Title: Inequalities in reported use of cervical screening in Estonia: results from cross-sectional
studies in 2004–2020
Version: 1 Date: 03 Oct 2022
Reviewer:  Clare Gilham
Reviewer's report:
This is an interesting paper which tackles the important subject of inequalities in cancer
screening. The data source is well described and is able to give an insight into the inequalities
which exist within the Estonian screening programme.
I have some major concerns regarding the data analysis methods, however, but these can be
rectified by a re-analysis of the data:
1. The outcome measure is lifetime uptake of screening. This would be reasonable at a single
time-point, but several cross-sectional surveys are being analysed. The outcome measure is
therefore cumulative and you would expect an increase over age and time even if there was no
improvement. It is therefore difficult to interpret the findings. Figure 1 shows that the time since
last screen was recorded. I would therefore suggest that a new outcome measure is defined such
as screening within a defined period of time, eg 3 or 5 years. This measure of screening coverage
is usual for comparing screening programmes across the world.
An example of where the interpretation is difficult is the discussion of uptake of screening among
non-Estonians. The comparison of lifetime screening is being made among Estonians who have
been offered screening since 2004 and non-Estonians who may only have been resident for a
few years.
2. Surveys such as these are usually analysed in STATA using the “svy” set of commands.
These allow the data to be weighted to adjust for the unequal selection probabilities. The
authors should consider this method of analysis in order to make inferences about the entire
Estonian population. For examples, see Liivlaid & Uusküla, Sex Transm Infect 2013;89:91–97.
doi:10.1136/sextrans-2012-050506, Martín-López et al (your ref 13) or the NATSAL surveys
from the UK.
In addition to the above, I have the following suggestions which could improve the paper:
3. There’s no denying that the incidence rates in Estonia are high and need to be addressed,
but it’s not fair to say that the organised screening in Estonia “has hardly been effective”. The
overall incidence rate over time is not an accurate measure of effectiveness of screening. First, it
must be compared to what would be expected in the absence of screening. Vaccarella et al (ref 6)
shows that in the absence of screening the incidence rate would be expected to be around 50 per
100,000 in 2020 in Estonia, (much higher than the observed rate of 27.5 per 100,000). Second,
following introduction and improvements in screening, incidence rates would be expected to
rise as existing cancer cases are detected. Therefore cervical cancer mortality is a more reliable
measure of assessing effectiveness of screening. I think mortality is decreasing in Estonia? These
data would be worth presenting.
4. It is no longer acceptable to define a level for statistical significance (eg p<0.05).
Dichotomising p values is discouraged and they should be used as a continuous measure to
indicate the strength of evidence.
5. To formally assess whether there have been changes over time, interaction terms could be
used. This would test the observed “uptake gaps”.
6. Please comment on the representativeness of the surveys over time. For example, how do the
proportions with health insurance compare to the real population-wide figures in 2004-2020? It
would be useful to compare survey responses for variables which may be known for the entire
population over the time period. It appears that the respondents have become healthier over time
– is that true of the entire population?
7. Screening among non-Estonians may be a function of time since residency. Do you have data
on this?
8. Table 1: give numbers as well as %.
9. Table 2: Add numbers and % of women being screened.
10. Table 2: it is not appropriate to highlight pairwise comparisons in bold with p<0.5. It would
be more informative to add a column of the p-values – one per variable (calculated using a
Likelihood Ratio Test for example) to represent the association between each variable and the
outcome.
11. Probably as a result of the previous point (10), the last two paragraphs of the results section
(lines 158-173) include several subgroup comparisons. These should be regarded with caution.
It is better to comment on trends across entire variables.
12. The uptake was compared to Spain and Switzerland (ref 13 and 14). What definitions of
uptake were used in these papers – do they compare? In Spain the uptake was 3 yearly coverage
for example.
13. Discussion (line 209) “Single, and especially widowed..” – statement unfounded as
widowed/divorced women had similar uptake to married women.
14. Discussion (line 217) – “gap between education…” This is an example of where an
interaction would be useful to formally test whether this is true or not.
15. Discussion (line 221). Very few women in this sample didn’t have health insurance, so
these estimates were based on small numbers. The OR is 0.83 indicating a lower uptake but the
Confidence Intervals were wide indicating insufficient power to draw conclusions.
16. Uptake appeared increased among women with increased access to healthcare. So women
with depression may undergo more regular health checks where they are encouraged to accept
their screening invitations rather than being more concerned with their health (ref discussion
line 238).
17. Define “Pap” in first line of introduction.
18. The depression variable may be easier to interpret if it were categorised as “yes/no”.
19. Figure 1: is the “>2 years” category only applicable for 2004? For subsequent surveys, which
category includes those with screening between 2 and 3 years ago? Amend the key to accurately
describe the categories.
20. Figure 2: as per the first comment above (1), this graph could be done for both 3 and 5 yearly
coverage regardless of which is chosen for the analysis.