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Exploring risk factors of pelvic organ prolapse at eastern of Democratic Republic of Congo: a case-control study

  • Eloge Ilunga-Mbaya,
  • Denis Mukwege,
  • Renaud De Tayrac,
  • Branly Mbunga,
  • Raha Maroyi,
  • Mukanire Ntakwinja,
  • Mushengezi Amani Dieudonné Sengeyi

摘要

Background

Pelvic organ prolapse is a common debilitating condition worldwide. Despite surgical treatment, its recurrence can reach up to 30%. It has multiple risk factors, some of which are particular for a low-resource settings. The identification these factors would help to devise risk models allowing the development of prevention policies. The objective of this study was to explore risk factors for pelvic organ prolapse in a population in eastern Democratic Republic of Congo (DRC).

Methods

This was an unmatched case-control study conducted between January 2021 and January 2022. The sample size was estimated to be a total of 434 women (217 with prolapse as cases and 217 without prolapse as controls). Data comparisons were made using the Chi-Square and Student T tests. Binary and multivariate logistic regressions were used to determine associated factors. A p < 0.05 was considered significant.

Results

Variables identified as definitive predictors of pelvic organ prolapse included low BMI (aOR 2.991; CI 1.419–6.307; p = 0.004), home birth (aOR 6.102; CI 3.526–10.561; p < 0.001), family history of POP (aOR 2.085; CI 1.107–3.924; p = 0.023), history of birth without an episiotomy (aOR 3.504; CI 2.031–6.048; p = 0), height ≤ 150 cm (aOR 5.328; CI 2.942–9.648; p < 0.001) and history of giving birth to a macrosomic baby (aOR 1.929; IC 1.121–3.321; p = 0.018).

Conclusions

This study identified that Body Mass Index and birth-related factors are definitive predictors of pelvic organ prolapse in a low-resource setting. These factors are potentially modifiable and should be targeted in any future pelvic organ prolapse prevention policy. Additionally, there seems to be a genetic predisposition for prolapse, which warrants further assessment in specifically designed large scale studies.