Study design
It was a national-based cross-sectional study utilizing the 2015-16 Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS) dataset. The study population was all women of reproductive age (aged 15-49 years). We used Individual file recode (TZIR7BFL) with a total of 13266 women who responded to the survey (97% response rate). To ensure we work with recent information, we then excluded all women who did not have recent birth (birth within a past year) which resulted in 2286 women.
Setting
The study was done among women who had given birth in a period of one year preceding the survey in Tanzania from August 22, 2015, through February 14, 2016. United Republic of Tanzania being the largest country in East Africa, cover 940,000 square kilometers, 60,000 of which are inland water. Tanzania lies south of the equator and shares borders with eight countries: Kenya and Uganda to the North; Rwanda, Burundi, the Democratic Republic of Congo, and Zambia to the West; and Malawi and Mozambique to the South.
Study population
Women of reproductive age who gave birth one year presiding the survey. In order to enhance recall and minimize recall bias women who had given birth one year prior the survey were included instead of those who had given birth five years prior
Sampling Technique
Two stages were used to obtain a sample for urban and rural areas in Tanzania Mainland and Zanzibar. In the first stage of sample selection, a total of 608 clusters were selected and in the second stage, a systematic selection of households was involved. A total of 22 households were then systematically selected from each cluster, yielding a representative probability sample of 13,376 households for the 2015-16 TDHS-MIS. To enhance representativeness Tanzania was divided into nine geographic zones. Grouping the regions into zones was done to reduce sampling error by increasing the number of people in the denominator. Zone were western zone (Tabora and Kigoma regions), Northern zone (Kilimanjaro, Tanga, and Arusha), Central zone (Dodoma, Singida and Manyara), Southern Highland zone (Iringa, Njombe, and Iringa), Southern zone (Lindi and Mtwara), South West Highland zone (Mbeya Rukwa and Katavi), Lake zone (Kagera, Mwanza, Geita, Mara, Simiyu, and Shinyanga), Eastern zone (Dar es Salaam, Pwani, and Morogoro) and Zanzibar (Kaskazini Unguja, Kusini Unguja, Mjini Magharibi, Kaskazini Pemba and Kusini Pemba).
Data Collection tool
The 2015-16 TDHS-MIS used household questionnaires and individual questionnaires. These questionnaires based on the Measure DHS standard AIDS Indicator Survey and Malaria Indicator Survey questionnaires standards. They were adapted and modified to reflect the Tanzanian population. They were translated into Kiswahili, Tanzania’s national language. The data presented in this study are from the individual questionnaire.
Study variables
Through a literature review, the conceptual framework was developed to guide the conceptualization. The conceptual framework had primary independent variables (socio-demographic and obstetric characteristics of a woman), the intermediate variable which was the antenatal services utilization and the outcome variable which was home-based delivery. The outcome variable was a dummy variable coded as 1 if women delivered at home and 0 otherwise. Independent variables included demographic and antenatal care practice variables among women.
Data Analysis
Data were analyzed using IBM SPSS version 20. We started by grouping women who had given birth within one year prior the survey was extracted from individuals file (TZIR7BFL) in DHS data. Women who had childbirth at home were coded as one and they were the reference population and those who had childbirth in health facility coded as 0. The reason for choosing this group was to minimize recall bias, it is the group we expected to find minimal missed data. Data analysis started by describing all study variables using frequencies and percentages, we then assessed the association between a dependent variable and independent variables using chi-square, and finally, we performed binary logistic regression analysis (univariate and multivariable) to determine significant predictors of the choice of place of delivery. All analyses were based at a 5% level of significance.
Study design
It was a national-based cross-sectional study utilizing the 2015-16 Tanzania Demographic and Health Survey and Malaria Indicator Survey (TDHS-MIS) dataset. The study population was all women of reproductive age (aged 15-49 years). We used Individual file recode (TZIR7BFL) with a total of 13266 women who responded to the survey (97% response rate). To ensure we work with recent information, we then excluded all women who did not have recent birth (birth within a past year) which resulted in 2286 women.
Setting
The study was done among women who had given birth in a period of one year preceding the survey in Tanzania from August 22, 2015, through February 14, 2016. United Republic of Tanzania being the largest country in East Africa, cover 940,000 square kilometers, 60,000 of which are inland water. Tanzania lies south of the equator and shares borders with eight countries: Kenya and Uganda to the North; Rwanda, Burundi, the Democratic Republic of Congo, and Zambia to the West; and Malawi and Mozambique to the South.
Study population
Women of reproductive age who gave birth one year presiding the survey. In order to enhance recall and minimize recall bias women who had given birth one year prior the survey were included instead of those who had given birth five years prior
Sampling Technique
Two stages were used to obtain a sample for urban and rural areas in Tanzania Mainland and Zanzibar. In the first stage of sample selection, a total of 608 clusters were selected and in the second stage, a systematic selection of households was involved. A total of 22 households were then systematically selected from each cluster, yielding a representative probability sample of 13,376 households for the 2015-16 TDHS-MIS. To enhance representativeness Tanzania was divided into nine geographic zones. Grouping the regions into zones was done to reduce sampling error by increasing the number of people in the denominator. Zone were western zone (Tabora and Kigoma regions), Northern zone (Kilimanjaro, Tanga, and Arusha), Central zone (Dodoma, Singida and Manyara), Southern Highland zone (Iringa, Njombe, and Iringa), Southern zone (Lindi and Mtwara), South West Highland zone (Mbeya Rukwa and Katavi), Lake zone (Kagera, Mwanza, Geita, Mara, Simiyu, and Shinyanga), Eastern zone (Dar es Salaam, Pwani, and Morogoro) and Zanzibar (Kaskazini Unguja, Kusini Unguja, Mjini Magharibi, Kaskazini Pemba and Kusini Pemba).
Data Collection tool
The 2015-16 TDHS-MIS used household questionnaires and individual questionnaires. These questionnaires based on the Measure DHS standard AIDS Indicator Survey and Malaria Indicator Survey questionnaires standards. They were adapted and modified to reflect the Tanzanian population. They were translated into Kiswahili, Tanzania’s national language. The data presented in this study are from the individual questionnaire.
Study variables
Through a literature review, the conceptual framework was developed to guide the conceptualization. The conceptual framework had primary independent variables (socio-demographic and obstetric characteristics of a woman), the intermediate variable which was the antenatal services utilization and the outcome variable which was home-based delivery. The outcome variable was a dummy variable coded as 1 if women delivered at home and 0 otherwise. Independent variables included demographic and antenatal care practice variables among women.
Data Analysis
Data were analyzed using IBM SPSS version 20. We started by grouping women who had given birth within one year prior the survey was extracted from individuals file (TZIR7BFL) in DHS data. Women who had childbirth at home were coded as one and they were the reference population and those who had childbirth in health facility coded as 0. The reason for choosing this group was to minimize recall bias, it is the group we expected to find minimal missed data. Data analysis started by describing all study variables using frequencies and percentages, we then assessed the association between a dependent variable and independent variables using chi-square, and finally, we performed binary logistic regression analysis (univariate and multivariable) to determine significant predictors of the choice of place of delivery. All analyses were based at a 5% level of significance.