Male Involvement in Family Planning Decisions in Malawi and Tanzania: What Are the Determinants?

International journal of environmental research and public health, Volume 20, No. 6, Year 2023

Interprétation

The participation of males in joint spousal decisions is urgently needed in achieving the fundamental indicators of reproductive health. The low involvement of males in family planning (FP) decision-making is a major determining factor in low FP usage in Malawi and Tanzania. Despite this, there are inconsistent findings regarding the extent of male involvement and the determinants that aid male participation in FP decisions in these two countries. The objective of this study was to assess the prevalence of male involvement in FP decisions and its associated determinants within the household context in Malawi and Tanzania. We used data from the 2015-2016 Malawi and Tanzania Demographic and Health Surveys (DHSs) to examine the prevalence and the determinants inhibiting male involvement in FP decisions. The total sample size of 7478 from Malawi and 3514 males from Tanzania aged 15-54 years was employed in the analysis by STATA version 17. Descriptive (graphs, tables and means), bi-variate (chi-square) and logistic regression analyses (unadjusted (U) and adjusted odds ratio (AOR)) were performed to identify the determinants associated with male involvement in FP decisions. The mean age of respondents in Malawi was 32 years (±8 SD) and in Tanzania, 36 years (±6 SD), with the prevalence of male involvement in FP decisions being 53.0% in Malawi and 26.6% in Tanzania. Being aged 35-44 years [AOR = 1.81; 95% CI: 1.59-2.05] and 45-54 years [AOR = 1.43; 95% CI: 1.22-1.67], educated (secondary/higher) [AOR = 1.62; 95% CI: 1.31-1.99], having access to media information [AOR = 1.35; 95% CI: 1.21-1.51] and having a female head of household [AOR = 1.79; 95% CI: 1.70-1.90] were determinant factors of male involvement in FP decisions in Malawi. Primary education [AOR = 1.94; 95% CI: 1.39-2.72], having a middle wealth index ranking [AOR = 1.46; 95% CI: 1.17-1.81], being married [AOR = 1.62; 95% CI: 1.38-1.90] and working [AOR = 2.86; 95% CI: 2.10-3.88] were higher predictors of male involvement in FP decisions in Tanzania. Increasing the role of males in FP decisions and involvement in FP utilization may improve uptake and continuity of FP usage. Therefore, the findings from this cross-sectional study will support redesigning the ineffective strategic FP programs that accommodate socio-demographic determinants that may increase the likelihood of male involvement in FP decisions, especially in the grassroots settings in Malawi and Tanzania. The demographic health survey (DHS) data of Malawi and Tanzania were used for this study (the 2015–2016 Malawi Demographic and Health Survey (2015–2016 MDHS); Tanzania Demographic and Health Survey (2015–2016 TDHS)) [43,44]. We used the most recent DHSs from each country as secondary data sources. These surveys are available through the DHS Program website. The DHS Program provides on-request public access to their data via an application programming interface (API), from which microdata for each country could systematically be downloaded. Further details regarding the DHS survey methodology and complex sampling can be reviewed on the DHS Program website (https://dhsprogram.com/methodology/ (accessed on 14 June 2022)). The DHS, a nationally representative survey, collects information on health and factors related to it, such as mortality, morbidity, use of family planning services, fertility, and maternal and child health. In short, DHSs follow standardized data collection procedures by employing similar questionnaires across different countries, allowing comparability between countries regarding the variables specifically studied (The DHS Program, 2022) [45]. In order to ascertain the point prevalence and contributing factors of male involvement in FP decisions in Malawi and Tanzania, the variables were taken from the literature and added together. The DHS employed a two-stage stratified sampling technique to select the study respondents. In the first stage, enumeration areas (EAs) were randomly selected, while in the second stage, households were selected. Each country’s survey consists of different datasets including men, women, children, birth, and household datasets, and for this study, we used the men’s datasets (MR file). This study included a weighted sample of 10,992 men aged 15–54 who were sexually active, knowledgeable about FP methods, and more likely to be involved in FP decisions or to have had prior experience with being involved in FP decisions in the five years prior to the survey. Regarding the limitations of the DHS datasets, these include reporting and recall bias, particularly for retrospective data relying on memory of a past event. The outcome variable for this study was male involvement in FP decisions. Men who in the five years preceding the survey had used any contraceptive methods (traditional and modern methods), or had knowledge that using condoms does not decrease men’s sexual desire, or believed that men should not care about contraception as it is a woman’s responsibility, or thought that having too many children was often detrimental to the mother’s health, or thought that men should share FP practices in the family, were selected. Male involvement in FP decisions was categorized into ‘Yes’ (involvement in FP decisions) or ‘No’ (non-involvement in FP decisions). The response variable for the ith male was represented by a random variable Yi, with two possible values coded as 1 and 0. Thus, the response variable of the ith male Yi was measured as a dichotomous variable with possible values Yi = 1, if ith man discussed FP with health workers or health professionals, and Yi = 0 if the male never discussed FP with health workers or health professionals in the last few months preceding the survey. The independent variables retrieved from the DHS were age, place of residence, education, wealth index, marital status, occupation, exposure to media, contraceptive knowledge, and the sex of the household head (Table 1). The years of the surveys were decided upon as an independent variable by using 2015–2016 as a reference because the DHSs of the countries of Tanzania and Malawi were taken into consideration at the same time. The years of the surveys were classified as 2015–2016 (Malawi) [43] and 2015–2016 (Tanzania) [44]. However, the bi-variable analysis with a p-value of > 0.2 were not eligible to be included in the multivariable analysis. The lists of independent variables and their definitions and measurements. STATA version 14 statistical software was used for data management and analysis. First, descriptive statistics were used to provide sample characteristics of the respondents. Bar graphs were used for the illustration of the point prevalence of male involvement in FP decisions in Malawi and Tanzania. Second, bi-variate analysis was performed using the Chi-Square (χ2) test statistic to define the statistical relationship of the outcome and the explanatory factors. Third, multivariate analysis (binary logistic regression) was performed to test the determinants associated with male involvement in FP decisions, which significantly predicts the outcome variable. The binary logistics regression model assesses the effect of socio-demographic factors on male involvement in FP decisions in a multiple regression framework. Following Tolles et al. (2016), the binary logistic regression model is defined as: which is an equation that describes the odds of being in the current category of interest and by definition, the odds for an event are π/(1 − π) such that p is the probability of the event. Thus, the multivariable logistic regression analysis took into account variables that had a p-value of less than 0.2 in the bivariate analysis. The unadjusted odds ratio (UOR) and the adjusted odds ratio (AOR) with 95% confidence interval (CI) were reported to declare the statistical significance and strength of association between the predicting determinants and the outcome variable (p < 0.05) in the multivariable logistic regression model. All analyses were weighted to account for differences in sampling probabilities. This study employed freely-accessible unidentified datasets, which suggests that the datasets themselves were not identified, rather than the respondents. One of the authors (corresponding author-Monica Ewomazino Akokuwebe) requested approval from the DHS Program/ICF International to download and use the dataset for all the countries analyzed in the study.

Résumé de l’IA

Study Justification:

– The low involvement of males in family planning (FP) decision-making is a major determining factor in low FP usage in Malawi and Tanzania.
– Increasing the role of males in FP decisions and involvement in FP utilization may improve uptake and continuity of FP usage.
– Redesigning ineffective strategic FP programs to accommodate socio-demographic determinants that may increase the likelihood of male involvement in FP decisions is necessary.

Study Highlights:

– The study assessed the prevalence of male involvement in FP decisions and its determinants in Malawi and Tanzania.
– Data from the 2015-2016 Malawi and Tanzania Demographic and Health Surveys (DHSs) were used for the analysis.
– The prevalence of male involvement in FP decisions was 53.0% in Malawi and 26.6% in Tanzania.
– Determinant factors of male involvement in FP decisions in Malawi included age, education, access to media information, and having a female head of household.
– Determinant factors of male involvement in FP decisions in Tanzania included education, wealth index, marital status, and occupation.

Study Recommendations:

– Increase awareness and education on family planning among males in both countries.
– Develop targeted interventions to address the determinants inhibiting male involvement in FP decisions, such as age, education, and access to media information.
– Strengthen the role of female heads of households in promoting male involvement in FP decisions.
– Improve access to family planning services and information for married men and those with middle wealth index ranking in Tanzania.
– Promote the integration of family planning discussions and decision-making within the context of work and occupation in Tanzania.

Key Role Players:

– Ministry of Health in Malawi and Tanzania
– Non-governmental organizations (NGOs) working in the field of reproductive health
– Community leaders and influencers
– Health workers and providers
– Educators and schools
– Media organizations and outlets

Cost Items for Planning Recommendations:

– Development and implementation of awareness and education campaigns
– Training programs for health workers and providers on promoting male involvement in FP decisions
– Production and dissemination of informational materials and resources
– Integration of family planning services within existing healthcare infrastructure
– Research and evaluation of program effectiveness
– Monitoring and evaluation systems for tracking progress and impact

Force de la preuve

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study used data from the 2015-2016 Malawi and Tanzania Demographic and Health Surveys, which are nationally representative surveys and provide reliable data. The study also employed descriptive, bi-variate, and logistic regression analyses to identify the determinants associated with male involvement in family planning (FP) decisions. However, the abstract does not provide information on the sampling methodology used in the surveys, which could affect the generalizability of the findings. Additionally, the abstract does not mention any limitations of the study, such as potential biases or confounding factors. To improve the strength of the evidence, it would be helpful to include more details on the sampling methodology and address any limitations of the study.

Abstrait

The demographic health survey (DHS) data of Malawi and Tanzania were used for this study (the 2015–2016 Malawi Demographic and Health Survey (2015–2016 MDHS); Tanzania Demographic and Health Survey (2015–2016 TDHS)) [43,44]. We used the most recent DHSs from each country as secondary data sources. These surveys are available through the DHS Program website. The DHS Program provides on-request public access to their data via an application programming interface (API), from which microdata for each country could systematically be downloaded. Further details regarding the DHS survey methodology and complex sampling can be reviewed on the DHS Program website (https://dhsprogram.com/methodology/ (accessed on 14 June 2022)). The DHS, a nationally representative survey, collects information on health and factors related to it, such as mortality, morbidity, use of family planning services, fertility, and maternal and child health. In short, DHSs follow standardized data collection procedures by employing similar questionnaires across different countries, allowing comparability between countries regarding the variables specifically studied (The DHS Program, 2022) [45]. In order to ascertain the point prevalence and contributing factors of male involvement in FP decisions in Malawi and Tanzania, the variables were taken from the literature and added together. The DHS employed a two-stage stratified sampling technique to select the study respondents. In the first stage, enumeration areas (EAs) were randomly selected, while in the second stage, households were selected. Each country’s survey consists of different datasets including men, women, children, birth, and household datasets, and for this study, we used the men’s datasets (MR file). This study included a weighted sample of 10,992 men aged 15–54 who were sexually active, knowledgeable about FP methods, and more likely to be involved in FP decisions or to have had prior experience with being involved in FP decisions in the five years prior to the survey. Regarding the limitations of the DHS datasets, these include reporting and recall bias, particularly for retrospective data relying on memory of a past event. The outcome variable for this study was male involvement in FP decisions. Men who in the five years preceding the survey had used any contraceptive methods (traditional and modern methods), or had knowledge that using condoms does not decrease men’s sexual desire, or believed that men should not care about contraception as it is a woman’s responsibility, or thought that having too many children was often detrimental to the mother’s health, or thought that men should share FP practices in the family, were selected. Male involvement in FP decisions was categorized into ‘Yes’ (involvement in FP decisions) or ‘No’ (non-involvement in FP decisions). The response variable for the ith male was represented by a random variable Yi, with two possible values coded as 1 and 0. Thus, the response variable of the ith male Yi was measured as a dichotomous variable with possible values Yi = 1, if ith man discussed FP with health workers or health professionals, and Yi = 0 if the male never discussed FP with health workers or health professionals in the last few months preceding the survey. The independent variables retrieved from the DHS were age, place of residence, education, wealth index, marital status, occupation, exposure to media, contraceptive knowledge, and the sex of the household head (Table 1). The years of the surveys were decided upon as an independent variable by using 2015–2016 as a reference because the DHSs of the countries of Tanzania and Malawi were taken into consideration at the same time. The years of the surveys were classified as 2015–2016 (Malawi) [43] and 2015–2016 (Tanzania) [44]. However, the bi-variable analysis with a p-value of > 0.2 were not eligible to be included in the multivariable analysis. The lists of independent variables and their definitions and measurements. STATA version 14 statistical software was used for data management and analysis. First, descriptive statistics were used to provide sample characteristics of the respondents. Bar graphs were used for the illustration of the point prevalence of male involvement in FP decisions in Malawi and Tanzania. Second, bi-variate analysis was performed using the Chi-Square (χ2) test statistic to define the statistical relationship of the outcome and the explanatory factors. Third, multivariate analysis (binary logistic regression) was performed to test the determinants associated with male involvement in FP decisions, which significantly predicts the outcome variable. The binary logistics regression model assesses the effect of socio-demographic factors on male involvement in FP decisions in a multiple regression framework. Following Tolles et al. (2016), the binary logistic regression model is defined as: which is an equation that describes the odds of being in the current category of interest and by definition, the odds for an event are π/(1 − π) such that p is the probability of the event. Thus, the multivariable logistic regression analysis took into account variables that had a p-value of less than 0.2 in the bivariate analysis. The unadjusted odds ratio (UOR) and the adjusted odds ratio (AOR) with 95% confidence interval (CI) were reported to declare the statistical significance and strength of association between the predicting determinants and the outcome variable (p < 0.05) in the multivariable logistic regression model. All analyses were weighted to account for differences in sampling probabilities. This study employed freely-accessible unidentified datasets, which suggests that the datasets themselves were not identified, rather than the respondents. One of the authors (corresponding author-Monica Ewomazino Akokuwebe) requested approval from the DHS Program/ICF International to download and use the dataset for all the countries analyzed in the study.

Matériaux

N/A

Innovations

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

1. Male involvement programs: Develop and implement programs that specifically target males to increase their involvement in family planning decisions. This can include educational campaigns, community outreach, and counseling services that aim to engage men in discussions about reproductive health and family planning.

2. Health worker training: Provide training to healthcare providers on how to effectively engage and communicate with male partners. This can help create a supportive environment where men feel comfortable discussing family planning and maternal health issues.

3. Mobile health (mHealth) interventions: Utilize mobile technology to deliver information and services related to maternal health to both men and women. This can include text message reminders for prenatal care appointments, educational videos on family planning, and access to teleconsultations with healthcare providers.

4. Community-based interventions: Implement community-based programs that promote awareness and education about maternal health, targeting both men and women. This can involve community health workers conducting home visits, organizing group discussions, and providing information on available maternal health services.

5. Policy changes: Advocate for policy changes that promote gender equality and encourage male involvement in family planning and maternal health. This can include policies that provide paternity leave, support male participation in antenatal care visits, and ensure equal access to reproductive health services for both men and women.

It is important to note that the effectiveness of these innovations may vary depending on the specific context and cultural norms of each country. Therefore, it is crucial to tailor these interventions to the local context and continuously evaluate their impact to ensure they are meeting the needs of the population.

AI Innovations Description

The recommendation that can be developed into an innovation to improve access to maternal health is to increase male involvement in family planning decisions. The study mentioned in the description found that the low involvement of males in family planning decision-making is a major factor contributing to low family planning usage in Malawi and Tanzania.

To address this issue, it is recommended to implement interventions that promote and encourage male participation in family planning decisions. This can be done through various strategies, such as:

1. Education and awareness campaigns: Conduct educational programs and awareness campaigns targeting men to increase their knowledge and understanding of family planning methods, benefits, and their role in decision-making. This can be done through community-based workshops, health talks, and media campaigns.

2. Couple counseling and communication: Provide counseling services that involve both partners in family planning decision-making. Encourage open and honest communication between couples to facilitate joint decision-making and shared responsibility.

3. Male-friendly healthcare services: Ensure that healthcare facilities are welcoming and accommodating to men. Train healthcare providers to engage and involve men in discussions about family planning and maternal health. This can help create a supportive environment for male involvement.

4. Engaging community leaders and influencers: Collaborate with community leaders, religious leaders, and influential individuals to promote the importance of male involvement in family planning. Their support and endorsement can help change social norms and attitudes towards male participation.

5. Targeted interventions for specific groups: Identify specific groups or populations where male involvement in family planning is particularly low, such as young men or men from marginalized communities. Develop tailored interventions to address their unique needs and barriers to involvement.

By implementing these recommendations, it is expected that male involvement in family planning decisions will increase, leading to improved access to maternal health services. This can ultimately contribute to better reproductive health outcomes for women and families in Malawi and Tanzania.

AI Innovations Methodology

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

1. Increase male involvement in family planning decisions: Encourage men to actively participate in discussions and decision-making regarding family planning methods. This can be done through educational campaigns, community outreach programs, and involving men in antenatal care visits.

2. Improve access to information: Ensure that both men and women have access to accurate and comprehensive information about maternal health, family planning methods, and the benefits of male involvement. This can be achieved through the use of various communication channels such as mass media, community health workers, and mobile health technologies.

3. Strengthen healthcare infrastructure: Invest in improving healthcare facilities and services, particularly in rural areas where access to maternal health services may be limited. This includes increasing the number of skilled healthcare providers, improving the availability of essential medicines and supplies, and ensuring the presence of well-equipped maternity wards.

4. Address cultural and social barriers: Address cultural norms and gender inequalities that may hinder male involvement in family planning decisions. This can be done through community engagement and awareness programs that challenge traditional gender roles and promote gender equality.

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 specific indicators that measure access to maternal health, such as the percentage of women receiving antenatal care, the percentage of births attended by skilled healthcare providers, or the contraceptive prevalence rate.

2. Collect baseline data: Gather data on the current status of these indicators in the target population. This can be done through surveys, interviews, or analysis of existing data sources such as the Demographic and Health Surveys (DHS).

3. Develop a simulation model: Create a simulation model that incorporates the potential impact of the recommendations on the selected indicators. This model should consider factors such as population demographics, healthcare infrastructure, cultural norms, and the level of male involvement in family planning decisions.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations. This can involve adjusting variables related to male involvement, access to information, healthcare infrastructure, and cultural barriers.

5. Analyze results: Analyze the results of the simulations to determine the potential impact of the recommendations on improving access to maternal health. This can include assessing changes in the selected indicators and identifying any potential challenges or limitations.

6. Refine and validate the model: Refine the simulation model based on the analysis of the results and validate it using additional data or expert input. This can help ensure the accuracy and reliability of the model’s predictions.

7. Communicate findings and make recommendations: Present the findings of the simulation analysis to relevant stakeholders, such as policymakers, healthcare providers, and community leaders. Use the results to inform decision-making and make recommendations for implementing interventions that can improve access to maternal health.

It is important to note that the methodology described above is a general framework and can be adapted based on the specific context and data availability.

Auteurs et co-auteurs

Statistics:

Authors: 3

Identifiers:

DOI: 10.3390/ijerph20065053

Research Areas:

Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Malawi, Multi-Countries, Tanzania

Study Design:

Case-Control Study, Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Male & Female

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