Non-identifiable data on the characteristics of respondents and non-respondents were provided by ONS. These data included age, marital status at birth registration, country of birth, IMD, region of residence, and parity. These variables were fitted in a binary logistic regression model with response/non-response as the outcome, and the resulting adjusted odds ratios were used to derive survey weights. The survey weights were applied to the data to reduce the effect of non-response bias.

Descriptive statistics were used to describe the characteristics of survey respondents and to evaluate prevalence of PTS-C and PTS-O, together with 95% confidence intervals (CI). Prevalence of each of the symptoms reported by the women with PTS-C and PTS-O was also estimated with 95% CI. Logistic regression was used to estimate the association between different sociodemographic, pregnancy- and childbirth-related factors and PTS-C or PTS-O. Each factor was fitted in a univariable binary logistic regression model with either no PTS/PTS-C or no PTS/PTS-O as the outcome. The factors that were significant at univariable level (p<0.1) were fitted in a binary multivariable logistic regression model. The factors that were significant at multivariable level (p<0.05), after mutually adjusting for all other factors, were retained in the model. The crude odds ratios (OR) were calculated for the univariable analyses and the adjusted odds ratios (AOR) were calculated for the multivariable analyses. All analyses were conducted in STATA version 15.

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