Strengths and limitations
This study has several limitations that need to be noted. First, given
the characteristics and temporal nature of our cross-sectional design,
it was impossible to make a causal inference between potential
associated factors and subfertility or infertility (presented as longer
TTP in our study). Second, information on TTP and other measures was
self-reported, which might introduce recall bias. To resolve this
problem, we designed a set of questions to collect accurate data on
duration among different populations, and logical errors were further
excluded from the final analysis. Third, we only enrolled couples who
conceived or were attempting to conceive at the time of the
investigation instead of couples without intent to become pregnant,
which might not be representative of women at risk for pregnancy.
However, not all couples who engaged in unprotected sex without
intention to conveive were whom truly desired to become pregnant, and
the quality of information on TTP in this population is probably poorer.
Finally, our data were collected from 2010 to 2011, when only couples
who were both “only children” were encouraged to have their second
child in China. Interpretation of the results for such group under
complicated political restrictions is challenging.
Nevertheless, our study has obvious strengths. First, the integration of
both retrospective and cross-sectional designs was applied. Thus, we not
only analyzed TTP in pregnant women but also utilized current approaches
to examine TTP in women attempting to conceive. Second, the couples in
our study were sampled from the general population with adequate
representation, making it possible to inquire the TTP in cases of
unsuccessful attempts and infertility. Third, dissimilar to other
several population-based studies that used traditional binary
classification to estimate infertility with “yes” or “no,” our study
acquired the total TTP distribution in couples at risk for pregnancy,
which provided a more sensitive indicator of fecundity and its
associated risk factors. Finally, this approach could facilitate
comparisons across different types of population-based and clinical
studies, which might help improve the public health guidelines and
clinical recommendations.