Background
Approximately 2.7 million new-borns die each year accounting to 43% of deaths of children under 5 years [
1]. Maternal and new-born deaths were the focus of millennium development goals 4 and 5 which were not met by 2015 and are currently captured in the sustainable development goals [
2,
3]. Kenya has a big neonatal mortality burden currently at 22 out of 1000 live births with Bungoma County at 31 per 1000 live births [
4]. Major contributors to the morbidity and mortality challenge include poor new-born health coverage, low levels of perinatal care [
5], adverse cultural practices including unskilled home deliveries, poor cord care comprising application of cow dung, lizard excreta, herbs and mud to ‘facilitate’ cord healing and early weaning. Additionally, the continuum of care from antenatal care to delivery and postnatal care and new-born health, is highly disjointed in most rural districts due to inadequate capacities (infrastructural, personnel and supplies) and strategies worsening the situation [
6‐
8].
A key pillar in supporting strategies to mitigate new-born morbidity and mortality is improvement of quality of care. Even though there is no clear definition of new-born quality of care, important elements include; skilled care during pregnancy and delivery encompassing skilled birth attendance, emergency obstetric care for maternal and new-born complications and post-natal care for mothers and babies [
9,
10]. In defining quality of care in regards to maternal health, Hulton et al.
, [
11] point to effective and timely access to services. The WHO has published a comprehensive quality of care guideline titled ‘Standards for improving quality of maternal and newborn care in health facilities’ that aims to set the bar especially for the developing world [
12]. Kenya has developed its own quality strategy called ‘The Kenya Quality Health Model’ that strives to guide different sectors of health provision including maternal and newborn on attaining International Standards Organisation certification. Nonetheless, the model does not cover process implementation but provides tools for continuous quality improvement, the Plan-Do –Check- Act cycle et cetera [
13]. The quality of care guidelines will of necessity be live to the different emerging strategies in increasing access to quality care especially in peripheral or poor resource settings factoring diverse metrics in their evaluation [
14]. These include mobile and telehealth applications, call centre services and other technologies.
The collaborative new-born support was conceived to address the unacceptably high new-born morbidity and mortality rates in Bungoma County with the purpose of enhancing survival of neonates, improve quality of care and accessibility of new-born care services in the nine sub-counties of Bungoma.
Anticipated impact
The project will contribute towards the two outcomes with activities linking as follows; 1. Increased access to and utilisation of quality neonatal health services since new-born special care units in 7 sub county hospitals will be set-up, introducing these services to the sub counties, establishing a mobile phone based follow up system for neonates will empower mothers to make timely health seeking decisions and avert complications. The community sensitization sessions will inform the members and stakeholders on availability of the services stimulating demand.
Secondly, the project will contribute to health system management strengthening to deliver quality neonatal health services because 90 medical staff will be trained, mentored and supported through a tele-health platform by paediatricians from Kenya Paediatric Association and Mount Kenya University. It is anticipated that this strategy will build the healthcare workers confidence in neonatal care and create a pool of trainers in the rural facilities. Moreover, repackaging and distributing 1000 copies of best practice neonatal clinical guidelines to facilities in the county will provide quick reference. A community referral system for neonatal health services will be enhanced working with community based midwives. Besides, it is anticipated that mothers and particularly with pre-natal challenges will choose to deliver in these facilities for better neonatal outcomes, raising skilled attendant deliveries.
Hypothesis
1.
Quality neonatal care, call centre service, tele-health and community engagement will reduce new-born morbidity and mortality in Bungoma county, Kenya
Study questions
1.
To what extent does increase in community knowledge on neonatal complications and availability of quality services affect mothers/new born caregivers utilization of hospital services in Bungoma County?
2.
Will improved quality of neonatal care lead to an increase in utilization of new born care services in Bungoma County?
3.
Does improved information flow amongst health practitioners result in better services for neonates in Bungoma County?
Methods
Study setting
Bungoma County (coordinates 0.8479° N, 34.7020° E), Western Kenya has a population of approximately 1.7 m and an area of 2069 km
2. The County has an urbanization rate of 21.7%, literacy levels of 60.5% with 87.6% of residents between the ages of 15–18 attending primary school. It has a poverty rate of 52.9% and access to electricity access of 4.5% [
4,
15]. The main ethnic groups in the county are the Bukusu and Sabaoti sub-tribes of Luhya and Kalenjin respectively.
The main economic activities include: Agriculture, manufacturing and retail services. Agriculture is the backbone of Bungoma County and most families rely on crop production and animal rearing. The main crops include maize, beans, finger millet, sweet potatoes, bananas, Irish potatoes and assorted vegetables. These are grown primarilly for subsistence with the excess sold to meet other family needs. On the other hand, the main cash crops include sugar cane, cotton, palm oil, coffee, sun flower and tobacco. Most families integrate livestock production with farming. The main livestock kept include cattle, sheep, goats, donkeys, pigs, poultry and bees. Most of this is on a small scale but some farmers also produce milk and poultry products for commercial use. Milk and sugarcane farmers sell their produce mainly through cooperative societies.
The County is comprised of 6 constituencies: Kimilili, Webuye East, Webuye West, Sirisia, Kanduyi, Bumula and Mt. Elgon. This guided the selection of project intervention sites. These sites are in the following facilities; Bungoma County referral hospital, Webuye Sub-County hospital, Kimilili, Sirisia, Bumula, Naitiri, Mt. Elgon, Sinoko and Chwele.
Study design
This intervention will take a quasi-experimental design approach with an experimental and control site. The project will involve pre- and post-intervention data collection with one comparison group to assess intervention effects.
The study will be conducted at level 4 and 5 hospitals, each hospital having high volume of clients and covering a sub-county, all with similar maternal, newborn and child health indicators. Five hospitals will be the implementation sites while 4 hospitals will be control sites in a step wedge approach. The implementation sites will be selected purposely. The control sites will receive the full intervention after 1 year when the first set of comparison with the intervention site has been completed.
The 5 experimental site hospitals will receive a set of interventions which include: refurbishing of new born units, installation of equipment such as incubators, respirators, Ambu bags, and radiators. Establishment of a tele-health platform to enable free flow of information amongst clinicians and obstetricians, establishment of a call center for follow up of patients, training of health care providers in neonatology, and creating awareness amongst the community will be key cross cutting interventions, implying they will benefit the intervention site as well as control site. The control sites will not receive the main intervention benefits (equipped specialized newborn units) until after 1 year of implementation in the experimental sites.
The project will undertake a baseline, mid-term and end-line surveys. The step wedge design which allows for the measurement of process, outcome and impact indicators within each site (at different time points) will be used. The design also allows a comparison between different combinations of the intervention components.
Primary outcome measure
Percentage reduction in newborn deaths in Bungoma County between October 2015 and December 2018. This will be measured by comparing the number of newborn deaths at the nine project facilities (in the period of implementation), with the number of deaths at the same facilities in the similar period before the intervention; converted to percentage and aggregated by facility.
Secondary outcome measures
1.
Percentage of mothers or care givers able to identify at least three danger signs in neonates in the project area; Numerator: Number of mothers/ care givers who can identify at least three danger signs in neonates in the project area. Denominator: Sampled mothers/care givers in selected community areas at baseline and end line.
2.
Proportion of neonates with complications referred to specialized neonatal units, through the call center; Numerator: Number of neonates with complications referred to specialized neonatal centers, through the call center. Denominator: Total number of neonates reached through the call center.
3.
Percentage of health providers in neonatal care units who adhere to expected neonatal standards of care (rapid and complete application of standard protocols); Numerator: Number of health workers in neonatal care who adhere to expected standards of neonatal care in the nine selected facilities. Denominator: Total number of health workers in neonatal care in the selected facilities.
4.
Percentage increase in neonates with severe complications in the specialized neonatal units; Percentage change in the number of admissions of neonates classified as having severe complications (using the 15-score index) over the project period.
5.
Percentage of neonates who stay in NBU beyond 5 days; Numerator: Number of neonates who stay beyond 5 days. Denominator: Total number of neonates admitted in the 9 specialized neonatal units
Sources of data collection
Qualitative
Tools
-
Baseline survey
-
Observations
-
Project information
Control group – Minimizing bias and contamination
To minimize bias and contamination, the intervention sites will be selected purposely from the nine county and sub- county hospitals. The sites selected will include the two facilities that have some level of neonatal facilities to ensure that the intervention sites and the control sites have distinct features separating the two.
We will measure the utilization of services in the selected facilities at baseline, mid-term and end-line levels.
To determine utilization of neonatal services, we will collect data directly from the source through a data abstraction form in the nine health facilities throughout the project period. Further, the community health workers and volunteers will also provide information of the number of cases they refer to specific facilities. Cases that fail to arrive at the health facilities will also be captured and the data from the facilities and the community health workers will be cleaned, cross checked and analyzed to provide required results for utilization of neonatal services.
Quality of care will be determined through observations of service provision by clinicians, as well as checking use of neonatal equipment and the severity of conditions managed by the health providers in the units.
Sample size determination
Bungoma County, the area targeted by community sensitization will be source area for target newborn caregivers/newborn mothers who will be simple randomly selected to participate in the survey. Statistical methods for determining sample size will be employed to ensure the correct sample size is picked to avoid biases and ensure the outcomes are valid. There are approximately 29,845 births in Bungoma County per annum [
16]. This number will be split twice for control and intervention group. The study will allow a margin of error of 5% at confidence level of 95% to obtain the correct sample size for the study. It is also expected that at least one of the variables of interest (e.g., knowledge of 3 danger signs in neonates) will be 50% among the respondents. Using the formula:
$$ x=Z{\left({}^c{/}_{100}\right)}^2r\left(100-r\right) $$
$$ n{=}^{N\ x}{{/_{\Big(\left(N-1\right)E}}^2}_{+x\Big)} $$
$$ E=\mathrm{Sqrt}\left[{}^{\left(N-n\right)x}{/}_{n\left(N-1\right)}\right] $$
where
N is the population size,
r is the fraction of responses that we are interested in, and
Z(
c/100) is the critical value for the confidence level
c, we get 375 as our ideal sample size in the control and intervention areas (i.e., four data collectors in each area conducting 8 surveys daily for 12 working days each). See Additional file
1.
Table 1
Outcomes and objectives
Outcomes | • Increased access and utilization of quality neonatal care services. • Health system management strengthened to deliver quality neonatal health services. |
Objectives | i) To setup 7 differentiated neonatal special care units in 7 sub-county hospitals in Bungoma. ii) To train 90 selected health workers (nurses and clinicians) based in 9 sub county hospitals in neonatal care to be able to diagnose and manage common new-born problems and make timely referral when appropriate. iii) Establishing a tele-health platform that will ease diagnostic and new-born clinical care consultant support iv) Re-packaging and distribution of best-practice clinical guidelines into user friendly formats. v) Develop a mobile phone based follow up system for all new-borns for early detection of neonatal problems and initiation of appropriate action. |
Selection of hospitals for the study
There are 177 health facilities in Bungoma County of which 12 are hospitals and the rest are health centers, dispensaries and clinics. In this study, nine high volume health facilities will be selected to participate in the study. Five Facilities will be intervention sites while four health facilities will be the control sites. The intervention sites will be selected purposely from the nine project facilities.
Selection of participants for the clinical observations of neonatal services
The sample size calculation is based on net change in the proportion of neonates who present with complications in health facilities. From the County HMIS data, it is estimated that a significant proportion of babies born with complications are missed out or only a few of them present to health facilities. In Bungoma County, currently only 21% present in health facilities. Through the Newborn collaborative support project, we seek to increase this by 50%, (21 to 32%). Given that births are 29,845 [
16] and current situation is 21% presenting in health facilities we get a population of 6268 neonates. This means we get a sample size of 125 neonates for intervention and 125 for control group. Data collection tools such as questionnaires will be administered randomly amongst the selected cases. See Additional file
2.
Selection of health care workers
Ninety health care providers who give informed consent to participate in the study and meet the requirement of the selection criteria will be interviewed through a questionnaire to determine their knowledge and skills levels in neonatal care. The health care providers have already been trained in their respective areas but may be deficient in neonatal care. It is approximated that their knowledge on neonatology is at 65% and this needs to be increased up to about 90%. See Additional file
3.
Participants for in-depth interviews
In- depth interviews will be conducted with key informants in the selected project sites (See Additional file
4). They include the following:
1 – County Director of health services
1 – Director of nursing services in the county
1 – County RH coordinator
1 –Pediatrician in the county
9 – Medical Superintendents/Facility –in- charges – one in each Hospital
9- Nurses in – charge on maternity – one in each Facility
9- Staff in- charge of new born units
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