Female migrants, family members and community socio-demographic characteristics influence facility delivery in Rufiji, Tanzania

BMC Pregnancy and Childbirth, Volume 14, No. 1, Year 2014

Interpretation

Background: Health professionals and public health experts in maternal and newborn health encourage women to deliver at health facilities in an effort to reduce maternal and newborn mortality. In the existing literature, there is scant information on how migration, family members and community influence facility delivery. This study addresses this knowledge gap using 10 years of longitudinal surveillance data from a rural district of Tanzania.Methods: Multilevel logistic regression was used to quantify the influence of hypothesized migration, family and community-level factors on facility delivery while adjusting for known confounders identified in the literature. We report adjusted odds ratios (AOR). Results: Overall, there has been an increase of 14% in facility delivery over the ten years, from 63% in 2001 to 77% in 2010 (p < .001). Women residing in households with female migrants from outside their community were more likely to give birth in a facility AOR = 1.2 (95% CI 1.11-1.29). Furthermore, the previous facility delivery of sisters and sisters-in-law has a significant influence on women's facility delivery; AOR = 1.29, 95% CI 1.15-1.45 and AOR = 1.7, 95% CI 1.35-2.13 respectively. Community level characteristics play a role as well; women in communities with higher socioeconomic status and older women of reproductive age had increased odds of facility delivery; AOR = 2.37, 95% CI 1.88-2.98 and AOR = 1.17, 95% CI 1.03-1.32 respectively.Conclusion: Although there has been an increase in facility delivery over the last decade in Rufiji, this study underscores the importance of female migrants, family members and community in influencing women's place of delivery. The findings of this study suggest that future interventions designed to increase facility delivery must integrate person-to-person facility delivery promotion, especially through women of the community and within families. Furthermore, the results suggest that investment in formal education of the community and increased community socio-economic status may increase facility delivery. This study utilizes delivery records of women who gave birth from 2001 to 2010 in the Rufiji Health and Demographic Surveillance System (RHDSS). The surveillance area was established in 1998 with an initial census conducted by enumerating the entire population. The surveillance system covers an area of 1,813 square kilometers with 33 communities (villages). The surveillance area, and lies about 178 km south of Dar es Salaam (the largest city in the country), and covers approximately 50% of the population of Rufiji District. The study area is geographically formed by 33 administrative villages which will be referred to as communities and serve as a unit of analysis in this study. Dominant ethnic groups are Ndengereko, Makonde and Sukuma. The surveillance system is managed by Ifakara Health Institute (IHI). Rufiji district was the site of two projects aimed at increasing facility delivery. From 1998 – 2003, the Ministry of Health implemented the Tanzania Essential Health Intervention Project (TEHIP). The goal of TEHIP was to increase district capacity to provide expectant mothers with access to basic obstetric and reproductive care. The project provided management tools such as training in management skills, team building, planning, and introduced management systems to help the district focus resources on areas in greatest need [24]. Following TEHIP, the Empower project (2006 – present) upgraded facility infrastructure and supplied equipment to increase access to comprehensive emergency obstetric care. It has trained staff to improve their service provision and increase retention. The population under surveillance in Rufiji is a dynamic cohort that enrolls individuals through birth and in-migration and who exit through death or out-migration. Each enumerated individual is given a unique identification number that is used to longitudinally track demographic events. Field teams visit each registered household three times annually to record dates of births, deaths, in and out -migrations and marital status changes that have occurred to each individual since the previous visit. Birth registration includes detailed information about mother’s place of delivery and assistance during delivery. More details about the surveillance system can be found elsewhere [25]. This paper uses data on all deliveries to women aged 15–49 between 2001 and 2010. A total of 20,049 deliveries were included in the analysis. Although the RHDSS started in 1998, data collection on place of delivery began in 2001. The dichotomous outcome variable compares delivery in a facility to delivery at home. The annual percent of facility deliveries was calculated by the ratio of facility deliveries with all deliveries recorded in the surveillance area. The change in the percent of facility delivery was calculated as difference in proportion in 2001 and 2010 at 5% significance level. The predictor variables of interest were constructed as follows. Presence of an in-migrant in a household was captured from the dataset documenting in and out-migration in the surveillance area. We construct a binary measure indicating presence of an adult female in-migrant in a household two years prior to delivery. Sisters’ place of delivery was constructed by first identifying sisters through matching parent identification numbers, and then determining place of delivery for sisters’ previous births using the birth registry data. We then construct a count variable for how many times she has given birth in a facility. The measure of sisters-in-law’s place of delivery is similarly constructed; women are linked to their husbands, and we then use husband’s identification number to link him to his sisters through matching parent identification numbers. We included sisters-in-law’s experience with facility delivery because Rufiji is a patrilineal society, and women are likely to reside in their husbands’ home village. To capture the influence of the community, we calculate the mean age of women of reproductive age, mean household wealth, and mean years of education of women of reproductive age by village using all residents in the village at the time of birth. Control measures were constructed as follows. Wealth measures were constructed using an asset index constructed through principal components analysis, based on asset ownership (such as bicycle, radio, bed) and structural characteristics of the home (such as roofing and building material) [26]. Asset data are collected annually. Maternal characteristics of education, work (paid/other) and marital status were captured at baseline or during routine updates of household data. Parity, multiple births, season of birth, place of delivery and outcome of every pregnancy were extracted from birth registration data. Distance to the nearest health facility was calculated from GPS points mapped at each household and health facility in the surveillance area. Mapping of geographic boundaries was conducted to locate all 33 communities and main roads. Proportion of facility delivery for each community was added to the surveillance map to geographically explore the facility delivery in relation to village main road connections. Exploratory analysis was conducted by plotting annual proportions of facility delivery (number of reproductive aged women who delivered at the facility/total number of deliveries) by socio-economic status, education, previous birth outcome and previous place of delivery. Multilevel logistic regression was used to estimate the hypothesized influence of in-migrants, family and community-level factors on facility delivery while adjusting for known confounders including mother’s age, birth order, season of birth, place of delivery of previous pregnancy, and marital status. Multilevel models were specified where children are nested within their mothers and mothers within communities. Mother and community level random effects were included because many individual and community-level factors that may influence facility delivery are not collected or are unobserved, such as husband’s approval of facility delivery and tribe. Maximum likelihood was used to estimate regression model coefficients and results are presented as odds ratios with 95% confidence interval. The study protocol was approved by Ifakara Health Institution Internal Review Board, National Institute of Health Research and Commission of Science and Technology, Tanzania. All participants were explained the demographic surveillance system study protocol and were enrolled through informed consent. Our research had adhered to the STROBE guidelines for observational cohort studies (Additional file 1).

AI Digest

Study Justification:

This study aims to fill a knowledge gap in the existing literature by examining how migration, family members, and community characteristics influence facility delivery in Rufiji, Tanzania. The study is important because health professionals and public health experts encourage women to deliver at health facilities to reduce maternal and newborn mortality. Understanding the factors that influence facility delivery can help inform interventions and policies to increase facility delivery rates.

Highlights:

– Over the ten-year period from 2001 to 2010, there was a 14% increase in facility delivery in Rufiji, Tanzania.
– Women residing in households with female migrants from outside their community were more likely to give birth in a facility.
– The previous facility delivery of sisters and sisters-in-law had a significant influence on women’s facility delivery.
– Community-level characteristics, such as higher socioeconomic status and older women of reproductive age, were associated with increased odds of facility delivery.

Recommendations for Lay Reader:

– Future interventions should focus on person-to-person facility delivery promotion, especially through women of the community and within families.
– Investment in formal education of the community and increased community socioeconomic status may increase facility delivery rates.

Recommendations for Policy Maker:

– Integrate person-to-person facility delivery promotion into interventions aimed at increasing facility delivery rates.
– Invest in formal education programs for the community to improve facility delivery rates.
– Consider strategies to improve the socioeconomic status of the community to increase facility delivery rates.

Key Role Players:

– Health professionals and public health experts
– Community leaders and influencers
– Women’s groups and organizations
– Local government officials
– Non-governmental organizations (NGOs) working in maternal and newborn health

Cost Items for Planning Recommendations:

– Training programs for health professionals and community leaders
– Educational materials and campaigns
– Infrastructure improvements in health facilities
– Community development programs to improve socioeconomic status
– Monitoring and evaluation activities to assess the impact of interventions

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it utilizes 10 years of longitudinal surveillance data and employs multilevel logistic regression to quantify the influence of migration, family, and community-level factors on facility delivery. The study also adjusts for known confounders identified in the literature. However, to improve the evidence, the abstract could provide more information on the sample size, data collection methods, and statistical analysis techniques used. Additionally, including information on the limitations of the study and potential biases would further strengthen the evidence.

Abstract

This study utilizes delivery records of women who gave birth from 2001 to 2010 in the Rufiji Health and Demographic Surveillance System (RHDSS). The surveillance area was established in 1998 with an initial census conducted by enumerating the entire population. The surveillance system covers an area of 1,813 square kilometers with 33 communities (villages). The surveillance area, and lies about 178 km south of Dar es Salaam (the largest city in the country), and covers approximately 50% of the population of Rufiji District. The study area is geographically formed by 33 administrative villages which will be referred to as communities and serve as a unit of analysis in this study. Dominant ethnic groups are Ndengereko, Makonde and Sukuma. The surveillance system is managed by Ifakara Health Institute (IHI). Rufiji district was the site of two projects aimed at increasing facility delivery. From 1998 – 2003, the Ministry of Health implemented the Tanzania Essential Health Intervention Project (TEHIP). The goal of TEHIP was to increase district capacity to provide expectant mothers with access to basic obstetric and reproductive care. The project provided management tools such as training in management skills, team building, planning, and introduced management systems to help the district focus resources on areas in greatest need [24]. Following TEHIP, the Empower project (2006 – present) upgraded facility infrastructure and supplied equipment to increase access to comprehensive emergency obstetric care. It has trained staff to improve their service provision and increase retention. The population under surveillance in Rufiji is a dynamic cohort that enrolls individuals through birth and in-migration and who exit through death or out-migration. Each enumerated individual is given a unique identification number that is used to longitudinally track demographic events. Field teams visit each registered household three times annually to record dates of births, deaths, in and out -migrations and marital status changes that have occurred to each individual since the previous visit. Birth registration includes detailed information about mother’s place of delivery and assistance during delivery. More details about the surveillance system can be found elsewhere [25]. This paper uses data on all deliveries to women aged 15–49 between 2001 and 2010. A total of 20,049 deliveries were included in the analysis. Although the RHDSS started in 1998, data collection on place of delivery began in 2001. The dichotomous outcome variable compares delivery in a facility to delivery at home. The annual percent of facility deliveries was calculated by the ratio of facility deliveries with all deliveries recorded in the surveillance area. The change in the percent of facility delivery was calculated as difference in proportion in 2001 and 2010 at 5% significance level. The predictor variables of interest were constructed as follows. Presence of an in-migrant in a household was captured from the dataset documenting in and out-migration in the surveillance area. We construct a binary measure indicating presence of an adult female in-migrant in a household two years prior to delivery. Sisters’ place of delivery was constructed by first identifying sisters through matching parent identification numbers, and then determining place of delivery for sisters’ previous births using the birth registry data. We then construct a count variable for how many times she has given birth in a facility. The measure of sisters-in-law’s place of delivery is similarly constructed; women are linked to their husbands, and we then use husband’s identification number to link him to his sisters through matching parent identification numbers. We included sisters-in-law’s experience with facility delivery because Rufiji is a patrilineal society, and women are likely to reside in their husbands’ home village. To capture the influence of the community, we calculate the mean age of women of reproductive age, mean household wealth, and mean years of education of women of reproductive age by village using all residents in the village at the time of birth. Control measures were constructed as follows. Wealth measures were constructed using an asset index constructed through principal components analysis, based on asset ownership (such as bicycle, radio, bed) and structural characteristics of the home (such as roofing and building material) [26]. Asset data are collected annually. Maternal characteristics of education, work (paid/other) and marital status were captured at baseline or during routine updates of household data. Parity, multiple births, season of birth, place of delivery and outcome of every pregnancy were extracted from birth registration data. Distance to the nearest health facility was calculated from GPS points mapped at each household and health facility in the surveillance area. Mapping of geographic boundaries was conducted to locate all 33 communities and main roads. Proportion of facility delivery for each community was added to the surveillance map to geographically explore the facility delivery in relation to village main road connections. Exploratory analysis was conducted by plotting annual proportions of facility delivery (number of reproductive aged women who delivered at the facility/total number of deliveries) by socio-economic status, education, previous birth outcome and previous place of delivery. Multilevel logistic regression was used to estimate the hypothesized influence of in-migrants, family and community-level factors on facility delivery while adjusting for known confounders including mother’s age, birth order, season of birth, place of delivery of previous pregnancy, and marital status. Multilevel models were specified where children are nested within their mothers and mothers within communities. Mother and community level random effects were included because many individual and community-level factors that may influence facility delivery are not collected or are unobserved, such as husband’s approval of facility delivery and tribe. Maximum likelihood was used to estimate regression model coefficients and results are presented as odds ratios with 95% confidence interval. The study protocol was approved by Ifakara Health Institution Internal Review Board, National Institute of Health Research and Commission of Science and Technology, Tanzania. All participants were explained the demographic surveillance system study protocol and were enrolled through informed consent. Our research had adhered to the STROBE guidelines for observational cohort studies (Additional file 1).

Materials

https://efashare.b-cdn.net/materials/eb7664a9855a3a9394907c479b868827f2cede1d6f6cfa46182b76e51618c830/12884_2014_1205_MOESM1_ESM.doc

Innovations

Based on the information provided, here are some potential innovations that could improve access to maternal health:

1. Person-to-person facility delivery promotion: This study suggests that future interventions should focus on promoting facility delivery through person-to-person communication. This could involve training women within the community to educate and encourage expectant mothers to deliver at health facilities.

2. Integration of family members in facility delivery promotion: The study found that the previous facility delivery of sisters and sisters-in-law had a significant influence on women’s facility delivery. Future interventions could involve engaging family members, particularly sisters and sisters-in-law, in promoting facility delivery and providing support to expectant mothers.

3. Investment in formal education and community socio-economic status: The study also found that women in communities with higher socioeconomic status and older women of reproductive age had increased odds of facility delivery. Therefore, investing in formal education of the community and improving community socio-economic status may help increase facility delivery rates.

4. Upgrading facility infrastructure and equipment: The Empower project mentioned in the study upgraded facility infrastructure and supplied equipment to increase access to comprehensive emergency obstetric care. Similar initiatives could be implemented to improve the quality and availability of maternal health services in rural areas.

5. Improving transportation and accessibility: Since the study area is located in a rural district, improving transportation options and accessibility to health facilities could help overcome geographical barriers and increase facility delivery rates.

6. Strengthening health systems and capacity building: The Tanzania Essential Health Intervention Project (TEHIP) mentioned in the study aimed to increase district capacity to provide expectant mothers with access to basic obstetric and reproductive care. Similar projects could be implemented to strengthen health systems and build the capacity of healthcare providers to deliver quality maternal health services.

It is important to note that these recommendations are based on the specific findings and context of the study mentioned. Further research and contextual analysis would be needed to tailor these recommendations to specific settings and populations.

AI Innovations Description

The study titled “Female migrants, family members and community socio-demographic characteristics influence facility delivery in Rufiji, Tanzania” explores the factors that influence facility delivery for maternal health in a rural district of Tanzania. The study utilizes 10 years of longitudinal surveillance data to identify the impact of migration, family members, and community-level factors on facility delivery.

The findings of the study highlight several important factors that influence facility delivery. Firstly, women residing in households with female migrants from outside their community were more likely to give birth in a facility. This suggests that the presence of female migrants can play a role in promoting facility delivery.

Secondly, the previous facility delivery of sisters and sisters-in-law also had a significant influence on women’s facility delivery. Women were more likely to give birth in a facility if their sisters or sisters-in-law had previously done so. This indicates the importance of social influence within families in promoting facility delivery.

Additionally, community-level characteristics were found to play a role in facility delivery. Women in communities with higher socioeconomic status and older women of reproductive age had increased odds of facility delivery. This suggests that community-level factors, such as education and socioeconomic status, can impact women’s access to facility delivery.

Based on these findings, the study recommends that future interventions aimed at improving access to maternal health should integrate person-to-person facility delivery promotion, particularly through women within the community and within families. Furthermore, the results suggest that investment in formal education of the community and increased community socioeconomic status may also contribute to increased facility delivery.

Overall, this study provides valuable insights into the factors that influence facility delivery for maternal health in a rural district of Tanzania and offers recommendations for developing interventions to improve access to maternal health services.

AI Innovations Methodology

Based on the provided study, here are some potential recommendations for innovations to improve access to maternal health:

1. Person-to-person facility delivery promotion: Integrate interventions that involve women of the community and within families to promote facility delivery. This could include training community members, such as female migrants, to provide information and support to pregnant women, encouraging them to deliver at health facilities.

2. Investment in formal education: Increase investment in formal education of the community to improve awareness and understanding of the importance of facility delivery. This could include educational campaigns, workshops, and programs that target both women and men in the community.

3. Improve community socio-economic status: Implement interventions that aim to increase the socio-economic status of the community. This could involve initiatives to improve income generation, access to resources, and overall living conditions, which may positively influence women’s decision to deliver at health facilities.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define the indicators: Identify key indicators that reflect access to maternal health, such as the proportion of facility deliveries, maternal mortality rates, or antenatal care coverage.

2. Collect baseline data: Gather data on the selected indicators before implementing the recommendations. This could involve conducting surveys, interviews, or analyzing existing data sources.

3. Implement the recommendations: Introduce the recommended innovations, such as person-to-person facility delivery promotion, investment in formal education, and initiatives to improve community socio-economic status.

4. Monitor and evaluate: Continuously monitor and evaluate the impact of the recommendations on the selected indicators. This could involve collecting data at regular intervals, conducting surveys or interviews with women in the community, and analyzing the data to assess any changes in access to maternal health.

5. Analyze the data: Use statistical analysis techniques to analyze the collected data and determine the impact of the recommendations. This could involve comparing the indicators before and after the implementation of the innovations, conducting regression analysis to identify associations, or using modeling techniques to simulate the potential impact on a larger scale.

6. Adjust and refine: Based on the findings from the analysis, make any necessary adjustments or refinements to the recommendations. This could involve modifying the interventions, targeting specific groups within the community, or addressing any barriers or challenges that may have been identified.

7. Repeat the process: Continuously repeat the monitoring, evaluation, and analysis process to assess the ongoing impact of the recommendations and make further improvements as needed.

By following this methodology, it would be possible to simulate the impact of the recommended innovations on improving access to maternal health and make evidence-based decisions for future interventions.

Authors & Co-Authors

Statistics:

Citations: 1

Authors: 3

Identifiers:

DOI: 10.1186/1471-2393-14-329

Research Areas:

Community Interventions, Disparities, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Technology and Innovations

Study Countries:

Tanzania

Study Design:

Case-Control Study, Cohort Study, Cross Sectional Study, Exploratory Study, Grounded Theory, randomised-control-trial

Participants Gender:

Female