Title: Comprehensive knowledge of mother-to-child HIV/AIDS transmission, prevention,
and associated factors among reproductive-age women in East Africa: insights from recent
demographic and national health surveys
Version: 1 Date: 13 Apr 2024
Reviewer's report:
There are two main concerns of the paper.
1) The authors recoded women who correctly responded to all the four questions (4/4) as 1,
and being knowledgeable and if a woman miss any of the questions, she is regarded as not
knowledgeable and coded as 0. There were about 4 questions in all. A woman who scored 3/4,
2/4, and 1/4 were all assume to be the same as a woman who scored 0/4.  But clearly, there is
a huge difference between these categories of women and lumping them as 0 will only make
your statistical analyses easier but you lose so much information. A woman who scored 3/4 in
knowledge related questions cannot be on the same level as a woman who scored 0/4. There is a
difference. At best, authors could have generated an ordinal outcome like Very good knowledge
(4/4), good knowledge (3/4), Acceptable knowledge (2/4), Poor knowledge (1/4) and Very Poor
knowledge (0/4) and fit ordinal logistic regression model or generalized ordered logit models
with survey adjusted sampling design (weighting, clustering , stratification). This will serve as
sensitivity analysis to the binary classification of the outcome measure where the authors fitted
logistic regression model.
2) Methods: The normalized weight is a relative weight which is valid for estimating means,
proportions and ratios, but not valid for estimating totals, and not valid for pooled data from
different surveys. If one needs to estimate population totals based on DHS data or to pool data
from different surveys together, the standard weight should be de-normalized.  The authors did
not denormalized the sampling weight
3) For a cross-sectional study, the preferred effect size of interest is prevalence ratio not
odds ratio as the odds ratio overestimate the effect size for common outcomes (when %
knolwedge is more than 20%) (Barros & Hirakata, 2003). You can generate prevalence ratio for
binary outcomes using log-binomial models, modified Poisson (Poisson with robust standard
errors), and Cox-proportional models with robust variance estimation(Barros & Hirakata, 2003;
Coutinho, Scazufca, & Menezes, 2008; Martinez et al., 2017; Talbot, Mésidor, Chiu, Simard,
& Sirois, 2022).
Barros, A. J., & Hirakata, V. N. (2003). Alternatives for logistic regression in cross-sectional
studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC
medical research methodology, 3(1), 1-13.
Coutinho, L., Scazufca, M., & Menezes, P. R. (2008). Methods for estimating prevalence ratios
in cross-sectional studies. Revista de saude publica, 42, 992-998.
Martinez, B. A. F., Leotti, V. B., Silva, G. d. S. e., Nunes, L. N., Machado, G., & Corbellini,
L. G. (2017). Odds ratio or prevalence ratio? An overview of reported statistical methods
and appropriateness of interpretations in cross-sectional studies with dichotomous outcomes in
veterinary medicine. Frontiers in veterinary science, 4, 193.
Talbot, D., Mésidor, M., Chiu, Y., Simard, M., & Sirois, C. (2022). An alternative perspective
on the robust Poisson method for estimating risk or prevalence ratios. Epidemiology, 34(1), 1-7.