Trends and Drivers of Unmet Need for Family Planning in Currently Married Tanzanian Women between 1999 and 2016

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Study Justification:
– The study aimed to investigate the trends and factors associated with the unmet need for family planning (FP) among married Tanzanian women between 1999 and 2016.
– Understanding the trends and drivers of unmet need for FP is crucial for developing effective policies and interventions to improve FP uptake and reduce unmet needs.
Study Highlights:
– The study used data from the Tanzania Demographic and Health Survey (TDHS) for the years 1999, 2004-2005, 2010, and 2015-2016.
– The percentage of married women with an unmet need for birth spacing remained unchanged between 1999 and 2016.
– The proportion of married women with an unmet need for limiting births decreased from 9.5% in 1999 to 6.6% in 2016.
– Factors positively associated with the unmet need for FP-spacing included residing in a rural area, parity between 1-4 and 5+, visiting a health facility within twelve months, and planning to have more children.
– Women with parity of 5+ were more likely to experience an unmet need for FP-limiting.
– Factors associated with lower odds of having an unmet need for FP-spacing included women’s age between 25-34 and 35-49 years, women’s employment status, watching television, women’s autonomy in household decisions, and planning to have more children.
– Factors associated with lower odds of having an unmet need for FP-limiting included women’s age between 25-34 years, watching television, autonomy, and planning to have more children.
Recommendations for Lay Reader and Policy Maker:
– Improving FP uptake among married Tanzanian women can reduce the unmet need for FP.
– Government policies and interventions should target women residing in rural areas and address modifiable risk factors such as parity, health facility visits, planning to have more children, employment, watching television, and women’s autonomy.
Key Role Players:
– Government of United Republic of Tanzania
– Global Affairs Canada
– United States Agency for International Development (USAID)
– National Bureau of Statistics (NBS)
– Office of the Chief Government Statistician (OCGS) in Zanzibar
– Inner-City Funds
Cost Items for Planning Recommendations:
– Funding for government policies and interventions
– Resources for targeting women residing in rural areas
– Training and capacity building for healthcare workers
– Awareness campaigns through television and other media channels
– Access to affordable and quality family planning services
– Monitoring and evaluation of the implemented interventions

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because the study used a large sample size and data from multiple years. The study also employed multivariable multinomial logistic regression models to investigate the association between various factors and the unmet need for family planning. To improve the evidence, the abstract could provide more information on the statistical significance of the associations found and the effect sizes of the factors. Additionally, it would be helpful to include information on any limitations of the study and suggestions for future research.

The current study investigated the trends and factors associated with the unmet need for family planning (FP) for limiting and spacing births among married Tanzanian women between 1999 and 2016. The study used Tanzania Demographic and Health Survey (TDHS) data for the years 1999 (N = 2653), 2004–2005 (N = 2950), 2010 (N = 6412), and 2015–2016 (N = 8210). Trends in the unmet need for FP were estimated over the study period. Multivariable multinomial logistic regression models were used to investigate the association between community-level, predisposing, enabling, and need factors with the unmet need for FP in Tanzania. The results showed no significant change in percentage of married women with an unmet need for birth spacing between 1999 and 2016. The proportion of married women with an unmet need for limiting births decreased from 9.5% (95% confidence interval (CI): 7.9%, 10.6%) in 1999 to 6.6% (95% CI: 5.9%, 7.3%) in 2016. Residing in a rural area, parity between 1–4 and 5+, visiting a health facility for any health services within twelve months, and planning to have more children (after two years and/or undecided) were factors positively associated with the unmet need for FP-spacing. Women with parity of 5+ were more likely to experience an unmet need for FP-limiting. Women’s age between 25–34 and 35–49 years, women’s employment status, watching television, women’s autonomy of not being involved in household decisions, and planning to have more children were factors associated with lower odds of having an unmet need for FP-spacing. Women’s age between 25–34 years, watching television, autonomy, and planning to have more children were factors with lower odds of having an unmet need for FP-limiting. Improving FP uptake among married Tanzanian women can reduce the unmet need for FP. Therefore, reducing unmet needs for FP is attainable if government policies and interventions can target women residing in rural areas and other modifiable risk factors, such as parity, health facility visits, planning to having more children, employment, watching television, and women’s autonomy.

The study used TDHS data from 1999 to 2016, 1999 (N = 2653), 2004/05 (N = 6950), 2010 (N = 6412), and 2015–16 (N = 8210). The National Bureau of Statistics (NBS), Office of the Chief Government Statistician (OCGS) in Zanzibar, and Inner-City Funds collected the data. The project was funded by the Government of United Republic of Tanzania, Global Affairs Canada, and the United States Agency for International Development (USAID) [16]. Data for maternal health, including FP, child health, infant nutrition, and other health-related data, were collected based on a nationally representative population in Tanzania [17,18,19,20]. The data were collected from eligible women aged 15–49 years who were married or cohabiting and were residents in the household 24 h prior to the survey using a two-stage stratified cluster sampling technique. In stage one, enumeration areas (EAs) were selected proportional to each geographical zone of Tanzania. The EAs were based on the 1988, 2002, and 2012 Tanzania Population and Housing Censuses, respectively [21,22,23]. In stage two, a systematic random sampling technique was used to select households after the complete household listing was conducted in each EAs. The response rates in the surveys were 98% in 1999, 97% in 2005–2004, 96% in 2010, and 97% in 2015–2016. The full methodological approaches used in the surveys are provided in the respective TDHS reports [17,18,19,20]. The present study was conducted based on a total weighted sample of 24,225 married and/or cohabiting women who were not using FP on the day of the interview but reported a willingness to use modern FP methods [24,25,26,27]. The United Republic of Tanzania has 31 regions (28 in mainland Tanzania and 3 in Tanzania Island) with a total of 184 districts. Geographically, Tanzania is divided into regions, districts, divisions, wards, and villages. Tanzania is the largest country in East Africa, covering a total of 947,300 km2. Based on 2022 National Census results, Tanzania has a total of 61,741,120 people, of whom 30,053,130 (48.8%) are men and 31,687,990 (51.3%) are women [28]. The 2015–2016 TDHS indicated that the total fertility rate was 5.2 [19] The study outcome was the unmet need for FP, measured as the proportion of women who: (1) are not pregnant or postpartum amenorrhoeic and are considered fecund and want to postpone their next birth for 2 or more years or stop childbearing altogether, but are not using a contraceptive method; (2) have a mistimed or unwanted current pregnancy; or (3) are postpartum amenorrhoeic and their last birth in the last 2 years was mistimed or unwanted [10]. The unmet need for FP was divided into: (i) unmet need for spacing births; and (ii) unmet need for limiting births (that is, women who want to space or limit births but are not currently using FP methods) [10]. Dividing the unmet need for FP into the need for spacing and limiting births provides additional and detailed information about issues related to non-use of FP methods among currently married women in Tanzania. In this study, we assumed that currently married women were sexually active, which was consistent with past studies [25,29,30,31,32,33]. The concept of unmet need for FP reflects the gap between the desire for childbearing and the use of FP methods. Using the Bradley et al. model [34], the unmet need for FP was computed. We categorized the unmet need for FP as “no unmet need or met need for FP (currently using FP),” “unmet need for spacing,” and “unmet need for limiting.” For this study, the exposure variables were selected based on previous studies from LMICs [24,25,26,27,35] and availability in the DHS data. The study factors were broadly classified as community-level, predisposing, enabling, and need factors based on the Anderson conceptual model of health service utilization and health-seeking behaviors [36]. The adopted conceptual model was consistent with past studies that examined the relationship between study factors and the unmet need for FP among married women [24,25,26,35,37,38]. Community-level factors included the place of residence, which was categorized as ‘rural’ or ‘urban’. Predisposing included sociodemographic and health knowledge factors. Sociodemographic factors included women’s age (grouped as ‘15–24 years,’ ‘25–34 years,’ or ‘35–49 years’), parity (grouped as ‘zero,’ ‘one to four,’ or ‘five or more’), women’s and husband’s education (grouped as ‘no schooling’ or ‘primary education or higher’), women’s and husband’s employment (grouped as ‘no employment,’ ‘formal employment,’ or ‘informal employment’), household wealth index (grouped as ‘poor,’ ‘middle,’ or ‘rich’), and the number of partners a woman has (grouped as ‘one’ or ‘more than one partner’). Household wealth index was calculated by the National Bureau of Statistics and Inner-City Funds using principal component analysis (PCA), considering the ownership of household assets such as toilets, electricity, television, radio, fridge, and bicycle, as well as the availability of the source of drinking water and the floor material used for the main house [39]. Health knowledge factors included listening to the radio, watching television, and reading newspapers/magazines (which were grouped as ‘yes’ or ‘no’), visit by health care workers within the last 12 months prior to the survey (grouped as ‘yes’ or ‘no’), and health facility visit within the last twelve months (grouped as ‘yes’ or ‘no’). Enabling factors included the distance to a health facility (grouped as ‘big problem’ or ‘not a big problem’ based on Measure DHS classification), women’s autonomy (grouped as ‘involved in household decision making’ or ‘not involved in household decision making’), household head (grouped as ‘male headed’ or ‘female headed’). The TDHS also collected information on reasons for not using FP methods, such as infrequent sex, sub-fecund, amenorrhea, breastfeeding, no need for more children, mother opposition, partner opposition, religion, not knowing FP methods, not knowing source of FP methods, fear of side effects, lack of access, too much cost, inconvenient to use FP methods, and interferes with body’s natural process (grouped as ‘yes’ or ‘no’). Need factors included planning to have more children (grouped as ‘do not want more,’ ‘want within two years,’ ‘want after two years and above or undecided’). All the study factors were grouped based on previous evidence from past studies from LMICs [24,25,26,27,31,38,40,41,42,43] (Figure 1). Conceptual model for unmet need for FP adopted from Anderson’s health service utilization model [36]. Our analytical approach was stepwise, based on previous studies conducted elsewhere [24,25,26,38,40,41,42,43]. The first step involved the estimation of frequencies and percentages of each study factor. Second, the prevalence of and trends in the unmet need for spacing and limiting births were calculated across the study factors (community-level, predisposing, enabling, and need factors) for each year from 1999 to 2016. Third, univariable logistic regression was conducted to establish associations between the study factors and the outcome variable of the unmet need for FP for spacing and limiting births. Fourth, multivariable logistic regression models were used to examine the associations between community-level, predisposing, enabling, and need factors with the outcome variable of the unmet need for FP. The multinomial regression model used in the analysis was based on the Anderson model [35] and was also based on other previous studies conducted in LMICs [24,25,26,31,35,37,38,40,41,42,43]. Community-level factor was entered into the stage 1 model to measure its association with the outcome variable, while adjusting for predisposing, enabling, and need factors. The same strategy was used for the predisposing factors (sociodemographic and health knowledge factors) model to establish the relationship with outcome variable, while adjusting for community-level, enabling, and need factors (stage 2). The corresponding modeling procedure was used for enabling factors and need factors in the third and fourth models (stage 3 and 4), respectively. In addition, the multivariable regression model was conducted for combining the TDHS data from 1999 to 2016: (i) to examine the trends and factors associated with the unmet need for spacing and limiting; (ii) to provide the exceptional opportunity to compare the unmet need for FP over time; (iii) to improve the statistical power of the study. Odds ratios (ORs) with 95% confidence intervals (CIs) were provided as the measure of association between the study variables and outcome variable. Analysis was performed using STATA version 14 software (Stata Corp, College Station, TX, USA), with the ‘svy’ command used to adjust for sampling weights, clustering, and stratification in both year-specific and combined datasets. The ‘mlogit’ function was used in the multinomial logistic regression models [44]. Furthermore, multi-collinearity was checked to examine for any influential variables related to each other using the ‘regress’ command [45] and reasons for not using FP were eliminated from the model due to high multi-collinearity.

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

1. Mobile Health (mHealth) Solutions: Develop mobile applications or SMS-based systems to provide information and reminders about family planning methods, antenatal care, and postnatal care. These tools can also facilitate communication between healthcare providers and pregnant women, allowing for remote consultations and follow-ups.

2. Community Health Workers: Train and deploy community health workers to provide education, counseling, and basic maternal healthcare services in rural areas where access to healthcare facilities is limited. These workers can also help identify and refer high-risk pregnancies for specialized care.

3. Telemedicine: Establish telemedicine networks to connect remote healthcare facilities with specialists in urban areas. This would enable healthcare providers in underserved areas to consult with experts and receive guidance on complicated cases, improving the quality of care for pregnant women.

4. Task Shifting: Train and empower nurses and midwives to perform certain tasks traditionally done by doctors, such as antenatal check-ups, basic obstetric care, and family planning counseling. This would help alleviate the shortage of skilled healthcare providers and increase access to maternal health services.

5. Financial Incentives: Implement financial incentives, such as conditional cash transfers or vouchers, to encourage pregnant women to seek antenatal care, deliver in healthcare facilities, and use family planning services. This can help overcome financial barriers and increase utilization of maternal health services.

6. Public-Private Partnerships: Foster collaborations between the government, private sector, and non-profit organizations to improve access to maternal health services. This could involve leveraging private sector resources and expertise to expand healthcare infrastructure, supply chain management, and service delivery in underserved areas.

7. Quality Improvement Initiatives: Implement quality improvement programs in healthcare facilities to ensure that maternal health services are delivered in a safe, respectful, and effective manner. This could involve training healthcare providers, improving infection control practices, and strengthening referral systems.

8. Health Information Systems: Develop and strengthen health information systems to collect, analyze, and disseminate data on maternal health indicators. This would enable policymakers and healthcare providers to make evidence-based decisions and monitor progress towards improving maternal health outcomes.

It’s important to note that the specific context and needs of the Tanzanian healthcare system should be taken into consideration when implementing these innovations.
AI Innovations Description
Based on the information provided, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Targeted interventions for rural areas: Since residing in a rural area was positively associated with the unmet need for family planning (FP) spacing, it is important to develop targeted interventions specifically designed for rural communities. This could include mobile clinics or outreach programs that provide FP services and education to women in rural areas.

2. Increasing awareness through media: Women who watched television had lower odds of having an unmet need for FP-spacing. Therefore, utilizing television and other media platforms to disseminate information about maternal health and family planning can help increase awareness and knowledge among women.

3. Empowering women: Women’s autonomy and involvement in household decision-making were factors associated with lower odds of having an unmet need for FP-spacing. Promoting women’s empowerment through education and economic opportunities can help improve access to maternal health services and increase the uptake of family planning methods.

4. Strengthening health facility services: Health facility visits within the last twelve months were positively associated with the unmet need for FP-spacing. Improving the quality and availability of maternal health services in health facilities can encourage women to seek care and access family planning services.

5. Addressing modifiable risk factors: Factors such as parity, planning to have more children, and employment status were associated with the unmet need for FP-spacing. Developing interventions that address these modifiable risk factors, such as providing counseling on birth spacing and family planning methods, can help reduce the unmet need for FP.

Overall, a comprehensive approach that combines targeted interventions, media campaigns, women’s empowerment, and strengthening health facility services can contribute to improving access to maternal health and reducing the unmet need for family planning in Tanzania.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Strengthening healthcare infrastructure: Investing in the development and improvement of healthcare facilities, particularly in rural areas, can help increase access to maternal health services. This includes ensuring the availability of skilled healthcare providers, essential medical equipment, and necessary supplies.

2. Enhancing community-based interventions: Implementing community-based programs that focus on educating and empowering women and their families about maternal health can improve access. These interventions can include awareness campaigns, health education sessions, and the establishment of support groups.

3. Improving transportation systems: Enhancing transportation systems, especially in remote areas, can help overcome geographical barriers and enable pregnant women to reach healthcare facilities in a timely manner. This can involve providing transportation subsidies, mobile clinics, or telemedicine services.

4. Strengthening health information systems: Developing robust health information systems can facilitate better monitoring and evaluation of maternal health services. This can help identify gaps and areas for improvement, leading to more targeted interventions.

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

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

2. Baseline data collection: Gather data on the selected indicators before implementing the recommendations. This can be done through surveys, health facility records, or existing data sources.

3. Introduce interventions: Implement the recommended interventions, such as strengthening healthcare infrastructure, community-based interventions, improving transportation systems, and enhancing health information systems.

4. Data collection after intervention: Collect data on the selected indicators after the interventions have been implemented. Ensure that the data collection methods are consistent with the baseline data collection.

5. Data analysis: Analyze the collected data to assess the impact of the interventions on the selected indicators. This can involve comparing the pre- and post-intervention data and conducting statistical analyses to determine the significance of any changes observed.

6. Interpretation and reporting: Interpret the findings of the data analysis and report on the impact of the interventions on improving access to maternal health. This can include summarizing the results, identifying any challenges or limitations, and making recommendations for further improvement.

By following this methodology, it would be possible to simulate the impact of the recommended interventions on improving access to maternal health and provide evidence-based insights for future decision-making and policy development.

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