Nairobi Newborn Study: A protocol for an observational study to estimate the gaps in provision and quality of inpatient newborn care in Nairobi City County, Kenya

BMJ Open, Volume 6, No. 12, Year 2016

Interpretation

Introduction Progress has been made in Kenya towards reducing child mortality as part of efforts aligned with the fourth Millennium Development Goal. However, little advancement has been made in reducing mortality among newborns, which now accounts for 45% of all child deaths. The frequently unanticipated nature of neonatal illness, its severity and the high dependency of sick newborns on skilled care make the provision of inpatient hospital services one key component of strategies to improve newborn survival. Methods and analyses This project aims to assess the availability and quality of inpatient newborn care in hospitals in Nairobi City County across the public, private and not-for-profit sectors and align this to the estimated need for such services, providing a description of the quantity and quality gaps between capacity and demand. The population level burden of disease will be estimated using morbidity incidence estimates from a literature review applied to subcounty estimates of population-adjusted births, providing a spatially disaggregated estimate of need within the county. This will be followed by a survey of neonatal services across all health facilities providing 24/7 inpatient newborn care in the county. The survey will include: a retrospective audit of admission registers to estimate the usage of facilities and case-mix of patients; a structural assessment of facilities to gain insight into capacity; a questionnaire to nursing staff focusing on the process of delivering key obstetric and neonatal interventions; and a retrospective case audit to assess adherence to guidelines by clinicians. Ethics and dissemination This study has been approved by the Kenya Medical Research Institute Scientific and Ethics Review Unit (SSC protocol No.2999). Results will be disseminated: to participating facilities through individualised reports and a joint workshop; to local and national stakeholders through meetings and a summary report; and to the international community through peer-review publication and international meetings. This study is primarily cross-sectional descriptive in design, drawing on a variety of methods including health services assessment, retrospective case audit and survey questionnaire. Literature review is also conducted as part of this study. The steps of the Nairobi Newborn Study are outlined in figure 1. Overview of study procedures. We will begin with estimating the population-level burden of neonatal conditions using morbidity incidence estimates from the literature review (figure 1, step 1). The available literature will be discussed with the expert group in order to decide on the incidence estimates that are most applicable to the Nairobi City County. Based on these findings, estimates from the literature will be combined (accounting for comorbidities/overlapping estimates where possible) to calculate the anticipated need for neonatal services. Combinations of subcounty population census data at varying spatial resolutions will be used to distribute the total population across the County boundaries. The units of interest will be wards.14 Wards are the lowest level of decision-making for the County and the lowest spatial scale for which variations in social inequalities are defined by the national government.14 15 Overall crude birth rates for Nairobi County, derived from the 2009 national census, will be compared with subcounty, special group estimates available from demographic surveillance sites within Nairobi.10 16 17 Adjusted estimates of new births within the County will be used to generate spatially representative denominators within the County. This process is summarised in figure 2. Estimating the need for neonatal services (study step 1). Empirical data collection will focus on quantifying the availability and quality of inpatient newborn services across all facilities in Nairobi City County that provide care for sick newborns 24 hours a day for 7 days a week (24/7). Facilities that might be eligible will initially be identified using the Kenya Master Facility list.18 19 This provisional list of eligible facilities will then be discussed with local obstetricians and paediatricians to exclude ineligible facilities and add missing ones. Facilities will be telephoned to evaluate their eligibility and finally visited by the research team to confirm eligibility. During this visit, advice will also be sought from facility staff on additional facilities that might be missed from our list. Data collection will be conducted in four steps (figure 1, steps 2–5). A retrospective audit of maternal and newborn admission registers will be conducted to estimate the usage of facilities and case-mix of neonatal patients (figure 1, step 2). Data from registers will be entered onsite in facilities by experienced and trained data clerks into a purpose-designed standardised data capture tool created in Research Electronic Data Capture (REDCap). This data capture tool will incorporate inbuilt data checks and predesigned cleaning scripts will be run daily and weekly to ensure data integrity. Where admission registers are not available, the admission information will be obtained from the patient medical records. A structural assessment of facilities will be performed in the maternity and neonatal units to gain insight into staffing, infrastructure and equipment capacity (figure 1, step 3). These data will be collected through direct observation by assistant research officers (clinically trained), recorded on a paper-based structured survey tool, and double data entered in a purpose-designed standardised REDCap tool. A knowledge questionnaire will be administered to nursing staff on duty in the maternity and neonatal units focusing on the process of delivering key obstetric and neonatal interventions (figure 1, step 4). Questions will include clinical scenarios where nurses are asked the steps that they would take (eg, keeping the newborn warm and newborn resuscitation), direct questions about clinical care guidelines (eg, cord clamping timing and baby checks during phototherapy treatment), and their opinions on availability of essential equipment and drugs (eg, intravenous fluids and oxygen) and frequency with which key newborn interventions are performed in the facility (eg, cleaning the cord with chlorhexidine digluconate and continuous positive airway pressure (CPAP) for respiratory distress in preterm newborns). The questionnaire will be conducted as a face-to-face interview by trained research assistants, who will read aloud questions and enter answers into a preprogrammed and custom-designed REDCap survey tool. Care will be taken to ensure minimal disruption to nurses while on duty and hence their care to patients. Interviews will be conducted during the nurse’s break (refreshments will be provided) or at the end of their shift. Finally, we will conduct a retrospective case audit of neonatal inpatient records to assess adherence to national guidelines by clinicians and documented evidence of patient monitoring (figure 1, step 5). Information of interest to be captured includes recording of patient signs and symptoms, diagnosis of patient, treatments and investigations prescribed, antibiotic dosage (by patient weight) prescribed, and dose and route of oxygen, fluids, and feeds prescribed. The same process as for entering data for admission registers will be applied. Where data are missing from records, data clerks will actively record these as missing to ensure that the adequacy of documentation can be quantified. The expected shortfall in supply to meet demand will be geospatially mapped in order to provide insight into the areas of the county with the greatest need for improved access to care. All data collection tools and standard operating procedures are available on request. In Nairobi City County, over half the population of 3.14 million people are estimated to live in low-income areas,20 inequality is massive and estimated neonatal mortality is considerably higher than the national average.10 Data collection for this study will take place in facilities within Nairobi City County that provide 24/7 inpatient newborn care. These facilities will be identified using the Kenyan Master Facility List and advice from local paediatricians and obstetricians.18 All eligible facilities will be invited to participate in the study. Information does not currently exist on the number of facilities providing inpatient newborn services in Nairobi or the capacity of facilities to provide this care. However, in consultation with local experts, we anticipate ∼30 facilities to be eligible. Step 1 of this study is a literature review and geospatial modelling exercise and step 3 is a structural assessment that will be conducted in all eligible facilities. Thus, sampling considerations only apply to steps 2, 4 and 5 of the study. All newborn admission registers (whether born within the facility, referred from another facility or brought from home) for a period of 1 year (July 2014 to July 2015) will be included in the usage and case-mix assessment. Additionally, maternal admission registers will be reviewed and information about residency and pregnancy outcome of women attending the facility to deliver will be obtained. Where facilities deliver 500 or fewer women per year, the register for a full year will be reviewed. Registers from those facilities that deliver more than 500 women during the sampling time frame will be sampled. A random list of weeks from the study period will be generated using Stata V.13. Starting from the top of this list, admissions from those weeks will be entered until a sample of 500 deliveries for that facility has been obtained. Although information is not available for all deliveries within potentially eligible facilities, experts advise that Pumwani Maternity Hospital (PMH) is the largest maternity hospital in Nairobi. Data from national statistics (District Health Information System 2) suggest annual deliveries of ∼25 000 per year. Hence, 500 deliveries at PMH would represent ∼2% of annual deliveries. In this scenario of the largest facility, with a sample of 500, we would be able to estimate a sample proportion (of residency and pregnancy outcome) of 50% with a 4.34% margin of error. This margin of error would be smaller for smaller facilities. Nursing staff on duty at the time of a scheduled research team visit providing care to sick newborns or on the maternity ward will be invited to participate in a questionnaire at a time of their convenience and minimal disruption to care. Where more than three nurses are on-duty in the maternity ward or newborn unit, a random sample of half (rounded upwards) of the nurses from that ward and/or unit will be selected for interview. A list of all on-duty nurses will be made in alphabetical order of their names. Every odd numbered (ie, 1, 3, 5, etc) nurse from the list will be invited to participate. If a nurse declines to participate, then another nurse, starting from the top of the list (ie, 2, 4, etc), will be invited in their place. Across all facilities taking part in this study, we plan to sample 800 newborn case records. With a total population of 100 000 admissions (above which there is little change in sample size estimations), 800 records would estimate a 50% sample proportion (example outcomes include correct antibiotic dosage, correct fluid/feed dosage and route, evidence of clinical monitoring) with a margin of error of 3.45%. Given that the data generated from these reports will be clustered in facilities, we might expect a design effect, which will be accounted for during analysis. Allowing for a design effect of 2, a sample size of 800 records would estimate a 50% sample proportion with a margin of error of 4.89%. Quality of newborn care will be assessed by classifying it into the three components defined by Donabedian:21 (1) structure, characteristics of the setting in which care is administered, (2) process, the essential procedures in the delivery of care, and (3) outcome, the effect of care on the health status of the patient or population. The quality of care indicators measured in this study are summarised in table 1. Structural indicators and the nursing knowledge questionnaire score will be available for the maternity and neonatal units; all other indicators are only for the neonatal unit. Quality indicators to be assessed in the Nairobi Newborn Study *Structural indicators and the nursing knowledge questionnaire score will be available for the maternity and neonatal units. Other indicators are only for the neonatal unit. Study data will be collected and managed using REDCap electronic data capture tools hosted at the Kenya Medical Research Institute (KEMRI) Wellcome Trust Research Programme. REDCap is a secure, web-based application designed to support data capture for research studies. Data will be exported for cleaning and analyses in Stata V.13 (Stata Corporation). The availability of care for sick newborns in Nairobi City County will be described. Facilities deemed to provide inpatient newborn care will be mapped. This map will include information on the type, ownership, size, workload and level of care provided by each facility. The level of care will be described in terms of the structural capacity (ie, availability of staff, cots/beds, type of newborn unit—discrete or part of maternity, equipment and drugs to provide interventions) of the facilities. This information on structure will be used to categorise facilities into basic and comprehensive levels of neonatal care in consultation with the expert advisory group. Quality of care indicators will be described to highlight key gaps in structure and process of neonatal care delivery in the maternity unit and neonatal inpatient unit across facilities. Variation in capacity and quality of care will be explored by health facility ownership: public/government, private, faith-based/not-for-profit. Quality of care will be considered in the context of ongoing international and national efforts to define measurable neonatal health service indicators.12 22–25 Most notably, findings from this study will be presented, where possible, in the context of the newborn and general indictors proposed by the WHO consultation on ‘Improving measurement of the quality of maternal, newborn and child care in health facilities’.22 It is anticipated that there will be a shortfall in service provision, with some ill newborns not receiving the standard of care they require. We will formally explore this by comparing an estimation of need for care with the information we collect in this study on the access to, and usage of, an appropriate level of care. The map of the magnitude and distribution of the burden of newborn conditions in Nairobi City County developed in step 1 of this study will be compared with information collected in step 2 about which patients are accessing and using care at facilities that have the ability to provide inpatient newborn care. The case-mix and numbers of patients attending these facilities will be compared with the estimated magnitude of the population-level burden of major neonatal conditions. Information about the residency of delivering women attending the health facility will be used to inform the estimated residency patterns of neonatal inpatients, therefore enabling a map of access to the facilities that provide inpatient newborn care to be created using geospatial mapping techniques. These facilities will be stratified by size and sector to enable estimates of service delivery patterns and geographic measures of access across the public, private and non-governmental sectors to be developed for Nairobi City County. This map of access will be compared with the map of burden of disease in order to provide details of the distribution of the gap and of areas within Nairobi City County with the greatest need for improved access to inpatient care for sick newborns. Finally, we will explore approaches to estimating the possible number of lives saved by providing adequate access to inpatient care for sick newborns in Nairobi City County using internationally developed, open access modelling tools.26

AI Digest

Study Justification:

– The study aims to address the high mortality rate among newborns in Nairobi City County, Kenya, which accounts for 45% of all child deaths.
– The provision of inpatient hospital services is crucial for improving newborn survival, as neonatal illness is often severe and requires skilled care.
– The study will assess the availability and quality of inpatient newborn care in hospitals across the public, private, and not-for-profit sectors in Nairobi City County.
– By identifying the gaps between capacity and demand, the study will provide valuable insights into the quantity and quality of care needed to improve newborn survival.

Study Highlights:

– The study will estimate the population-level burden of neonatal conditions using morbidity incidence estimates from a literature review.
– A survey will be conducted to assess neonatal services in all health facilities providing 24/7 inpatient newborn care in Nairobi City County.
– The survey will include a retrospective audit of admission registers, a structural assessment of facilities, a questionnaire to nursing staff, and a retrospective case audit to assess adherence to guidelines by clinicians.
– The study will provide information on the availability, quality, and capacity of facilities to provide inpatient newborn care.
– The findings will be disseminated to participating facilities, local and national stakeholders, and the international community through reports, workshops, meetings, and publications.

Study Recommendations:

– Based on the study findings, recommendations can be made to improve the provision and quality of inpatient newborn care in Nairobi City County.
– These recommendations may include increasing the number of facilities providing inpatient newborn care, improving staffing, infrastructure, and equipment capacity, and enhancing adherence to clinical guidelines.
– The recommendations should be implemented in collaboration with key role players and stakeholders.

Key Role Players:

– Local and national government health departments
– Public, private, and not-for-profit healthcare facilities
– Obstetricians and pediatricians
– Nursing staff
– Research team and data clerks

Cost Items for Planning Recommendations:

– Facility expansion and renovation
– Equipment procurement and maintenance
– Staff training and capacity building
– Data collection and analysis
– Dissemination of findings
– Monitoring and evaluation

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is primarily based on a descriptive cross-sectional study design, which limits the strength of the evidence. However, the study includes multiple methods such as health services assessment, retrospective case audit, and survey questionnaire, which increases the reliability of the findings. To improve the strength of the evidence, the study could consider incorporating a longitudinal design to assess changes over time and establish causal relationships. Additionally, including a control group or comparison group would enhance the ability to draw conclusions about the effectiveness of interventions. Finally, ensuring a representative sample and minimizing bias in data collection would further improve the validity of the study.

Abstract

This study is primarily cross-sectional descriptive in design, drawing on a variety of methods including health services assessment, retrospective case audit and survey questionnaire. Literature review is also conducted as part of this study. The steps of the Nairobi Newborn Study are outlined in figure 1. Overview of study procedures. We will begin with estimating the population-level burden of neonatal conditions using morbidity incidence estimates from the literature review (figure 1, step 1). The available literature will be discussed with the expert group in order to decide on the incidence estimates that are most applicable to the Nairobi City County. Based on these findings, estimates from the literature will be combined (accounting for comorbidities/overlapping estimates where possible) to calculate the anticipated need for neonatal services. Combinations of subcounty population census data at varying spatial resolutions will be used to distribute the total population across the County boundaries. The units of interest will be wards.14 Wards are the lowest level of decision-making for the County and the lowest spatial scale for which variations in social inequalities are defined by the national government.14 15 Overall crude birth rates for Nairobi County, derived from the 2009 national census, will be compared with subcounty, special group estimates available from demographic surveillance sites within Nairobi.10 16 17 Adjusted estimates of new births within the County will be used to generate spatially representative denominators within the County. This process is summarised in figure 2. Estimating the need for neonatal services (study step 1). Empirical data collection will focus on quantifying the availability and quality of inpatient newborn services across all facilities in Nairobi City County that provide care for sick newborns 24 hours a day for 7 days a week (24/7). Facilities that might be eligible will initially be identified using the Kenya Master Facility list.18 19 This provisional list of eligible facilities will then be discussed with local obstetricians and paediatricians to exclude ineligible facilities and add missing ones. Facilities will be telephoned to evaluate their eligibility and finally visited by the research team to confirm eligibility. During this visit, advice will also be sought from facility staff on additional facilities that might be missed from our list. Data collection will be conducted in four steps (figure 1, steps 2–5). A retrospective audit of maternal and newborn admission registers will be conducted to estimate the usage of facilities and case-mix of neonatal patients (figure 1, step 2). Data from registers will be entered onsite in facilities by experienced and trained data clerks into a purpose-designed standardised data capture tool created in Research Electronic Data Capture (REDCap). This data capture tool will incorporate inbuilt data checks and predesigned cleaning scripts will be run daily and weekly to ensure data integrity. Where admission registers are not available, the admission information will be obtained from the patient medical records. A structural assessment of facilities will be performed in the maternity and neonatal units to gain insight into staffing, infrastructure and equipment capacity (figure 1, step 3). These data will be collected through direct observation by assistant research officers (clinically trained), recorded on a paper-based structured survey tool, and double data entered in a purpose-designed standardised REDCap tool. A knowledge questionnaire will be administered to nursing staff on duty in the maternity and neonatal units focusing on the process of delivering key obstetric and neonatal interventions (figure 1, step 4). Questions will include clinical scenarios where nurses are asked the steps that they would take (eg, keeping the newborn warm and newborn resuscitation), direct questions about clinical care guidelines (eg, cord clamping timing and baby checks during phototherapy treatment), and their opinions on availability of essential equipment and drugs (eg, intravenous fluids and oxygen) and frequency with which key newborn interventions are performed in the facility (eg, cleaning the cord with chlorhexidine digluconate and continuous positive airway pressure (CPAP) for respiratory distress in preterm newborns). The questionnaire will be conducted as a face-to-face interview by trained research assistants, who will read aloud questions and enter answers into a preprogrammed and custom-designed REDCap survey tool. Care will be taken to ensure minimal disruption to nurses while on duty and hence their care to patients. Interviews will be conducted during the nurse’s break (refreshments will be provided) or at the end of their shift. Finally, we will conduct a retrospective case audit of neonatal inpatient records to assess adherence to national guidelines by clinicians and documented evidence of patient monitoring (figure 1, step 5). Information of interest to be captured includes recording of patient signs and symptoms, diagnosis of patient, treatments and investigations prescribed, antibiotic dosage (by patient weight) prescribed, and dose and route of oxygen, fluids, and feeds prescribed. The same process as for entering data for admission registers will be applied. Where data are missing from records, data clerks will actively record these as missing to ensure that the adequacy of documentation can be quantified. The expected shortfall in supply to meet demand will be geospatially mapped in order to provide insight into the areas of the county with the greatest need for improved access to care. All data collection tools and standard operating procedures are available on request. In Nairobi City County, over half the population of 3.14 million people are estimated to live in low-income areas,20 inequality is massive and estimated neonatal mortality is considerably higher than the national average.10 Data collection for this study will take place in facilities within Nairobi City County that provide 24/7 inpatient newborn care. These facilities will be identified using the Kenyan Master Facility List and advice from local paediatricians and obstetricians.18 All eligible facilities will be invited to participate in the study. Information does not currently exist on the number of facilities providing inpatient newborn services in Nairobi or the capacity of facilities to provide this care. However, in consultation with local experts, we anticipate ∼30 facilities to be eligible. Step 1 of this study is a literature review and geospatial modelling exercise and step 3 is a structural assessment that will be conducted in all eligible facilities. Thus, sampling considerations only apply to steps 2, 4 and 5 of the study. All newborn admission registers (whether born within the facility, referred from another facility or brought from home) for a period of 1 year (July 2014 to July 2015) will be included in the usage and case-mix assessment. Additionally, maternal admission registers will be reviewed and information about residency and pregnancy outcome of women attending the facility to deliver will be obtained. Where facilities deliver 500 or fewer women per year, the register for a full year will be reviewed. Registers from those facilities that deliver more than 500 women during the sampling time frame will be sampled. A random list of weeks from the study period will be generated using Stata V.13. Starting from the top of this list, admissions from those weeks will be entered until a sample of 500 deliveries for that facility has been obtained. Although information is not available for all deliveries within potentially eligible facilities, experts advise that Pumwani Maternity Hospital (PMH) is the largest maternity hospital in Nairobi. Data from national statistics (District Health Information System 2) suggest annual deliveries of ∼25 000 per year. Hence, 500 deliveries at PMH would represent ∼2% of annual deliveries. In this scenario of the largest facility, with a sample of 500, we would be able to estimate a sample proportion (of residency and pregnancy outcome) of 50% with a 4.34% margin of error. This margin of error would be smaller for smaller facilities. Nursing staff on duty at the time of a scheduled research team visit providing care to sick newborns or on the maternity ward will be invited to participate in a questionnaire at a time of their convenience and minimal disruption to care. Where more than three nurses are on-duty in the maternity ward or newborn unit, a random sample of half (rounded upwards) of the nurses from that ward and/or unit will be selected for interview. A list of all on-duty nurses will be made in alphabetical order of their names. Every odd numbered (ie, 1, 3, 5, etc) nurse from the list will be invited to participate. If a nurse declines to participate, then another nurse, starting from the top of the list (ie, 2, 4, etc), will be invited in their place. Across all facilities taking part in this study, we plan to sample 800 newborn case records. With a total population of 100 000 admissions (above which there is little change in sample size estimations), 800 records would estimate a 50% sample proportion (example outcomes include correct antibiotic dosage, correct fluid/feed dosage and route, evidence of clinical monitoring) with a margin of error of 3.45%. Given that the data generated from these reports will be clustered in facilities, we might expect a design effect, which will be accounted for during analysis. Allowing for a design effect of 2, a sample size of 800 records would estimate a 50% sample proportion with a margin of error of 4.89%. Quality of newborn care will be assessed by classifying it into the three components defined by Donabedian:21 (1) structure, characteristics of the setting in which care is administered, (2) process, the essential procedures in the delivery of care, and (3) outcome, the effect of care on the health status of the patient or population. The quality of care indicators measured in this study are summarised in table 1. Structural indicators and the nursing knowledge questionnaire score will be available for the maternity and neonatal units; all other indicators are only for the neonatal unit. Quality indicators to be assessed in the Nairobi Newborn Study *Structural indicators and the nursing knowledge questionnaire score will be available for the maternity and neonatal units. Other indicators are only for the neonatal unit. Study data will be collected and managed using REDCap electronic data capture tools hosted at the Kenya Medical Research Institute (KEMRI) Wellcome Trust Research Programme. REDCap is a secure, web-based application designed to support data capture for research studies. Data will be exported for cleaning and analyses in Stata V.13 (Stata Corporation). The availability of care for sick newborns in Nairobi City County will be described. Facilities deemed to provide inpatient newborn care will be mapped. This map will include information on the type, ownership, size, workload and level of care provided by each facility. The level of care will be described in terms of the structural capacity (ie, availability of staff, cots/beds, type of newborn unit—discrete or part of maternity, equipment and drugs to provide interventions) of the facilities. This information on structure will be used to categorise facilities into basic and comprehensive levels of neonatal care in consultation with the expert advisory group. Quality of care indicators will be described to highlight key gaps in structure and process of neonatal care delivery in the maternity unit and neonatal inpatient unit across facilities. Variation in capacity and quality of care will be explored by health facility ownership: public/government, private, faith-based/not-for-profit. Quality of care will be considered in the context of ongoing international and national efforts to define measurable neonatal health service indicators.12 22–25 Most notably, findings from this study will be presented, where possible, in the context of the newborn and general indictors proposed by the WHO consultation on ‘Improving measurement of the quality of maternal, newborn and child care in health facilities’.22 It is anticipated that there will be a shortfall in service provision, with some ill newborns not receiving the standard of care they require. We will formally explore this by comparing an estimation of need for care with the information we collect in this study on the access to, and usage of, an appropriate level of care. The map of the magnitude and distribution of the burden of newborn conditions in Nairobi City County developed in step 1 of this study will be compared with information collected in step 2 about which patients are accessing and using care at facilities that have the ability to provide inpatient newborn care. The case-mix and numbers of patients attending these facilities will be compared with the estimated magnitude of the population-level burden of major neonatal conditions. Information about the residency of delivering women attending the health facility will be used to inform the estimated residency patterns of neonatal inpatients, therefore enabling a map of access to the facilities that provide inpatient newborn care to be created using geospatial mapping techniques. These facilities will be stratified by size and sector to enable estimates of service delivery patterns and geographic measures of access across the public, private and non-governmental sectors to be developed for Nairobi City County. This map of access will be compared with the map of burden of disease in order to provide details of the distribution of the gap and of areas within Nairobi City County with the greatest need for improved access to inpatient care for sick newborns. Finally, we will explore approaches to estimating the possible number of lives saved by providing adequate access to inpatient care for sick newborns in Nairobi City County using internationally developed, open access modelling tools.26

Materials

N/A

Innovations

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

1. Telemedicine: Implementing telemedicine services can help connect healthcare providers with pregnant women in remote or underserved areas. This technology allows for virtual consultations, remote monitoring, and access to medical advice, improving access to maternal health services.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information and resources on maternal health can empower pregnant women to take control of their own health. These apps can provide educational content, appointment reminders, and access to healthcare professionals.

3. Community health workers: Training and deploying community health workers can help bridge the gap between healthcare facilities and pregnant women in rural or underserved areas. These workers can provide basic prenatal care, education, and referrals to appropriate healthcare services.

4. Transport services: Establishing transportation services specifically for pregnant women can ensure that they have access to timely and safe transportation to healthcare facilities for prenatal care, delivery, and postnatal care.

5. Maternal health clinics: Setting up dedicated maternal health clinics in areas with limited access to healthcare facilities can provide comprehensive prenatal, delivery, and postnatal care services in one location, making it easier for pregnant women to access the care they need.

6. Public-private partnerships: Collaborating with private healthcare providers to expand access to maternal health services can help increase the availability of quality care in underserved areas. This can involve subsidizing services, providing training and resources, and improving coordination between public and private healthcare providers.

7. Health information systems: Implementing robust health information systems can improve data collection and analysis, allowing for better monitoring and evaluation of maternal health services. This can help identify gaps in access and quality of care, leading to targeted interventions and improvements.

These are just a few examples of innovations that can be used to improve access to maternal health. It’s important to consider the specific context and needs of the population when implementing these innovations to ensure their effectiveness and sustainability.

AI Innovations Description

The recommendation to improve access to maternal health based on the Nairobi Newborn Study is to address the gaps in provision and quality of inpatient newborn care in Nairobi City County, Kenya. This can be achieved through the following steps:

1. Assess the availability and quality of inpatient newborn care in hospitals across the public, private, and not-for-profit sectors in Nairobi City County. This will involve conducting a survey of neonatal services in all health facilities providing 24/7 inpatient newborn care in the county.

2. Estimate the population-level burden of neonatal conditions using morbidity incidence estimates from a literature review. This will provide a spatially disaggregated estimate of the need for neonatal services within the county.

3. Conduct a retrospective audit of admission registers to estimate the usage of facilities and case-mix of neonatal patients. This will provide information on the demand for inpatient newborn care.

4. Perform a structural assessment of facilities to gain insight into staffing, infrastructure, and equipment capacity. This will help identify gaps in the provision of care.

5. Administer a questionnaire to nursing staff focusing on the process of delivering key obstetric and neonatal interventions. This will provide insights into the quality of care being provided.

6. Conduct a retrospective case audit of neonatal inpatient records to assess adherence to national guidelines by clinicians and documented evidence of patient monitoring. This will help evaluate the quality of care provided.

7. Geospatially map the expected shortfall in supply to meet demand in order to identify areas within the county with the greatest need for improved access to care.

8. Disseminate the results of the study to participating facilities, local and national stakeholders, and the international community through individualized reports, workshops, meetings, summary reports, peer-reviewed publications, and international meetings.

By implementing these recommendations, it is expected that access to inpatient newborn care in Nairobi City County will be improved, leading to a reduction in neonatal mortality and improved maternal health outcomes.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Invest in improving the capacity and quality of healthcare facilities, particularly in areas with high population density and limited access to maternal health services. This could involve building new facilities, upgrading existing ones, and ensuring the availability of essential equipment and supplies.

2. Enhancing healthcare workforce: Increase the number of skilled healthcare providers, such as doctors, nurses, midwives, and community health workers, to ensure adequate coverage and availability of maternal health services. This could be achieved through training programs, recruitment, and retention strategies.

3. Promoting community-based interventions: Implement community-based programs that focus on educating and empowering women and families about maternal health, promoting early antenatal care, and encouraging the use of skilled birth attendants. This could involve community health workers conducting home visits, organizing health education sessions, and establishing referral systems.

4. Improving transportation and logistics: Address transportation barriers by improving access to ambulances, emergency transport services, and road infrastructure. Additionally, ensure the availability and timely delivery of essential medicines, supplies, and equipment to healthcare facilities.

5. Strengthening health information systems: Develop and implement robust health information systems to collect, analyze, and utilize data on maternal health indicators. This could help identify gaps in service provision, monitor progress, and inform evidence-based decision-making.

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

1. Baseline assessment: Conduct a comprehensive assessment of the current state of maternal health access, including data on healthcare facilities, workforce, transportation, and health outcomes. This will serve as a baseline for comparison.

2. Define indicators: Identify key indicators to measure the impact of the recommendations, such as the number of healthcare facilities per population, the availability of skilled healthcare providers, the percentage of women receiving antenatal care, and maternal mortality rates.

3. Data collection: Collect data on the identified indicators before and after implementing the recommendations. This could involve surveys, interviews, facility assessments, and analysis of existing data sources.

4. Analysis: Analyze the collected data to determine the changes in the identified indicators. Compare the baseline data with the post-intervention data to assess the impact of the recommendations on improving access to maternal health.

5. Interpretation and dissemination: Interpret the findings of the analysis and communicate the results to relevant stakeholders, including policymakers, healthcare providers, and the community. Use the findings to inform future decision-making and prioritize further interventions.

It is important to note that the specific methodology may vary depending on the context and available resources.

Authors & Co-Authors

Statistics:

Citations: 12

Authors: 8

Identifiers:

DOI: 10.1136/bmjopen-2016-012448

Research Areas:

Community Interventions, Disparities, Health System and Policy, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Workforce

Study Countries:

Kenya

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Mixed-methods, Quantitative, Systematic Review

Participants Gender:

Female