Title: Improving intervention design to promote cervical cancer screening among hard-to-
reach women: assessing beliefs and predicting individual attendance probabilities in Bogotá,
Colombia.
Version: 1 Date: 12 Oct 2021
Reviewer's report:
The study problem is important, because attendance to cervical cancer screening is low in many
countries. In the study population there is on-going an intervention to increase participation
(home visit programme) and participation is indeed much better (about 60% among the home-
visited women) than in many other populations in low-resource settings. There is available
extensive, interview-based data on health beliefs related to cervical cancer screening, and health-
record-based data, too. The authors have performed a multitude of analyses. However, models
have not been described well enough, and the presentation of the data is inadequate. It is therefore
not possible for a reader to sufficiently comprehend the data patterns and the models. The paper
is already rather long, therefore it would make sense to reduce the contents. Main comments:
Comment 1. Page 4, lines 84-91 aims of the study; and Sections 3.1. (concerning the principal
component analysis) and 3.2. Assessing the beliefs: There are too many aims and analyses
for one article, they should be preferably reduced. The principal component analysis in the
sections 3.1. and 3.2. could be deleted and possibly a separate manuscript prepared about them.
Alternatively, if those parts could be shortened meaningfully, their main results could possibly
be included as an Annex and referred in the main text only rather shortly. Shortening would make
sense because the results of the principal component analysis were not used in the modelling of
attendance probabilities. Comment 2. More effort and space should be given to the aim to analyze
the individual ‘no-show’ probabilities (preferably, the individual attendance probabilities) which
seemed to be yet the main aim of the study. The three LASSO regression models should be
specified more in detail, and the algorithms used for model selection, parameter estimation,
prediction, and evaluation of the model performance should be described better. To obtain
a better interpretation of the odds ratios, cross-tabulations should be presented between the
explanatory variables and the outcome (i.e., attendance status of screening). Also, univariate
distributions should be provided for the study material by values of all the variables used in
the models.  More detailed comments: Comment 3. Abstract and the discussion section should
be re-written after the re-organization of the manuscript as proposed above. Comment 4. Page
5, line 115, it was not clear why the values 1,3,5 (not, e.g., 1,2,3) were given to the three
response alternatives. Anyway, the LASSO model was parametric, with discrete variables only
where scoring for the responses did not matter Comment 5. Page 6, lines 127-143, see the
second main comment, add information about the models. Mention also if the LASSO model
was using logistic regression. Why so; would there have been alternatives; and what were the
consequences for the point estimates of attendance probability (considering that attendance
was rather common)? Add also more information about fitting the models (e.g., explain more
precisely what you mean with conducting multiple experiments, and analyzing the average odds
ratios). Mention in detail which variables were included in each of the models, with their coding
details and describe also the ‘sequential model’ similarly to other models. Preferably, explain
also for a reader what is the AUROC score and why it was used as the main indicator in assessing
model performance. Comment 6. Page 6, lines 139-141, information on the pap smear tests:
apparently the 23,384 tests during 2017-2019 included also other population(s) than women
responding to the interview survey, right? Did you use information about it only for the survey
participants? How did you classify if there were more than one invitation per women in that
time, i.e. did the ‘no-show’ category include only those who had never participated in the years
2017-2019, or had at least one invitation without participation during these years? Comment
7. Table 5: it seems that the results of the table 5 show ORs from separate univariate models
for the selected items from the survey, or did you adjust for any other variables? Would any
adjustments be needed? No information is provided about statistical significance/precision of
the OR estimates; such information would be necessary for assessing whether the likelihood of
attendance was higher/lower than the reference.