New natal health facility and Care

1. MODELLING GEOGRAPHICAL ACCESSIBILITY AND ASSESSMENT OF
BYPASSING BEHAVIOUR FOR INPATIENT NEONATAL CARE
IN BUNGOMA COUNTY, KENYA.
A COLLABORATIVE NEWBORN SUPPORT PROJECT
Mount Kenya University

2. Background
• Approximately 2.6 million babies die annually during the neonatal period
• Sub-Saharan Africa (SSA) leads in the number of newborn deaths
• In Kenya, NMR still high at 22/1000 live births. Global NMR; 18/1000 live births.
• Bungoma County, with highest NMR of 31/1000 live births.
NMR- Neonatal Mortality Rate

3. Under-five mortality rate
(deaths per 1,000 live births)
>100
75 to 100
50 to 75
25 to 50
≤25
No data
Under-five mortality rate by country, 2017
Neonatal mortality accounts for 46% of all under-five deaths globally
https://data.unicef.org/wp-content/uploads/2018/09/UN-IGME-Child-Mortality-Report-2018.pdf

4. The good news…..
• 60-70 % of the neonatal deaths can be averted through cost
effective interventions if 90-99% of the population have access to
these services

5. Major causes of neonatal deaths globally
35%
24%
15%
11%
7%
6%
1%
1%
Neonatal Conditions
Preterm
Intrapartum
Sepsis
Cogenitals
Others
Pneumonia
Tetanus
Diarrhoea
Preterm birth complications lead in cause of neonatal deaths
https://www.unicef.org/publications/index_101071.html

6. Global and Regional Comparison of NMR
0
5
10
15
20
25
30
35
40
45
50
1990 1995 2000 2005 2010 2015 2016
Percentage(per1000livebirths)
Global and SSA neonatal mortality rates
SSA Global
https://www.unicef.org/publications/index_101071.html
• NMR declined in the last two
decades
• Numbers are still stagnant
• Attributed to:
• high birth rate in the region
• high rate of home deliveries
conducted by unskilled birth
attendants

7. Neonatal and under-5 mortality rates of Kenya
0
20
40
60
80
100
120
140
1993 1998 2003 2008 2014
Mortalityper1000livebirths
Neonatal
Under-5
https://www.unicef.org/publications/index_101071.html
Ascribed to:
• lack of quality services rendered in the health facilities
• lack of transportation
• long travel time distances to nearest neonatal care facility
• cultural practices
High rates of neonatal deaths in the country

8. Kenya Health targets
• Target set out of reducing neonatal mortality rate from 31 in 2013:
to 25 in 2016 and
to 15 in 2018.
• Targets not attained by 2018
• Universal health care rolled out; all citizens access to care by 2020
• Financial burden in the access to newborn care will be reduced
• Access to packages approximated to reduce newborn deaths by two thirds

9. • Identified as a multidimensional concept
• Influenced by:-
o supply and demand
o population health status
o demographic characteristics
o socioeconomic status
o geographical barrier between population and health facilities
• Access to healthcare as a fusion of 4 pillars:-
o accessibility
o availability
o affordability
o acceptability
Access to Healthcare

10. Bypassing phenomena
• Definition: patients seek to travel far away in search for better service than use their
nearest health
• Implications:
Financial travel cost burden
Loss of time travelling to the far away facility
To the nearest health facility, it leads to
olow demand of the local health facility
odecline in the local economy
o“desert health facility” (in extreme cases)
To the higher tier facility, it leads to
ooverstretching of resources in the facility
ohampering its efficiencies in conducting specialized treatment as its core mandate

11. Study Rationale
• Defining bypassing determinants of care seekers helps:
o identify challenges specific NBUs face leading to underutilisation
o policy makers in allocation of resources and
o policy makers in customizing of solutions to specific needs
• Bypassing concept incorporates the supply side with little consideration on
the demand.
• This study seeks to:
o bridge the gap by modelling geographic accessibility to the NBUs
o incorporate the bypassing concept to quantify the newborns who have access to NBUs
o understand the spatial patterns of the care-seekers in seeking inpatient neonatal care.

12. Study Objectives
Assess the care-seeking bypassing behaviours for the inpatient neonatal
care and the access and spatial patterns of the care-seekers.
Specific objectives
• Identify care-seeking behaviour for inpatient neonatal care.
• Compute spatial accessibility to NBU using location of NBUs, roads and
land covers.
• Characterize spatial patterns of care-seekers in accessing inpatient
neonatal care.

13. Study area : Bungoma County
Methodology

14. Methodology
• Study sites: seven NBUs- Bungoma, Webuye, Kimilili, Chwele, Bumula, Mt.Elgon, Naitiri
• Inclusion criteria :Mothers with NBU admitted newborns
• Questionnaires: structured mixed open and close ended
• Sample size: 400
• Field survey data collection: May 2018 to August 2018
• Sample of questions:
Questions
Is this the nearest Newborn Unit to your home?
Sociodemographic characteristics (maternal age, education level, occupation, marital status &
residence location):
Type of admission (referral or non-referral):
Reasons for the newborn admission:

15. Methodology: Analysis
Statistical analysis
• Field data analysed using IBM SPSS Statistics Version 20 (SPSS Inc., Chicago, IL, USA).
• Characteristics of mothers analysed and compared between bypassers and non-bypassers.
• Frequencies and percentages used in analysing the categorical data
• Mean and standard deviations used for continuous data.
Care seeking spatial pattern
The geographical coordinates of the nearest schools and villages information from the
participants were extracted from Google Earth and mapped using Quantum GIS (QGIS)

16. Spatial Data
Spatial locations of the eight newborn facilities

17. Road Network
Road class Description Speed (Km/hr)
Primary (Motorized)
Secondary
(Motorized)
County (Cycling)
Rural (walking)
A
B
C
D
E
G
L
R
U
Roads connecting international
boundaries at specific border points.
Connects county headquarters and other
national economic centres or towns to
class A road
Link county headquarters to each other
and to either class A or class B
Link town centers and other sub county
centers to each other and links them to
Class A, B or C
Major feeder roads that link constituency
centers
Serve to transport farm produce to the
markets
Connects local roads to the arterial roads
Roads in rural areas
Unclassified rural roads
50
50
30
30
11
11
11
5
5

18. Distribution of Livebirths
(at 100 x 100m spatial resolution)
• Approximately 21% of all newborn
require inpatient services

19. Results
Hospital No. respondent (%)
Bungoma 196 49
Webuye 128 32
Kimilili 35 8.8
Chwele 18 4.5
Bumula 11 2.8
Mt. Elgon 11 2.8
Naitiri 1 0.3
Total 400 100
A total of 400 participants were interviewed across the seven newborn units
Distribution of respondents across the NBUs
Facility Frequency (n=41)
Bumula 9 (22%)
Kimilili 9 (22%)
Chwele 6 (14%)
Sirisia 3 (0.8%)
Mt. Elgon 2 (0.5%)
Webuye 2 (0.5%)
Bungoma 1 (0.3%)
Webuye 2 (0.5%)
Naitiri 1 (0.3%)
Others facilities 8 (2.0%)
Bypassed facilities

20. Results
Facilities preferred by Bypassers Bypassing Determinants
Majority of those who bypassed their nearest facilities went to
Bungoma County Referral and Webuye County hospital
Distrust of health personnel in the bypassed facilities was cited
as the main determinant for bypassing

21. Results
Referral cases in Kimilili NBU
Referral In Facility
1 Mt. Elgon
Referral Out
4 Bungoma NBU
12 Webuye NBU
Referrals in Chwele NBU
Referral In Facility
Nil
Referral Out
8 Bungoma NBU
6 Webuye NBU

22. Results
Referrals in Bumula NBU
Referral In Facility
1 Muanda H/c
Referral Out
13 Bungoma
Referrals in Mt. Elgon NBU
Referral In Facility
1 Sambocho Health Centre
Referral Out
2 Webuye
2 Bungoma
1 Kimilili

23. Results
Referrals in Webuye NBU
Referral In Facility
12 Kimilili
6 Chwele
2 Mt. Elgon
11 Other Facilities
Referral Out
3 Bungoma
Newborn Unit Frequency (n =113)
Bumula 11.5% (13)
Chwele 7% (8)
Kimilili 3.5% (4)
Sirisia 3.5% (4)
Webuye 2.6% (3)
Mt. Elgon 1.7% (2)
Other Health Facilities 43.3% (49)
Referrals in Bungoma NBU

24. Referral Network within the county health facilities
Results
Transportation mode N (%)
Private car 24 (20.2)
Ambulance 73 (61.3)
Public transport 2 (2)
Motorcycle 20 (16.8)
Mode of transport during referrals

25. Approximate population newborn who require inpatient care
Results
within 30 minutes access to nearest NBU. within 60 minutes access to nearest NBU.

26. Spatial patterns of the care-seekers to the NBUs
Results

27. Summary
1. In seeking care for their newborn babies 11% of mothers bypassed their nearest NBUs
2. Bumula and Kimilili were the most bypassed facilities
3. Distrust among personnel and lack diagnostic services main bypassing determinants
4. Majority of population of newborns who require inpatient services can access within 1 hour
5. Bungoma and Webuye NBUs were the most preferred health facilities
6. In Bumula and Sirisia diagnostic services available but not operational due to lack of back up
energy source.
7. Childbirth deliveries conducted by skilled birth attendants within health facilities was higher
than the national and SSA estimation

28. • Distrust among the health personnel involved how the mothers are treated after
delivery and other cultural issues such as placenta disposal in the facilities
• In extreme cases, women may prefer to deliver at home than using the facilities,
increasing the chances of neonatal deaths
• Increase in the number of health facility deliveries can be attributed to the free
maternity services offered in the public health facilities since 2013
• Areas that are near the county boundary are the most affected in accessing care
• In this study none of the demographic characteristics statistically had an influence on
the bypassing phenomena.
Discussion

29. Recommendations
• Future studies ought to incorporate the following dynamics in assessing bypassing factors
 Use of insurance cover
 Use of private health facilities where the public health facilities and NBUs are not
functional
• The Ministry of Health ought to customize solutions to the bypassing determinants to
reduce bypassing and enhance utilization of the sub-county level newborn units.

30. Acknowledgments
Mount Kenya University
-Study participants
-Health care workers in the Special Care Units