Title: Geospatial inequalities in women’s malnutrition in Pakistan
Version: 1 Date: 15 Feb 2024
Reviewer's report:
I am always impressed when researchers attempt to present findings of Bayesian Geo-Additive
Quantile analysis for the general reader and even moreso for policymakers.  Simplifying the
findings of complex relationships,  helping understand the relationship between health and
location,  considering not averages but quantile levels of data, and using our apriori hunches
with the new knowledge to better understand connections and make predictions.   My hope
always is that the researcher doesn't get bog down with churning out statistics but works towards
presenting meaningful conclusions.
As is my usual habit, I looked at the title and then went straight to the tables and figures.   Neither
the headings of the tables & figures nor the legends were self-explanatory.  My first impression
was that the paper was going looking at the malnutrition status of women across the provinces
of Pakistan.
Section-wise comments:
Background:   The intro spent too much time letting the audience know about maternal
malnutrition and its consequences, something that could have been shortened in favor of more
information about the distribution of  BMI categories  across the provinces from previous
research and PDHS data.  There could be more references to the usefulness of using Bayesian
geo-additive quantile regression and how this may have been used elsewhere (eg Bangladesh,
Nepal for women and quite a few places for under-5 year olds).  The literature references can
be updated.
The rationale was weak and emphasis was given to analysis not including pregnant and lactating
women but not stating that that one of the reasons for utilizing the Bayesian geo-additive model
rather than the traditional linear regression in the study is its flexibility to incorporate the spatial
and non-linear effects in a model.
Methods
Sample and Data:  Again too much information on what indicators the PDHS provides.  This is
all standard stuff that anyone familiar with DHS knows.
Variable Description: Line 222 mentions that the paper used Asian BMIs but in line 97 it
mentions using the WHO cut-off.  Again too much information given that could be reduced on
the variables that are self explanatory.   With 53% illiteracy, why was the full data-set used for
the read newspaper variable?
Statistical Analysis & Empirical Model:   It is better to replace the generic terms used with what
its supposed to represent, eg replace respondent with women etc.
Results:
Generally, the formatting of the tables needs to be improved with bold and indents.
Table 1: Glaring omission of the distribution of malnutrition.  No where in the paper did the
reader get an idea of what proportion had a normal BMI.  If the paper intended to look at urban-
rural differences, we didn't get any idea of the differences in distribution.
Table 2.  The results were presented with a lot of repetition of the tables.  The reader not well
versed with this type of statistics would not even be able to understand that the linear associations
being show are referenced to normal weight.  No where is this mentioned.
Fig 1.  It is when we start looking at the results of the spatial mapping that we realize that the
division of maps is based on provinces and not urban-rural as stated earlier.  Line 199 states
that "...districts in Southern Pakistan, including districts from Punjab, Sindh and Baluchistan are
more likely....".   Further down lines 208-213 discuss at the provincial level. The visualization
nor the interpretation gives the reader any confidence that the authors are even aware of urban/
rural boundaries that they set out to look into.    This could be because the statistician doing
the analysis is not from Pakistan and thus depended on the local authors to help differentiate
and interpret.
Fig 2.  The non-linear effects of women's ages on the 4 BMI categories are not explained. The
roller coaster graph for severely thin needs explanation but the authors fail to  discuss it.
Discussion & Conclusion:  Suddenly in the discussion, the interpretations switch to urban and
rural even though there is no evidence in the geo-spatial results.  I am not going to comment
about it because the authors are not really discussing their results.