Neurodevelopmental Outcomes of Extremely Low Birth Weight Survivors in Johannesburg, South Africa

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Study Justification:
The study aimed to investigate the neurodevelopmental outcomes of extremely low birth weight infants (ELBWI) in a neonatal unit in Johannesburg, South Africa. This was important because improved survival rates of ELBWI in Sub-Saharan Africa raised concerns about potential adverse neurodevelopmental outcomes in these infants. Understanding the outcomes would provide valuable information for healthcare providers and policymakers to improve the care and support provided to ELBWI.
Highlights:
– The study included 723 ELBWI admissions, with 292 (40.4%) surviving to hospital discharge and 85 (29.1%) attending the follow-up clinic.
– The mean birth weight of the infants was 857.7 g, and the mean gestational age was 27.5 weeks.
– None of the infants had major complications of prematurity.
– The Bayley Scales of Infant and Toddler Development (version III) were used to assess neurodevelopmental outcomes.
– The mean composite scores for cognition, language, and motor skills fell within the normal range.
– However, 28 (36.8%) infants were classified as “at risk” for neurodevelopmental delay.
– The study demonstrated good neurodevelopmental outcomes in a small group of surviving ELBWI, but the results should be interpreted considering the high mortality rate in this population.
Recommendations:
Based on the study findings, the following recommendations can be made:
1. Enhance follow-up care: Implement strategies to improve the rates of follow-up for ELBWI, such as sending text message reminders and providing transportation support.
2. Early intervention: Establish a system for identifying and referring infants with developmental problems to appropriate interventions by allied medicine teams, including physiotherapy, occupational therapy, and speech therapy.
3. Long-term monitoring: Develop a long-term monitoring program to track the neurodevelopmental progress of ELBWI beyond the assessed age range, considering the potential for delayed or subtle developmental issues to emerge later in childhood.
Key Role Players:
To address the recommendations, the following key role players are needed:
1. Neonatologists and pediatricians: Provide medical expertise and guidance in the follow-up care and intervention for ELBWI.
2. Physiotherapists, occupational therapists, and speech therapists: Assess and provide appropriate interventions for infants with developmental problems.
3. Nurses and support staff: Assist in coordinating follow-up appointments, providing transportation support, and ensuring effective communication with parents.
Cost Items for Planning Recommendations:
While the actual costs may vary, the following budget items should be considered in planning the recommendations:
1. Transportation support: Allocate funds to refund transport costs for families attending follow-up appointments.
2. Staff training: Budget for training healthcare professionals in the assessment and intervention of neurodevelopmental outcomes in ELBWI.
3. Equipment and resources: Allocate funds for necessary equipment and resources for developmental assessments and interventions.
4. Long-term monitoring program: Consider the costs associated with establishing and maintaining a long-term monitoring program for ELBWI.
Please note that the provided information is based on the given description and may not include all details from the original study.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study was a prospective follow-up study conducted at a neonatal unit in Johannesburg, South Africa. The study included a relatively large sample size of 85 extremely low birth weight infants (ELBWI) who survived to hospital discharge. The neurodevelopmental outcomes were assessed using the Bayley Scales of Infant and Toddler Development (version III) at 9 to 12 months and 18 to 24 months. The mean composite scores for cognition, language, and motor skills fell within the normal range. However, the study only included a small group of surviving ELBWI, and the results should be interpreted in the context of the high mortality in this group. To improve the strength of the evidence, future studies could include a larger sample size and a control group for comparison.

Background: Improved survival in extremely low birth weight infants (ELBWI) in Sub-Saharan Africa has raised the question whether these survivors have an increased chance of adverse neurodevelopmental outcomes. Objectives: To describe neurodevelopmental outcomes of ELBWI in a neonatal unit in South Africa. Methods: This was a prospective follow-up study. All ELBWI who survived to discharge between 1 July 2013 and 31 December 2017 were invited to attend the clinic. Bayley Scales of Infant and Toddler Development (version III) were conducted at 9 to 12 months and 18 to 24 months. Results: There were 723 ELBWI admissions during the study period, 292 (40.4%) survived to hospital discharge and 85/292 (29.1%) attended the neonatal follow up clinic. The mean birth weight was 857.7 g (95% CI: 838.2–877.2) and the mean gestational age was 27.5 weeks (95% CI 27.1–27.9). None of the infants had any major complication of prematurity. A total of 76/85 (89.4%) of the infants had a Bayley-III assessment at a mean corrected age of 17.21 months (95% CI: 16.2–18.3). The mean composite scores for cognition were 98.4 (95% CI 95.1–101.7), language 89.9 (95% CI 87.3–92.5) and motor 97.6 (95% CI 94.5–100.6). All mean scores fell within the normal range, The study found 28 (36.8%) infants to be “at risk” for neurodevelopmental delay. Conclusion: Our study demonstrates good neurodevelopmental outcome in a small group of surviving ELBWI, but these results must be interpreted in the context of the high mortality in this group of infants.

This was a prospective follow-up study of ELBWI born between 1 July 2013 and 31 December 2017. The study was conducted at the neonatal unit of a tertiary hospital in Johannesburg, South Africa. Charlotte Maxeke Johannesburg Academic Hospital (CMJAH) is a public sector hospital that serves a low socioeconomic community that does not have access to private health insurance All ELBWI who survived to hospital discharge were invited to enrol. Enrolment was done at the first clinic visit. The ELBWI study group were seen at the study clinic every 3 months until the corrected age of 24 months. To improve rates of follow-up, text messages were sent to parents of enrolled participants as reminders of follow-up appointments. Transport costs were refunded and defaulting patients were traced and rebooked where possible. Appropriately trained paediatricians and physiotherapist performed the developmental assessments using the Bayley Scales of Infant and Toddler Development, version III (Bayley-III) (11). The Bayley III was validated in the same setting (14). The first Bayley-III assessment was conducted between 9 and 12 months; the second between 18 and 24 months (if patient still attended the follow up clinic). The Bayley-III assessment would be done at the next visit if a child defaulted a study clinic visit. The Bayley-III scores were calculated using the age corrected for prematurity. The gestational age was assessed by maternal menstrual history and clinical assessment using the Ballard score (11). The Cronbach’s alpha interclass correlation between different observers for neurodevelopment assessment was 0.89 (14). Infants with congenital abnormalities that were likely to affect neurodevelopment, for example Trisomy 21, were subsequently excluded from the study. Developmental delay was classified “at risk” if a composite Bayley-III score was below 85 on any of the cognitive, language or motor sub-scales and as “delayed” if a composite Bayley-III score was below 70 on any of the sub-scales (14). Cerebral palsy was diagnosed if there was a delay in motor milestones together with abnormal movement and/or posture (14). Hearing and vision were indirectly assessed as part of the Bayley-III language and motor assessment. Where developmental problems were identified, the child was referred for appropriate intervention by the allied medicine team (physiotherapy, occupational therapy and speech therapy). Data were entered and managed using Research Electronic Data Capture (REDCap™) software, hosted by the University of Witwatersrand (15). Maternal variables included demographics, antenatal care (ANC), obstetric history, place and mode of delivery. Neonatal variables included gestational age, birth weight, sex, duration of ventilation and stay, neonatal morbidity, late sepsis and outcome. The data were exported into SPSS version 23 (IBM, USA) for statistical analysis. The latest Bayley-III score for each child was used for analysis. The composite cognitive, language and motor scores were used as outcome variables. If continuous variables were normally distributed, the data was described using mean and 95% confidence intervals (95% CI). Skewed data was described using median and interquartile range (IQR). Categorical variables were described using frequency and percentages. Survivors and non-survivors were compared–continuous variables were compared using unpaired t test or Mann Whitney U depending on the data distribution. Categorical variables were compared using Chi Square. Only valid cases were analysed for each variable (i.e., missing data was excluded). Written informed consent was obtained from the parents of each participant prior to study enrolment. The Human Research Ethics Committee of the University of the Witwatersrand, Johannesburg, approved the study (reference numbers M120623 & M170702).

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

1. Mobile Health (mHealth) Solutions: Implementing mobile health technologies, such as text message reminders for follow-up appointments, can help improve rates of follow-up and reduce missed appointments.

2. Transportation Support: Refunding transport costs for patients can help remove a barrier to accessing maternal health services, particularly for low-income individuals who may face financial constraints.

3. Patient Tracing and Rebooking: Developing systems to trace and rebook defaulting patients can help ensure that they receive the necessary care and follow-up, even if they miss their scheduled appointments.

4. Allied Medicine Team Referrals: Establishing a multidisciplinary team of allied healthcare professionals, including physiotherapists, occupational therapists, and speech therapists, can ensure that infants with developmental problems receive appropriate interventions and support.

5. Research Electronic Data Capture (REDCap™) Software: Utilizing electronic data capture software, such as REDCap™, can streamline data management and analysis, making it easier to track and analyze maternal health outcomes.

6. Collaboration with Public Sector Hospitals: Partnering with public sector hospitals, like Charlotte Maxeke Johannesburg Academic Hospital, can help reach and serve low socioeconomic communities that may not have access to private health insurance.

It’s important to note that these are general recommendations based on the information provided. The specific context and needs of the maternal health system in Johannesburg, South Africa, should be taken into consideration when implementing any innovations.
AI Innovations Description
Based on the provided description, the recommendation to improve access to maternal health and potentially develop an innovation could be to implement a comprehensive follow-up program for extremely low birth weight infants (ELBWI) in low socioeconomic communities. This program could include the following components:

1. Establishing dedicated follow-up clinics: Create specialized clinics within public sector hospitals, like Charlotte Maxeke Johannesburg Academic Hospital, specifically for ELBWI. These clinics would provide regular check-ups and developmental assessments for infants who have been discharged from the neonatal unit.

2. Utilizing reminder systems: Implement a system, such as text messages, to send reminders to parents of enrolled participants about follow-up appointments. This can help improve attendance rates and ensure that infants receive the necessary care and assessments.

3. Refunding transport costs: Recognize that transportation can be a barrier for families in low socioeconomic communities. Refund transport costs for families who attend follow-up appointments, making it easier for them to access the necessary healthcare services.

4. Tracing and rebooking defaulting patients: Develop a process to trace and rebook patients who have missed their follow-up appointments. This can help ensure that all infants receive the necessary assessments and interventions.

5. Collaborating with allied medicine teams: Establish partnerships with physiotherapists, occupational therapists, and speech therapists to provide appropriate interventions for infants who are identified with developmental problems. This multidisciplinary approach can support the overall neurodevelopmental outcomes of ELBWI.

6. Using electronic data capture software: Implement a system, such as Research Electronic Data Capture (REDCap™) software, to efficiently manage and analyze data related to maternal variables, neonatal variables, and developmental outcomes. This can help streamline the process and ensure accurate data collection.

By implementing these recommendations, healthcare providers can improve access to maternal health for ELBWI in low socioeconomic communities, leading to better neurodevelopmental outcomes and potentially serving as an innovation in maternal healthcare.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Mobile Clinics: Implementing mobile clinics that can travel to remote areas or underserved communities to provide maternal health services. This can help overcome transportation barriers and ensure that pregnant women have access to prenatal care, screenings, and necessary interventions.

2. Telemedicine: Utilizing telemedicine technologies to provide virtual consultations and remote monitoring for pregnant women. This can be especially beneficial for women in rural areas who may not have easy access to healthcare facilities.

3. Community Health Workers: Training and deploying community health workers who can provide education, support, and basic healthcare services to pregnant women in their own communities. These workers can help bridge the gap between healthcare facilities and pregnant women, ensuring that they receive the necessary care and information.

4. Maternal Health Education: Implementing comprehensive maternal health education programs that target both pregnant women and their families. These programs can focus on topics such as prenatal care, nutrition, breastfeeding, and postpartum care. By increasing knowledge and awareness, women can make informed decisions about their health and seek appropriate care.

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

1. Define the target population: Identify the specific population that will be impacted by the recommendations, such as pregnant women in a particular region or community.

2. Collect baseline data: Gather data on the current state of maternal health access in the target population. This can include information on healthcare facilities, availability of services, utilization rates, and health outcomes.

3. Define indicators: Determine key indicators that will be used to measure the impact of the recommendations. This can include metrics such as the number of prenatal visits, rates of complications during pregnancy and childbirth, and maternal and infant mortality rates.

4. Simulate the impact: Use modeling techniques to simulate the potential impact of the recommendations on the defined indicators. This can involve creating scenarios that reflect the implementation of the recommendations and estimating the resulting changes in the indicators.

5. Analyze the results: Evaluate the simulated impact of the recommendations on improving access to maternal health. Compare the projected outcomes with the baseline data to assess the potential benefits and identify any potential challenges or limitations.

6. Refine and adjust: Based on the analysis, refine the recommendations and simulation methodology as needed. Consider factors such as feasibility, cost-effectiveness, and scalability to ensure that the proposed innovations are practical and sustainable.

7. Implement and monitor: Once the recommendations have been refined, implement them in the target population. Continuously monitor and evaluate the impact of the interventions to assess their effectiveness and make any necessary adjustments.

By following this methodology, stakeholders can gain insights into the potential impact of innovations on improving access to maternal health and make informed decisions about their implementation.

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