Title: Prevalence and determinants of exposure to family planning messages among
reproductive age women in five Sub-Saharan African countries: A Multilevel Analysis of
2021-2023 Demographic and Health Surveys
Version: 2 Date: 10 Oct 2024
Reviewer's report:
As a statistician, my review will be focused exclusively on the statistical analysis and
methodology presented in the study. I will not be providing comments or assessments related to
the clinical aspects or interpretations of the results, as those are outside my area of expertise.
Strengths of the Study
This paper addresses an important public health topic by examining factors associated with
exposure to family planning messages among women of reproductive age across five sub-
Saharan African countries. The use of large-scale DHS data, combined with the multilevel
analysis approach, is commendable and provides valuable insights into both individual- and
community-level factors that influence family planning awareness. The study’s focus on the
differences across countries and its use of mixed-effects modeling to capture regional variations
strengthen its contribution to the literature.
Comments on Statistical Analysis
1. Candidate Threshold for Univariate Analysis:
In the "Data source, study population and sampling technique" subsection of the Methods
section, it is mentioned that a p-value threshold of <0.25 was used for candidate selection in the
univariate analysis. The rationale for choosing this threshold over the more conventional 0.05
should be clearly justified. My concern is that 0.25 is somewhat high and may lead to overfitting
by including too many variables in the multivariable model.
2. Clarification of "95% Significance Threshold":
In the same subsection, the term "95% significance threshold" is used when reporting factors
influencing family planning messages. This term is unclear. Typically, results are reported using
95% confidence intervals (CI) or p-values to assess statistical significance. It is important to
clarify whether the 95% CI or p-value is being referred to here.
3. Outcome Variable Coding:
In the "Outcome Variable" subsection under "Variables," the binary coding of family planning
message exposure ("Yes = 1" and "No = 0") is standard. However, more clarity is needed on
how the outcome variable was constructed across the five countries. Were the definitions and
sources of media exposure (e.g., newspaper, magazine, television, radio) consistent across all
countries? If not, this could introduce bias into the analysis.
4. ICC Assumptions:
In the "Random Effects" section, the formula for the intra-class correlation coefficient (ICC)
assumes that the variance of the standard logistic distribution is fixed at 3.29. While this is
typically acceptable, the study should provide a discussion on whether this assumption holds in
the context of the current study.
5. Interpretation of MOR and PCV:
In the same section, it would be helpful to provide more context for interpreting the median
odds ratio (MOR) and proportional change in variance (PCV). For example, what would be
considered a small or large MOR in this context? Including further details on the interpretation
of these metrics beyond their equations would provide valuable guidance to readers.
6. Random Effect Structure:
The mixed-effects model used in the study does not clarify whether only random intercepts were
included for clusters or whether random slopes were also considered. Random slopes could be
relevant if the effects of certain predictors vary across clusters (e.g., the effect of education
may differ between countries). Additionally, it is important to check for and report potential
interactions between individual- and community-level factors, as failing to account for such
interactions could result in model misspecification.
7. Model Comparison Metrics:
You mentioned that deviance was used to compare models, which is appropriate. However, it
would be helpful to clarify whether other model comparison metrics, such as AIC or BIC, were
also considered. Unlike deviance, AIC and BIC penalize model complexity, which helps avoid
overfitting. Given the number of predictors included, using penalized likelihood criteria might
be more informative.
8. Multicollinearity Concerns (VIF):
The paragraph states that variance inflation factors (VIF) were checked for multicollinearity
with an acceptable range of 1–10. However, a VIF threshold of 10 is too high. Typically, a lower
threshold of 5 is more appropriate for detecting multicollinearity. If any variables exceed this
threshold, further investigation or adjustment should be considered to reduce collinearity, as
strong multicollinearity can distort regression coefficients.
9. Missing Data and Sensitivity Analysis:
Is there any missing data in the study? If so, how was it handled? Additionally, conducting
sensitivity analyses to ensure the robustness of the findings would be valuable, particularly in
cases where missing data might impact results.
10. Sample Size Discrepancy:
In the results section, the total sample size is reported as 93,266 women, while the abstract and
methods sections list the sample size as 63,266. This discrepancy needs to be addressed to avoid
confusion about the actual sample size used. Additionally, the calculation and definition of "poor
community media exposure" (52.69%) need to be clearly explained.
11. Potential Confounding:
There is potential confounding between individual- and community-level variables, such as
maternal education, husband's education, wealth, and community poverty. These variables may
be correlated, leading to multicollinearity. While VIF checks were mentioned, actual values were
not reported, nor were any steps taken to address potential collinearity discussed. Reporting VIF
values in the supplemental materials and addressing this issue would strengthen the analysis.
12. Practical Significance vs. Statistical Significance:
Given the large sample size, statistically significant results are likely even for small effects. It
is important to assess and discuss the practical significance of the findings, rather than focusing
solely on statistical significance. This would provide a more meaningful interpretation of the
results.
13. Country-Level Effects and Clustering:
While country is included as a variable in the model, it is unclear whether random effects for
countries or clustering at the country level were considered. Given the differing policies and
infrastructures related to family planning in the five sub-Saharan African countries, accounting
for country-level clustering may be important.
General Formatting Issues
1. Document Inconsistencies:
Some paragraphs, particularly in the introduction section of the abstract, contain incomplete
sentences and require revision. Additionally, I noticed discrepancies between the Word
document and PDF versions of the manuscript—certain paragraphs and sentences differ between
the two. Please ensure consistency across both documents and revise accordingly.
2. Table 2 Formatting:
In Table 2, there are several issues with the format and reporting of frequencies. For example,
for maternal age (15-19), the frequency is listed as "18,688.154," which is unclear. Does this
refer to 18,688 subjects, or is there another explanation for the decimal places? Additionally,
the labels in the community-level section are inconsistent, with frequency being labeled as (%)
and percentage as (n). Please revise to clarify the exact numbers and ensure consistency.
3. Consistency in Reporting Confidence Intervals:
Confidence intervals are inconsistently reported. For example, some intervals are presented to
two decimal places (e.g., 1.36, 1.64), while others are reported with one decimal place (e.g., 1.61,
2.05). Consistency in reporting is essential for clarity and professionalism in scientific reporting.
4.  Inconsistencies in Table Formatting:
There are some inconsistencies in the formatting of tables and results. For example, the total
percentages in some rows (such as "total children ever born") do not sum to 100%, and rounding
is inconsistent across the tables. Consistent rounding and ensuring that totals are accurate would
enhance the professionalism of the results presentation.