Title: A Cross-Sectional Analysis of Factors Associated with Cervical Cancer Screening in a
Large Midwest Primary Care Setting
Version: 2 Date: 12 Feb 2025
Reviewer's report:
A Cross-Sectional Analysis of Multi-level Factors Associated with Cervical Cancer Screening
in a Large Midwest Primary Care Setting
Dear Authors,
Thank you for the opportunity to review this manuscript examining factors associated with
cervical cancer screening (CCS) in a primary care setting in Southeast Minnesota. This study is
timely and has the potential to inform strategies for improving CCS uptake. However, substantial
revisions are needed to enhance clarity, ensure methodological rigor, and align the study's aims
with its analysis. Below are my detailed suggestions for improvement:
General Comments:
• The manuscript is overly wordy, particularly in the introduction. It would benefit from more
concise writing to improve readability.
• The title does not accurately reflect the study’s scope. The term multi-level should be removed,
as it has a distinct meaning in scientific research.
Abstract:
1. Remove the word "lower" in line 75 for accuracy.
2. Replace "subjects" with "participants" in line 80.
3. Add "years" after 21–65 in line 80 for clarity.
4. Replace "online portal" with "patient portal" in line 92.
5. Replace "variables" with "factors/characteristics" in line 97.
6. The conclusion should be revised to clearly highlight factors associated with CCS, rather than
referring to disparities, as this study does not conduct a disparities analysis. If disparities are of
interest, additional in-depth analyses on race/ethnicity, socioeconomic status (SES), or country
of origin would be necessary.
Introduction:
7. Ensure consistency by correcting "underscreened" to "under screened" throughout the
manuscript.
8. Revise lines 136–138 for clarity and conciseness: "Approximately 50% of newly diagnosed
cervical cancer cases occur in individuals with inadequate past screening, and 25% of eligible
individuals in the U.S. are under screened."
9. The introduction could be more concise, with some of the background on race, SES, and
immigrant populations moved to the discussion section to support findings.
10. Lines 178–193 should be moved to the discussion or substantially shortened, as there is no
data on community health workers (CHW) or self-sampling in this manuscript. A brief mention
in the discussion on how CHWs and self-sampling could improve CCS would be sufficient.
Methods:
12. The aims (lines 196–197) should be revised to align with the study’s actual scope, which
primarily examines sociodemographic and clinical factors rather than systemic or area-level
factors.
13. Expand on how comorbidities were assessed via the Charlson Comorbidity Index, by listing
specific conditions (e.g., heart failure, diabetes) and describe the score interpretation e.g. mild-
severe.
14. Given that the study population is derived from a primary care clinic, a supplemental
table showing comorbidity prevalence across Charlson Comorbidity Index categories should be
included.
15. Provide a table/figure reporting CCS type (Pap smear, HPV screening, etc.).
16. Clarify the definition of healthcare utilization (line 214–215). Does it refer to years since the
last clinic visit or number of visits in the past five years?
17. At first mention of HOUSES, provide its full name (Housing-Based Index of Socioeconomic
Status). Justify its use as a surrogate for SES and, if possible, conduct a sensitivity analysis
comparing it to conventional SES measures (occupation, education, or income) if any of these
are available in your data.
18. Describe how HOUSES is derived, what factors are included, and how scores (Q1–Q4) are
interpreted.
19. Clarify the data source for HOUSES and whether data linkage was conducted. A
supplemental table detailing variables and cutoffs for Q1–Q4 would be helpful.
21. Describe ADI, source, variables used in calculation, whether data linkage was conducted,
interpretation of percentages i.e 0-100%, 50% indicating national average
22. Conduct a sensitivity analysis on missing data distribution for ADI and HOUSES, stratified
by race/ethnicity.
23. List all covariates (continuous or categorical) and confounders in the methods. Describe
how confounders were selected for regression models. A single predictor model is insufficient
—additional models adjusted for appropriate confounders should be included.
24. Mention statistical analysis test used and how missing data were handled
25. Verify if RStudio version 2023.12.1+402.pro is suitable for general use. The website
indicates it is primarily for testing purposes as it may contain unstable features or bugs. If
possible, consider using the latest RStudio version released for everyday work.
Results:
26. Provide a breakdown (%) of CCS types (Pap smear, HPV screening, etc.) in the results.
27. To minimize bias, include adjusted models that account for age, health insurance, and other
relevant confounders. Models for language, and country of origin can additionally adjust for
provider gender (because some cultures prefer certain genders as health providers ). Consider
using Directed Acyclic Graphs (DAGs) for confounder selection.
Discussion:
28. Update the discussion based on findings from newly adjusted models and sensitivity
analyses.
29. Lines 307–310 should be rewritten for clarity and conciseness.
30. Address multicollinearity appropriately. The suggested reference by Gregorich et al. (2021)
Regression with Highly Correlated Predictors: Variable Omission Is Not the Solution. Int J
Environ Res Public Health. doi: 10.3390/ijerph18084259. PMID: 33920501; may be useful.
31. The future directions section (lines 334–338) does not sound like future directions and can
be deleted from this section.
32. The conclusion should reflect the study's actual findings and scope. Lines 347–349 would
be more appropriate in the discussion.
Tables & Figures:
33. Ensure tables and figures are self-explanatory, including footnotes defining terms such as
“others” (list categories if available), the interpretation of Charlson Comorbidity Index scores,
and HOUSES scores, confounders adjusted for in each model. Titles should accurately describe
data/results in the table or figure, and include the data collection period.