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.
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