Determinants of low birth weight in neonates born in three hospitals in Brong Ahafo region, Ghana, 2016- an unmatched case-control study

BMC Pregnancy and Childbirth, Volume 19, No. 1, Year 2019

Ukuhunyushwa

Background: Each year, about 20 million Low Birth Weight (LBW) babies are born with very high proportion (96.5%) occuring in developing countries. In the last decade, the incidence of LBW in Ghana has not declined. Brong Ahafo Region of Ghana recorded a LBW prevalence of 11% which was higher than the the national average of 10%. This study identifed determinants of LBW delivery in the Brong Ahafo Region. Methods: We conducted a 1:2 unmatched case control study among mothers with singleton deliveries in 3 major health facilities in the Brong Ahafo Region. A case was defined as a mother who delivered a baby weighing less than 2500g in any of the three selected health facilities between 1st December, 2015 and 30th April, 2016. A control was defined as a mother who within 24 h of delivery by a case, delivered a baby weighing at least 2500g and not exceeding 3400g in the same health facility. Deliveries that met the inclusion criteria for cases were selected and two controls were randomly selected from the pool of deliveries that meet criteria for controls within 24 h of delivery of a case. A total of 120 cases and 240 control were recruited for the study. We computed odds ratios at 95% confidence level to determine the associations between low birth weight and the dependent factors. Results: After controlling for confounders such as planned pregnancy, mode of delivery, parity and previous LBW in stepwise backward logistic regression, first trimester hemoglobin < 11 g/dl (aOR 3.14; 95%CI: 1.50-6.58), delivery at 32-36 weeks gestation (aOR 13.70; 95%CI: 4.64-40.45), delivery below 32 weeks gestation (aOR 58.5; 95%CI 6.7-513.9), secondary education of mothers (aOR 4.19; 95%CI 1.45-12.07), living with extended family (aOR 2.43; 95%CI 1.15-5.10, living alone during pregnancy (aOR 3.9; 95%CI: 1.3-11.7), and not taking iron supplements during pregnancy (aOR 3.2; 95%CI: 1.1-9.5) were found to be significantly associated with LBW. Conclusion: Determinants of LBW were: preterm delivery, mothers with secondary education, living alone during pregnancy, not taking daily required iron supplementation and mothers with first trimester hemoglobin below 11 g/dl. Education during antenatal sessions should be tailored to address the identified risk factors in the mother and child health care services. We carried out a 1:2 an unmatched case control study in three major hospitals in Brong Ahafo Region, Ghana from 1st December 2015 to 30th April 2016. These hospitals recorded the largest number of deliveries per year in the region and serve as the major referral centres in the region. The study was conducted in the Brong Ahafo Regional Hospital, Sunyani Municipal Hospital and the Holy Family Hopsital. Brong Ahafo Regional Hospital is the main referral centre in the region for patients requiring secondary healthcare services. In 2015, data from DHIMS revealed that the hospital recorded a total of 3261 live births with 12.6% (410/3261) being low birth weight. The hospital has one gynecological theatre, one labour ward, three post-delivery wards and one neonatal unit. Holy family hospital serves as another referral centre for medical conditions including obstetric and gynecological conditions. It is located in Techiman, which is considered the busiest trading centre of the region. Data from the DHIMS for 2015 showed that the hospital recorded a total of 5152 live births with 12.4% (641/5152) low birth weight babies. The hospital one labour ward, a post-delivery ward and one neonatal unit. The Sunyani Municipal Hospital is the third referral hospital and it serves the Sunyani Municipality. It recorded a total of 1662 live births with 5.9% (98/1662) low birth weights for 2015 (DHIMS 2015). It has one theatre, one labour ward and one post-delivery ward. All the data collected from this hospital were in the records of the post-delivery ward. A case was defined as a mother who delivered a baby weighing less than 2500g in any of the three selected health facilities between 1st December, 2015 and 30th April, 2016. A control was defined as a mother who within 24 h of delivery by a case, delivered a baby weighing at least 2500g and not exceeding 3400g in the same health facility. Mothers with singleton deliveries of babies whose weight is 3400 g or less. Mothers who consent to participate in the study. Babies with congenital abnormalities or still births. Mothers who are critically ill. A sample size calculation formula for unmatched case control study was used with the following parameters: power of the study was 80%, Zβ = 0.84 and at 0.05 significance level, Zα = 1.96. The proportion exposed in the control group used was 33%, thus the exposure was nulliparity in a study carried out in The Gambia (Jammeh et al., 2011). Minimal detectable odds ratio that was used was 2. Based on this a total of 360 case control respondent pairs, that is a 1:2 unmatched case control pairs was arrived at. An additional 15% was added to adjust for missing data. A data collection questionnaire was designed to collect data from mothers who delivered and met the criteria to be included in the study. The questionnaire obtained both primary and secondary data. We obtained the secondary data by reviewing the antenatal and postnatal health records of the mother. The questionnaire was pretested in a health facility with similar characteristics as the study sites. The questionnaire was revised to improve clarity of some of the questions. The required data from mothers with low birth weight babies were collected within 24 h upon delivery. Data was collected each time a low birth weight baby was delivered until the required sample size was obtained. The data collection officer visited the post-delivery, labour, and neonatal wards three times each day (morning, afternoon and evening) to identify study participants. Also, the staff on duty at the post delivery, labour, and neonatal units alerted the data collection officer each time a delivery meeting the case definition and inclusion criteria occurred. Data was collected by administering the structured questionnaire to the mother and also recording information from the mothers’ antenatal records book and the maternity ward records. Data were collected concurrently in all the three health facilities until the total required sample size was obtained. Two controls were selected on the same day of delivery of a case by a simple random method. Where more than two controls were delivered within 24 h after delivery of the case, the controls are assigned numbers which were entered into a random digit selection software to select the controls randomly. This was done concurrently in all the three health until the required sample size was reached. Consenting mothers were taken to an office within the ward for the questionnaire to be administered to ensure confidentiality. The data collected included: Socio demographic information: age, occupation of mother, educational status, income status, baby’s sex, marital status, social support status, height, weight, residence, and planning of pregnancy. Obstetric data included: gestation at booking, gestation at delivery, mode of delivery, family planning methods used, previous abortions, previous delivery of a LBW baby, parity, number of Antenatal Care (ANC) visits. Medical status information: any chronic medical condition, illness during pregnancy, hospital admissions during pregnancy, intake of required daily dose of iron supplementation, appetite during pregnancy, use of herbal medications during pregnancy and alcohol intake during pregnancy. Data collection was carried out with the help of two data collection officers. The officers were health professionals trained in the area of maternal and child health. They were selected from the facilities used for the study. They were trained a week prior to commencement of the data collection. They were then introduced to the heads of departments where recruitment was done. Data was cross-checked for errors and entered using EpiInfo 7 software. Data was saved in password protected files and no one had access to it except for cross-referencing. Filled questionnaires were kept in locked cabinets. Participants were identified by codes. Data were analyzed using STATA software Version 13. Continuous variables were summarized into means and proportions, whiles categorical variables were summarized into frequencies. Bivariate analysis was done between birthweight and each of the independent variables to determine the associations using the chi-square test of proportions. The odds ratios and confidence intervals were reported using 95% level of significance. All variables in the bivariate analysis with a p-value of less than 0.05 was considered for multiple logistic regression analysis. The backward stepwise logistic regression model was used to test for the determinant predictors for LBW. The level of significance for regression analysis was set at 95%. We obtained ethical approval from the Ghana Health Service Ethics Review Committee (GHS/ERS 071015). Also, permission was obtained from the Brong Ahafo Regional Health Directorate and the Heads of the Sunyani Regional Hospital, Sunyani Municipal Hospital and Holy Family Hospital to access the participants and their records. The study was explained to participants and their concerns addressed. A written informed consent was obtained from all participants. Each consenting participant signed or thumb printed on the consent form before the questionnaire was administered. For participant who were under 18, consent was sought form their guardians, and the participant provided a written assent to take part in the study. Both the mother or guardian and participant signed a consent form before the interviews were conducted.

I-AI Digest

Study Justification:

– Each year, a high number of low birth weight (LBW) babies are born, with a majority in developing countries.
– The incidence of LBW in Ghana has not declined in the last decade.
– The Brong Ahafo Region of Ghana has a higher prevalence of LBW compared to the national average.
– This study aimed to identify the determinants of LBW delivery in the Brong Ahafo Region.

Highlights:

– The study was conducted in three major hospitals in the Brong Ahafo Region, which are the main referral centers in the region.
– A total of 120 cases (mothers who delivered LBW babies) and 240 controls (mothers who delivered normal weight babies) were recruited for the study.
– After controlling for confounders, the following factors were found to be significantly associated with LBW: first trimester hemoglobin < 11 g/dl, delivery at 32-36 weeks gestation, delivery below 32 weeks gestation, secondary education of mothers, living with extended family, living alone during pregnancy, and not taking iron supplements during pregnancy.
– The study recommends tailoring education during antenatal sessions to address the identified risk factors in mother and child healthcare services.

Recommendations for Lay Reader and Policy Maker:

– Lay Reader: The study highlights the factors associated with low birth weight in the Brong Ahafo Region of Ghana. It emphasizes the importance of early detection and management of anemia during pregnancy, adequate prenatal care, and iron supplementation. The findings can help expectant mothers make informed decisions and take necessary precautions to prevent LBW.
– Policy Maker: The study provides evidence-based recommendations to improve maternal and child healthcare services in the Brong Ahafo Region. It suggests the need for targeted education programs for pregnant women, especially those with secondary education, living alone, and not taking iron supplements. Policy interventions should focus on improving access to prenatal care, promoting iron supplementation, and addressing social factors that contribute to LBW.

Key Role Players:

– Healthcare providers: Obstetricians, gynecologists, midwives, nurses, and other healthcare professionals involved in maternal and child healthcare services.
– Public health officials: Officials responsible for planning and implementing public health programs, policies, and interventions.
– Community leaders and organizations: Leaders and organizations involved in community health promotion and education.
– Government agencies: Ministries of Health and Education, responsible for policy development and implementation.
– Non-governmental organizations (NGOs): NGOs working in the field of maternal and child health, providing support and resources.

Cost Items for Planning Recommendations:

– Education and training programs for healthcare providers: Budget for organizing workshops, seminars, and training sessions to update healthcare providers on LBW prevention and management.
– Awareness campaigns: Budget for developing and implementing public awareness campaigns to educate pregnant women and their families about the importance of prenatal care, iron supplementation, and healthy lifestyle choices.
– Access to prenatal care: Budget for improving access to prenatal care services, including infrastructure development, staffing, and equipment.
– Iron supplementation programs: Budget for providing iron supplements to pregnant women, including procurement, distribution, and monitoring.
– Social support programs: Budget for implementing programs to address social factors contributing to LBW, such as providing support for pregnant women living alone or with extended family.

Please note that the provided cost items are general suggestions and may vary based on the specific context and resources available in the Brong Ahafo Region.

Amandla Obufakazi

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a detailed description of the study design, sample size calculation, data collection methods, and statistical analysis. However, it does not mention the specific statistical tests used for the associations between low birth weight and the independent variables. To improve the evidence, the abstract could include the specific statistical tests used and provide more information on the validity and reliability of the data collection methods.

Abstract

We carried out a 1:2 an unmatched case control study in three major hospitals in Brong Ahafo Region, Ghana from 1st December 2015 to 30th April 2016. These hospitals recorded the largest number of deliveries per year in the region and serve as the major referral centres in the region. The study was conducted in the Brong Ahafo Regional Hospital, Sunyani Municipal Hospital and the Holy Family Hopsital. Brong Ahafo Regional Hospital is the main referral centre in the region for patients requiring secondary healthcare services. In 2015, data from DHIMS revealed that the hospital recorded a total of 3261 live births with 12.6% (410/3261) being low birth weight. The hospital has one gynecological theatre, one labour ward, three post-delivery wards and one neonatal unit. Holy family hospital serves as another referral centre for medical conditions including obstetric and gynecological conditions. It is located in Techiman, which is considered the busiest trading centre of the region. Data from the DHIMS for 2015 showed that the hospital recorded a total of 5152 live births with 12.4% (641/5152) low birth weight babies. The hospital one labour ward, a post-delivery ward and one neonatal unit. The Sunyani Municipal Hospital is the third referral hospital and it serves the Sunyani Municipality. It recorded a total of 1662 live births with 5.9% (98/1662) low birth weights for 2015 (DHIMS 2015). It has one theatre, one labour ward and one post-delivery ward. All the data collected from this hospital were in the records of the post-delivery ward. A case was defined as a mother who delivered a baby weighing less than 2500g in any of the three selected health facilities between 1st December, 2015 and 30th April, 2016. A control was defined as a mother who within 24 h of delivery by a case, delivered a baby weighing at least 2500g and not exceeding 3400g in the same health facility. Mothers with singleton deliveries of babies whose weight is 3400 g or less. Mothers who consent to participate in the study. Babies with congenital abnormalities or still births. Mothers who are critically ill. A sample size calculation formula for unmatched case control study was used with the following parameters: power of the study was 80%, Zβ = 0.84 and at 0.05 significance level, Zα = 1.96. The proportion exposed in the control group used was 33%, thus the exposure was nulliparity in a study carried out in The Gambia (Jammeh et al., 2011). Minimal detectable odds ratio that was used was 2. Based on this a total of 360 case control respondent pairs, that is a 1:2 unmatched case control pairs was arrived at. An additional 15% was added to adjust for missing data. A data collection questionnaire was designed to collect data from mothers who delivered and met the criteria to be included in the study. The questionnaire obtained both primary and secondary data. We obtained the secondary data by reviewing the antenatal and postnatal health records of the mother. The questionnaire was pretested in a health facility with similar characteristics as the study sites. The questionnaire was revised to improve clarity of some of the questions. The required data from mothers with low birth weight babies were collected within 24 h upon delivery. Data was collected each time a low birth weight baby was delivered until the required sample size was obtained. The data collection officer visited the post-delivery, labour, and neonatal wards three times each day (morning, afternoon and evening) to identify study participants. Also, the staff on duty at the post delivery, labour, and neonatal units alerted the data collection officer each time a delivery meeting the case definition and inclusion criteria occurred. Data was collected by administering the structured questionnaire to the mother and also recording information from the mothers’ antenatal records book and the maternity ward records. Data were collected concurrently in all the three health facilities until the total required sample size was obtained. Two controls were selected on the same day of delivery of a case by a simple random method. Where more than two controls were delivered within 24 h after delivery of the case, the controls are assigned numbers which were entered into a random digit selection software to select the controls randomly. This was done concurrently in all the three health until the required sample size was reached. Consenting mothers were taken to an office within the ward for the questionnaire to be administered to ensure confidentiality. The data collected included: Socio demographic information: age, occupation of mother, educational status, income status, baby’s sex, marital status, social support status, height, weight, residence, and planning of pregnancy. Obstetric data included: gestation at booking, gestation at delivery, mode of delivery, family planning methods used, previous abortions, previous delivery of a LBW baby, parity, number of Antenatal Care (ANC) visits. Medical status information: any chronic medical condition, illness during pregnancy, hospital admissions during pregnancy, intake of required daily dose of iron supplementation, appetite during pregnancy, use of herbal medications during pregnancy and alcohol intake during pregnancy. Data collection was carried out with the help of two data collection officers. The officers were health professionals trained in the area of maternal and child health. They were selected from the facilities used for the study. They were trained a week prior to commencement of the data collection. They were then introduced to the heads of departments where recruitment was done. Data was cross-checked for errors and entered using EpiInfo 7 software. Data was saved in password protected files and no one had access to it except for cross-referencing. Filled questionnaires were kept in locked cabinets. Participants were identified by codes. Data were analyzed using STATA software Version 13. Continuous variables were summarized into means and proportions, whiles categorical variables were summarized into frequencies. Bivariate analysis was done between birthweight and each of the independent variables to determine the associations using the chi-square test of proportions. The odds ratios and confidence intervals were reported using 95% level of significance. All variables in the bivariate analysis with a p-value of less than 0.05 was considered for multiple logistic regression analysis. The backward stepwise logistic regression model was used to test for the determinant predictors for LBW. The level of significance for regression analysis was set at 95%. We obtained ethical approval from the Ghana Health Service Ethics Review Committee (GHS/ERS 071015). Also, permission was obtained from the Brong Ahafo Regional Health Directorate and the Heads of the Sunyani Regional Hospital, Sunyani Municipal Hospital and Holy Family Hospital to access the participants and their records. The study was explained to participants and their concerns addressed. A written informed consent was obtained from all participants. Each consenting participant signed or thumb printed on the consent form before the questionnaire was administered. For participant who were under 18, consent was sought form their guardians, and the participant provided a written assent to take part in the study. Both the mother or guardian and participant signed a consent form before the interviews were conducted.

Izinto zokwakha

N/A

Emisha

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women with access to information and resources related to maternal health. These apps can provide educational materials, appointment reminders, and access to healthcare professionals through telemedicine.

2. Community Health Workers: Train and deploy community health workers to provide education, support, and basic healthcare services to pregnant women in remote or underserved areas. These workers can conduct home visits, provide antenatal care, and refer women to appropriate healthcare facilities when necessary.

3. Telemedicine: Establish telemedicine services to connect pregnant women in remote areas with healthcare professionals. This can help overcome geographical barriers and provide access to prenatal care, consultations, and monitoring.

4. Maternal Health Vouchers: Implement voucher programs that provide pregnant women with subsidized or free access to essential maternal health services, such as antenatal care, delivery, and postnatal care. This can help reduce financial barriers and increase utilization of healthcare services.

5. Transportation Support: Develop transportation initiatives to ensure pregnant women have access to transportation to healthcare facilities. This can include providing free or subsidized transportation services or partnering with existing transportation providers to offer discounted rates for pregnant women.

6. Maternal Health Education Campaigns: Launch targeted education campaigns to raise awareness about the importance of maternal health and encourage women to seek timely and appropriate care. These campaigns can use various channels, such as radio, television, social media, and community outreach programs.

7. Strengthening Healthcare Infrastructure: Invest in improving and expanding healthcare facilities, particularly in underserved areas, to ensure access to quality maternal health services. This can include building or renovating maternity wards, equipping facilities with necessary medical equipment, and training healthcare providers.

8. Maternity Waiting Homes: Establish maternity waiting homes near healthcare facilities to accommodate pregnant women who live far away and need to travel for delivery. These homes can provide a safe and comfortable place for women to stay during the final weeks of pregnancy, ensuring timely access to healthcare services.

9. Task Shifting: Train and empower non-physician healthcare providers, such as nurses and midwives, to perform certain tasks traditionally done by doctors. This can help alleviate the shortage of healthcare professionals and increase access to maternal health services.

10. Public-Private Partnerships: Foster collaborations between the public and private sectors to improve access to maternal health services. This can involve leveraging private sector resources, expertise, and infrastructure to expand healthcare services and reach underserved populations.

It is important to note that the implementation of these innovations should be context-specific and tailored to the specific needs and challenges of the Brong Ahafo Region in Ghana.

AI Innovations Description

Based on the study findings, here is a recommendation that can be developed into an innovation to improve access to maternal health:

Develop a targeted education program during antenatal sessions: Based on the identified determinants of low birth weight (LBW) in the Brong Ahafo Region, it is recommended to develop a targeted education program during antenatal sessions. This program should focus on addressing the identified risk factors, such as preterm delivery, mothers with secondary education, living alone during pregnancy, not taking daily required iron supplementation, and mothers with first trimester hemoglobin below 11 g/dl. The education program should provide information and guidance on the importance of regular antenatal care visits, proper nutrition, iron supplementation, and the potential risks associated with preterm delivery. It should also emphasize the importance of social support during pregnancy and encourage mothers to seek support from their extended family or community. By providing targeted education, pregnant women can be empowered with the knowledge and resources to make informed decisions and take necessary actions to improve their maternal and child health outcomes.

AI Innovations Methodology

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

1. Strengthen antenatal care services: Enhance the quality and accessibility of antenatal care services by ensuring that pregnant women receive comprehensive and timely care. This can include regular check-ups, health education, and early detection and management of risk factors.

2. Improve access to iron supplementation: Increase awareness and availability of iron supplementation during pregnancy to address the association between low birth weight and not taking daily required iron supplements. This can be achieved through targeted education campaigns and ensuring the availability of iron supplements in healthcare facilities.

3. Enhance education during antenatal sessions: Tailor antenatal education sessions to address the identified risk factors for low birth weight, such as preterm delivery, mothers with secondary education, and living alone during pregnancy. This can help pregnant women make informed decisions and adopt healthy behaviors to reduce the risk of low birth weight.

4. Strengthen referral systems: Improve the referral systems between primary healthcare facilities and referral hospitals to ensure that pregnant women with complications receive timely and appropriate care. This can involve training healthcare providers, establishing clear communication channels, and improving transportation options for referrals.

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 can measure the impact of the recommendations, such as the percentage increase in the number of pregnant women receiving iron supplementation or the reduction in the percentage of low birth weight babies.

2. Collect baseline data: Gather data on the current status of access to maternal health services, including the number of pregnant women receiving antenatal care, the percentage of women taking iron supplementation, and the prevalence of low birth weight.

3. Implement the recommendations: Roll out the recommended interventions, such as strengthening antenatal care services, improving access to iron supplementation, enhancing education during antenatal sessions, and strengthening referral systems.

4. Monitor and evaluate: Continuously monitor the implementation of the recommendations and collect data on the indicators identified in step 1. This can involve regular data collection through surveys, interviews, or health facility records.

5. Analyze the data: Analyze the collected data to assess the impact of the recommendations on improving access to maternal health. This can involve comparing the baseline data with the data collected after the implementation of the recommendations.

6. Interpret the results: Interpret the findings to understand the extent to which the recommendations have improved access to maternal health. Identify any challenges or barriers that may have affected the outcomes.

7. Adjust and refine: Based on the findings, make any necessary adjustments or refinements to the recommendations to further improve access to maternal health. This can involve modifying strategies, reallocating resources, or addressing specific challenges identified during the evaluation.

8. Repeat the process: Continuously repeat the monitoring and evaluation process to assess the long-term impact of the recommendations and make further improvements as needed.

Umbhali

Dima Health

Statistics:

Citations: 16

Identifiers:

DOI: 10.1186/s12884-019-2315-6

Research Areas:

Disparities, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Substance Abuse

Study Countries:

Gambia, Ghana, Multi-Countries

Study Design:

Case-Control Study, Cohort Study, Cross Sectional Study, Grounded Theory, randomised-control-trial

Study Approach:

Quantitative

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