Case series | Volume 37, Article 30, 08 Sep 2020 | 10.11604/pamj.2020.37.30.25205

The determinants of premature birth during the year 2018 at the General Reference Hospital of Malemba in the Democratic Republic of Congo

Fiston Ilunga Mbayo, Yannick Bienge Nsenga, Gloire Kasongo Lupitshi, Kennedy Twite Nyemba, Cédrick Mwamba Mpingisha, Justin Batambe Kambala, Gaetan Kabongo Muzinga

Corresponding author: Fiston Ilunga Mbayo, Malemba General Reference Hospital, Malemba, Democratic Republic of Congo

Received: 26 Jul 2020 - Accepted: 11 Aug 2020 - Published: 08 Sep 2020

Domain: Obstetrics and gynecology

Keywords: Premature delivery, determinants, Malemba

©Fiston Ilunga Mbayo et al. Pan African Medical Journal (ISSN: 1937-8688). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Cite this article: Fiston Ilunga Mbayo et al. The determinants of premature birth during the year 2018 at the General Reference Hospital of Malemba in the Democratic Republic of Congo. Pan African Medical Journal. 2020;37:30. [doi: 10.11604/pamj.2020.37.30.25205]

Available online at: https://www.panafrican-med-journal.com/content/article/37/30/full

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Case series

The determinants of premature birth during the year 2018 at the General Reference Hospital of Malemba in the Democratic Republic of Congo

The determinants of premature birth during the year 2018 at the General Reference Hospital of Malemba in the Democratic Republic of Congo

Fiston Ilunga Mbayo1,2,&, Yannick Bienge Nsenga1,2, Gloire Kasongo Lupitshi1,2, Kennedy Twite Nyemba1,2, Cédrick Mwamba Mpingisha1,2, Justin Batambe Kambala1,2, Gaetan Kabongo Muzinga1,2

 

1Malemba General Reference Hospital, Malemba, Democratic Republic of Congo, 2University of Malemba, Malemba, Democratic Republic of Congo

 

 

&Corresponding author
Fiston Ilunga Mbayo, Malemba General Reference Hospital, Malemba, Democratic Republic of Congo

 

 

Abstract

Premature birth remains a major concern for obstetric and pediatric teams. The objective of our work is to determine the causes and frequencies of premature childbirth in our environment, more particularly at the HGR Malemba. This is a descriptive cross-sectional study conducted in the obstetrics and gynecology department of the HGR Malemba. Qui included 24 premature deliveries between 1st January and 31st December 2018. The hospital frequency of preterm birth was 4.6%, the average age of childbirth was 28 ± 6.7 years, married births were 75%, housewives were 45.8%, the women born were large multiparous in 41.7%, the pathologies occurring during pregnancy: urinary tract infections (75%); malaria (25%), maternal age <18 years or >35 years and urinary tract infections constitute important risk factors for preterm birth. The study we conducted shows that the rate of premature delivery remains high in the city of Malemba. The early identification of the cause allows their reduction through the improvement of the health care system in our environment.

 

 

Introduction    Down

Preventing premature childbirth is at the forefront of obstetrics concerns, so uncertain the future of premature babies. This prematurity is a major risk factor for morbidity and mortality Neonatal tale. The frequency of premature childbirth is high, variable depending on the place and time. It depends above all on the prevention and treatment of the threat of preterm birth. The Democratic Republic of Congo is one of the 10 countries in the world with the highest rates of premature births with 341,400 premature babies each year and half of premature babies born in 32 weeks of amenorrhea die [1]. A number of extremely varied risk factors contribute to the occurrence of preterm birth. This is why, as a scrutineer and out of scientific curiosity, we have chosen to enter into the intricacies of premature childbirth in order to seize the ins and outs.

 

General objective: study the determinants of premature delivery in our study environment.

 

Specific objectives: determine the frequency of premature deliveries at the Malemba General Referral Hospital; determine the factors influencing premature deliveries within the said hospital.

 

 

Methods Up    Down

Study framework: our study was carried out in the maternity unit of the obstetric gynecology service of the Malemba General Referral Hospital located in the Haut Lomami Province in the Democratic Republic of Congo.

 

Type and period of study

 

Study type: this is a retro-prospective cross-sectional descriptive study on premature deliveries in the obstetric gynecology department of Malemba Hospital.

 

Study period: our study took place over a period from 1st January to 31st December 2018, 12 months.

 

Population and sampling: pregnant women admitted or referred for preterm delivery in the obstetric gynecology department of the HGR Malemba during the period of our research.

 

Sampling

 

Inclusion criteria: pregnant women received in the delivery room at a term between 22 to 37 weeks; parturientes presenting a complete file.

 

Exclusion criteria: pregnant women who gave birth before 22 weeks old and after 37 weeks old, parturients with an incomplete file, pregnant women who gave birth out of service.

 

Data management and collection: data were collected from partograms, hospitalization records and the HGR Malemba delivery register during our study period. At the end of this collection, a sample of 24 premature deliveries was selected.

 

Ethical considerations: the anonymity of all the women born was respected, the consent was obtained by the management of the Hospital of Malemba and the department of obstetric gynecology.

 

 

Results Up    Down

Frequency: during our study we recorded 24 premature deliveries from 1st January to 31st December 2018, a total of 517 deliveries a frequency of 4.6%.

 

Age: it emerges from this table (Table 1) that 17 delivered or 70.8% were between 18 and 35 years old against 19 delivered or 8.4% were under 18 years old. The mean age was 28.7 ± 6.7 years with the extremes being 15 and 46 years.

 

Marital status: analysis of this table (Table 2) shows that 18 delivered, i.e. 75% were married against 1 childbirth, or 4.1% was widowed; single and divorced women represented 12.5% and 4.2% respectively.

 

Profession: it emerges from this table (Table 3) that 45.8% of our deliveries were housewives with a number of 11 deliveries against a childbirth who was a civil servant, i.e. 4.2%; 10 women delivered were liberal or 41.7% and 2 women delivered were pupils or students or 8.3%.

 

Parity: it emerges from this table (Table 4) that 10 delivered or 41.7% were large multiparous against 2 delivered or 8.3% were pauciparous; 8 delivered or 33.3% were multiparous; and first-time mothers were 4, i.e. 16.7%.

 

Gesture: this table (Table 5) shows that 9 delivered or 37.5% were large multigestants against 4 women delivered or 16.7% were paucigestous; the multigestes and the primigestes were respectively 6(25%) and 5(20.8%).

 

Pathologies during pregnancy: analysis of this table (Table 6) shows that urinary tract infection represented the most frequent pathology during pregnancy with 75%, i.e. 18 women delivered against one childbirth or 4.2% had diabetes; malaria and hypertension accounted for 25%, or 6 deliveries and 8.3%, or 2 deliveries, respectively.

 

Type of pregnancy: this table (Table 7) shows that 20 childbirths or 83.3% had a single fetal pregnancy against one childbirth or 4.2% had a multiple pregnancy, 3 women born or 12.5% had a twin pregnancy.

 

 

Discussion Up    Down

Frequency: our study shows that out of 517 pregnant women who gave birth at the Malemba General Hospital in DR Congo during the period from January to December 2018 (12 months), we recorded 24 premature deliveries, i.e. a frequency of 4.6%. This frequency is explained by factors related to parturients, medical factors and socio-economic factors. Confronted with data from the literature, our frequency is higher than that of Sanogo [2] with 1.9% and that of Traoré [3] with 1.26% and is proportional to that of Pambou at the Brazzaville CHU [4] which found 4.7% as a frequency. Our increase in the premature birth rate could be explained by the late and inappropriate management of PAD by the department, resulting in premature birth; ignorance of parturients; lack of prenatal care; the unfavorable socio-economic conditions of women giving birth.

 

Age of childbirth: the maternal age group the most exposed during our study corresponds well to that of the woman in full genital activity in 70.8%, i.e. 17 women born. But premature childbirth was also found in adolescents and elderly women with 8.4% and 20.8% respectively. Contrary to a study carried out in France, the age group most represented for premature birth is that of ≤18 years and that of over 40 years [5] and after (Ancel, 2002) [6], these are underage women over 35 years of age who are at risk of PAD and therefore susceptible to premature delivery. At the Brazzaville University Hospital (Pambou, 2006) [4], the maternal age group most exposed is that of 14-20 years old in 27% of cases, those over 35 representing only 9%. These discrepancies noted by these authors in relation to our results could be explained by the recruitment criteria used. In our traditions, women are only valued by the number of children they have, hence their procreation from adolescence to old age (46 years in our study) Traoré [3].

 

Profession: in our study, 45.8% of the women born were housewives, 41.7% of the women born had a liberal profession. The rest of the population was made up of pupils and students with 8.3% and civil servants with 4.2%. Our results are close to those of Traore [3] who found 45.89% and those of Sanogo [2] with 47.3% for housewives in the general population. This could be explained by the predominance of housewives in the general population in our series.

 

Marital status: in relation to marital status, our study reveals that it is the married births who are more represented 75% followed by single women 12.5%. This rate is consistent with the work already carried out by Traoré [3]. But unlike the study carried out by Mvondo Nicole who found 50.5% of single births [7]. This could be explained by the fact that women, living alone, are often not able to meet all their needs because of low income, compared to women living in a conjugal relationship or not with a man whose psychological support in addition to financial is a strong contributor to monitoring the pregnancy. Other authors had found that single women were more likely to have premature babies. According to El-Sayed, in the United States, the risk of prematurity was high among married women while this risk decreased among unmarried women. But after adjusting certain parameters, marriage had become less protective against prematurity [8].

 

Parity and gesture: parity would not miss a role in the occurrence of premature childbirth either, it reveals that 41.7% of the women born were large multiparas, 33.3% of multiparas, 16.7% of first-time mothers and 8.3%. Pauciparas, although according to Papiernick in France [9], the risk coefficient of preterm birth had a low positive predictive value of 8% in nulliparas and 33% in multiparas, respectively. The most plausible explanation would be cervical strain. Contrary to the study carried out by Mvondo in Yaoundé, primiparity increased the risk of premature childbirth in an increased manner [7]. Amri had found 26.2% of cases in first-time mothers and 25.4% of cases in multipara (≤4 pregnancies) [10]. Large multigestes with 37.5% of cases were the strongest population in this series.

 

Associated pathologies: analysis of our results showed that 75% of our deliveries had a urinary tract infection which led to premature rupture of the membrane. Our results are far superior to that of Sanogo [2] in Bamako in commune V with 27.3%. However, malaria and hypertension play a non-negligible role in the occurrence of premature childbirth, 54.4% of cases of urinary infection were found by Sidibé [11]. In Morocco, infection is incriminated as the most frequent factor of premature delivery with 40.2% of cases including 15.8% urinary tract infections, 14.1% for genital infection and 3.8% for chorioamnionitis [12]. The risk of preterm birth is ten times higher for multiple pregnancies than for single pregnancies. The explanations are mostly uterine distension and premature rupture of membranes more common for years the multiple pregnancy [11,13].

 

 

Conclusion Up    Down

We carried out a cross-sectional descriptive study on the causes and frequency of premature deliveries, the objectives of which were to determine the frequency and causes of premature deliveries at the Malemba General Referral Hospital: the incidence of premature delivery was 4.6%; the maternal factors that influenced preterm delivery in our study were: urinary tract infections, parity >4, certain pathologies during pregnancy such as malaria; the fetal factors that influenced preterm birth were twin and multiple pregnancies; the early and effective management of PAD would help reduce the rate of premature birth which has serious consequences for both the premature and the mother. Its reduction can only result from the simultaneous combined improvement of health education objectified by the attendance of women in ANC and of the quality of care at the level of health centers.

What is known about this topic

  • Clinical epidemiological profile of preterm deliveries.

What this study adds

  • Determine the determinants of premature deliveries at the Malemba General Referral Hospital;
  • Determine the factors influencing premature deliveries within the said hospital.

 

 

Competing interests Up    Down

The authors have read and agreed to the final manuscript.

 

 

Authors' contributions Up    Down

All the authors contributed to the realization of this work. All the authors have read and agreed to the final manuscript.

 

 

Acknowledgments Up    Down

We sincerely thank the Medical Director and the medical officer of staff who accepted that this inquiry can be made from the Malemba General Reference Hospital.

 

 

Tables Up    Down

Table 1: distribution of women giving birth by age

Table 2: distribution of women giving birth by marital status

Table 3: breakdown of women giving birth by profession

Table 4: distribution of women giving birth according to parity

Table 5: breakdown of women giving birth according to pregnancy

Table 6: distribution of women giving birth according to the pathologies occurring during the current pregnancy

Table 7: breakdown of women giving birth by type of pregnancy

 

 

References Up    Down

  1. Hugo L. Prématurité une nouvelle génération d'enfants: l'homme 2.0: l'être humain réparé, transformé, augmente. Pour la science. 2012;422:74-80. Google Scholar

  2. Sanogo CT. étude épidémio-clinique de l‘accouchement prématuré au centre de sante de référence de la commune v de 2002 à 2003 à propos de 238 cas. Bamako: thèse de médecine. 2005;2-50. Google Scholar

  3. Maiga DB. Aspect épidémio-clinque de l´accouchement prématuré dans le service de gynécologie obstétrique du centre de santé de référence de la commune II du district de Bamako. Thesis Med Bamako. 2006. Google Scholar

  4. Pamboo O, Ntsika-Kaya P, Ekoundzola JR, Mayanda F. Premature births at the Brazzaville CHU. Francophone Studies and Research Papers/Health. 2006;16(3):185-189. PubMed | Google Scholar

  5. Kalenga MK, Kakudji L. Obstetrics, unpublished course, fac. Med UNILU. 2013.

  6. Cabrol D, Goffinet F, Carbonne B, Dreyfus M, D'Ercole C, Ancel PY. La menace d'accouchement prématuré (MAP) à membranes intactes (2002). J Gynecol Obstet Biol Reprod. 2002. Google Scholar

  7. Mvondo N. Facteurs de risque et devenir hospitalier des nouveau-nés prématurés à l'Hôpital Gynéco-Obstétrique et Pédiatrique de Yaoundé. Memoire Online. 2012. Google Scholar

  8. EL-Sayed AM, Galea S. Changes in the relationship between marriage and preterm birth, 1989-2006. Public Health Rep. 2011 Sep-Oct;126(5):717-25. PubMed | Google Scholar

  9. Papiernik E. Le coefficient de risque d'accouchement prématuré. Presse Med. 1969;77:793.

  10. Amri F, Fatnassi R, Negra S, Khammari S. Prise en charge du nouveau-né prématuré. Journal of Pediatrics and Pediatrics. 2008;5(21);227-231. Google Scholar

  11. Sidibe B. Contribution à l´étude des accouchements prématurés en milieu obstétrical Bamakois (a propos de 140cas). Thèse Méd Bamako. 1976.

  12. Alsubari A. Epidémiologie de la prématurité dans notre contexte a propos de 18 cas. Doctoral thesis Rabat-Morocco Mohammed V. 2012.

  13. Lawnj J, Coussens S, Zupan J, Lancet Neonatal Survival Steering Team. 4 million neonatal deaths: when, Where, Why. Lancet. 2005;365(9462):891-900. PubMed | Google Scholar

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Case series

The determinants of premature birth during the year 2018 at the General Reference Hospital of Malemba in the Democratic Republic of Congo

Case series

The determinants of premature birth during the year 2018 at the General Reference Hospital of Malemba in the Democratic Republic of Congo

Case series

The determinants of premature birth during the year 2018 at the General Reference Hospital of Malemba in the Democratic Republic of Congo