08 8.84 Religion Christian Muslim Traditional 8,843 24,101 139 26.73 72.85 0.42 Use of internet Never Used 30,939 2,144 93.52 6.48 Wealth status Poor Middle Rich 17,364 6,485 9,234 52.49 19.60 27.91 Perinatal mortality No Yes 19,757 13,326 72.16 27.84 Total 33,038 100 Table 2: Distribution of Respondents by migration status and migration pattern Characteristics Weighted Frequency (N= 33,083) Percent (%) Status Migrant Non-migrant 15,024 18,059 45.41 54.59 Migration pattern Rural-rural Rural-urban Urban-rural Urban-urban 6,670 1,523 2,588 4,243 44.40 10.14 17.23 28.23 Table 3: Chi-square analysis of women socio-demographics by perinatal mortality in Nigeria. Characteristics PERINATAL MORTALITY Statistics χ 2 NO Frequency and percentage (%) YES Frequency and percentage (%) P value (χ 2 ) Age 15-19 20-24 25-29 30-34 35-39 40-44 45-49 255 (00.77) 1724 (05.21) 4357 (13.17) 5905 (17.85) 7222 (21.83) 662 (02.00) 7004 (21.17) 275 (00.83) 1700 (05.14) 4701 (14.21) 6868 (20.76) 7761 (23.46) 16024 (8.21) 5753 (17.39) 101.56 P<0.001 Access/ distance to health facility Big problem Not a big problem 9061 (27.39) 24022 (72.61) 9482 (28.66) 23601 (71.34) 5.35 Antenatal care visits Less than 4 4 and above 23441 (98.06) 642 (01.94) 32236 (97.44) 847 (02.56) 0.15 Currently working No Yes 8856 (26.77) 24227 (73.23) 8036 (24.29) 25047 (75.71) 21.11 Ethnicity Hausa/Fulani Igbo Yoruba Others 19221 (58.10) 2580 (07.80) 2478 (07.49) 8803 (26.61) 17696 (53.49) 3159 (09.55) 2961 (08.95) 9267 (28.01) 72.05 P<0.01 Exposure to mass media Exposed Not exposed 14255 (43.09) 18828 (56.91) 12370 (37.39) 20713 (62.61) 88.52 P<0.001 Level of education No education Primary Secondary Higher 19354 (58.50) 6170 (18.65) 5892 (17.81) 1667 (05.04) 17326 (52.37) 6203 (18.75) 7239 (21.88) 2316 (07.00) 147.13 P<0.001 Place of delivery Not in hospital Hospital 20505 (61.98) 12578 (38.02) 19406 (58.66) 13677 (41.34) 6.78 Place of residence Urban Rural 10917 (33.00) 22166 (67.00) 12529 (37.87) 20554 (62.13) 69.38 P<0.001 Region North central North east North west South east South south South west 3417 (10.33) 6441 (19.47) 16247 (49.11) 1978 (05.98) 2203 (06.66) 2796 (08.45) 3537 (10.69) 7010 (21.19) 14193 (42.90) 2385 (07.21) 2696 (08.15) 3262 (09.86) 115.49 P<0.001 Religion Christian Muslim Traditional 8393 (25.37) 24538 (74.17) 152 (00.46) 10012 (30.26) 22966 (69.42) 106 (00.32) 82.59 P<0.001 Use of internet Never Used 31246 (94.45) 1836 (05.55) 30145 (91.12) 2938 (08.88) 120.89 P<0.001 Wealth status Poor Middle Rich 17948 (54.25) 6531 (19.74) 8605 (26.01) 8605 (47.91) 6368 (19.25) 10864 (32.84) 161.98 P<0.001 *p<0.05, **p<0.01, ***p<0.001 Table 4: Chi-square analysis of female migration streams and migration status by perinatal mortality among women in Nigeria Characteristics PERINATAL MORTALITY Statistics NO Frequency and percentage (%) YES Frequency and percentage (%) χ 2 Migration pattern Rural-rural Rural-urban Urban-rural Urban-urban Rural non-migrant Urban non-migrant 6742(20.38) 1512(04.57) 2554(07.72) 4026(12.17) 12889(38.98) 5353(16.18) 6445(19.48) 1555(04.70) 2696(08.15) 4923(14.88) 11096(33.54) 6368(19.25) 120.01 P<0.001 Status Migrants Non-migrants 14722 (44.50) 18361 (55.50) 15807 (47.78) 17276 (52.22) 13.78 *p<0.05, **p<0.01, ***p<0.001 Table 5: Odds ratio based on Binary Logistic Regression Analysis of Female Migration Status, Migration pattern and Socio-demographics by Perinatal Mortality in Nigeria. Characteristics Model 1 Model 2 Perinatal mortality Perinatal mortality Odds Ratio Lower-Upper Confidence Interval Odds Ratio Lower-Upper Confidence Interval Migration streams Rural-rural Rural-urban Urban-rural Urban-urban Rural non-migrant Urban non-migrant RC 0.93 0.91 0.78* 1.11 0.80* 0.70-1.24 0.74-1.11 0.65-0.95 0.96-1.28 0.67-0.97 RC 1.10 1.02 1.01 1.07 0.94 0.80-1.52 0.83-1.25 0.80-1.27 0.93-1.24 0.75-1.18 Age 15-19 20-24 25-29 30-34 35-39 40-44 45-49 RC 1.05 1.05 1.01 1.20 1.27 1.43 0.71-1.56 0.71-1.53 0.68-1.48 0.74-1.64 0.85-1.89 0.94-2.19 Ethnicity Hausa/Fulani Igbo Yoruba Others RC 1.02 0.89 1.04 0.68-1.54 0.60-1.32 0.84-1.28 Exposure to mass media Exposed Not exposed RC 0.91 0.79-1.04 Place of residence Urban Rural RC 1.01 0.71-1.22 Religion Christian Muslim Traditional RC 1.15 1.28 0.94-1.39 0.41-4.02 Level of education No education Primary Secondary Higher RC 1.02 0.94 0.91 0.86-1.22 0.77-1.56 0..67-1.22 Region North central North east North west South east South south South west RC 0.85 1.16 1..04 1.05 1.31 0.68-1.06 0.92-1.47 0.68-1.57 0.82-1.34 0.92-1.86 Use of internet Never Used RC 0.83 0.65-1.04 Wealth status Poor Middle Rich RC 0.93 0.78 0.78-1.12 0.64-0.96 *p<0.05, **p<0.01, ***p<0.001. 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The World Health Organization (WHO) defines perinatal mortality as the sum of stillbirths and early neonatal deaths per 1,000 total births, commencing at 22 completed weeks (154 days) of gestation (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Studies indicate that perinatal mortality is influenced by the quality of healthcare received by pregnant women and their infants (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Beyond statistics, it has serious economic, psychological, and social consequences for mothers and families (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe United Nations' Sustainable Development Goal 3 (SDG 3) aims to ensure healthy lives and promote well-being for all ages. However, the global burden of perinatal mortality remains high, with approximately 2\u0026nbsp;million stillbirths and 1.8\u0026nbsp;million early neonatal deaths recorded in 2019 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This burden is particularly pronounced in low- and middle-income countries (LMICs), notably sub-Saharan Africa and South Asia (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Nigeria, like many LMICs, faces significant challenges in reducing perinatal mortality.\u003c/p\u003e\u003cp\u003eData from the 2018 Nigeria Demographic and Health Survey (NDHS) highlight a concerning trend: while infant and under-five mortality rates have declined, perinatal mortality has increased over the same period (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Studies conducted within Nigerian health facilities also report consistently high perinatal mortality rates (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), posing a major obstacle to achieving SDG 3.2 targets. Understanding the factors driving this trend is critical. One emerging factor is population mobility, or migration, which significantly influences livelihoods, economic opportunities, and healthcare access (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMigration, particularly rural-urban movement, has played a key role in Nigeria\u0026rsquo;s urbanization, driven by limited employment opportunities in rural areas and the lure of higher incomes in cities (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Women, in particular, migrate frequently within Nigeria, seeking improved opportunities (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, while previous research has linked migration patterns to child mortality, its impact on perinatal mortality remains underexplored. This study aims to fill this gap by investigating how female migration patterns influence perinatal mortality in Nigeria. By doing so, it contributes to a deeper understanding of this critical public health issue and informs targeted maternal and child health interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy Design\u003c/h2\u003e\n\u003cp\u003eThis study utilizes secondary data from the 2018 Nigeria Demographic and Health Survey (NDHS) to examine the relationship between female migration patterns and perinatal mortality in Nigeria. The NDHS survey follows a descriptive research design, specifically a sample survey approach, aimed at providing a comprehensive understanding of naturally occurring phenomena. Data were systematically collected from a nationally representative sample using structured questionnaires.\u003c/p\u003e\n\u003ch2\u003eSample Size and Data Source\u003c/h2\u003e\n\u003cp\u003eThe study\u0026apos;s weighted sample size consists of 33,083 women of reproductive age (15\u0026ndash;49 years) who participated in the NDHS 2018. These women were drawn from all 36 states and the Federal Capital Territory (FCT) of Abuja, covering Nigeria\u0026apos;s six geopolitical zones. The NDHS dataset is a robust source for demographic, socioeconomic, and health-related information.\u003c/p\u003e\n\u003ch3\u003eSampling Method\u003c/h3\u003e\n\u003cp\u003eThe NDHS survey employed a two-stage stratified sampling design. In the first stage, enumeration areas (EAs) were selected using probability proportional to size (PPS). In the second stage, households were systematically selected within each EA, ensuring adequate national representation.\u003c/p\u003e\n\u003ch3\u003eVariable Measurement\u003c/h3\u003e\n\u003ch4\u003eDependent Variable\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003ePerinatal Mortality (PM):\u0026nbsp;\u003c/strong\u003eDefined as the sum of stillbirths (fetal deaths) and early neonatal deaths (ENND)\u0026mdash;the latter referring to live newborns who die within the first seven completed days of life. The Perinatal Mortality Rate (PMR) is calculated as:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003ch4\u003eAdditional Independent Variables (Confounders)\u003c/h4\u003e\n\u003cp\u003eKey covariates include age, access to healthcare, current employment status, ethnicity, exposure to mass media, education level, occupation, region, religion, wealth status, and internet usage.\u003c/p\u003e\n\u003ch3\u003eData Analysis\u003c/h3\u003e\n\u003cp\u003eStatistical analysis was performed using STATA version 12.0, following a three-step approach:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eUnivariate Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e○\u0026nbsp; \u0026nbsp;\u0026nbsp;Descriptive statistics (frequencies, percentages, tables) were used to summarize socio-demographic characteristics.\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eBivariate Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e○\u0026nbsp; \u0026nbsp;\u0026nbsp;Chi-square (\u0026chi;\u0026sup2;) tests were conducted to assess relationships between perinatal mortality and migration status, alongside other socio-demographic variables.\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eMultivariate Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e○ \u0026nbsp; \u0026nbsp;Binary logistic regression was employed to estimate odds ratios (ORs), examining the association between perinatal mortality and independent variables while controlling for potential confounders.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eUnivariate Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of the Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Table 1 below presents the socio-demographic characteristics of women aged 15-49 and perinatal mortality. The age distribution of the respondents highlights that the majority of women fell into the 35-39 years age group, accounting for 22.28% of the sample. The next most prevalent age group was 45-49 years, comprising 20.12% of the respondents. Following closely were women aged 40-44 years and 30-34 years, representing 19.50% and 18.66% respectively. Women in the age groups of 25-29 years and 20-24 years accounted for 13.46% and 5.19% respectively, while those between 15-19 years were the least represented, making up only 0.79% of the sample.\u003c/p\u003e\n\u003cp\u003eRegarding access to healthcare facilities, a significant proportion of women reported that it was not a problem, constituting 77.26% of the respondents. In contrast, 22.74% of the women indicated that accessing healthcare was a significant challenge for them. The distribution of respondents based on the number of antenatal care visits revealed that the majority of women (97.89%) had less than four visits, while only a small proportion (2.11%) reported having four or more visits.\u003c/p\u003e\n\u003cp\u003eSimilarly, the data indicates that a higher percentage of women in the study were currently employed compared to those who were not working, accounting for 73.92% and 26.08% respectively. In terms of ethnicity, Hausa/Fulani women were the most represented group, constituting 56.82% of the sample. This was followed by women from other ethnic groups and the Igbo ethnic group, comprising 27.00% and 8.28% respectively. The Yoruba ethnic group had the lowest representation, accounting for 7.90% of the respondents.\u003c/p\u003e\n\u003cp\u003eRegarding exposure to mass media, a majority of women reported being exposed, representing 58.45% of the sample. In contrast, 41.55% of women indicated that they were not exposed to mass media. The distribution of respondents based on educational attainment revealed that the highest proportion of women had no formal education, accounting for 56.78% of the sample. Following this, women with secondary education and primary education levels were reported at 18.94% and 18.68% respectively. The lowest reported educational level was women with a higher level of education, representing 5.59% of the respondents.\u003c/p\u003e\n\u003cp\u003eWhen considering the place of delivery, the majority of women reported giving birth outside of a hospital setting, constituting 61.02% of the respondents. In contrast, 38.98% of women stated that they delivered in a hospital. In terms of residential location, a higher percentage of women resided in rural areas compared to those residing in urban areas, accounting for 65.64% and 34.36% respectively.\u003c/p\u003e\n\u003cp\u003eExamining the regional distribution of women, it was found that a higher percentage of women resided in the North-west and North-east regions, constituting 47.38% and 19.95% respectively. Following this, women residing in the North-central and South-west regions accounted for 10.43% and 8.84% respectively. Women residing in the South-south region were reported at 7.08%, while the lowest reported region was the South-east, representing 6.32% of the respondents.\u003c/p\u003e\n\u003cp\u003eIn terms of religious affiliation, the majority of women identified as Muslims, accounting for 72.85% of the sample. Christian women constituted 26.73% of the respondents, while a small percentage of women identified as traditionalists, representing 0.42% of the sample.\u003c/p\u003e\n\u003cp\u003eRegarding internet usage, a higher percentage of women reported never using the internet, representing 93.52% of the sample. In contrast, 6.48% of women reported using the internet. Considering wealth status, the majority of women were classified as poor, accounting for 52.49% of the respondents. Rich women represented 27.91% of the sample, while those classified as middle wealth status were reported at 19.69%. Lastly, when examining the experience of perinatal mortality, a higher percentage of women reported not having experienced perinatal mortality, representing 72.16% of the sample. In contrast, 27.84% of women reported having experienced perinatal mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBivariate Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the distribution of women in Nigeria based on their migration status and different migration patterns. Examining the migration status of women, the majority, accounting for 54.59% of the weighted sample, reported themselves as non-migrants. On the other hand, 45.41% of women reported that they are migrants, totaling approximately 15,024 women. Among the 15,024 women who identified themselves as migrants, the distribution of various migration patterns was as follows: rural-rural migration pattern was the most frequently reported, accounting for 44.40% of the migrant population. The second most common migration pattern was urban-urban, reported by 28.23% of the migrant population. Urban-rural migration pattern was reported by 17.23% of the migrants, while rural-urban migration pattern was the least reported, accounting for 10.14% of the migrant population.\u003c/p\u003e\n\u003cp\u003eTable 3 provides an analysis of the socio-demographic factors associated with perinatal mortality among women in Nigeria. The results indicate significant associations between women\u0026apos;s age and perinatal mortality (\u0026chi;2=101.56, P\u0026lt;0.001). Women between the ages of 35-39 years and 30-34 years appear to be at higher risk of perinatal mortality in Nigeria, accounting for 23.46% and 20.76% respectively. This is followed by women aged 40-44 years and 45-49 years, with rates of 18.21% and 17.39% respectively. Women aged 25-29 years and 20-24 years have lower rates of perinatal mortality at 14.21% and 5.14% respectively, while women aged 15-19 years have the lowest reported rate at 0.83%.\u003c/p\u003e\n\u003cp\u003eEthnicity is significantly associated with perinatal mortality (\u0026chi;2=72.05, P\u0026lt;0.001). Women from the Hausa/Fulani ethnic group tend to be at higher risk of perinatal mortality compared to women from other ethnic groups, with rates of 53.49% and 28.01% respectively. Women from the Igbo ethnic group have a rate of 9.55%, while women from the Yoruba ethnic group have the lowest reported rate at 8.95%.\u003c/p\u003e\n\u003cp\u003eExposure to mass media is also significantly associated with perinatal mortality (\u0026chi;2=88.52, P\u0026lt;0.001). Women who are not exposed to mass media tend to be at higher risk of perinatal mortality compared to women who are exposed, with rates of 62.61% and 37.39% respectively. The level of education of women is significantly associated with perinatal mortality (\u0026chi;2=147.13, P\u0026lt;0.001). Women with no formal education have a higher risk of perinatal mortality at 52.37%. Similarly, women with secondary and primary education levels have higher rates of perinatal mortality (21.88% and 18.75%) compared to women with higher education levels (7.00%).\u003c/p\u003e\n\u003cp\u003ePlace of residence is also associated with perinatal mortality (\u0026chi;2=69.38, P\u0026lt;0.001). Women residing in rural areas tend to be at higher risk of perinatal death compared to women residing in urban areas, with rates of 62.1% and 37.87% respectively. The region of women in Nigeria is significantly associated with perinatal mortality (\u0026chi;2=115.49, P\u0026lt;0.001). Women from the North-west and North-east regions have higher rates of perinatal mortality at 42.90% and 21.19% respectively. Women from the North-central and South-west regions have rates of 10.69% and 9.86% respectively, while women from the South-south region have a rate of 8.15%. The South-east region has the lowest reported rate of perinatal mortality at 7.214%.\u003c/p\u003e\n\u003cp\u003eReligion is associated with perinatal mortality (\u0026chi;2=82.59, P\u0026lt;0.05). Muslim women face higher risks (69.42%), while Christian women have a higher risk than other religious groups (30.26% vs. 0.32%). Internet usage is significantly associated with perinatal mortality (\u0026chi;2=120.89, P\u0026lt;0.001). Non-users face higher risks (91.12%) compared to users (8.88%). Wealth status is significantly associated with perinatal mortality (\u0026chi;2=161.98, P\u0026lt;0.001). Poor women face higher risks (47.91%), and rich women have higher risks than middle wealth status (32.84% vs. 19.25%).\u003c/p\u003e\n\u003cp\u003eThese findings highlight the importance of considering socio-demographic factors in understanding perinatal mortality trends and developing targeted interventions to reduce perinatal mortality rates in Nigeria.\u003c/p\u003e\n\u003cp\u003eTable 4 below shows the chi-square analysis to examine the association between female migration streams and migration status in relation to perinatal mortality among women in Nigeria. The chi-square statistic (\u0026chi;2) for migration pattern is 17.65, indicating a statistically significant association between migration pattern and perinatal mortality. This suggests that the likelihood of perinatal mortality varies significantly based on the specific migration pattern. Also, the chi-square statistic (\u0026chi;2) for migration status is 28.57, indicating a statistically significant association between migration status and perinatal mortality. This suggests that there is a significant difference in perinatal mortality rates between migrants and non-migrants.\u003c/p\u003e\n\u003cp\u003eTable 5 presents the binary logistic regression models analyzing the association between various female migration patterns, migration status, socio-demographic characteristics, and perinatal mortality among women in Nigeria. The significance levels are indicated by p-values of \u0026lt;0.05, 0.01, and 0.001. Two models were employed: Model 1, which was unadjusted, focused on the relationship between female migration pattern, migration status, and perinatal mortality in Nigeria. Model 2, adjusted for socio-demographic characteristics, aimed to determine the relationship between these factors and perinatal mortality.\u003c/p\u003e\n\u003cp\u003eIn Model 1, the results indicate that women who migrated from rural to urban areas were 1.03 times more likely to be at risk of perinatal mortality compared to those who migrated from rural to rural places (OR=1.03, CI=0.76-1.40). Similarly, women who migrated from urban to rural areas had a 1.06 times higher risk of perinatal mortality compared to rural-rural migrants (OR=1.06, CI=0.87-1.30). Additionally, women who migrated from urban to urban areas were 1.19 times more likely to face perinatal mortality risk than rural-rural migrants in Nigeria (OR=1.19, CI=0.99-1.44). Conversely, women who did not migrate had a 0.87 times lower likelihood of perinatal mortality risk compared to migrants (OR=0.87, CI=0.79-0.97).\u003c/p\u003e\n\u003cp\u003eIn Model 2, after adjusting for migration status and socio-demographic characteristics, a significant relationship between these factors and perinatal mortality was observed. Women who migrated from rural to urban areas were 0.98 times less likely to experience perinatal death than rural-rural migrants (OR=0.99, CI=0.63-1.53). On the other hand, urban-rural migrants had a 1.07 times higher likelihood of perinatal death compared to rural-rural migrants (OR=1.07, CI=0.85-1.33). Similarly, women who migrated from urban to urban areas had a 1.07 times higher risk of perinatal death than rural-rural migrants in Nigeria (OR=1.07, CI=0.82-1.39). Furthermore, non-migrant women were 0.95 times less likely to face perinatal mortality compared to migrant women (OR=0.95, CI=0.85-1.05).\u003c/p\u003e\n\u003cp\u003eFurthermore, the analysis revealed additional factors associated with perinatal mortality. Women who reported that access to healthcare is not a significant problem were 0.86 times less likely to be at risk of perinatal death compared to those who perceived it as a major issue (OR=0.86, CI=0.75-0.98). Moreover, women who had more than four antenatal care visits had a 1.62 times higher likelihood of perinatal death compared to those with less than four visits (OR=1.62, CI=0.96-2.74).\u003c/p\u003e\n\u003cp\u003eAge was also found to be a contributing factor. Women between the ages of 20-24 years and 25-29 years were 0.88 times and 0.90 times less likely to experience perinatal death, respectively, compared to women aged 15-19 years (OR=0.88, CI=0.61-1.27; and OR=0.90, CI=0.63-1.31). Similarly, women in the age groups 30-34 years and 35-39 years had a lower risk of perinatal mortality, with odds ratios of 0.98 (CI=0.68-1.41) and 0.89 (CI=0.61-1.30), respectively, compared to the reference group of women aged 15-19 years. Additionally, women aged 40-44 years and 45-49 years had a decreased likelihood of perinatal mortality, with odds ratios of 0.76 (CI=0.53-1.12) and 0.70 (CI=0.47-1.04), respectively, compared to women aged 15-19 years. Lastly, women who stated that access to health facilities was not a significant problem were 0.72 times less likely to face perinatal death compared to those who identified it as a major barrier (OR=0.72**, CI=0.60-0.87).\u003c/p\u003e\n\u003cp\u003eAnalysis of the working status of women revealed noteworthy findings. Women who are currently employed were found to be 1.08 times more likely to be at risk of perinatal mortality compared to those who are not working (OR=1.08, CI=0.93-1.25). Additionally, women belonging to the Igbo and Other ethnic groups exhibited a decreased risk of perinatal mortality, with odds ratios of 0.90 (CI=0.60-1.36) and 0.92 (CI=0.75-1.56), respectively, in comparison to women from the Hausa/Fulani ethnic group. Conversely, women from the Yoruba ethnic group had a slightly higher likelihood of perinatal death, with an odds ratio of 1.04 (CI=0.70-1.56), compared to women from the Hausa/Fulani ethnic group.\u003c/p\u003e\n\u003cp\u003eFurthermore, women who reported no exposure to mass media were 1.13 times more likely to be at risk of perinatal mortality than women who were exposed to mass media (OR=1.13, CI=0.99-1.30). Additionally, women who delivered in a hospital setting had a 1.15 times higher likelihood of perinatal mortality than women who did not give birth in a hospital (OR=1.15, CI=1.00-1.32). On the other hand, women residing in rural areas had a decreased risk of perinatal mortality, with an odds ratio of 0.80 (CI=0.71-0.92), compared to women residing in urban areas.\u003c/p\u003e\n\u003cp\u003eAn examination of the regional distribution of women revealed significant insights. Women from the North-East region were found to be 1.27 times more likely to be at risk of perinatal mortality compared to those from the North-Central region (OR=1.27, CI=0.97-1.67). Conversely, women from the North-West and South-East regions exhibited a decreased risk of perinatal mortality, with odds ratios of 0.97 (CI=0.72-1.31) and 0.97 (CI=0.50-1.20), respectively, in comparison to women from the North-Central region. Similarly, women from the South-South and South-West regions had lower odds of perinatal mortality, with odds ratios of 0.93 (CI=0.70-1.28) and 0.81 (CI=0.54-1.22), respectively, compared to women from the North-Central region.\u003c/p\u003e\n\u003cp\u003eRegarding religious affiliation, Muslim and traditional women displayed reduced likelihoods of perinatal mortality, with odds ratios of 0.86 (CI=0.71-1.05) and 0.78 (CI=0.29-2.15), respectively, in comparison to women from the Christian religion. Additionally, women with primary and higher levels of education were less likely to experience perinatal mortality, with odds ratios of 0.93 (CI=0.73-1.18) and 0.97 (CI=0.67-1.41), respectively, compared to women with no formal education. Conversely, women with a secondary level of education exhibited a slightly higher likelihood of perinatal mortality, with an odds ratio of 1.06 (CI=0.82-1.38), compared to those with no formal education.\u003c/p\u003e\n\u003cp\u003eFurthermore, women who reported internet usage were 1.23 times more likely to be at risk of perinatal mortality than those who had never used the internet (OR=1.23, CI=0.91-1.68). Lastly, women with middle and rich wealth statuses demonstrated lower odds of perinatal mortality, with odds ratios of 0.98 (CI=0.78-1.26) and 1.36 (CI=1.05-1.77), respectively, compared to women from poor wealth status.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study conducted a thorough examination of female migration patterns and their association with perinatal mortality in Nigeria. The research aimed to shed light on the prevalence of perinatal mortality among migrant women, the proportion of women who are migrants, and the potential link between female migration and perinatal mortality in Nigeria. The findings revealed several key insights that are vital for understanding the complex relationship between migration, socio-demographic factors, and perinatal mortality.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003ePrevalence of Perinatal Mortality\u003c/h2\u003e\u003cp\u003eOne of the most striking findings of this study is the high prevalence of perinatal mortality in Nigeria, which stands at 27.84%. This alarming rate underscores the urgency of addressing perinatal mortality as a major public health concern. Perinatal mortality is not only a significant indicator of maternal and child health but also a substantial contributor to under-five mortality rates. This finding aligns with previous research by (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) in Sub-Saharan Africa, which highlighted that many countries in the region face challenges related to inadequate healthcare services, conflict, and weak healthcare systems, resulting in a surge in perinatal mortality. Conflict, in particular, has been identified as a barrier to facility-based deliveries and has led to the destruction of health infrastructure and a shortage of skilled healthcare personnel, thereby increasing the risk of perinatal mortality due to unassisted births and a lack of timely emergency perinatal services (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eMigration Patterns and Perinatal Mortality\u003c/h2\u003e\u003cp\u003eThe study's analysis of migration patterns revealed important associations with perinatal mortality. Women who migrated from urban to urban areas were found to be 0.78 times less likely to experience perinatal death compared to those who migrated from rural to rural locations. This result suggests that urban areas may offer better access to healthcare facilities and services, contributing to improved perinatal outcomes. The migrant adaptation theory also plays a role, as migrants tend to adopt urban lifestyles and healthcare-seeking behaviors over time, similar to urban non-migrants (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConversely, women who followed migration patterns from urban to rural or urban to urban areas exhibited a higher risk of perinatal death. Urban-rural and urban-urban migration were associated with 1.01 times and 1.07 times higher odds of perinatal mortality than rural-rural migration, indicating that migration can disrupt social networks and support systems, leading to reduced access to healthcare and potentially contributing to perinatal mortality. Urban non-migrant women had a lower risk of perinatal mortality compared to women who had a rural to rural migration pattern, emphasizing the importance of social networks and support for pregnant women.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eSocio-Demographic Factors and Perinatal Mortality\u003c/h2\u003e\u003cp\u003eThe study also uncovered several socio-demographic factors associated with perinatal mortality. Notably, women who reported that access to healthcare was not a significant problem had a lower risk of perinatal death. This finding underscores the importance of improving healthcare access, especially in rural areas where access may be limited (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eImpact of Maternal Age\u003c/h2\u003e\u003cp\u003eThe study observed a significant association between maternal age and perinatal mortality. As women get older from the age group 20\u0026ndash;24 years to 45\u0026ndash;49 years, they are more likely at increased risk of perinatal death compared to younger mothers (\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This finding aligns with the work of Ezeh and colleagues, who also noted that increasing maternal age is associated with better perinatal outcomes (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). A possible explanation for this could be related to medical risk factors, such as antepartum hemorrhage, hypertension, operative vaginal delivery and gestational diabetes which were previously reported to be associated with advanced maternal age (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eRole of Education and Media Exposure\u003c/h2\u003e\u003cp\u003eEducation emerged as a crucial determinant of perinatal mortality, with women having secondary and higher levels of education exhibiting a lower risk of perinatal death. This finding is consistent with the research conducted by (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), which emphasized the importance of maternal education in child survival. The study also highlighted that women who are not exposed to mass media are more at risk of perinatal deaths than women who are exposed to mass media. The finding of this study is dissimilar with existing studies of Girma and colleagues, who emphasized that media plays a significant role in disseminating vital healthcare information and promoting positive health behaviors (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The differences observed in this study could be due to the nature of the data that was used in understanding the association between perinatal death and media exposure.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eWealth Status\u003c/h2\u003e\u003cp\u003eThe study uncovered that women from middle and rich wealth statuses experience lower risk of perinatal mortality as compared to women who are poor as their economic factor. These findings are in agreement with Akombi and Renzaho, who noted that perinatal mortality was highest in Nigeria among west African countries and second highest in Sub-Saharan Africa due to the high level of poverty experienced in the country (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Similarly, Mmusi-Phetoe (2016), poverty is an underlying factor for poor birth outcomes among women (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Hence, it is important that poverty being a multidimensional phenomenon be addressed to curb the undesired risk of perinatal mortality.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eReligion and Its Influence\u003c/h2\u003e\u003cp\u003eThe study found that women of Muslim and traditional religions had different perinatal mortality risk profiles compared to Christian women. This observation may be related to cultural practices, such as early marriage in the northern part of Nigeria among Muslim communities which have allowed children not matured enough to either take care of themselves nor the pregnancy they have as noted by Envuladu and colleagues (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). These findings underscore the need for culturally sensitive interventions to address perinatal mortality disparities across religious groups.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eImplications and Policy Recommendations\u003c/h2\u003e\u003cp\u003eThe outcomes of this study hold crucial implications for healthcare policies in Nigeria, specifically in addressing the prevalent issue of perinatal mortality. Policymakers are urged to consider a multifaceted approach to tackle this challenge effectively. Foremost among the recommendations is the promotion of maternal education, emphasizing the need to encourage girls' education and enhance women's literacy. This approach is seen as pivotal in empowering women with the knowledge and decision-making capabilities essential for optimal maternal and child health outcomes.\u003c/p\u003e\u003cp\u003eIn addition, the study suggests the intensification of health education programs and media campaigns, focusing on disseminating critical information related to maternal and child health practices. Key aspects include emphasizing the significance of antenatal care, facility-based delivery, and postnatal care. The importance of community-based support systems is highlighted, especially for pregnant women, with a particular focus on providing emotional and social support, particularly for migrants who may lack access to their original support structures.\u003c/p\u003e\u003cp\u003eFurthermore, the study emphasises the need to address regional disparities in perinatal mortality rates. Tailored interventions are recommended, particularly for vulnerable regions like the North-East, necessitating targeted efforts to improve healthcare infrastructure and outcomes. Lastly, strategies aimed at reducing wealth-related disparities in perinatal mortality should be a priority, ensuring that economically disadvantaged women have equitable access to healthcare services through targeted interventions.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eFirstly, the retrospective nature of the survey may introduce recall bias, as respondents might not accurately remember past events or experiences. Additionally, migration status was derived from the available variables in the dataset, which could potentially introduce measurement error. The cross-sectional design of the study also limits the ability to establish causality between variables, as it only provides a snapshot at a specific point in time. Furthermore, some variables of interest were not collected in the NDHS dataset, which constrains the scope of analysis and may limit the depth of the findings.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFurther Research\u003c/strong\u003e\u003cp\u003eFurther research is needed to explore the unexpected findings related to hospital delivery and antenatal care visits. Qualitative studies and in-depth investigations may provide insights into the reasons behind these associations.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides critical insights into the relationship between female migration patterns and perinatal mortality in Nigeria. Our findings highlight the significant role of migration status in shaping maternal and newborn health outcomes. Specifically, rural-urban migrants were found to have lower perinatal mortality rates than rural-rural migrants, underscoring the potential benefits of urban healthcare access and socio-economic opportunities. However, disruptions in social support systems among urban-rural and urban-urban migrants were linked to an increased risk of perinatal mortality, emphasizing the complexities of migration and health.\u003c/p\u003e\u003cp\u003eBeyond migration, the study identifies key socio-demographic factors influencing perinatal mortality. Limited healthcare access, lower maternal education, poverty, and lack of media exposure were strongly associated with higher perinatal mortality rates. Additionally, cultural and religious influences, particularly early marriage and restricted healthcare autonomy among certain groups, further exacerbated risks. These findings reinforce the urgent need for targeted interventions that address not only healthcare accessibility but also education, economic empowerment, and culturally sensitive maternal health programs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e● \u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e● \u003cb\u003eDHS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDemographic and Health Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e● \u003cb\u003eEA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEnumeration Area\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e● \u003cb\u003eENND\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEarly Neonatal Death\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e● \u003cb\u003eFCT\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFederal Capital Territory\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e● \u003cb\u003eLMICs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLow\u0026ndash;and Middle\u0026ndash;Income Countries\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e● \u003cb\u003eNDHS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNigeria Demographic and Health Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e● \u003cb\u003eOR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e● \u003cb\u003ePPS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProbability Proportional to Size\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e● \u003cb\u003ePM\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePerinatal Mortality\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e● \u003cb\u003ePMR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePerinatal Mortality Rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e● \u003cb\u003eSDG\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSustainable Development Goal\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e● \u003cb\u003eWHO\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWorld Health Organization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe data used in this study were obtained from the 2018 Nigeria Demographic and Health Survey (NDHS), a publicly available dataset. Ethical approval for the survey was granted by the National Health Research Ethics Committee of Nigeria and the ICF Institutional Review Board. Written informed consent was obtained from all adult participants, and for participants under 18 years of age, consent was provided by their parent(s) or legal guardian(s) prior to participation.\u003c/p\u003e\n\u003ch3\u003eEthics Approval and Consent to Participate\u003c/h3\u003e\n\u003cp\u003eThis study was based on secondary data analysis of the 2018 Nigeria Demographic and Health Survey (NDHS), which was publicly available and de-identified. Ethical approval for the original NDHS survey was obtained from the National Health Research Ethics Committee of Nigeria (NHREC) and the Institutional Review Board of ICF International. Written informed consent was obtained from all participants by the NDHS survey team before data collection. Given that this study utilized secondary data with no direct involvement of human participants, additional ethical approval was not required.\u003c/p\u003e\n\u003cp\u003eAlso, since the study is not a Clinical trial. \u003c/p\u003e\n\u003ch3\u003eConsent for Publication\u003c/h3\u003e\n\u003cp\u003eNot applicable. This study does not include identifiable individual participant data.\u003c/p\u003e\n\u003ch3\u003eAvailability of Data and Materials\u003c/h3\u003e\n\u003cp\u003eThe dataset used in this study is publicly available through the Demographic and Health Surveys (DHS) Program and can be accessed upon request via the MEASURE DHS website: https://www.dhsprogram.com/data/available-datasets.cfm.\u003c/p\u003e\n\u003cp\u003eThe STATA DO-File for all analyses performed in this study can be assessed here\u003c/p\u003e\n\u003ch3\u003eCompeting Interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch3\u003eAuthors' Contributions\u003c/h3\u003e\n\u003cp\u003e● Opeyemi Agboola \u0026amp; Sunday Tuwatimi: Conceptualized the study, performed data analysis, and wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e● Opeyemi Agboola, Sunday Tuwatimi \u0026amp; Lalita Kumari Sah: Contributed to study design, data interpretation, and manuscript revision.\u003c/p\u003e\n\u003cp\u003e● Rajeeb Kumar Sah \u0026amp; Lalita Kumari Sah: Provided critical feedback, reviewed statistical methods, and revised the final manuscript.\u003cbr\u003e All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eThe authors appreciate the DHS Program for granting access to the 2018 NDHS dataset.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Neonatal and Perinatal Mortality: Country, Regional and Global Estimates. Geneva: WHO; 2006. \u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Neonatal and perinatal mortality: country, regional, and global estimates. Geneva: WHO; 2006. doi:10.2471/BLT.15.168047. \u003c/li\u003e\n\u003cli\u003eLeon B, Suswardany D, Michener K, Tarigan I, Suhaemi A, Siregar A. Utility of local health registers in measuring perinatal mortality: A case study in rural Indonesia. BMC Pregnancy Childbirth. 2011;11(20). \u003c/li\u003e\n\u003cli\u003eLawn J, Shibuya K, Stein C. No cry at birth: Global estimates of intrapartum stillbirths and intrapartum-related neonatal deaths. Bull World Health Organ. 2005;83:409\u0026ndash;17. \u003c/li\u003e\n\u003cli\u003eGausia K, Moran AC, Ali M, Ryder D, Fisher C, Koblinsky M. Psychological and social consequences among mothers suffering from perinatal loss: perspective from a low-income country. BMC Public Health. 2011;11:1-9. \u003c/li\u003e\n\u003cli\u003eUnited Nations Inter-agency Group for Child Mortality Estimation (UN IGME). A Neglected Tragedy: The global burden of stillbirths. 2020. \u003c/li\u003e\n\u003cli\u003eLawn JE, Yakoob MY, Haws RA, Soomro T, Darmstadt GL, Bhutta ZA. 3.2 million stillbirths: epidemiology and overview of the evidence review. BMC Pregnancy Childbirth. 2009;9:1-17. \u003c/li\u003e\n\u003cli\u003eNational Population Commission. Nigeria Demographic and Health Survey 2018. NPC, ICF; 2019. \u003c/li\u003e\n\u003cli\u003eOji S, Odimegwu C. Perinatal mortality in Nigeria: do place of delivery and delivery assistants matter? Open Demogr J. 2011;4:110. \u003c/li\u003e\n\u003cli\u003eAdimora GN, Odetunde IO. Perinatal mortality in University of Nigeria Teaching Hospital (UNTH) Enugu at the end of the last millennium. Niger J Clin Pract. 2007;10:19\u0026ndash;23. \u003c/li\u003e\n\u003cli\u003eSuleiman MB, Mokuolu OA. Perinatal mortality in a Northwestern Nigerian city: A wake-up call. Front Pediatr. 2014;2:105. \u003c/li\u003e\n\u003cli\u003eUnited Nations. Human Development Report 2009: Overcoming Barriers: Human Mobility and Development. New York: United Nations Development Program; 2009. \u003c/li\u003e\n\u003cli\u003eWorld Bank. World Development Report 2009: Reshaping Economic Geography. Washington, DC: The World Bank; 2009. \u003c/li\u003e\n\u003cli\u003eWorld Bank. From Oil to Cities: Nigeria\u0026rsquo;s Next Transformation. Directions in Development. Washington, DC: World Bank; 2016. doi:10.1596/978-1-4648-0792-3. \u003c/li\u003e\n\u003cli\u003eGoldin I, Reinert KA. Globalization for development: trade, finance, aid, migration, and policy. Washington, DC: World Bank Publications; 2006. \u003c/li\u003e\n\u003cli\u003eAkombi BJ, Renzaho AM. Perinatal mortality in sub-Saharan Africa: a meta-analysis of demographic and health surveys. Ann Glob Health. 2019;85(1). \u003c/li\u003e\n\u003cli\u003eRobert S, De-jong GF, Stokes CS. The effect of female migration on infant and child survival in Uganda. Popul Res Policy Rev. 2002;21:403\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eDe Haas H. The impact of international migration on social and economic development in Moroccan sending regions: A review of the empirical literature. IMI Working Paper No. 3. Oxford: International Migration Institute, University of Oxford; 2007. \u003c/li\u003e\n\u003cli\u003eBerhan Y, Berhan A. Commentary: Reasons for persistently high maternal and perinatal mortalities in Ethiopia: Part III\u0026mdash;Perspective of the \u0026quot;three delays\u0026quot; model. Ethiop J Health Sci. 2014;24 Suppl(0 Suppl):137\u0026ndash;48. PMID: 25489188; PMCID: PMC4249213. \u003c/li\u003e\n\u003cli\u003eEzeh OK, Uche-Nwachi EO, Abada UD, Agho KE. Community- and proximate-level factors associated with perinatal mortality in Nigeria: evidence from a nationwide household survey. BMC Public Health. 2019;19:1-9. \u003c/li\u003e\n\u003cli\u003eBhandari S, Raja EA, Shetty A, Bhattacharya S. Maternal and perinatal consequences of antepartum haemorrhage of unknown origin. BJOG. 2014;121(1):44-52. \u003c/li\u003e\n\u003cli\u003eIssaka AI, Agho KE, Renzaho AM. Correction: The impact of internal migration on under-five mortality in 27 sub-Saharan African countries. PLoS One. 2017;12(2):e0171766. \u003c/li\u003e\n\u003cli\u003eGirma D, Abita Z, Fetene G, Birie B. Individual and community-level factors of perinatal mortality in the high-mortality regions of Ethiopia: a multilevel mixed-effect analysis. BMC Public Health. 2022;22(1):247. \u003c/li\u003e\n\u003cli\u003eMmusi-Phetoe RM. Social factors determining maternal and neonatal mortality in South Africa: A qualitative study. Curationis. 2016;39(1):1\u0026ndash;8. doi:10.4102/curationis.v39i1.1571. \u003c/li\u003e\n\u003cli\u003eEnvuladu E, Umaru R, Iorapuu N, Osagie I, Okoh E, Zoakah A. Determinants and effect of girl child marriage: A cross-sectional study of school girls in Plateau State, Nigeria. Int J Med Biomed Res. 2016;5(3):122-9. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Distribution of Respondents by Socio-Demographic Characteristics and perinatal mortality.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eWeighted Frequency\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n= 33,083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003ePercent\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15-19\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20-24\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25-29\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e30-34\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35-39\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40-44\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45-49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003cp\u003e1,717\u003c/p\u003e\n \u003cp\u003e4 ,453\u003c/p\u003e\n \u003cp\u003e6,172\u003c/p\u003e\n \u003cp\u003e7,371\u003c/p\u003e\n \u003cp\u003e6,452\u003c/p\u003e\n \u003cp\u003e6,657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003cp\u003e5.19\u003c/p\u003e\n \u003cp\u003e13.46\u003c/p\u003e\n \u003cp\u003e18.66\u003c/p\u003e\n \u003cp\u003e22.28\u003c/p\u003e\n \u003cp\u003e19.50\u003c/p\u003e\n \u003cp\u003e20.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eAccess/ distance to health facility\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBig problem\u003c/p\u003e\n \u003cp\u003eNot a big problem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9,178\u003c/p\u003e\n \u003cp\u003e23,905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22.74\u003c/p\u003e\n \u003cp\u003e77.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eAntenatal care visits\u003c/p\u003e\n \u003cp\u003eLess than 4\u003c/p\u003e\n \u003cp\u003e4 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32,385\u003c/p\u003e\n \u003cp\u003e698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e97.89\u003c/p\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eCurrently working\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8,628\u003c/p\u003e\n \u003cp\u003e24,455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26.08\u003c/p\u003e\n \u003cp\u003e73.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003cp\u003eHausa/Fulani\u003c/p\u003e\n \u003cp\u003eIgbo\u003c/p\u003e\n \u003cp\u003eYoruba\u003c/p\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18,797\u003c/p\u003e\n \u003cp\u003e2,741\u003c/p\u003e\n \u003cp\u003e2,614\u003c/p\u003e\n \u003cp\u003e8,931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e56.82\u003c/p\u003e\n \u003cp\u003e8.28\u003c/p\u003e\n \u003cp\u003e7.90\u003c/p\u003e\n \u003cp\u003e27.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eExposure to mass media\u003c/p\u003e\n \u003cp\u003eNot exposed\u003c/p\u003e\n \u003cp\u003eExposed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13,731\u003c/p\u003e\n \u003cp\u003e19,352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e41.55\u003c/p\u003e\n \u003cp\u003e58.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eLevel of education\u003c/p\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18,788\u003c/p\u003e\n \u003cp\u003e6,179\u003c/p\u003e\n \u003cp\u003e6,267\u003c/p\u003e\n \u003cp\u003e1,848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e56.79\u003c/p\u003e\n \u003cp\u003e18.68\u003c/p\u003e\n \u003cp\u003e18.94\u003c/p\u003e\n \u003cp\u003e5.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003ePlace of delivery\u003c/p\u003e\n \u003cp\u003eNot in hospital\u003c/p\u003e\n \u003cp\u003eHospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20,187\u003c/p\u003e\n \u003cp\u003e12,896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e61.02\u003c/p\u003e\n \u003cp\u003e38.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003ePlace of residence\u003c/p\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11,366\u003c/p\u003e\n \u003cp\u003e21,717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e34.36\u003c/p\u003e\n \u003cp\u003e65.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003cp\u003eNorth central\u003c/p\u003e\n \u003cp\u003eNorth east\u003c/p\u003e\n \u003cp\u003eNorth west\u003c/p\u003e\n \u003cp\u003eSouth east\u003c/p\u003e\n \u003cp\u003eSouth south\u003c/p\u003e\n \u003cp\u003eSouth west\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3,450\u003c/p\u003e\n \u003cp\u003e6,599\u003c/p\u003e\n \u003cp\u003e15,674\u003c/p\u003e\n \u003cp\u003e2,092\u003c/p\u003e\n \u003cp\u003e2,341\u003c/p\u003e\n \u003cp\u003e2,926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10.43\u003c/p\u003e\n \u003cp\u003e19.95\u003c/p\u003e\n \u003cp\u003e47.38\u003c/p\u003e\n \u003cp\u003e6.32\u003c/p\u003e\n \u003cp\u003e7.08\u003c/p\u003e\n \u003cp\u003e8.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eReligion\u003c/p\u003e\n \u003cp\u003eChristian\u003c/p\u003e\n \u003cp\u003eMuslim\u003c/p\u003e\n \u003cp\u003eTraditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8,843\u003c/p\u003e\n \u003cp\u003e24,101\u003c/p\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26.73\u003c/p\u003e\n \u003cp\u003e72.85\u003c/p\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eUse of internet\u003c/p\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003cp\u003eUsed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e30,939\u003c/p\u003e\n \u003cp\u003e2,144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93.52\u003c/p\u003e\n \u003cp\u003e6.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eWealth status\u003c/p\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003cp\u003eRich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17,364\u003c/p\u003e\n \u003cp\u003e6,485\u003c/p\u003e\n \u003cp\u003e9,234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e52.49\u003c/p\u003e\n \u003cp\u003e19.60\u003c/p\u003e\n \u003cp\u003e27.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003ePerinatal mortality\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19,757\u003c/p\u003e\n \u003cp\u003e13,326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e72.16\u003c/p\u003e\n \u003cp\u003e27.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e33,038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e100\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\u003e\u003cstrong\u003eTable 2: Distribution of Respondents by migration status and migration pattern\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable class=\"MsoNormalTable\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 291px;\"\u003e\n \u003cp class=\"MsoNormal\"\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp class=\"MsoNormal\"\u003eWeighted Frequency\u0026nbsp;\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e(N= 33,083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp class=\"MsoNormal\"\u003ePercent\u0026nbsp;\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 291px;\"\u003e\n \u003cp class=\"MsoNormal\"\u003eStatus\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003eMigrant\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003eNon-migrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e15,024\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e18,059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e45.41\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e54.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 291px;\"\u003e\n \u003cp class=\"MsoNormal\"\u003eMigration pattern\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003eRural-rural\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003eRural-urban\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003eUrban-rural\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003eUrban-urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e6,670\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e1,523\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e2,588\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e4,243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e44.40\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e10.14\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e17.23\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e28.23\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\u003e\u003cstrong\u003eTable 3: Chi-square analysis of women socio-demographics by perinatal mortality in Nigeria.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003ePERINATAL MORTALITY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eStatistics\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003cp\u003eFrequency and percentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003cp\u003eFrequency and percentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003cp\u003e(\u0026chi;\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15-19\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20-24\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25-29\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e30-34\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35-39\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40-44\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e255 (00.77) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1724 (05.21) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4357 (13.17) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5905 (17.85) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7222 (21.83) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e662 (02.00) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7004 (21.17) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e275 (00.83)\u003c/p\u003e\n \u003cp\u003e1700 (05.14)\u003c/p\u003e\n \u003cp\u003e4701 (14.21)\u003c/p\u003e\n \u003cp\u003e6868 (20.76)\u003c/p\u003e\n \u003cp\u003e7761 (23.46)\u003c/p\u003e\n \u003cp\u003e16024 (8.21)\u003c/p\u003e\n \u003cp\u003e5753 (17.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e101.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eAccess/ distance to\u003c/p\u003e\n \u003cp\u003ehealth facility\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBig problem\u003c/p\u003e\n \u003cp\u003eNot a big problem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9061 (27.39)\u003c/p\u003e\n \u003cp\u003e24022 (72.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9482 (28.66)\u003c/p\u003e\n \u003cp\u003e23601 (71.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eAntenatal care visits\u003c/p\u003e\n \u003cp\u003eLess than 4\u003c/p\u003e\n \u003cp\u003e4 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23441 (98.06)\u003c/p\u003e\n \u003cp\u003e642 (01.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32236 (97.44)\u003c/p\u003e\n \u003cp\u003e847 (02.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eCurrently working\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8856 (26.77) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24227 (73.23) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8036 (24.29)\u003c/p\u003e\n \u003cp\u003e25047 (75.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e21.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003cp\u003eHausa/Fulani\u003c/p\u003e\n \u003cp\u003eIgbo\u003c/p\u003e\n \u003cp\u003eYoruba\u003c/p\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19221 (58.10)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2580 (07.80) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2478 (07.49) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8803 (26.61) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17696 (53.49)\u003c/p\u003e\n \u003cp\u003e3159 (09.55)\u003c/p\u003e\n \u003cp\u003e2961 (08.95)\u003c/p\u003e\n \u003cp\u003e9267 (28.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e72.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eP\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eExposure to mass\u003c/p\u003e\n \u003cp\u003emedia\u003c/p\u003e\n \u003cp\u003eExposed\u003c/p\u003e\n \u003cp\u003eNot exposed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14255 (43.09) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18828 (56.91) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12370 (37.39)\u003c/p\u003e\n \u003cp\u003e20713 (62.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e88.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eLevel of education\u003c/p\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19354 (58.50) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6170 (18.65) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5892 (17.81) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1667 (05.04) \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17326 (52.37)\u003c/p\u003e\n \u003cp\u003e6203 (18.75)\u003c/p\u003e\n \u003cp\u003e7239 (21.88)\u003c/p\u003e\n \u003cp\u003e2316 (07.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e147.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003ePlace of delivery\u003c/p\u003e\n \u003cp\u003eNot in hospital\u003c/p\u003e\n \u003cp\u003eHospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20505 (61.98) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12578 (38.02) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19406 (58.66)\u003c/p\u003e\n \u003cp\u003e13677 (41.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003ePlace of residence\u003c/p\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10917 (33.00)\u003c/p\u003e\n \u003cp\u003e22166 (67.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12529 (37.87)\u003c/p\u003e\n \u003cp\u003e20554 (62.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e69.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003cp\u003eNorth central\u003c/p\u003e\n \u003cp\u003eNorth east\u003c/p\u003e\n \u003cp\u003eNorth west\u003c/p\u003e\n \u003cp\u003eSouth east\u003c/p\u003e\n \u003cp\u003eSouth south\u003c/p\u003e\n \u003cp\u003eSouth west\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3417 (10.33) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6441 (19.47) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16247 (49.11) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1978 (05.98) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2203 (06.66) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2796 (08.45) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3537 (10.69)\u003c/p\u003e\n \u003cp\u003e7010 (21.19)\u003c/p\u003e\n \u003cp\u003e14193 (42.90)\u003c/p\u003e\n \u003cp\u003e2385 (07.21)\u003c/p\u003e\n \u003cp\u003e2696 (08.15)\u003c/p\u003e\n \u003cp\u003e3262 (09.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e115.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eReligion\u003c/p\u003e\n \u003cp\u003eChristian\u003c/p\u003e\n \u003cp\u003eMuslim\u003c/p\u003e\n \u003cp\u003eTraditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8393 (25.37) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24538 (74.17) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e152 (00.46) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10012 (30.26)\u003c/p\u003e\n \u003cp\u003e22966 (69.42)\u003c/p\u003e\n \u003cp\u003e106 (00.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e82.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eUse of internet\u003c/p\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003cp\u003eUsed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e31246 (94.45) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1836 (05.55) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e30145 (91.12)\u003c/p\u003e\n \u003cp\u003e2938 (08.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e120.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eWealth status\u003c/p\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003cp\u003eRich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17948 (54.25)\u003c/p\u003e\n \u003cp\u003e6531 (19.74)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8605 (26.01) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8605 (47.91)\u003c/p\u003e\n \u003cp\u003e6368 (19.25)\u003c/p\u003e\n \u003cp\u003e10864 (32.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e161.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Chi-square analysis of female migration streams and migration status by perinatal mortality among women in Nigeria\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003ePERINATAL MORTALITY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eStatistics\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; NO\u003c/p\u003e\n \u003cp\u003eFrequency and percentage (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; YES\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFrequency and percentage (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eMigration pattern\u003c/p\u003e\n \u003cp\u003eRural-rural\u003c/p\u003e\n \u003cp\u003eRural-urban\u003c/p\u003e\n \u003cp\u003eUrban-rural\u003c/p\u003e\n \u003cp\u003eUrban-urban\u003c/p\u003e\n \u003cp\u003eRural non-migrant\u003c/p\u003e\n \u003cp\u003eUrban non-migrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6742(20.38)\u003c/p\u003e\n \u003cp\u003e1512(04.57)\u003c/p\u003e\n \u003cp\u003e2554(07.72)\u003c/p\u003e\n \u003cp\u003e4026(12.17)\u003c/p\u003e\n \u003cp\u003e12889(38.98)\u003c/p\u003e\n \u003cp\u003e5353(16.18)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6445(19.48)\u003c/p\u003e\n \u003cp\u003e1555(04.70)\u003c/p\u003e\n \u003cp\u003e2696(08.15)\u003c/p\u003e\n \u003cp\u003e4923(14.88)\u003c/p\u003e\n \u003cp\u003e11096(33.54)\u003c/p\u003e\n \u003cp\u003e6368(19.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e120.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eStatus\u003c/p\u003e\n \u003cp\u003eMigrants\u003c/p\u003e\n \u003cp\u003eNon-migrants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14722 (44.50)\u003c/p\u003e\n \u003cp\u003e18361 (55.50) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15807 (47.78)\u003c/p\u003e\n \u003cp\u003e17276 (52.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Odds ratio based on Binary Logistic Regression Analysis of Female Migration Status, Migration pattern and Socio-demographics by Perinatal Mortality in Nigeria.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;Perinatal mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003ePerinatal mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eLower-Upper Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eLower-Upper Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eMigration streams\u003c/p\u003e\n \u003cp\u003eRural-rural\u003c/p\u003e\n \u003cp\u003eRural-urban\u003c/p\u003e\n \u003cp\u003eUrban-rural\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUrban-urban\u003c/p\u003e\n \u003cp\u003eRural non-migrant\u003c/p\u003e\n \u003cp\u003eUrban non-migrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRC\u003c/p\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003cp\u003e0.78*\u003c/p\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003cp\u003e0.80*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.70-1.24\u003c/p\u003e\n \u003cp\u003e0.74-1.11\u003c/p\u003e\n \u003cp\u003e0.65-0.95\u003c/p\u003e\n \u003cp\u003e0.96-1.28\u003c/p\u003e\n \u003cp\u003e0.67-0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRC\u003c/p\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.80-1.52\u003c/p\u003e\n \u003cp\u003e0.83-1.25\u003c/p\u003e\n \u003cp\u003e0.80-1.27\u003c/p\u003e\n \u003cp\u003e0.93-1.24\u003c/p\u003e\n \u003cp\u003e0.75-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15-19\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20-24\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25-29\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e30-34\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35-39\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40-44\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRC\u003c/p\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.71-1.56\u003c/p\u003e\n \u003cp\u003e0.71-1.53\u003c/p\u003e\n \u003cp\u003e0.68-1.48\u003c/p\u003e\n \u003cp\u003e0.74-1.64\u003c/p\u003e\n \u003cp\u003e0.85-1.89\u003c/p\u003e\n \u003cp\u003e0.94-2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003cp\u003eHausa/Fulani\u003c/p\u003e\n \u003cp\u003eIgbo\u003c/p\u003e\n \u003cp\u003eYoruba\u003c/p\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRC\u003c/p\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.68-1.54\u003c/p\u003e\n \u003cp\u003e0.60-1.32\u003c/p\u003e\n \u003cp\u003e0.84-1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eExposure to mass media\u003c/p\u003e\n \u003cp\u003eExposed\u003c/p\u003e\n \u003cp\u003eNot exposed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRC\u003c/p\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.79-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003ePlace of residence\u003c/p\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRC\u003c/p\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.71-1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eReligion\u003c/p\u003e\n \u003cp\u003eChristian\u003c/p\u003e\n \u003cp\u003eMuslim\u003c/p\u003e\n \u003cp\u003eTraditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRC\u003c/p\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.94-1.39\u003c/p\u003e\n \u003cp\u003e0.41-4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eLevel of education\u003c/p\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRC\u003c/p\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.86-1.22\u003c/p\u003e\n \u003cp\u003e0.77-1.56\u003c/p\u003e\n \u003cp\u003e0..67-1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003cp\u003eNorth central\u003c/p\u003e\n \u003cp\u003eNorth east\u003c/p\u003e\n \u003cp\u003eNorth west\u003c/p\u003e\n \u003cp\u003eSouth east\u003c/p\u003e\n \u003cp\u003eSouth south\u003c/p\u003e\n \u003cp\u003eSouth west\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRC\u003c/p\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003cp\u003e1..04\u003c/p\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.68-1.06\u003c/p\u003e\n \u003cp\u003e0.92-1.47\u003c/p\u003e\n \u003cp\u003e0.68-1.57\u003c/p\u003e\n \u003cp\u003e0.82-1.34\u003c/p\u003e\n \u003cp\u003e0.92-1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eUse of internet\u003c/p\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003cp\u003eUsed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRC\u003c/p\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.65-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eWealth status\u003c/p\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003cp\u003eRich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRC\u003c/p\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.78-1.12\u003c/p\u003e\n \u003cp\u003e0.64-0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001.\u003c/p\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":"Perinatal Mortality, Female Migration, Nigeria Demographic and Health Survey (NDHS), Maternal and Child Health, and Socio-Demographic Factors","lastPublishedDoi":"10.21203/rs.3.rs-7479175/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7479175/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Perinatal mortality remains a critical public health concern in Nigeria, serving as a key indicator of maternal and child health outcomes. Despite efforts to reduce perinatal deaths, rates remain high, reflecting persistent healthcare disparities and socio-economic inequalities. Understanding the role of female migration in shaping perinatal mortality is crucial, as migration influences healthcare access, social support systems, and economic stability. However, research on migration and perinatal mortality in Nigeria remains limited. This study explores how different female migration patterns impact perinatal mortality, providing insights into potential policy and healthcare interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This study utilized data from the 2018 Nigeria Demographic and Health Survey (NDHS), analyzing a weighted sample of 33,083 women aged 15-49 years. Female migration was categorized into six groups: rural-rural, rural-urban, urban-rural, urban-urban migrants, rural non-migrants, and urban non-migrants. Various socio-demographic factors were considered as potential confounders. Data analysis involved univariate, bivariate, and multivariate statistical techniques to examine associations between migration patterns and perinatal mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The prevalence of perinatal mortality in Nigeria was 40.28%, highlighting a significant public health burden. Among the study population, 45.41% were internal migrants. Rural-urban migrants had a lower likelihood of perinatal mortality than rural-rural migrants, likely due to better healthcare access and socio-economic opportunities in urban areas. Increasing maternal age was protective, while easier healthcare access reduced perinatal mortality risk. However, attending more than four antenatal care visits was linked to higher odds of perinatal mortality, suggesting healthcare quality concerns. Muslim women faced higher perinatal mortality risk than Christians, possibly due to cultural practices like early marriage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Female migration patterns play a significant role in perinatal mortality outcomes in Nigeria. Improving healthcare accessibility, particularly in rural areas, and addressing cultural and systemic healthcare challenges are essential for reducing perinatal mortality. Targeted policy interventions focusing on maternal education, healthcare quality, and culturally sensitive health programs are crucial for mitigating perinatal mortality among migrant and non-migrant populations.\u003c/p\u003e","manuscriptTitle":"Association between Female Migration and Perinatal Mortality in Nigeria: An analysis of 2018 Nigeria Demographic and Health Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 07:51:53","doi":"10.21203/rs.3.rs-7479175/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":"6a2f7ae8-5b46-441b-b1de-85eb06b98678","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T02:08:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 07:51:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7479175","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7479175","identity":"rs-7479175","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}