Title: Number of Children Ever-born and Its Associated Factors among Currently Married
Ethiopian Women: Evidence From the 2019 EMDHS Using Negative Binomial Regression
Version: 2 Date: 17 Nov 2023
Reviewer's report:
PEER REVIEWER ASSESSMENTS:
OBJECTIVE - Full research articles: is there a clear objective that addresses a testable research
question(s) (brief or other article types: is there a clear objective)?
Yes - there is a clear objective
DESIGN - Is the current approach (including controls and analysis protocols) appropriate for
the objective?
No - there are minor issues
EXECUTION - Are the experiments and analyses performed with technical rigor to allow
confidence in the results?
No - there are minor issues
STATISTICS - Is the use of statistics in the manuscript appropriate?
No - there are issues with the statistics in the study
INTERPRETATION - Is the current interpretation/discussion of the results reasonable and not
overstated?
No - there are major issues
OVERALL MANUSCRIPT POTENTIAL - Is the current version of this work technically
sound? If not, can revisions be made to make the work technically sound?
Maybe - with major revisions
PEER REVIEWER COMMENTS:
GENERAL COMMENTS: A very interesting study of public health relevance. The aim of
this study is to assess number of children ever-born and its associated factors among currently
married Ethiopian women using a nationally representative sample from the 2019 Ethiopian
Mini Demographic and Health Survey.
See my comment below.
REQUESTED REVISIONS:
I have to insert line numbers so I can refer you to the lines where I have comments:
L8: justify the inclusion of only “currently married women”.
L15: impossible confidence interval….. 3.81(95CI%:  8.74, 8.89)….parameter is outside the
interval. Besides, CEB are usually skewed. So mean is inappropriate, use median and its IQR.
L19-25: How can completing primary education (IRR= 0.822, 95% CI: 0.793, 0.853), secondary
education(IRR=0.675, 95%CI: 0.610, 0.725), higher education(IRR=0.556, 95%CI: 0.506,
0.610); you need to recast and mention the comparison group
being from female-headed household(IRR=0.924, 95%CI: 0.88, 0.962), because the man is no
longer alive.
How can being from a poorer household(IRR=0.935, 95%CI: 0.897, 0.976), being from
a middle income household(IRR= 0.924, 95%CI: 0.884, 0.966), being from a richer
household(IRR=0.878, 95% CI: 0.839, 0.918), and being from a richest household(IRR=0.761,
95%CI: 0.716,0.809)  all at the same be negatively associated with bearing more children.
L25: region accounted for 6% …what about the remaining variability?
L112-114: delete.
L128: what are the justifications for use of this independent variables? Any reference?
Why was use of modern contraceptive, women autonomy, wantedness of last pregnancy
excluded from the list of potential determinants of CEB?
It appears you didn’t explore enough literature to arrive your list.
L145: Besides over-dispersion, what are the other justifications for using negative binomial
regression? Why not zero inflated Poisson? How does NB model differ from others? why a two-
level mixed-effect and not three?
L146 -147: what about women individual levels?
L161:Typically, CEB are skewed. So mean is inappropriate, use median. Corroborated in your
L145.
Inconsistency in use of decimal points….somewhere 1, others 2 and others 3 and 4, etc. e.g.,
(IRR=1.238593, in L179 and in same line???
Your result interpretation did not suggest if you controlled for other variables or the IRRs are
crude values.
What are the public health implications of your findings?
Table 1: indicate that the percentage beside the total is column percentage.
Table 1:The numbers of CEB from 1 to 15 is non-informative. Rather present their row
percentages.
Table1: The totals for each variable is not needed, delete them. Rather present information about
missing values if any in the respective last rows. But retain the overall total.
Table 2 can be seen clearly from the last row of Table 1, if my suggestions above are taken.
Table 3: what are the other models you considered. You stated in your methods that  a two-level
NB regression was the best. Where are the others?
Table 3: are these the crude values or adjusted IRRs?
Table 3: Insert 1.000 under IRR column for the reference categories.
Table 3: the two levels and the amount of variability they contributed is not spelt out…you have
to create one row for each of the levels. You may wish see Approximation of the Cox survival
regression model by MCMC Bayesian Hierarchical Poisson modelling of factors associated with
childhood mortality in Nigeria | Scientific Reports (nature.com).
Figure 1: Mean is incorrect, use median.