Title: The varying estimation of infertility in Ethiopia: The need for a comprehensive definition
Version: 0 Date: 27 Dec 2023
Reviewer's report:
Summary
This is a well-written manuscript that addresses a critical question related to infertility
estimation. The researchers applied two methodologic approaches (demographic and current
duration) to Ethiopian DHS data to estimate infertility prevalence. Given the variation in
definitions, the magnitude of infertility varied (even when applied to the same data source);
however, both approaches indicate a higher magnitude of secondary infertility compared to
primary. Overall, I find few weaknesses with this paper, but offer some suggestions below to
improve the overall approach and interpretation of the findings. The main limitation is that
the authors could have applied accelerated failure time (AFT) regression models with current
duration values as the outcome to provide associations with sociodemographic characteristics.
This would allow for more direct comparison of associations between the two approaches –
demographic (using logistic regression) and current duration (using AFT models). Then one can
determine whether associations are similar or different across the two approaches. Similar to
what was done for this current duration paper: https://pubmed.ncbi.nlm.nih.gov/23290741/
Introduction
p.4, lines 38-39. The most up-to-date reference might be the systematic review by Cox et al. This
is the primary source and basis for the WHO report (reference 25 in this manuscript). https://
academic.oup.com/hropen/article/2022/4/hoac051/6825316
p.4, line 41. Although the concept of “barrenness amid plenty” has been used in the literature,
the term “barren” may be stigmatizing to some and doesn’t accurately depict infertility as a
clinical concept. I think it is still important to convey the concept of high infertility in areas
of high fertility, but without using this terminology. Additionally, it makes sense intuitively as
fertility/fertility attempts are higher, infertility may be more prevalent because it is more likely
to be recognized. That is, if childbearing is complete at a fewer number of children (e.g., 2 kids),
infertility that develops later will not be recognized or prevalent at the population level.
p.5, lines 70-71. The description of the demographic approach only describes the population at
risk, but not how infertility is defined. The population is “have been married for at least 5 years”
AND not had a live birth.
Methods
p.6 Infertility definition. Why is only primary and secondary infertility defined, but not overall
infertility? One could first describe overall infertility and then differentiate based on primary or
secondary, or describe primary and secondary and then overall as a combination of the two.
p.9, lines 124-127. This describes exclusion criteria for the analytic sample, but additionally the
current duration values subtract the max time of amenorrhea or postpartum abstinence from the
total duration for those who had a live birth. Some details of how the current duration measure
is derived and calculated would strengthen this section. This can be found in the original Polis
et al. paper with an updated Table in the OSF documentation cited in this manuscript.
p.9, line 127. Parametric survival analysis – details of the distribution applied should be included.
p.9 lines 128-129. While subgroup comparisons make sense here, accelerated failure
time regression models provide associations in a way that is more comparable to
logistic regression. This has been done in previous current duration approaches: https://
pubmed.ncbi.nlm.nih.gov/16755258/ and https://pubmed.ncbi.nlm.nih.gov/23290741/
Results
- Table 2. Table 2 also looks at overall infertility. Include some summary of relevant findings.
- Table 3. How does CD sample compare to those not included? Include some summary of
relevant findings from Table 3.
- Table 4 and 5. While these are informative, one of these tables could be replaced (or moved to
supplement) with AFT regression model analysis as a main Table.
- General comment: Some tables lack decimals, likely the ones that end in “.0” These should be
added to be consistent with other cells in the Table.
Discussion
General comment: I think another takeaway that wasn’t mentioned is that the patterns of primary
and secondary infertility are similar in that secondary infertility is higher regardless of approach.
It would be expected that the magnitude would vary given that one uses a 5-year cut-point,
while the current duration uses 12-36 month cut-points, but the patterns are similar with regard
to primary/secondary. Additionally, if the current duration approach did not censor after 36
months and could be estimated at a 5-year cut-off, would the prevalence be similar? Perhaps.
In addition to comparing primary/secondary infertility patterns, sociodemographic associations
may be similar regardless of approach, which could be compared with AFT regression for current
duration and logistic regression for the demographic approach.
p.12, lines 198-199. I don’t understand how these definitions from population-based data would
lead to over or under-management from a clinical perspective. Some re-framing of this takeaway
is needed. Rather, it may relate to resource allocation or planning for services…However, I
think in even with the definition that shows higher a magnitude, infertility is under-resourced
regardless of definition applied. Perhaps it suggests unmet need for resources…?
p.15, lines 256-259. Because the current duration approach applies a parametric survival
function that is consistent with TTP distributions from prospective studies (exponential decay),
the finding that it continually decreases is a function of the distribution applied to the data (i.e.,
it has to decrease until you reach larger time values when the distribution flattens out, but that is
after the point of censoring). Other study designs (prospective studies) might better inform this
conclusion, particularly as it relates to age. Suggest removing these lines.
p.16, lines 280. This should be re-framed. Infertility is inherently a couple-based measure. The
problem with only asking female respondents, is that it lacks the male perspective (i.e., what
subgroups of men are more likely to experience infertility). But from an estimation point-of-
view, the magnitude should represent a couple-based measure and men (in theory if reporting
was reliable) would have a comparable estimate of prevalence. Could re-frame this to state that
lack of information from men limits understanding of infertility among men and who is most
affected.
p.16, line 290. “can lead to over or underestimation and treatment of infertility.” Same comment
as above – I don’t understand how these population-based approaches affect treatment of
infertility.