Transcript Slide 1

The District Health Barometer 2005/06
Fiorenza Monticelli, HST, 21 February 2007, District management meeting, Kwazulu-Natal
The District Health Barometer
What is it?
• A report that provides a snapshot of
performance in key health areas at a specific
time across the 53 health districts in SA
• A tool for all managers at district, province and
national level for :
– Monitoring and evaluation
– Strategic planning and annual performance
planning
– Identifying quality issues in DHIS data that
need to be addressed.
What is the purpose of the District Health
Barometer?
It functions as a TOOL to monitor progress and
support improvement of equitable provision of
primary health care by:
– Illustrating important aspects of the health
system at district level through analysis of
indicators.
– Ranking , classifying and analysing health
districts (in various groupings eg. metros,
provinces, ISRDP sites), by indicators
– Comparing these indicators over time.
What is measured? (DHB year 1)
Nurse clinical work load (PHC)
Per capita health expenditure (Public sector)
Caesarean section rate
Male condom distribution rate
Immunisation coverage <1y
Immunisation drop out rate (DTP-Hib 1 – DTP-Hib 3)
Proportion antenatal clients tested for HIV
HIV prevalence among antenatal clients tested
Nevirapine uptake rate among HIV+ pregnant women
Nevirapine uptake rate among babies born to HIV+
women
PHC Utilisation rate
TB cure rate
Smear conversion rate
Incidence of diarrhoea under 5 years (per 1000).
Stillbirth Rate
What is new in the DHB year 2 (2005/06)?
Socioeconomic background data
– Household access to piped water
– Deprivation indices and quintiles
Financial data
– % of DH services expenditure on management
– % of DH services expenditure on district hospitals
Health indicators - 3 financial years and additional
indicators
– Incidence of STIs treated
– Underweight rate for age < 5 years
– Delivery rate in facility
– Perinatal mortality rate in facility
– Average length of stay
– Bed utilisation rate
Facility data
– Number of public and private facilities
– Number of public and private beds.
Publication Contents
Background, Introduction & Overview , Methodology
Section A : Indicator Comparisons by district
– For 53 districts, 6 metro and 13 rural nodes
– Change in performance 2003/04 to 2005/06
Section B : District and province profiles
– Map, data table & summary analysis
Appendices :
– Definitions, Deprivation indices detail,
indicator correlations, maps, data table,
resources and references.
Districts by deprivation index
Deprivation index, 2001
West Coast DM
Overberg DM
Cape Winelands DM
Eden DM
Central Karoo DM
Namakw a DM
Cape Tow n MM
Siyanda DM
Johannesburg MM
N Mandela MM
Sedibeng DM
Pixley ka Seme DM
Frances Baard DM
Tshw ane MM
Ekurhuleni MM
West Rand DM
Cacadu DM
Metsw eding DM
Xhariep DM
eThekw ini MM
Southern DM
Fezile Dabi DM
Motheo DM
Lejw eleputsw a DM
Nkangala DM
uMgungundlovu DM
Bojanala Platinum DM
Amajuba DM
Waterberg DM
G Sibande DM
T Mof utsanyane DM
Ehlanzeni DM
Central DM
Bophirima DM
Amathole DM
Capricorn DM
Kgalagadi DM
Mopani DM
C Hani DM
Bohlabela DM
iLembe DM
Ukhahlamba DM
Uthungulu DM
Uthukela DM
Vhembe DM
Ugu DM
Sisonke DM
Gr Sekhukhune DM
Zululand DM
Umkhanyakude DM
Umzinyathi DM
A Nzo DM
O Tambo DM
Metros
SOCIOECONOMIC
INDICATOR
uMgundundlovu
EC
FS
GP
KZN
LP
MP
NC
NW
WC
Umkanyakude
Umzinyathi
A Nzo & O R
Tambo all are
ISRDS nodes
0
1
better
2
Index
3
4
w orse
5
Per Capita Health Expenditure (PHC public sector)
2005/06
South Africa
Bophirima DM
Namakw a DM
Xhariep DM
Cape Tow n MM
Central Karoo DM
Umkhanyakude DM
West Coast DM
Central DM
Sisonke DM
Ekurhuleni MM
eThekw ini MM
Southern DM
Kgalagadi DM
C Hani DM
Johannesburg MM
Eden DM
Amathole DM
Uthungulu DM
Pixley ka Seme DM
Vhembe DM
Motheo DM
T Mofutsanyane
Mopani DM
Ugu DM
Sedibeng DM
Bojanala Platinum
Zululand DM
uMgungundlovu
Umzinyathi DM
Ehlanzeni DM
iLembe DM
O Tambo DM
Fezile Dabi DM
Cape Winelands
Ukhahlamba DM
Frances Baard DM
Overberg DM
N Mandela MM
Tshw ane MM
Cacadu DM
Uthukela DM
A Nzo DM
Lejw eleputsw a DM
Capricorn DM
Waterberg DM
Nkangala DM
Amajuba DM
West Rand DM
Bohlabela DM
G Sibande DM
Siyanda DM
Metsw eding DM
Gr Sekhukhune DM
SA ave 2005 =
R232
SA ave in 2001=
R168
R309
EC
FS
GP
KZN
LP
MP
NC
NW
WC
SA
R166
R115
0
100
200
300
Rand
400
500
INPUT
INDICATOR
Change in per capita health expenditure 2001-2005
Change in per capita expenditure 2001/02 to 2005/06
Umkhanyakude
R143 more
Amajuba R18 more
South Africa
Xhariep DM
Ukhahlamba DM
T Mofutsanyane DM
C Hani DM
Namakw a DM
Umkhanyakude DM
Central DM
Nkangala DM
Cacadu DM
O Tambo DM
Fezile Dabi DM
Kgalagadi DM
A Nzo DM
Vhembe DM
Amathole DM
Motheo DM
Capricorn DM
Sisonke DM
G Sibande DM
iLembe DM
Bohlabela DM
Uthungulu DM
Lejw eleputsw a DM
Mopani DM
Umzinyathi DM
eThekw ini MM
Ehlanzeni DM
Bojanala Platinum
Pixley ka Seme DM
Sedibeng DM
N Mandela MM
Zululand DM
Frances Baard DM
Ugu DM
Waterberg DM
Southern DM
Uthukela DM
Bophirima DM
West Coast DM
Gr Sekhukhune DM
uMgungundlovu DM
Siyanda DM
Amajuba DM
Tshw ane MM
Cape Winelands DM
West Rand DM
Central Karoo DM
Cape Tow n MM
Eden DM
Overberg DM
Metsw eding DM
Johannesburg MM
Ekurhuleni MM
-150
EC
FS
GP
KZN
LP
MP
NC
NW
WC
SA
Metros and
less deprived
districts
spending less
-100
-50
0
50
100
Rand (change)
150
200
250
Perinatal mortality rate 2005/06
Unrealistic/
data
problems
Perinatal mortality rate, 2005/06
IMPACT
INDICATOR
South Africa
Metsw eding DM
Southern DM
Bojanala Platinum DM
Bophirima DM
Central DM
Kgalagadi DM
Pixley ka Seme DM
Overberg DM
Vhembe DM
Cape Tow n MM
West Coast DM
Cape Winelands DM
C Hani DM
Eden DM
Johannesburg MM
Frances Baard DM
Namakw a DM
Sedibeng DM
Umkhanyakude DM
Tshw ane MM
Waterberg DM
Uthukela DM
Uthungulu DM
Mopani DM
Fezile Dabi DM
Central Karoo DM
uMgungundlovu DM
Ehlanzeni DM
West Rand DM
Ugu DM
Ekurhuleni MM
Umzinyathi DM
Capricorn DM
Siyanda DM
Xhariep DM
eThekw ini MM
Gr Sekhukhune DM
Motheo DM
T Mofutsanyane DM
G Sibande DM
Sisonke DM
Amathole DM
Bohlabela DM
Nkangala DM
Lejw eleputsw a DM
iLembe DM
Cacadu DM
Ukhahlamba DM
Amajuba DM
O Tambo DM
A Nzo DM
Zululand DM
N Mandela MM
Below SA
ave of
34/1000
EC
FS
GP
KZN
LP
MP
NC
NW
WC
SA
More deaths than
SA ave and over 40
0
20
40
60
Per 1000 births
80
100
Unrealistic/
data
problems
Immunisation coverage 2005/06
SA ave 90.3% up
from 80.7% 2003
Immunisation coverage 2005/06
South Africa
C Hani DM
Ugu DM
uMgungundlovu DM
Frances Baard DM
N Mandela MM
A Nzo DM
Sisonke DM
O Tambo DM
Vhembe DM
Pixley ka Seme DM
Amathole DM
Uthungulu DM
iLembe DM
Tshwane MM
Fezile Dabi DM
Umkhanyakude DM
Kgalagadi DM
Capricorn DM
Central Karoo DM
Cacadu DM
Sedibeng DM
Mopani DM
Cape Town MM
Motheo DM
T Mofutsanyane DM
Bohlabela DM
Umzinyathi DM
Gr Sekhukhune DM
Johannesburg MM
Ukhahlamba DM
Eden DM
Bophirima DM
Siyanda DM
Lejweleputswa DM
Nkangala DM
Ekurhuleni MM
eThekwini MM
Amajuba DM
Bojanala Platinum DM
Cape Winelands DM
Xhariep DM
Uthukela DM
Overberg DM
Zululand DM
West Coast DM
Southern DM
G Sibande DM
Central DM
Ehlanzeni DM
Namakwa DM
West Rand DM
Waterberg DM
Metsweding DM
OUTPUT
INDICATOR
EC
FS
GP
KZN
LP
MP
NC
NW
WC
SA
Ugu 117.9 %
Zululand 79.5 %
0
20
40
60
80
Percentage
100
120
140
TB cure rate 2004
SA target 2005/6 = 65%
SA ave 2004 = 50.8%,
SA ave 2003 = 56.7%
TB Cure rate, 2004
South Af rica
Overberg DM
Eden DM
Waterberg DM
Bohlabela DM
Vhembe DM
West Coast DM
West Rand DM
Central Karoo DM
Metsw eding DM
Lejw eleputsw a DM
Capricorn DM
Xhariep DM
Bophirima DM
T Mof utsanyane DM
Cape Tow n MM
Motheo DM
Cape Winelands DM
Tshw ane MM
Johannesburg MM
Amajuba DM
Sedibeng DM
Fezile Dabi DM
Kgalagadi DM
Namakw a DM
Bojanala Platinum DM
Central DM
Ekurhuleni MM
Umzinyathi DM
Gr Sekhukhune DM
Cacadu DM
Mopani DM
Ukhahlamba DM
Zululand DM
Sisonke DM
Southern DM
C Hani DM
N Mandela MM
Pixley ka Seme DM
iLembe DM
Siyanda DM
Uthukela DM
Ehlanzeni DM
A Nzo DM
O Tambo DM
Umkhanyakude DM
eThekw ini MM
Ugu DM
Amathole DM
G Sibande DM
uMgungundlovu DM
Frances Baard DM
Uthungulu DM
Nkangala DM
Overberg
84.5%
61.1%
EC
FS
GP
KZN
LP
MP
34 districts
achieved
over 50%
cure rate
Sisonke 50.6%
from 23.7%
uMgungundlovu 23%
Uthungulu 18.1%
Nkangala 12%
0
10
20
30
40
50
60
70
80
90
100
NC
NW
WC
SA
OUTCOME
INDICATOR
District Profiles
Vhembe
• 1.3 mil in lowest socio-economic quintile
• 86.4% households access to piped water.
• Per capita PHC expenditure R237 - highest in
province.
• Nurse clinical workload 26.6 patients p/d
• PHC utilisation rate 4.1 visits p/y - highest in
Limpopo, 2nd highest in SA
• TB cure rate 63.5% to 75.1%
• Smear conversion 65% to 73.1%
• proportion of pregnant mothers tested for HIV
(47.1%).
• Condom distribution rate.
• incidence of STI’s has been consistently high
at 10.4% , which is the highest in SA.
• Highest Caesarean section rate in Limpopo
(15.4%).
• Decline in the stillbirth and perinatal mortality
rates, lowest in the province.
• Immunisation has improved
• increase in immunisation drop out rate.
DC27 Umkhanyakude
• 586 000 with v. poor socio-economic status
• PHC expenditure per person=R309,
incr of R143 from 2001, rank = 6
• PHC utilisation 2.2 visits pp/y.
• Nurse clinical workload 22.7 in 2005 from 38.8 in
2004.
• TB cure rate improved from 30.0% in 2003 to
34.9% in 2004 , rank 45
• Smear conversion rate 47.1% in 2004 to 44% in
2005 !!!!!
• Pregnant mothers tested for HIV decr to 58.6%
from 73.1 in 2004!
• HIV+ve mothers receiving NVP from 57.5 % in
2003 to 68.5% in 2005.
• NVP - newborn babies decr to 77%! Rank 45
• Male condom distribution ave 10.9 per man.
• Perinatal mortality declining 47.7 – 30.8 – 30.1
rank 18
• Immunisation coverage 96.8, Drop out rate 5.1.
Data Quality!
• DHB is considered a valuable tool (National DOH,
Treasury, WHO, health managers at all levels)
• Data extracted is the official dataset for 2005/06,
(extracted June 2006) as submitted by the provinces to
Treasury and the NDOH
• Bad quality data is brought to light in DHB
• Report could be more useful with better quality & reliable
DHIS data – decision making improved
• Improving quality of DHIS data is ongoing - BUT leads to
multiple data extractions / sources.
Use the DHB to identify areas which require
improvement in data quality and improvement in
performance.
How to use the DHB
• Gives an idea which areas to focus your attention on
– Explain the data, look at the trends, look for
underlying reasons why, how can it be improved in
the future, set a new target and steps to achieve it.
• Can compare your district to other districts – what can
one learn from one another.
• Monitor and evaluate progress.
• Identify and improve data quality issues
We acknowledge the National Department of
Health for access to and use of their data for
this publication and Atlantic Philanthropies
for funding the project
Your comments / suggestions are valued!
e-mail [email protected]
Thank you