Risk factors for obstructed labour in Eastern Uganda: A case control study

PLoS ONE, Volume 15, No. 2, Year 2020

Interpretation

Introduction Obstructed labour (OL) is an important clinical and public health problem because of the associated maternal and perinatal morbidity and mortality. Risk factors for OL and its associated obstetric squeal are usually context specific. No epidemiological study has documented the risk factors for OL in Eastern Uganda. This study was conducted to identify the risk factors for OL in Mbale Hospital. Objective To identify the risk factors for OL in Mbale Regional Referral and Teaching Hospital, Eastern Uganda. Methods We conducted a case control study with 270 cases of women with OL and 270 controls of women without OL. We consecutively enrolled eligible cases between July 2018 and February 2019. For each case, we randomly selected one eligible control admitted in the same 24-hour period. Data was collected using face-to-face interviews and a review of patient notes. Logistic regression was used to identify the risk factors for OL. Results The risk factors for OL were, being a referral from a lower health facility (AOR 6.80, 95% CI: 4.20–11.00), prime parity (AOR 2.15 95% CI: 1.26–3.66) and use of herbal medicines in active labour (AOR 2.72 95% CI: 1.49–4.96). Married participants (AOR 0.59 95% CI: 0.35–0.97) with a delivery plan (AOR 0.56 95% CI: 0.35–0.90) and educated partners (AOR 0.57 95% CI: 0.33–0.98) were less likely to have OL. In the adjusted analysis, there was no association between four or more ANC visits and OL, adjusted odds ratio [(AOR) 0.96 95% CI: 0.57–1.63)]. Conclusions Prime parity, use of herbal medicines in labour and being a referral from a lower health facility were identified as risk factors. Being married with a delivery plan and an educated partner were protective of OL. Increased frequency of ANC attendance was not protective against obstructed labour. We conducted this study in the labour suite of Mbale regional referral Hospital in Eastern Uganda. This hospital, serves 14 districts in the Elgon zone with an estimated population of 4 million people. This is a government run, not-for-profit, charge-free, 470-bed hospital with 52 maternity beds. Annually, about 12,000 childbirths occur in this hospital with a caesarean section rate of 35% and nearly 500 mothers have OL. About two thirds of these mothers with OL are referrals in active labour from the lower health units. Unmatched case control design with incidence density sampling of the controls admitted in the same delivery suite. All patients admitted to the labour suite in active labour at term (≥ 37 weeks of gestation) were screened. A Medical Officer or Obstetrician diagnosed OL using the American College of Obstetricians and Gynecologists (ACOG) guideline for arrest of labour [14] and local protocols. A case was defined as; a cervical dilatation ≥ 6cm with ruptured membranes, having adequate contractions lasting > 4hrs with no change in cervical dilatation in the first stage of labour. For the second active stage of labour, arrest was defined as a delay of > 2 hours for the nullipara and > 1 hour for the multipara with adequate uterine contractions. In addition, a case had to have any two of the following obvious signs of severe obstruction: caput formation, Bandl’s ring, sub-conjunctival hemorrhages and edematous vulva. Controls were women admitted to the labour suit within the same 24-hour period in active labour without obstruction. We used the formula described by Fleiss with a continuity correction to estimate the sample size[15]. The exposure factor was the proportion of pregnant women who attended < 4 ANC visits. We enrolled 270 cases and 270 controls based on the following assumptions: two-sided 95% confidence level, power of 95%, ratio 1:1 to detect an odds ratio of at least 2 for the risk of OL among pregnant women who attended < 4 ANC visits as the main exposure variable[16–18]. We further assumed that controls were like any other pregnant woman in Uganda who attended at least 4 ANC visits (60%) according to the Uganda demographic and health survey [9]. We consecutively enrolled all eligible incident cases between July 2018 and February 2019. We used simple random sampling to select one control from a list of admissions in active labour immediately after enrolling each case. Before recruitment, all respondents gave us written informed consent and pregnant adolescents below the legal age of 18 years were taken as emancipated minors[19]. We used unique study numbers issued at enrolment to identify each respondent. Cases were women with OL carrying singleton, term pregnancies in cephalic presentation. Controls were women in active labour without obstruction carrying singleton, term pregnancies in cephalic presentation. We excluded women with other obstetric emergencies such as antepartum haemorrhage, Pre-eclampsia and eclampsia (defined as elevated blood pressure of at least 140/90 mmHg, urine protein of at least 2+, any of the danger signs and fits), premature rupture of membranes and intrauterine fetal death. We also excluded all women from outside the Hospital catchment area of 14 districts as either cases or controls. The socio-demographic factors highlighted in the literature to predispose women to OL were the participant’s age, marital status, occupation, level of education, the occupation and education level of the spouse as well as distance to the nearest health facility and the place of residence[10,12,17,20,21]. The obstetric factors were gravidity, number of ANC visits, having a delivery plan in place, a history of being referred from a lower health facility and use of herbal medications during labour[16,17]. Physical examination included the respondent’s height and fetal birth weight. Our main exposure was the number of ANC visits attended as indicated on the ANC card, the other covariates were considered as confounders. We used an interviewer-administered questionnaire running on an open data kit (ODK) platform. Trained research assistants (RA’s) who are qualified midwives administered the questionnaire to all participants in the local dialect. We blinded all the RA’s to the hypothesis of the study. Available records such as the antenatal cards, facility registers and case report files were reviewed by the RA’s to crosscheck some of the verbal responses. The principal investigator (PI) would, on a daily basis access and review the data from the Google Aggregate server for completeness. The data was uploaded to a password protected server to which only the PI or his designee had access. Assisted by a statistician, the data was downloaded into an excel spreadsheet and exported to Stata version 14 for further cleaning and analysis. Baseline socio-demographic, physical and obstetric characteristics of the cases and controls were compared, to identify any differences. Normality of the continuous variables was tested for using the Shapiro-Wilk test. We summarised continuous variables using means and standard deviations. Whereas frequencies and percentages were used for the categorical variables. We used logistic regression (LR) to estimate Odds ratios, and 95% confidence intervals to examine the association between the number of ANC visits (< 4 Vs ≥ 4) and the different socio-demographic, physical and obstetric covariates in bivariable and multivariable analysis. We included all factors that are known to confound the relationship between the frequency of ANC attendance and OL in the multivariable LR model, based on biological plausibility. In order to control for potential residual confounding due to factors that we had not previously hypothesized to be confounders, we also included those variables for which bivariable analysis returned a p-value equal to or less than 0.25. We reasoned that a cut-off of 0.25 would allow us to test the effect of any factors previously not known to have a confounding effect on the relationship between OL and the frequency of ANC attendance, without including those factors that were reasonably least likely [22]. Multicollinearity between explanatory variables was assessed using the variance inflation factor (VIFs), and they were all less than 1.5. In the final adjusted multivariable model, we included all the statistically significant covariates (being a referral, a history of using herbal medicines, having a delivery plan, prime parity and partner education level). Confounding was considered present, if the difference between the crude and adjusted OR was ≥ 10 percentage points[23,24]. The Makerere University School of Medicine Research and Ethics Committee (#REC REF 2017–103) and the Uganda National Council for Science and Technology (HS217ES) approved the protocol. The Mbale Hospital Research and Ethics Committee (MRRH-REC IN-COM 00/2018) gave us administrative clearance. The hospital protocols were followed in management emergencies during the study.

AI Digest

Study Justification:

– Obstructed labour (OL) is a significant clinical and public health problem due to its associated maternal and perinatal morbidity and mortality.
– Risk factors for OL and its complications are usually specific to the context.
– No epidemiological study has documented the risk factors for OL in Eastern Uganda.

Study Highlights:

– Conducted a case control study with 270 cases of women with OL and 270 controls of women without OL.
– Identified risk factors for OL in Mbale Regional Referral and Teaching Hospital, Eastern Uganda.
– Risk factors for OL included being a referral from a lower health facility, prime parity, and use of herbal medicines in active labour.
– Being married with a delivery plan and an educated partner were protective against OL.
– Increased frequency of antenatal care (ANC) visits was not protective against obstructed labour.

Study Recommendations:

– Improve referral systems from lower health facilities to reduce the risk of obstructed labour.
– Provide education and support for prime parity women to reduce their risk of OL.
– Raise awareness about the potential risks of using herbal medicines during labour.
– Promote marriage, delivery planning, and education of partners as protective factors against OL.
– Emphasize the importance of ANC visits for overall maternal health, even though it may not directly prevent obstructed labour.

Key Role Players:

– Obstetricians and gynecologists
– Midwives and other healthcare providers
– Hospital administrators and policymakers
– Community health workers and educators
– Maternal health organizations and NGOs

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers on identifying and managing risk factors for obstructed labour.
– Development and implementation of referral systems and protocols.
– Education and awareness campaigns targeting pregnant women and their partners.
– Provision of resources and support for delivery planning.
– Strengthening ANC services and promoting regular attendance.
– Monitoring and evaluation of interventions to assess their effectiveness.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because the study design is a case-control study, which is a lower level of evidence compared to randomized controlled trials or systematic reviews. However, the study has a large sample size and includes both cases and controls, which strengthens the evidence. To improve the evidence, the study could have used a prospective cohort design to establish a temporal relationship between the risk factors and obstructed labour. Additionally, the abstract could provide more details on the methods used for data collection and analysis.

Abstract

We conducted this study in the labour suite of Mbale regional referral Hospital in Eastern Uganda. This hospital, serves 14 districts in the Elgon zone with an estimated population of 4 million people. This is a government run, not-for-profit, charge-free, 470-bed hospital with 52 maternity beds. Annually, about 12,000 childbirths occur in this hospital with a caesarean section rate of 35% and nearly 500 mothers have OL. About two thirds of these mothers with OL are referrals in active labour from the lower health units. Unmatched case control design with incidence density sampling of the controls admitted in the same delivery suite. All patients admitted to the labour suite in active labour at term (≥ 37 weeks of gestation) were screened. A Medical Officer or Obstetrician diagnosed OL using the American College of Obstetricians and Gynecologists (ACOG) guideline for arrest of labour [14] and local protocols. A case was defined as; a cervical dilatation ≥ 6cm with ruptured membranes, having adequate contractions lasting > 4hrs with no change in cervical dilatation in the first stage of labour. For the second active stage of labour, arrest was defined as a delay of > 2 hours for the nullipara and > 1 hour for the multipara with adequate uterine contractions. In addition, a case had to have any two of the following obvious signs of severe obstruction: caput formation, Bandl’s ring, sub-conjunctival hemorrhages and edematous vulva. Controls were women admitted to the labour suit within the same 24-hour period in active labour without obstruction. We used the formula described by Fleiss with a continuity correction to estimate the sample size[15]. The exposure factor was the proportion of pregnant women who attended < 4 ANC visits. We enrolled 270 cases and 270 controls based on the following assumptions: two-sided 95% confidence level, power of 95%, ratio 1:1 to detect an odds ratio of at least 2 for the risk of OL among pregnant women who attended < 4 ANC visits as the main exposure variable[16–18]. We further assumed that controls were like any other pregnant woman in Uganda who attended at least 4 ANC visits (60%) according to the Uganda demographic and health survey [9]. We consecutively enrolled all eligible incident cases between July 2018 and February 2019. We used simple random sampling to select one control from a list of admissions in active labour immediately after enrolling each case. Before recruitment, all respondents gave us written informed consent and pregnant adolescents below the legal age of 18 years were taken as emancipated minors[19]. We used unique study numbers issued at enrolment to identify each respondent. Cases were women with OL carrying singleton, term pregnancies in cephalic presentation. Controls were women in active labour without obstruction carrying singleton, term pregnancies in cephalic presentation. We excluded women with other obstetric emergencies such as antepartum haemorrhage, Pre-eclampsia and eclampsia (defined as elevated blood pressure of at least 140/90 mmHg, urine protein of at least 2+, any of the danger signs and fits), premature rupture of membranes and intrauterine fetal death. We also excluded all women from outside the Hospital catchment area of 14 districts as either cases or controls. The socio-demographic factors highlighted in the literature to predispose women to OL were the participant’s age, marital status, occupation, level of education, the occupation and education level of the spouse as well as distance to the nearest health facility and the place of residence[10,12,17,20,21]. The obstetric factors were gravidity, number of ANC visits, having a delivery plan in place, a history of being referred from a lower health facility and use of herbal medications during labour[16,17]. Physical examination included the respondent’s height and fetal birth weight. Our main exposure was the number of ANC visits attended as indicated on the ANC card, the other covariates were considered as confounders. We used an interviewer-administered questionnaire running on an open data kit (ODK) platform. Trained research assistants (RA’s) who are qualified midwives administered the questionnaire to all participants in the local dialect. We blinded all the RA’s to the hypothesis of the study. Available records such as the antenatal cards, facility registers and case report files were reviewed by the RA’s to crosscheck some of the verbal responses. The principal investigator (PI) would, on a daily basis access and review the data from the Google Aggregate server for completeness. The data was uploaded to a password protected server to which only the PI or his designee had access. Assisted by a statistician, the data was downloaded into an excel spreadsheet and exported to Stata version 14 for further cleaning and analysis. Baseline socio-demographic, physical and obstetric characteristics of the cases and controls were compared, to identify any differences. Normality of the continuous variables was tested for using the Shapiro-Wilk test. We summarised continuous variables using means and standard deviations. Whereas frequencies and percentages were used for the categorical variables. We used logistic regression (LR) to estimate Odds ratios, and 95% confidence intervals to examine the association between the number of ANC visits (< 4 Vs ≥ 4) and the different socio-demographic, physical and obstetric covariates in bivariable and multivariable analysis. We included all factors that are known to confound the relationship between the frequency of ANC attendance and OL in the multivariable LR model, based on biological plausibility. In order to control for potential residual confounding due to factors that we had not previously hypothesized to be confounders, we also included those variables for which bivariable analysis returned a p-value equal to or less than 0.25. We reasoned that a cut-off of 0.25 would allow us to test the effect of any factors previously not known to have a confounding effect on the relationship between OL and the frequency of ANC attendance, without including those factors that were reasonably least likely [22]. Multicollinearity between explanatory variables was assessed using the variance inflation factor (VIFs), and they were all less than 1.5. In the final adjusted multivariable model, we included all the statistically significant covariates (being a referral, a history of using herbal medicines, having a delivery plan, prime parity and partner education level). Confounding was considered present, if the difference between the crude and adjusted OR was ≥ 10 percentage points[23,24]. The Makerere University School of Medicine Research and Ethics Committee (#REC REF 2017–103) and the Uganda National Council for Science and Technology (HS217ES) approved the protocol. The Mbale Hospital Research and Ethics Committee (MRRH-REC IN-COM 00/2018) gave us administrative clearance. The hospital protocols were followed in management emergencies during the study.

Materials

Innovations

Based on the information provided, here are some potential innovations that can be used to improve access to maternal health:

1. Telemedicine: Implementing telemedicine services can improve access to maternal health by allowing pregnant women in remote areas to consult with healthcare professionals through video calls or phone calls. This can help in early detection of risk factors and provide necessary guidance and support.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information and resources related to maternal health can empower women with knowledge and enable them to make informed decisions about their health. These apps can provide information on prenatal care, nutrition, and warning signs during pregnancy.

3. Community health workers: Training and deploying community health workers in rural areas can improve access to maternal health services. These workers can provide basic prenatal care, educate women about healthy practices during pregnancy, and facilitate referrals to healthcare facilities when necessary.

4. Transportation services: Lack of transportation is a major barrier to accessing maternal health services, especially in remote areas. Implementing transportation services, such as ambulances or community transport systems, can ensure that pregnant women have timely access to healthcare facilities for prenatal care, delivery, and emergency obstetric care.

5. Mobile clinics: Setting up mobile clinics that travel to remote areas can provide essential maternal health services to women who have limited access to healthcare facilities. These clinics can offer prenatal check-ups, vaccinations, and basic obstetric care.

6. Health education programs: Implementing comprehensive health education programs in communities can raise awareness about the importance of maternal health and encourage women to seek timely care. These programs can include workshops, community meetings, and educational materials targeting pregnant women and their families.

7. Strengthening referral systems: Improving the referral systems between lower health facilities and regional referral hospitals can ensure that pregnant women with complications are promptly referred to higher-level facilities for specialized care. This can help reduce delays in accessing emergency obstetric services.

It is important to note that the specific implementation of these innovations would require careful planning, collaboration with local stakeholders, and consideration of the local context and resources available.

AI Innovations Description

Based on the study titled “Risk factors for obstructed labour in Eastern Uganda: A case control study,” the following recommendations can be developed into an innovation to improve access to maternal health:

1. Strengthen referral systems: Since being a referral from a lower health facility was identified as a risk factor for obstructed labor, it is important to improve the referral systems between lower health facilities and regional referral hospitals. This can be done by establishing clear protocols and communication channels for timely and efficient referrals.

2. Increase awareness about the risks of herbal medicines in labor: The study found that the use of herbal medicines in active labor was a risk factor for obstructed labor. To address this, there should be targeted health education campaigns to raise awareness among pregnant women and healthcare providers about the potential risks associated with the use of herbal medicines during labor.

3. Promote delivery planning: The study showed that having a delivery plan in place was protective against obstructed labor. To encourage more pregnant women to have a delivery plan, innovative approaches such as mobile phone applications or community-based education programs can be developed to provide information and support for creating and implementing a delivery plan.

4. Enhance partner involvement and education: The study found that having an educated partner was protective against obstructed labor. To promote partner involvement and education, innovative strategies such as antenatal education programs specifically targeting partners can be implemented. This can include providing information on the importance of ANC visits, signs of labor complications, and the role of partners in supporting maternal health.

5. Improve access to antenatal care (ANC): Although the study did not find a significant association between the number of ANC visits and obstructed labor, it is still important to ensure that pregnant women have access to quality ANC services. This can be achieved by strengthening ANC clinics, increasing the number of skilled healthcare providers, and addressing barriers to ANC utilization such as distance to health facilities and socio-economic factors.

By implementing these recommendations, it is possible to develop innovative solutions that can improve access to maternal health and reduce the incidence of obstructed labor in Eastern Uganda.

AI Innovations Methodology

Based on the information provided, here are some potential recommendations for improving access to maternal health:

1. Strengthen referral systems: Given that a significant number of mothers with obstructed labor are referrals from lower health facilities, it is important to strengthen the referral systems between these facilities and regional referral hospitals. This can include improving communication channels, providing training to healthcare providers on identifying and managing obstructed labor, and ensuring timely and efficient transportation for referrals.

2. Increase awareness and education: Educating pregnant women and their partners about the risk factors for obstructed labor can help them make informed decisions and seek appropriate care. This can be done through community outreach programs, antenatal care sessions, and mass media campaigns. Additionally, providing information about the importance of regular antenatal care visits and the potential complications of obstructed labor can encourage women to seek timely and appropriate care.

3. Improve access to antenatal care: Although the study did not find a significant association between the number of antenatal care (ANC) visits and obstructed labor, it is still important to ensure that pregnant women have access to and attend the recommended number of ANC visits. This can be achieved by addressing barriers such as distance to health facilities, cost of care, and availability of healthcare providers. Mobile clinics, community-based ANC services, and financial support programs can help improve access to ANC.

4. Enhance availability of skilled birth attendants: Having skilled birth attendants present during childbirth is crucial for early detection and management of obstructed labor. Efforts should be made to increase the number of skilled birth attendants, particularly in areas with high rates of obstructed labor. This can be achieved through training and deploying more midwives and other skilled healthcare providers to underserved areas.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the indicators: Identify specific indicators that reflect access to maternal health, such as the number of referrals from lower health facilities, the percentage of women attending the recommended number of ANC visits, or the availability of skilled birth attendants.

2. Collect baseline data: Gather data on the current status of the identified indicators. This can be done through surveys, interviews, or analysis of existing data sources.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the indicators. This model should consider factors such as population size, healthcare infrastructure, and resource availability.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations. Vary the parameters to explore different scenarios and assess the sensitivity of the results.

5. Analyze results: Analyze the simulation results to determine the potential impact of the recommendations on improving access to maternal health. This can include quantifying changes in the identified indicators and assessing the feasibility and cost-effectiveness of implementing the recommendations.

6. Refine and validate the model: Refine the simulation model based on the analysis of the results and validate it using additional data or expert input. This will help ensure the accuracy and reliability of the simulation results.

7. Communicate findings and make recommendations: Present the findings of the simulation study to relevant stakeholders, such as policymakers, healthcare providers, and community members. Use the results to make evidence-based recommendations for improving access to maternal health and advocate for their implementation.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific context and available data.

Author

Dima Health

Statistics:

Citations: 6

Identifiers:

DOI: 10.1371/journal.pone.0228856

Research Areas:

Health System and Policy, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Social Determinants, Technology and Innovations, Workforce

Study Countries:

Uganda

Study Design:

Case-Control Study, Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female