26 Section A: Management Inpatients 3 Management Inpatients Nazia Peer, Lesley Bamford and Peter Barron The district hospital plays a central role in the district health system as it supports primary health care and serves as an entry to more specialist care. The indicators in this chapter have been reported individually, but should be viewed by hospital and district management teams as being linked, and should be interpreted collectively. District hospitals generally admit patients with acute, relatively uncomplicated illnesses. More complicated cases are transferred to a regional or tertiary hospital. Hospital utilisation 3.1 Inpatient bed utilisation rate (district hospitals) Bed utilisation rate (BUR) measures the occupancy of available beds and therefore indicates how efficiently a hospital is using its available capacity. It is calculated as follows: the number of inpatient days is added to half the number of day patients, and divided by the usable bed days; this is expressed as a percentage. In 2014/15, the national inpatient BUR was 65.8%; this value has been stable for the past five years. Figure 1 shows that there is great inter-provincial variation between the highest value in the Western Cape (WC) (89.3%) and the lowest value in the Eastern Cape (EC) (59.0%). The Northern Cape (NC) BUR of 62.4% obscures the fact that it has districts with both the highest and lowest BUR. These positions on the league table are the same as in 2013/14. Figure 2 and Map 1 show the district BUR for 2014/15. The three districts with the highest BUR were Cape Town (WC) (99.4%), Namakwa (NC) (97.6%), and Eden (WC) (83.1%). Fezile Dabi in the Free State (FS) moved from the third-highest value in 2013/14 (95.1%) to a middle of the league table value of 68.7%. This change requires further analysis. Although Namakwa has a very high BUR (97.6%), this is now a realistic value, unlike the outlying value in 2013/14 of 137.4% created as a result of missing data for Calvinia Hospital. The three districts that had the lowest BUR in 2013/14 all moved out of the lowest positions on the league table. Amajuba in KwaZulu-Natal (KZN) increased from 44.3% in 2013/14 to 62.6% in 2014/15; uThungulu (KZN) rose slightly from 47.2% in 2013/14 to 50.0% in 2014/15; and Capricorn in Limpopo (LP) rose from 48.5% in 2013/14 to 69.5% in 2014/15. The Capricorn results in 2013/14 (48.5%) were due to data errors where one hospital had denominators 30 times the normal value for two months. Basically the BUR has been stable at around 70% for two years. This illustrates the value and necessity of managers using their data for decision making on a regular basis as this error could have been detected and corrected way before the next financial year. Frances Baard (NC) dropped from 51.9% in 2013/14 to 36.3% in 2014/15 but the indicator had fluctuated for several years, probably due to data quality issues. All the Eastern Cape districts have a BUR between 70.4% (Joe Gqabi) and 50.2% (Chris Hani), with the majority of them towards the lower half of the table. All the Western Cape districts are in the top one-third of the table. Figure 3 presents BUR trends over time and shows that the Western Cape has an overall BUR of over 80%, with a steady increase over the last five years. Cape Town had the highest BUR of 99.4% in 2014/15. All the other provinces had BURs between 60% and 70% and generally showed no real changes over time. Mpumalanga (MP) districts do not show much variation, apart from Gert Sibande with peaks in 2006/07 and 2007/08, and the lowest BUR for a second consecutive year. In the Free State, Fezile Dabi increased steadily from 2010/11 and reached a high in 2013/14. However, it dropped sharply to 68.7% as a result of an increase in the number of usable bed days (denominator). Northern Cape showed great inter-district variation, with Namakwa, ZF Mgcawu and Frances Baard decreasing, and Pixley Ka Seme and John Taolo Gaetsewe increasing. This could be attributable to the small population in the Northern Cape or poor data quality. Although there is much variation within socio-economic quintiles (SEQs), overall the average BUR remains distinctly higher in SEQs 4 and 5 compared with SEQs 1 and 2. This suggests that hospital resources in poorer socio-economic districts need to be used much more efficiently (Figure 4).
The district hospital plays a central role in the district health system as it supports primary health care and serves as an entry to more specialist care. The indicators in this chapter have been reported individually, but should be viewed by hospital and district management teams as being linked, and should be interpreted collectively. District hospitals generally admit patients with acute, relatively uncomplicated illnesses. More complicated cases are transferred to a regional or tertiary hospital.
Hospital utilisation
3.1 Inpatient bed utilisation rate (district hospitals)
Bed utilisation rate (BUR) measures the occupancy of available beds and therefore indicates how efficiently a hospital is using its available capacity. It is calculated as follows: the number of inpatient days is added to half the number of day patients, and divided by the usable bed days; this is expressed as a percentage.
In 2014/15, the national inpatient BUR was 65.8%; this value has been stable for the past five years. Figure 1 shows that there is great inter-provincial variation between the highest value in the Western Cape (WC) (89.3%) and the lowest value in the Eastern Cape (EC) (59.0%). The Northern Cape (NC) BUR of 62.4% obscures the fact that it has districts with both the highest and lowest BUR. These positions on the league table are the same as in 2013/14.
Figure 2 and Map 1 show the district BUR for 2014/15. The three districts with the highest BUR were Cape Town (WC) (99.4%), Namakwa (NC) (97.6%), and Eden (WC) (83.1%). Fezile Dabi in the Free State (FS) moved from the third-highest value in 2013/14 (95.1%) to a middle of the league table value of 68.7%. This change requires further analysis. Although Namakwa has a very high BUR (97.6%), this is now a realistic value, unlike the outlying value in 2013/14 of 137.4% created as a result of missing data for Calvinia Hospital.
The three districts that had the lowest BUR in 2013/14 all moved out of the lowest positions on the league table. Amajuba in KwaZulu-Natal (KZN) increased from 44.3% in 2013/14 to 62.6% in 2014/15; uThungulu (KZN) rose slightly from 47.2% in 2013/14 to 50.0% in 2014/15; and Capricorn in Limpopo (LP) rose from 48.5% in 2013/14 to 69.5% in 2014/15. The Capricorn results in 2013/14 (48.5%) were due to data errors where one hospital had denominators 30 times the normal value for two months. Basically the BUR has been stable at around 70% for two years. This illustrates the value and necessity of managers using their data for decision making on a regular basis as this error could have been detected and corrected way before the next financial year. Frances Baard (NC) dropped from 51.9% in 2013/14 to 36.3% in 2014/15 but the indicator had fluctuated for several years, probably due to data quality issues.
All the Eastern Cape districts have a BUR between 70.4% (Joe Gqabi) and 50.2% (Chris Hani), with the majority of them towards the lower half of the table. All the Western Cape districts are in the top one-third of the table.
Figure 3 presents BUR trends over time and shows that the Western Cape has an overall BUR of over 80%, with a steady increase over the last five years. Cape Town had the highest BUR of 99.4% in 2014/15. All the other provinces had BURs between 60% and 70% and generally showed no real changes over time.
Mpumalanga (MP) districts do not show much variation, apart from Gert Sibande with peaks in 2006/07 and 2007/08, and the lowest BUR for a second consecutive year. In the Free State, Fezile Dabi increased steadily from 2010/11 and reached a high in 2013/14. However, it dropped sharply to 68.7% as a result of an increase in the number of usable bed days (denominator). Northern Cape showed great inter-district variation, with Namakwa, ZF Mgcawu and Frances Baard decreasing, and Pixley Ka Seme and John Taolo Gaetsewe increasing. This could be attributable to the small population in the Northern Cape or poor data quality.
Although there is much variation within socio-economic quintiles (SEQs), overall the average BUR remains distinctly higher in SEQs 4 and 5 compared with SEQs 1 and 2. This suggests that hospital resources in poorer socio-economic districts need to be used much more efficiently (Figure 4).
Inpatient bed utilisation rate (district hospitals) by NHI district, 2014/15
Percentage
T Mofutsanyana: DC19
uMzinyathi: DC24
OR Tambo: DC15
Tshwane: TSH
Amajuba: DC25
Pixley ka Seme: DC7
G Sibande: DC30
Dr K Kaunda: DC40
Vhembe: DC34
uMgungundlovu: DC22
Eden: DC4
20 40 60 80 100
55.5
50.6
62.4
79.6
53.6
62.6
69.0
66.7
64.8
67.4
83.1
SA average: 65.8
ProvincesECFSGPKZNLPMPNCNWWC
Inpatient bed utilisation rate (district hospitals) by province, 2014/15
Percentage [Source: DHIS]
EC
FS
GP
NC
KZN
NW
LP
MP
WC
20 40 60 80 100
59.0
60.0
60.7
62.8
69.1
71.0
62.4
64.2
89.3
SA average: 65.8
ProvincesECFSGPKZNLPMPNCNWWC
27
Section A: Management Inpatients
Figure 1: Inpatient bed utilisation rate by province, 2014/15
Map 1: Inpatient bed utilisation rate by district, 2014/15
Inpatient bed utilisation rate (district hospitals) by district, 2014/15
Figure 2: Inpatient bed utilisation rate by district, 2014/15
Annual trends: Inpatient bed utilisation rate (district hospitals)
Perc
enta
ge
40
60
80
100
120
140EC FS
● ● ● ●●
●
●● ●
●
GP
40
60
80
100
120
140KZN
● ● ● ●●
●●
●● ●
LP MP
40
60
80
100
120
140
2005
/06
2006
/07
2007
/08
2008
/09
2009
/10
2010
/11
2011
/12
2012
/13
2013
/14
2014
/15
NC
2005
/06
2006
/07
2007
/08
2008
/09
2009
/10
2010
/11
2011
/12
2012
/13
2013
/14
2014
/15
NW20
05/0
620
06/0
720
07/0
820
08/0
920
09/1
020
10/1
120
11/1
220
12/1
320
13/1
420
14/1
5●
●
●
● ● ●
●●
● ●
WC
EC A NzoEC AmatholeEC Buffalo CityEC C HaniEC Joe GqabiEC N Mandela BayEC OR TamboEC S BaartmanFS Fezile DabiFS LejweleputswaFS MangaungFS T MofutsanyanaFS Xhariep
GP EkurhuleniGP JohannesburgGP SedibengGP TshwaneGP West RandKZN AmajubaKZN eThekwiniKZN Harry GwalaKZN iLembeKZN UguKZN uMgungundlovuKZN uMkhanyakudeKZN uMzinyathi
KZN uThukelaKZN uThunguluKZN ZululandLP CapricornLP MopaniLP SekhukhuneLP VhembeLP WaterbergMP EhlanzeniMP G SibandeMP NkangalaNC Frances BaardNC JT Gaetsewe
NC NamakwaNC Pixley ka SemeNC ZF MgcawuNW BojanalaNW Dr K KaundaNW NM MolemaNW RS MompatiWC Cape TownWC Cape WinelandsWC Central KarooWC EdenWC OverbergWC West Coast
●
●
●
29
Section A: Management Inpatients
Figure 3: Annual trends: Inpatient bed utilisation rate
S E QS E Q 1 (most deprived)
S E Q 2 (deprived)
S E Q 3
S E Q 4 (well off)
S E Q 5 (least deprived)
F Y 2006 F Y 2007 F Y 2008 F Y 2009 F Y 2010 F Y 2011 F Y 2012 F Y 2013 F Y 2014 F Y 2015F inancial year
Usable bedutil (DH)
60
65
70
75
80In
dica
tor
valu
e
74.4
63.162.9
64.2
67.4
61.1
68.4
72.1
67.6
60.1
30
Section A: Management Inpatients
Figure 4: Trends in average district values by SEQ for inpatient bed utilisation rate
3.2 Average length of stay (district hospitals)
Average length of stay (ALOS) refers to the average number of days that patients spend in hospital. It is generally calculated as follows: total number of inpatient days during a year plus half the number of day patients, divided by the number of separations (deaths, discharges and transfers out).
A consistently high ALOS implies that patients are being kept in hospital for too long. Some of the most important reasons for this are as follows:
✦ Shortage of doctors leads to a lack of regular ward rounds, which can result in inappropriate patient management, such as patients not being discharged or transferred.
✦ In rural areas the disease profile, including high HIV, TB and mental health burdens, results in patients having to stay longer in hospital.
✦ Some orthopaedic cases also require unusually long hospital stays because they cannot be transferred to a higher level hospital.
✦ Some district hospitals have no laboratories, which forces patients to stay in hospital until results are available.
✦ Social cases, where families do not collect patients after they are discharged or where patients have long distances to travel home and no transport is available, also result in patients staying longer than they should.
✦ In general, factors in rural areas contribute more to a higher ALOS than factors in urban areas.
✦ A high ALOS can also be due to data quality issues such as an undercount of the number of discharges, which results in a false elevation.
Low ALOS could imply that the quality of care afforded to patients is sub-standard and patients are being discharged without appropriate care. It may also mean that too many patients are being referred to other hospitals without proper investigation. Both too-long and too-short ALOS warrant further investigation.
The national average for ALOS decreased slightly from 4.7 days in 2013/14 to 4.6 days in 2014/15 (Figure 5). The provincial pattern shows that KwaZulu-Natal (5.8 days) and the Eastern Cape (5.3 days) had the longest ALOS; both provinces have a significant number of rural district hospitals. This was the second year that KwaZulu-Natal and the Eastern Cape had the longest ALOS.
Figure 6 and Map 2 show the distribution of ALOS among districts. The 13 districts with the longest ALOS were in KwaZulu-Natal (n=9) and Eastern Cape (n=4). OR Tambo district (EC) had the longest ALOS of 6.4 days, followed by uThungulu (KZN) at 6.3 days. The 10 districts with the shortest ALOS were from the Free State (n=3), Northern Cape (n=4) and the Western Cape (n=3). Frances Baard (NC) continued to increase from 1.1 days in 2012/13, to 2.4 days in 2013/14, to 2.9 days in 2014/15, and was no longer among the three districts with the shortest ALOS. This could be attributable to improvements in the quality of care. Although slightly decreased from 2013/14, Tshwane district in Gauteng (GP) and Cape Town (WC) had the longest ALOS in their provinces, at 4.9 days and 4.3 days respectively. eThekwini (KZN) was the metro with the longest ALOS, probably partly due to all districts in KwaZulu-Natal having high ALOS, possibly due to burden of HIV and TB. In Cape Town the number of patient days increased substantially as a result of the two new large district hospitals in Mitchell’s Plain and Khayelitsha. This resulted in greater patient access and decreased burden on higher levels of care.
Average length of stay (district hospitals) by NHI district, 2014/15
Days
OR Tambo: DC15
uMzinyathi: DC24
uMgungundlovu: DC22
Tshwane: TSH
Amajuba: DC25
Vhembe: DC34
G Sibande: DC30
Eden: DC4
Dr K Kaunda: DC40
Pixley ka Seme: DC7
T Mofutsanyana: DC19
2 4 6
6.38
2.81
4.85
5.55
6.07
4.84
4.28
4.00
3.25
3.44
3.57SA average: 4.61 Provinces
ECFSGPKZNLPMPNCNWWC
Average length of stay (district hospitals) by province, 2014/15
Days [Source: DHIS]
KZN
EC
NW
MP
GP
LP
WC
NC
FS
2 4 6
5.26
3.16
4.28
5.83
4.25
4.32
3.54
4.71
3.76
SA average: 4.61
ProvincesECFSGPKZNLPMPNCNWWC
31
Section A: Management Inpatients
Figure 6 shows the variation in ALOS among the National Health Insurance (NHI) districts. OR Tambo’s value was 2.7 times greater than that of Thabo Mofutsanyana district in the Free State, which had the shortest ALOS of the NHI districts. Apart from Tshwane, the NHI districts with the longest ALOS were all rural.
The annual ALOS trends for Gauteng, Limpopo, Mpumalanga, North West (NW) and the Western Cape do not exhibit much inter-district variation (Figure 7). In the Free State, the pattern among the districts was constant, apart from Manguang. The Eastern Cape and KwaZulu-Natal displayed great variation. OR Tambo in the Eastern Cape had the longest ALOS of 10.0 days in 2002/03; this dropped to 4.8 days in 2011/12, followed by a rise to 6.9 days in 2014/15. This variation is most likely explained by poor data quality. In KwaZulu-Natal, uMkhanyakude and Zululand districts showed the greatest variation, with the former peaking at 7.5 days in 2008/09 and dropping to 5.5 days in 2013/14. Although the ALOS in Amajuba increased from 2011/12, it remained the shortest ALOS in KwaZulu-Natal.
In previous years, a long ALOS was generally seen in the most deprived districts (SEQ1), and the shortest ALOS in the least deprived districts (SEQ5). In 2014/15, SEQ1 districts clearly had the longest ALOS, with OR Tambo at 6.9 days and uMzinyathi at 5.1 days. There was a declining gradient from SEQ2 to SEQ4 (see Figure 8).
Figure 5: Average length of stay by province, 2014/15
Average length of stay (district hospitals) by district, 2014/15
Days [Source: DHIS]
OR Tambo: DC15uThungulu: DC28
eThekwini: ETHuMzinyathi: DC24
uMkhanyakude: DC27iLembe: DC29
Zululand: DC26Ugu: DC21
C Hani: DC13uMgungundlovu: DC22
Joe Gqabi: DC14Amathole: DC12uThukela: DC23
RS Mompati: DC39Bojanala: DC37
A Nzo: DC44Harry Gwala: DC43
Buffalo City: BUFTshwane: TSH
Amajuba: DC25Nkangala: DC31
West Rand: DC48Mopani: DC33
Capricorn: DC35Ehlanzeni: DC32
NM Molema: DC38JT Gaetsewe: DC45
Ekurhuleni: EKUCape Town: CPT
Vhembe: DC34Waterberg: DC36
S Baartman: DC10G Sibande: DC30Mangaung: MAN
Fezile Dabi: DC20Sekhukhune: DC47
N Mandela Bay: NMAEden: DC4
Johannesburg: JHBSedibeng: DC42
Central Karoo: DC5Dr K Kaunda: DC40
Namakwa: DC6Pixley ka Seme: DC7
Overberg: DC3ZF Mgcawu: DC8West Coast: DC1
Cape Winelands: DC2Frances Baard: DC9
T Mofutsanyana: DC19Lejweleputswa: DC18
Xhariep: DC16
2 4 6
NHI
NHI
NHI
NHI
NHI
NHI
NHI
NHI
NHI
NHI
NHI
4.92
4.03
5.47
5.56
5.55
6.38
5.18
3.67
2.192.422.81
3.903.95
3.50
4.76
4.30
3.56
4.85
5.70
5.55
5.39
6.07
4.84
5.88
6.04
6.25
5.97
5.14
6.12
4.42
4.28
4.39
4.17
3.89
4.00
4.80
4.37
4.31
3.423.25
3.04
2.90
5.19
4.32
5.29
3.44
4.29
3.032.98
3.13
3.57
3.49
SA average: 4.61
ProvincesECFSGPKZNLPMPNCNWWC
32
Section A: Management Inpatients
Figure 6: Average length of stay by district, 2014/15
33
Section A: Management Inpatients
Map 2: Average length of stay by district, 2014/15
Annual trends: Average length of stay (district hospitals)
Day
s
2
4
6
8
EC FS
● ●● ● ● ●
●●
● ●
GP
2
4
6
8
KZN
● ● ● ●● ●
●● ● ●
LP MP
2
4
6
8
2005
/06
2006
/07
2007
/08
2008
/09
2009
/10
2010
/11
2011
/12
2012
/13
2013
/14
2014
/15
NC
2005
/06
2006
/07
2007
/08
2008
/09
2009
/10
2010
/11
2011
/12
2012
/13
2013
/14
2014
/15
NW
2005
/06
2006
/07
2007
/08
2008
/09
2009
/10
2010
/11
2011
/12
2012
/13
2013
/14
2014
/15
● ●●
●
●● ● ●
● ●
WC
EC A NzoEC AmatholeEC Buffalo CityEC C HaniEC Joe GqabiEC N Mandela BayEC OR TamboEC S BaartmanFS Fezile DabiFS LejweleputswaFS MangaungFS T MofutsanyanaFS Xhariep
GP EkurhuleniGP JohannesburgGP SedibengGP TshwaneGP West RandKZN AmajubaKZN eThekwiniKZN Harry GwalaKZN iLembeKZN UguKZN uMgungundlovuKZN uMkhanyakudeKZN uMzinyathi
KZN uThukelaKZN uThunguluKZN ZululandLP CapricornLP MopaniLP SekhukhuneLP VhembeLP WaterbergMP EhlanzeniMP G SibandeMP NkangalaNC Frances BaardNC JT Gaetsewe
NC NamakwaNC Pixley ka SemeNC ZF MgcawuNW BojanalaNW Dr K KaundaNW NM MolemaNW RS MompatiWC Cape TownWC Cape WinelandsWC Central KarooWC EdenWC OverbergWC West Coast
●
●
●
34
Section A: Management Inpatients
Figure 7: Annual trends: Average length of stay
F Y 2006 F Y 2007 F Y 2008 F Y 2009 F Y 2010 F Y 2011 F Y 2012 F Y 2013 F Y 2014 F Y 2015F inancial year
Avg length ofstay (DH)
3
4
5
6
Indi
cato
r va
lue
3.5
3.0
4.1
6.1
5.5
4.8
3.93.7
4.2
4.6
S E QS E Q 1 (most deprived)
S E Q 2 (deprived)
S E Q 3
S E Q 4 (well off)
S E Q 5 (least deprived)
35
Section A: Management Inpatients
Figure 8: Trends in average district values by SEQ for average length of stay
3.3 Expenditure per patient day equivalent (district hospitals)
Expenditure per patient day equivalent (PDE) is a composite process indicator that connects financial data with service-related data from the hospital admissions and outpatients records. This indicator measures how the resources available to the hospital are being spent, and is a marker of efficiency. The indicator measures the average cost per PDE at a district hospital, and is expressed as Rand per PDE. The indicator value is calculated by dividing the total expenditure of the hospital (within budget programme 2: district health services, as recorded in the Basic Accounting System (BAS)) by the number of PDEs. PDEs are calculated by adding the number of inpatients, plus half of day patients, plus one-third of outpatients and emergency room visits, as recorded in the District Health Information Software (DHIS).
Comparative analysis of costs for performing the same activity across facilities is important for monitoring efficiency of performance.a It is assumed that the average cost of one inpatient day is equivalent to that of three outpatient visits. Historically, under-utilisation of hospital services, and over-staffing with fixed costs like salaries, are common causes of high PDEs. This results in high expenditures with low utilisation. As expenditure per PDE is a ratio between costs and services, improved performance is possible if costs are reduced or utilisation increased.
The expenditure per PDE can be compared across similar hospitals within or between districts. Rural hospitals, particularly those located in more remote areas, may have difficulty attracting and retaining staff for a number of reasons, including poor physical infrastructure, lack of staff accommodation, uneven remuneration of staff working in different rural locations, and poor road and transport networks.b Some of these hospitals are therefore poorly utilised, with a low BUR, and may have high expenditure per PDE since almost all fixed costs remain even if facilities are not fully utilised. Certain district hospitals, particularly in KwaZulu-Natal and the Eastern Cape, offer some regional and tertiary hospital services. This may result in higher expenditure per PDE since expenditure per PDE generally increases with the level of care. Another possible reason for a high expenditure per PDE is that some district hospitals provide specialist services and employ full-time specialists, interns and clinical managers as part of their staff.
In 2014/15, the average expenditure per PDE in South Africa for all district hospitals was R2 136, which is higher than the 2013/14 value of R1 969 and the 2012/13 value of R1 926 respectively. All values for this indicator are given in real 2014/15 costs, meaning that the effect of inflation has already been adjusted for. This steady rise is to be expected since medical inflation has traditionally been higher than the consumer price index (CPI).
Figure 9 shows the provincial expenditure per PDE. There is almost 30% difference between Gauteng, which has the highest provincial value of R2 605, and the Western Cape, which has the lowest value of R1 903. This has significant implications for equity as it implies that the Western Cape is getting much more for its money than Gauteng. These data suggest that the Western Cape is more efficient, and service indicators (e.g. neonatal and other child mortality indicators of hospital performance) suggest that they are more effective as well. South Rand District Hospital in Johannesburg (GP) has had consistently high expenditure per PDE values, with a peak in 2011.
Another possible reason for inaccurate values is the quality of the data. Outpatient Department (OPD) headcount values could be incorrect because visits to allied health workers are not included. Some expenditures per PDE values could also be an underestimation because budgeting in some provinces (for example Limpopo) is not widely decentralised,
a Adams I, Darko D, Accorsi S. Accessing efficiency in service delivery. Bulletin of Health Information. 2004, 1 (1) Available from: http://www.ghanahealthservice.org/includes/upload/publications/Assessing%20efficiency%20in%20service%20delivery.pdf [accessed 13 August 2015].
b Barron P, Monticelli F. Key district health indicators in Primary Health Care. Volume 1. Durban: Health Systems Trust, April 2007.
Expenditure per patient day equivalent (district hospitals) by NHI district, 2014/15
Rand (real 2014/15 prices)
Eden: DC4
uMgungundlovu: DC22
Pixley ka Seme: DC7
G Sibande: DC30
Amajuba: DC25
T Mofutsanyana: DC19
OR Tambo: DC15
uMzinyathi: DC24
Tshwane: TSH
Vhembe: DC34
Dr K Kaunda: DC40
1000 2000 3000
1856
2685
1903
2437
1749
1949
1966
1835
2211
1987
2354
SA avg: 2136
ProvincesECFSGPKZNLPMPNCNWWC
Expenditure per patient day equivalent (district hospitals) by province, 2014/15
Rand (real 2014/15 prices) [Source: BAS, DHIS]
WC
KZN
EC
MP
NC
FS
NW
LP
GP
1000 2000 3000
2145
2511
2275
2006
1903
2004
2164
2006
2605
SA avg: 2136
ProvincesECFSGPKZNLPMPNCNWWC
36
Section A: Management Inpatients
resulting in some expenditure (for example on medicines and HIV) not being recorded per facility. Another reason for underestimation could be due to no laboratory expenses being recorded at facility level (for example in KwaZulu-Natal).
Figure 10 and Map 3 show the expenditure per PDE for the 52 districts. The districts with the highest values are predomin-ately in Gauteng (n=3) and Limpopo (n=3). The Western Cape districts dominate the lower values in the district league table, and KwaZulu-Natal districts are also mainly in the lower half of the table. Johannesburg has the highest expenditure per PDE of R3 596, 2.2 times the value of the lowest, namely the West Coast (WC) with an expenditure per PDE of R1 602. Three districts with high expenditure per PDEs remained among the top four districts in 2012/13, 2013/14 and 2014/15. These are Nelson Mandela Bay (EC), Frances Baard (NC) and Waterberg (LP). In Limpopo, all the districts are above the SA average, with three of them appearing in the top 10 districts with the highest expenditure per PDE. Similarly in the North West, all the districts are above the SA average. As in previous years, the Northern Cape has the most inter-district variation, with districts in the high, low and middle positions of the table. In 2013/14, Eastern Cape districts were predominantly in the top third of the table, with Joe Gqabi having the lowest expenditure per PDE in 2013/14. In 2014/15, Joe Gqabi is in the middle of the league table, with other Eastern Cape districts spread throughout the table. As most districts have a number of district hospitals, these district variations conceal the much greater variations that exist between individual hospitals.
Inter-district variations (Figure 11) over the last 10 years show that in the Western Cape there were steady increases to 2010/11, with the exception of Cape Town, which had dramatic increases to 2010. Thereafter the expenditure per PDE has been remarkably constant in all WC districts. The very wide fluctuations in expenditure per PDE in the Northern Cape are predominantly in Frances Baard and John Taolo Gaetsewe. This is suggestive of either inadequate quality of data (for example expenditure not being linked accurately to individual facilities) or variations in service provision. Several facilities in the Northern Cape have undergone renovations, restructuring or reclassification, resulting in periods of reduced services. Within each district in the Northern Cape, individual hospitals have tended to differ widely in their expenditure per PDE.
KwaZulu-Natal districts show little variation apart from sporadic rises in uMgungundlovu and uThukela, although overall there has been a steady increase in the expenditure per PDE over time. In NM Molema in North West, some hospitals seem to be reporting expenditure jointly, although the PDEs remain separate, leading to extreme changes in the indicator values per facility. Expenditure per PDE has declined in RS Mompati (NW), where Joe Morolong Memorial Hospital has been reclassified from a district to a regional hospital.
In Limpopo, all the districts have had a steady increase in expenditure per PDE over time. The Free State has also had steady increases, apart from Lejweleputswa where there was a sharp rise in 2009/10. In the Eastern Cape, Nelson Mandela Bay has been an outlier, with an expenditure per PDE much greater than the other districts. All the districts have increased over time, with Nelson Mandela Bay maintaining its expenditure per PDE above the others. In Mpumalanga, expenditure per PDEs for the different districts have converged over time, and have plateaued since 2009/10. In Gauteng, Johannesburg and West Rand have shown the most variation. There is little inter-district variation in expenditure per PDE, with the exception of Johannesburg, which had a sharp increase in 2014/15 that warrants further investigation.
Figure 9: Expenditure per patient day equivalent by province, 2014/15
Expenditure per patient day equivalent (district hospitals) by district, 2014/15
Figure 10: Expenditure per patient day equivalent by district, 2014/15
38
Section A: Management Inpatients
Map 3: Expenditure per patient day equivalent by district, 2014/15
Annual trends: Expenditure per patient day equivalent (district hospitals)
Ran
d (re
al 2
014/
15 p
rices
)
1000
2000
3000
EC FS
●
●
●● ●
●●
● ●●
GP
1000
2000
3000
KZN
●
● ●
●
●
●● ●
●●
LP MP
1000
2000
3000
2005
/06
2006
/07
2007
/08
2008
/09
2009
/10
2010
/11
2011
/12
2012
/13
2013
/14
2014
/15
NC
2005
/06
2006
/07
2007
/08
2008
/09
2009
/10
2010
/11
2011
/12
2012
/13
2013
/14
2014
/15
NW20
05/0
620
06/0
720
07/0
820
08/0
920
09/1
020
10/1
120
11/1
220
12/1
320
13/1
420
14/1
5●
●
●●
●
●
●●
●●
WC
EC A NzoEC AmatholeEC Buffalo CityEC C HaniEC Joe GqabiEC N Mandela BayEC OR TamboEC S BaartmanFS Fezile DabiFS LejweleputswaFS MangaungFS T MofutsanyanaFS Xhariep
GP EkurhuleniGP JohannesburgGP SedibengGP TshwaneGP West RandKZN AmajubaKZN eThekwiniKZN Harry GwalaKZN iLembeKZN UguKZN uMgungundlovuKZN uMkhanyakudeKZN uMzinyathi
KZN uThukelaKZN uThunguluKZN ZululandLP CapricornLP MopaniLP SekhukhuneLP VhembeLP WaterbergMP EhlanzeniMP G SibandeMP NkangalaNC Frances BaardNC JT Gaetsewe
NC NamakwaNC Pixley ka SemeNC ZF MgcawuNW BojanalaNW Dr K KaundaNW NM MolemaNW RS MompatiWC Cape TownWC Cape WinelandsWC Central KarooWC EdenWC OverbergWC West Coast
●
●
●
39
Section A: Management Inpatients
Figure 11: Annual trends: Expenditure per patient day equivalent
OPD new client not referred rate (district hospitals) by NHI district, 2014/15
Percentage
Pixley ka Seme: DC7
T Mofutsanyana: DC19
G Sibande: DC30
Vhembe: DC34
uMzinyathi: DC24
OR Tambo: DC15
uMgungundlovu: DC22
Amajuba: DC25
Eden: DC4
Tshwane: TSH
Dr K Kaunda: DC40
20 40 60 80 100
52.0
69.6
24.5
44.1
61.7
43.8
63.7
64.5
73.1
22.0
29.1SA average: 60.7 Provinces
ECFSGPKZNLPMPNCNWWC
OPD new client not referred rate (district hospitals) by province, 2014/15
Percentage [Source: DHIS]
LP
FS
NC
MP
EC
KZN
NW
GP
WC
20 40 60 80 100
64.3
71.2
46.9
53.0
75.2
67.0
68.7
47.9
29.5SA average: 60.7
ProvincesECFSGPKZNLPMPNCNWWC
40
Section A: Management Inpatients
3.4 OPD new client not referred rate
This indicator is being reported for the third year. OPD new client not referred rate, refers to the percentage of new outpatient clients who enter a hospital without a referral letter. The percentage is calculated by dividing new OPD cases that are not referred (numerator) by all new OPD cases (denominator). OPD follow-up and emergency clients are excluded from the denominator. OPD new client not referred rate, monitors the utilisation trends of clients who by-pass primary health care (PHC) facilities. There is no target set for this indicator.
High OPD new client not referred rate values could imply overburdened PHC facilities or inadequately performing facilities resulting in poor referral systems. High values could also indicate a lack of referral systems, or clients being resistant to change. If a patient needs to see a doctor, he or she will have to go to a hospital because most doctors do not render a medical service at PHC facilities. In some instances, the quality of care provided by the nurses is not up to standard and these patients are unnecessarily referred to a doctor. Drug stock-outs at clinics and strict opening and closing times without extended after-hour services can contribute to patients attending hospital without going via the clinic. Lastly, in some areas, a hospital may be geographically closer to patients than the PHC facilities, which could be why the patients go there first.
Figure 12 shows the greater than two-fold difference between the Western Cape with the lowest rate (29.5%), and Limpopo with the highest rate (75.2%). In 2013/14, the same provinces had the lowest and highest values; however, the rates had increased to 40.2% (WC) and 78.6% (LP) respectively.
Figure 13 shows that in 2014/15 the average OPD new client not referred rate was 60.7%, a very small but sustained decrease from the 61.5% in 2013/14 and 64.1% in 2012/13. The highest rate was in Waterberg (LP) at 93.9%, followed by Frances Baard (NC) at 88.4%, and the lowest was in the Central Karoo (WC) at 4.6% and Nelson Mandela Bay (EC) at 8.9%. The Western Cape has five districts among the 10 with the lowest not referred rate (Map 4). Johannesburg, which reported for the first time in 2014/15, has the sixth-highest value. Central Karoo rates have fluctuated very dramatically in the two years in which the district has reported, with the fluctuations probably due to poor data quality.
Figure 13 also shows that of the NHI districts, Pixley Ka Seme (NC) has the highest OPD new client not referred rate (73.1%), but this was less than the rate of 80.7% in 2013/14. The rate for Dr K Kaunda (NW) was 22.0% in 2014/15; although the lowest of the NHI districts, it doubled from the 2013/14 value of 10.8%. This is of particular concern as a sustained improvement in the PHC system should decrease the value of this indicator.
There are no real distinct provincial patterns, with substantial intra and inter-provincial variations. The exception is the districts in Limpopo, which have been fairly consistent over time (Figure 14). Although there is a wide variation in the rate within each socio-economic quintile, an associative pattern cannot be seen between specific quintiles and the OPD new client not referred rate.
Figure 12: OPD new client not referred rate by province, 2014/15
OPD new client not referred rate (district hospitals) by district, 2014/15
Figure 13: OPD new client not referred rate by district, 2014/15
42
Section A: Management Inpatients
Map 4: OPD new client not referred rate by district, 2014/15
Annual trends: OPD new client not referred rate (district hospitals)
Perc
enta
ge
20
40
60
80
100EC FS
●
●
●
● ●
GP
20
40
60
80
100KZN
● ● ● ● ●
LP MP
20
40
60
80
100
2010
/11
2011
/12
2012
/13
2013
/14
2014
/15
NC
2010
/11
2011
/12
2012
/13
2013
/14
2014
/15
NW20
10/1
1
2011
/12
2012
/13
2013
/14
2014
/15
●
●
WC
EC A NzoEC AmatholeEC Buffalo CityEC C HaniEC Joe GqabiEC N Mandela BayEC OR TamboEC S BaartmanFS Fezile DabiFS LejweleputswaFS MangaungFS T MofutsanyanaFS Xhariep
GP EkurhuleniGP JohannesburgGP SedibengGP TshwaneGP West RandKZN AmajubaKZN eThekwiniKZN Harry GwalaKZN iLembeKZN UguKZN uMgungundlovuKZN uMkhanyakudeKZN uMzinyathi
KZN uThukelaKZN uThunguluKZN ZululandLP CapricornLP MopaniLP SekhukhuneLP VhembeLP WaterbergMP EhlanzeniMP G SibandeMP NkangalaNC Frances BaardNC JT Gaetsewe
NC NamakwaNC Pixley ka SemeNC ZF MgcawuNW BojanalaNW Dr K KaundaNW NM MolemaNW RS MompatiWC Cape TownWC Cape WinelandsWC Central KarooWC EdenWC OverbergWC West Coast
●
●
●
43
Section A: Management Inpatients
Figure 14: Annual trends: OPD new client not referred rate
Inpatient crude death rate by NHI district, 2014/15
Percentage
OR Tambo: DC15
T Mofutsanyana: DC19
uMzinyathi: DC24
Dr K Kaunda: DC40
Amajuba: DC25
uMgungundlovu: DC22
Vhembe: DC34
G Sibande: DC30
Tshwane: TSH
Pixley ka Seme: DC7
Eden: DC4
2 4 6 8
7.34
6.91
4.73
5.54
6.16
5.99
5.49
5.02
4.64
6.01
3.59
SA average: 5.24 ProvincesECFSGPKZNLPMPNCNWWC
Inpatient crude death rate by province, 2014/15
Percentage [Source: DHIS]
NW
EC
FS
MP
LP
GP
KZN
NC
WC
2 4 6 8
6.31
6.08
5.35
5.11
5.55
5.56
5.04
6.64
3.14SA average: 5.24
ProvincesECFSGPKZNLPMPNCNWWC
44
Section A: Management Inpatients
3.5 Inpatient crude death rate
The inpatient crude death rate (ICDR) is an impact indicator that refers to the proportion of all inpatient separations due to death. Inpatient separations include inpatient transfers out, deaths, and inpatient discharges. The indicator therefore includes deaths from all causes that occur in a health facility.
The average ICDR for all hospitals in South Africa was 5.2% in 2014/15 (Figure 15). Previous values were 5.1% in 2011/12, and 5.8% in 2012/13 and 5.4% in 2013/14. North West (6.6%) and the Eastern Cape (6.3%) recorded the highest values in 2014/15. Figure 16 shows the district league table for ICDR. Chris Hani (EC) has the highest ICDR of 8.1%, followed by Bojanala (NW) with 7.9%. All six Western Cape districts are among the 10 districts with the lowest ICDR (Map 5). The lowest ICDR is in Overberg (2.4%), followed by West Coast (3.0%). Other provinces show more variation in their values. Five of the 10 districts with the highest ICDRs are from the Eastern Cape, two from North West and another two from the Free State.
The ICDR is generally lowest in the least deprived districts (SEQ5) and higher in the most deprived (SEQ1) (Figure 17).
Figure 15: Inpatient crude death rate by province, 2014/15
Inpatient crude death rate by district, 2014/15
Percentage [Source: DHIS]
C Hani: DC13Bojanala: DC37
OR Tambo: DC15T Mofutsanyana: DC19
Joe Gqabi: DC14Fezile Dabi: DC20
NM Molema: DC38Nkangala: DC31
A Nzo: DC44Amathole: DC12
Lejweleputswa: DC18uThungulu: DC28
RS Mompati: DC39Capricorn: DC35
uMzinyathi: DC24uThukela: DC23
West Rand: DC48Dr K Kaunda: DC40
Amajuba: DC25Ugu: DC21
Frances Baard: DC9Buffalo City: BUF
Zululand: DC26Sedibeng: DC42Ekurhuleni: EKU
Mopani: DC33uMgungundlovu: DC22
JT Gaetsewe: DC45Vhembe: DC34
Ehlanzeni: DC32Harry Gwala: DC43
Mangaung: MANWaterberg: DC36
Johannesburg: JHBN Mandela Bay: NMA
G Sibande: DC30uMkhanyakude: DC27
Sekhukhune: DC47Tshwane: TSH
Pixley ka Seme: DC7S Baartman: DC10
ZF Mgcawu: DC8eThekwini: ETH
iLembe: DC29Eden: DC4
Xhariep: DC16Cape Winelands: DC2
Namakwa: DC6Cape Town: CPT
Central Karoo: DC5West Coast: DC1
Overberg: DC3
2 4 6 8
NHINHI
NHI
NHI
NHI
NHI
NHI
NHI
NHI
NHI
NHI
5.83
4.62
6.52
8.10
6.75
7.34
6.57
5.09
3.48
6.52
6.91
6.72
5.41
5.67
6.07
5.57
5.38
4.73
5.88
5.54
6.116.16
5.99
5.69
4.92
6.38
4.14
5.42
4.20
5.55
5.49
6.32
5.39
4.86
5.02
6.58
5.47
5.52
3.31
4.64
4.61
5.87
7.86
6.61
6.38
6.01
3.09
3.00
3.33
2.44
3.59
3.05 SA average: 5.24
ProvincesECFSGPKZNLPMPNCNWWC
45
Section A: Management Inpatients
Figure 16: Inpatient crude death rate by district, 2014/15
F Y 2008 F Y 2009 F Y 2010 F Y 2011 F Y 2012 F Y 2013 F Y 2014 F Y 2015F inancial year
C rude deathrate
4
5
6
7
Indi
cato
r va
lue
5.7
4.4
6.5
6.1
5.2
5.6
5.8
5.6
6.3
4.1
S E QS E Q 1 (most deprived)
S E Q 2 (deprived)
S E Q 3
S E Q 4 (well off)
S E Q 5 (least deprived)
46
Section A: Management Inpatients
Map 5: Inpatient crude death rate by district, 2014/15
Figure 17: Trends in average district values by SEQ for inpatient crude death rate
Facility crude death rate (all facilities) versus Expenditure per PDE (district hospitals), 2014/15
ProvEC
FS
GP
KZN
LP
MP
NC
NW
WC
ZA
ProvEC
FS
GP
KZN
LP
MP
NC
NW
WC
ZA
Sum of Exppde Dh 2014/15 vs. sum of Fac Cdr 2014/15. Color shows details about Prov. Shape shows details about Prov. The marks are labeled by Prov.
47
Section A: Management Inpatients
Combined performance
If the performance on crude death rate (CDR) and expenditure per patient day equivalent (PDE) are combined in a scatter graph with four quadrants, then the bottom left block represents low death rates and low expenditure per patient day (‘good performance’). The top right quadrant represents both a high death rate and a high expenditure per patient day (‘poor performance’).
Figure 18 shows the combined performance of the indicators for the provinces. This shows the stark difference between the Western Cape, which has exceptionally low values for both indicators, and the other provinces. KwaZulu-Natal also falls in the ’good performance’ quadrant, while Gauteng, Limpopo, Free State and North West provinces fall in the ‘poor performance’ quadrant.
Figure 18: Hospital crude death rate versus cost per PDE by province, 2014/15
Figure 19 shows the combined performance of the two indicators for the districts.
All six Western Cape districts fall into the ‘good performance’ category. They are accompanied by three districts from the Northern Cape and one district each from KwaZulu-Natal, Mpumalanga, and the Eastern and Northern Cape provinces.
In the ‘poor performance’ quadrant are four districts each from Limpopo and Gauteng, three districts each from the Free State and North West, and one district each from the Northern Cape, Mpumalanga and KwaZulu-Natal.
Figure 19: Hospital crude death rate versus cost per PDE by district, 2014/15
Child under 5 years mortality indicators
Improving the survival and well-being of young children remains an important health and development goal.
The end of the Millennium Development Goals (MDG) period provides an opportunity to reflect on progress made in reducing child mortality during the past quarter-century on a global, national and local level. Globally, there has been a substantial reduction in child deaths; by 2013 the number of under-5-year deaths worldwide had declined from 12.7 million in 1990 to 6.3 million, an overall decline of 50.3%.c Progress has therefore been substantial, but insufficient to reach the MDG4 target of a two-thirds reduction in the under-5- mortality rate. However, it should also be noted that even though national under-5 mortality rates have been reduced in many countries, disparity in mortality rates between poor and rich has generally increased, and child deaths (and under-nutrition) are therefore becoming increasingly concentrated in the poorest and most deprived communities.d
c Statistics South Africa. Mortality and causes of death in South Africa, 2010: Findings from death notification. Pretoria: Statistics South Africa, 2013.
d You D, Jones G, Hill K, Wardlaw T, Chopra M. Levels and trends in child mortality, 1990-2009. Lancet. 2010;376:931-3.
49
Section A: Management Inpatients
In South Africa, under-5 mortality rates fell modestly during the early 1990s, but began rising in the mid-1990s. In 2003, South Africa was one of just five countries with under-5 mortality rates that were higher than those of 1990,e in all likelihood a consequence of the HIV epidemic impacting on under-5 mortality. However, this trend was reversed in the mid-2000s and child mortality rates fell rapidly between 2007 and 2011. The rates have since stabilised at levels of approximately 40 per 1 000 live births and 28 per 1 000 live births for under-5 and infant mortality respectively.f
The National Development Plan calls for a reduction in under-5 child mortality to below 30 per 1 000 live births by 2030.g This is well-aligned with Goal 3 of the Sustainable Development Goals, which focuses on “ensuring healthy lives and promoting wellbeing for all at all ages”. The specific child survival target is to end preventable deaths of newborns and under-5 children by 2030.
Three child health mortality indicators are reported below, namely the case fatality rates (CFRs) for diarrhoea, pneumonia and severe acute malnutrition (SAM) in children under 5 years of age. These conditions are important causes of preventable mortality in young children, and together with deaths in the newborn period and HIV infection, account for the majority of deaths in children under 5 years of age.
The CFR for the priority childhood illnesses (pneumonia, diarrhoea and SAM), is the proportion of all children under 5 years admitted to hospital with these conditions that die during the admission. High CFRs reflect poor case management following admission and/or late presentation of children with these conditions to health facilities. Conversely, a declining CFR suggests better management of children who present to health facilities with these conditions, and/or earlier presentation, i.e. children are less ill at the time of presentation and therefore more likely to respond to standard treatment.
It should be noted that there is a relationship between the number of deaths due to a condition and the number of admissions for that condition, with the two variables often increasing or declining in proportion to one another. For example, during a diarrhoea outbreak it is likely that both the number of admissions for diarrhoea and the number of deaths due to diarrhoea will increase. Because a stable CFR may mask substantial increases or declines in these variables, it is useful to monitor not only the CFRs, but also the number of admissions and deaths.
It should be noted that only deaths that occur in health facilities (almost exclusively hospitals) are captured, and that a high proportion of registered deaths occur outside of health facilities.h
Information on each of the three causes of mortality is provided separately, followed by combined discussion and recommendations.
3.6 Child under 5 years diarrhoea case fatality rate
The child under 5 years diarrhoea CFR has declined steadily since 2006/07 (see Table 1). This decline is continuing, with a national rate of 3.3% being reported during 2014/15. This was in line with the national target of less than 3.5%.
A total of 1 513 deaths from diarrhoea in children under 5 years was reported during 2014/15. It is encouraging to note that this represents a significant decline when compared with the numbers reported in 2013/14 (1 775 deaths). Furthermore, the number of deaths due to diarrhoea in children under 5 years has more than halved in the past eight years (from 3 228 in 2007/08 to 1 513 in 2014/15).
Following a substantial increase in the number of young children being admitted for diarrhoea between 2012/13 and 2013/14 (from 35 692 to 45 880), the number of admissions remained relatively constant in 2014/15 (45 787). The decline in the CFR therefore resulted from a decline in the number of deaths (unlike the situation in 2013/14 when the CFR declined despite an increase in the number of deaths).
e UNICEF. Levels and trends in child mortality report 2012: estimates developed by the UN Inter-agency Group for Child Mortality Estimation. New York: UNICEF, the World Health Organization, the World Bank, and the United Nations Population Division; 2012. Available at: http://www.childinfo.org/files/Child_Mortality_Report_2012.pdf [accessed 6 August 2015].
f Dorrington RE, Bradshaw D, Laubscher R, Nannan N. Rapid mortality surveillance report 2013. Cape Town: South African Medical Research Council; 2014.
g National Development Plan, 2030. Available at: http://www.gov.za/sites/www.gov.za.pdf [accessed 6 August 2015].
h Statistics South Africa. Mortality and causes of death in South Africa, 2013: Findings from death notification. Pretoria: Statistics South Africa, 2014.
50
Section A: Management Inpatients
Table 1: Diarrhoea admissions, deaths and case fatality rate in children under 5 years (nationally), 2007/08 – 2014/15
Provincial figures are shown in Table 2 and in Figure 20. Five provinces (Western Cape, Gauteng, KwaZulu-Natal, Northern Cape and North West) reported CFRs that were below the national target of 3.5%.
Table 2: Diarrhoea admissions, deaths and case fatality rate in children under 5 years by province, 2014/15
Mpumalanga and the Eastern Cape continued to report high rates (5.3% and 5.2% respectively). The situation in Mpumalanga is of particular concern as the province reported an increase in both the number of deaths (189 compared with 163) and the CFR (5.3% compared with 4.9%) (see Table 3). Case fatality rates declined in seven of the remaining provinces (Northern Cape experienced a modest increase). A similar pattern was evident with regard to the number of deaths, with Limpopo being the only other province to report a modest rise.
Three provinces (Gauteng, Limpopo and Mpumalanga) reported significant increases in the number of admissions, with the other provinces reporting modest declines. Notwithstanding the increase, the number of admissions in Gauteng remains low, suggesting that reporting may not be complete.
Table 3: Diarrhoea deaths and case fatality rate in children under 5 years by province, 2013/14 – 2014/15
The district rankings are shown in Figure 21 and Map 6. Thirty-one of the 52 districts reported CFRs below the national target of 3.5%. All six districts in the Western Cape but none of the three districts in Mpumalanga achieved the national target.
51
Section A: Management Inpatients
No deaths were reported in Overberg District (WC). Five additional districts, four in the Western Cape and one in the Free State (Xhariep), reported CFRs below 1%. OR Tambo District (EC) reported the highest CFR (9.6%).
Case fatality rates fell in 34 districts and increased in 15 districts, with Dr K Kaunda (NW) reporting the highest reduction (68%). The CFR more than doubled in two districts (Tshwane (GP) (from 0.7% to 1.8%) and Amajuba (KZN) (from 0.9% to 3.0%), although it should be noted that the CFRs in these districts were still below the national target of 3.5% (Figure 22).
Of the five worst-performing districts, three had improved rates when compared with those of the previous financial year. These were: OR Tambo (EC) (34.7% reduction), Mopani (LP) (45.1% reduction), and Alfred Nzo (EC) (23% reduction). Two of the remaining worst-performing districts experienced a significant increase in the CFR, namely, Mangaung (FS) (84% increase) and Mopani (LP) (45% increase).
Case fatality rates in non-metro districts were more than double those in metro districts (3.9% versus 1.9%). There was also a considerable gradient across socio-economic quintiles, with districts in SEQ1 reporting a CFR of 5.0% compared with 1.5% in districts in SEQ5 (Table 4). Just over one-third of all diarrhoeal deaths therefore occurred in districts in the lowest socio-economic quintile.
Table 4: Children under 5 admissions, deaths and case fatality rates for diarrhoea by socio-economic quintile, 2014/15
Districts with the highest numbers of deaths from diarrhoea are shown in Table 5. Diarrhoea accounted for 47.2% of all deaths in children under five in these districts.
Table 5: Districts with highest numbers of deaths from diarrhoea in children under 5 years of age, 2014/15
Child under 5 years diarrhoea case fatality rate by NHI district, 2014/15
Percentage
OR Tambo: DC15
T Mofutsanyana: DC19
G Sibande: DC30
uMzinyathi: DC24
Vhembe: DC34
Amajuba: DC25
uMgungundlovu: DC22
Pixley ka Seme: DC7
Tshwane: TSH
Dr K Kaunda: DC40
Eden: DC4
2 4 6 8 10
9.58
4.52
1.84
2.55
3.76
2.96
3.10
4.46
2.50
1.27
0.24
SA average: 3.3
Target: 3.5ProvincesECFSGPKZNLPMPNCNWWC
Child under 5 years diarrhoea case fatality rate by province, 2014/15
Percentage [Source: DHIS]
MP
EC
LP
FS
NW
NC
KZN
GP
WC
2 4 6 8 10
5.17
4.05
2.93
3.00
4.66
5.26
3.40
3.42
0.16SA average: 3.3
Target: 3.5 ProvincesECFSGPKZNLPMPNCNWWC
52
Section A: Management Inpatients
Figure 20: Child under 5 years diarrhoea case fatality rate by province, 2014/15
Map 6: Child under 5 years diarrhoea case fatality rate by district, 2014/15
Child under 5 years diarrhoea case fatality rate by district, 2014/15
Percentage [Source: DHIS]
OR Tambo: DC15Mopani: DC33
A Nzo: DC44Mangaung: MANBojanala: DC37
JT Gaetsewe: DC45Ehlanzeni: DC32
Frances Baard: DC9Capricorn: DC35
Johannesburg: JHBNkangala: DC31
uMkhanyakude: DC27Zululand: DC26
T Mofutsanyana: DC19G Sibande: DC30
C Hani: DC13Sekhukhune: DC47
uMzinyathi: DC24Waterberg: DC36uThungulu: DC28
NM Molema: DC38Ekurhuleni: EKUVhembe: DC34
Harry Gwala: DC43Amathole: DC12
Joe Gqabi: DC14Amajuba: DC25
West Rand: DC48uThukela: DC23
Lejweleputswa: DC18uMgungundlovu: DC22
Sedibeng: DC42Pixley ka Seme: DC7
Ugu: DC21Fezile Dabi: DC20
RS Mompati: DC39Buffalo City: BUF
iLembe: DC29Tshwane: TSH
eThekwini: ETHZF Mgcawu: DC8
S Baartman: DC10N Mandela Bay: NMA
Dr K Kaunda: DC40Central Karoo: DC5
Namakwa: DC6Xhariep: DC16
Eden: DC4Cape Winelands: DC2
West Coast: DC1Cape Town: CPT
Overberg: DC3
2 4 6 8 10
NHI
NHI
NHI
NHI
NHI
NHI
NHI
NHI
NHI
NHI
NHI
2.21
1.52
3.03
4.36
2.99
9.58
7.67
1.42
0.75
2.74
4.52
2.41
6.86
2.53
2.87
3.41
4.92
1.84
2.49
2.55
2.82
3.76
2.96
4.744.77
3.75
1.87
3.09
1.77
7.85
3.10
5.18
3.76
3.81
4.46
4.90
6.056.21
1.15
2.50
1.62
5.71
6.30
3.64
2.28
1.27
0.110.140.15
0.00
0.24
1.23
SA average: 3.3
Target: 3.5
ProvincesECFSGPKZNLPMPNCNWWC
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Figure 21: Child under 5 years diarrhoea case fatality rate by district, 2014/15
Annual trends: Child under 5 years diarrhoea case fatality rate
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EC A NzoEC AmatholeEC Buffalo CityEC C HaniEC Joe GqabiEC N Mandela BayEC OR TamboEC S BaartmanFS Fezile DabiFS LejweleputswaFS MangaungFS T MofutsanyanaFS Xhariep
GP EkurhuleniGP JohannesburgGP SedibengGP TshwaneGP West RandKZN AmajubaKZN eThekwiniKZN Harry GwalaKZN iLembeKZN UguKZN uMgungundlovuKZN uMkhanyakudeKZN uMzinyathi
KZN uThukelaKZN uThunguluKZN ZululandLP CapricornLP MopaniLP SekhukhuneLP VhembeLP WaterbergMP EhlanzeniMP G SibandeMP NkangalaNC Frances BaardNC JT Gaetsewe
NC NamakwaNC Pixley ka SemeNC ZF MgcawuNW BojanalaNW Dr K KaundaNW NM MolemaNW RS MompatiWC Cape TownWC Cape WinelandsWC Central KarooWC EdenWC OverbergWC West Coast
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Figure 22: Annual trends: Child under 5 years diarrhoea case fatality rate
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3.7 Child under 5 years pneumonia case fatality rate
The national average for the child under 5 years pneumonia CFR was 2.9% (Table 6). No official target was set for 2014/15; however, a national target of below 3% has been set for 2015/16. The pneumonia CFR has declined steadily since 2009/10.
Table 6: Pneumonia admissions, deaths and case fatality rate in children under 5 years (nationally), 2009/10 – 2014/15
Considerable variation is apparent in the number of hospital admissions for children with pneumonia, with a large increase being reported between 2012/13 and 2013/14, followed by a further substantial increase (to 48 383) in 2014/15.
The number of deaths has generally declined, with 1 411 deaths being reported in 2014/15. While this upward trend in admissions should be monitored, the continued decline in the number of deaths is encouraging.
Provincial data for 2014/15 are shown in Table 7 and Figure 23. Four provinces reported CFRs below 3%. These were the Western Cape (0.4%), Gauteng (2.1%), KwaZulu-Natal (2.7%) and Northern Cape (2.9%). Mpumalanga reported the highest CFR (5.2%), while the Eastern Cape and Limpopo both reported rates of 4.6%.
Table 7: Pneumonia admissions, deaths and case fatality rate in children under 5 years by province, 2014/15
Case fatality rates remained constant or improved in all provinces between 2013/14 and 2014/15 (see Table 8). The number of deaths decreased in all provinces, with the exception of Gauteng where a modest rise was reported. Four provinces (Eastern Cape, Gauteng, KwaZulu-Natal and Western Cape) reported substantial increases in the number of admissions.
Table 8: Pneumonia deaths and case fatality rate in children under 5 years by province, 2012/13 – 2014/15
Case fatality rates for districts are shown in Figure 24 and Map 7. Two districts (Xhariep (FS) and Central Karoo (WC)) reported no deaths from pneumonia, and an additional seven districts reported rates below 1%.
The highest rate was reported in Alfred Nzo (EC) (6.7%), with an additional eight districts reporting rates above 5%. These districts included an additional district from the Eastern Cape (OR Tambo), as well as two districts from both the Free State (Lejweleputswa and T Mofutsanyane) and Mpumalanga (Ehlanzeni and Nkangala), and one each from Limpopo (Mopani), Northern Cape (John Taolo Gaetsewe) and KwaZulu-Natal (uThungulu).
Case fatality rates fell in 31 districts and increased in 19 districts (Figure 25). Xhariep (FS) reported the highest reduction (100%), with significant reductions also being reported in Joe Gqabi (EC) (96%), West Rand (GP) (62%), uMzinyathi (KZN) (60%), Harry Gwala (KZN) (59%), Amathole (EC) (57%) and Dr K Kaunda (NW) (54%). The CFR increased significantly in the following districts: Cape Winelands (WC) (365%), Namakwa (NC) (149%), Lejweleputswa (FS) (91%) and Tshwane (GP) (72%).
Case fatality rates were much higher in non-metro districts than metro districts (3.4% versus 1.9% respectively). There was also a considerable gradient across socio-economic quintiles, with districts in SEQ1 reporting a CFR of 3.9% compared with 1.8% in districts in SEQ5 (Table 9).
Table 9: Admissions, deaths and case fatality rates in children under 5 years of age for pneumonia by socio-economic quintile, 2014/15
Districts with the highest numbers of deaths from pneumonia are shown in Table 10. Pneumonia accounted for 46.8% of all deaths in these districts among children under 5 years.
Table 10: Districts with highest numbers of deaths from pneumonia in children under 5 years of age, 2014/15
Figure 24: Child under 5 years pneumonia case fatality rate by district, 2014/15
Annual trends: Child under 5 years pneumonia case fatality rate
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EC A NzoEC AmatholeEC Buffalo CityEC C HaniEC Joe GqabiEC N Mandela BayEC OR TamboEC S BaartmanFS Fezile DabiFS LejweleputswaFS MangaungFS T MofutsanyanaFS Xhariep
GP EkurhuleniGP JohannesburgGP SedibengGP TshwaneGP West RandKZN AmajubaKZN eThekwiniKZN Harry GwalaKZN iLembeKZN UguKZN uMgungundlovuKZN uMkhanyakudeKZN uMzinyathi
KZN uThukelaKZN uThunguluKZN ZululandLP CapricornLP MopaniLP SekhukhuneLP VhembeLP WaterbergMP EhlanzeniMP G SibandeMP NkangalaNC Frances BaardNC JT Gaetsewe
NC NamakwaNC Pixley ka SemeNC ZF MgcawuNW BojanalaNW Dr K KaundaNW NM MolemaNW RS MompatiWC Cape TownWC Cape WinelandsWC Central KarooWC EdenWC OverbergWC West Coast
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Figure 25: Annual trends: Child under 5 years pneumonia case fatality rate
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3.8 Child under 5 years severe acute malnutrition case fatality rate
The child under 5 years severe acute malnutrition (SAM) case fatality rate (CFR) was 11.6% during 2014/15, which is well above the national target of 8%. During 2014/15 there was an increase in the number of children admitted with SAM as well as the number of deaths associated with SAM (see Table 11). A total of 1 852 children under 5 years were recorded as having died from SAM.
Trends in the number of children admitted with SAM, as well as the number of deaths, are shown in Table 11. Admissions remained relatively stable for the period 2009/10 through 2012/13, but increased in the last two financial years. Deaths due to SAM decreased substantially between 2009/10 and 2011/12; this decline was then reversed, with a slow increase for a number of years followed by a sharper rise during 2014/15.
It is difficult to determine the extent to which these increases reflect better identification and reporting of children with SAM, as opposed to real increases in the number of children admitted with and dying from severe malnutrition (see discussion below).
Table 11: SAM admissions, deaths and case fatality rate in children under 5 years (nationally), 2009/10 – 2014/15
Provincial data are shown in Table 12 and Figure 26. The Western Cape was the only province that achieved the national target of a CFR below 8%. The extremely high rate in Mpumalanga (19.1%) is of particular concern, while a further five provinces reported rates above 10%: Limpopo (14.9%), North West (12.3%), Free State (12.2%), Eastern Cape (11.8%) and KwaZulu-Natal (10.4%). The low number of admissions (1 350) and deaths (126) reported in Gauteng suggest that under-reporting may be a factor.
Table 12: SAM admissions, deaths and case fatality rate in children under 5 years by province, 2014/15
Six provinces reported an increase in the number of deaths in children admitted with SAM (table 13). This was again most marked in Mpumalanga, where the number of deaths increased from 144 to 233.
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Table 13: SAM deaths and case fatality rate in children under 5 years, 2012/13 – 2014/15
The district rankings are shown in Figure 27 and Map 8. No SAM deaths were reported in the West Coast, Overberg and Central Karoo districts in the WC, while the highest CFR (21.9%) was reported in Gert Sibande District (MP).
Only 11 of the 52 districts achieved the national target of a CFR below 8%. Four of these 11 districts were metros, and no districts in the Eastern Cape, Limpopo, Mpumalanga and North West reported a CFR for SAM of 8% or below.
Six districts reported CFRs of 20% or higher. These were: Gert Sibande (MP) (21.9%), Lejweleputswa (FS) (21.5%), Mopani (LP) (21.1%), Sekhukhune (LP) (20.7%), Fezile Dabi (FS) (20.5%), and Zululand (KZN) (20.3%).
Case fatality rates fell in 19 districts and increased in 30 districts (Figure 28). Eden (WC) reported the highest reduction (91%), with significant reductions also being reported in OR Tambo (EC) (47%), Capricorn (LP) (45%), Xhariep (FS) (42%), and Cape Town (WC) (39%). The CFR more than doubled in five districts. These were Pixley ka Seme (NC) (fivefold increase), Cape Winelands (WC) (275% increase), Tshwane (GP) (162% increase), iLembe (KZN) (110% increase), and Sarah Baartman (EC) (107% increase).
Case fatality rates in non-metro districts were much higher than in metro districts (12.6% versus 7.5% respectively). The rates were also higher in lower socio-economic quintiles (especially SEQ3) when compared with those in SEQ4 and SEQ5. The highest number of deaths (512 or 27.6% of the total) occurred in districts belonging to SEQ1 (Table 14).
Table 14: Admissions, deaths and case fatality rates in children under 5 years of age for SAM by socio-economic quintile, 2014/15
Districts with the highest numbers of death from SAM are shown in Table 15. Diarrhoea accounted for 43.6% of all deaths in these districts among children under 5 years.
Table 15: Districts with highest numbers of deaths from SAM in children under 5 years of age 2014/15
Table 17: Districts with highest number of deaths from diarrhoea, pneumonia and SAM in children under 5 years of age, 2014/15
Diarrhoea N Pneumonia N SAM NOR Tambo 173 OR Tambo 105 OR Tambo 119Ehlanzeni 95 Ehlanzeni 94 Ehlanzeni 111Mopani 86 eThekwini 71 Vhembe 86Alfred Nzo 57 Mopani 66 Gert Sibande 82Gert Sibande 54 Vhembe 65 NM Molema 81Zululand 52 uThungulu 53 Mopani 70Chris Hani 50 Nkangala 53 Alfred Nzo 69eThekwini 50 Nelson Mandela Bay 53 eThekwini 67Vhembe 50 Gert Sibande 51 uThungulu 64Capricorn 47 Ekurhuleni 50 Waterberg 58Total for 10 districts 714 661 807% of all deaths 47.2% 46.8% 43.6%
Twelve districts improved across all three case fatality indicators. These districts were: Xhariep (FS) (average reduction of 66%), Joe Gqabi (EC) (58% reduction), Dr K Kaunda (NW) (45% reduction), OR Tambo (EC) (42% reduction), Ugu (KZN) (33%), uMzinyathi (KZN) (32%), John Taolo Gaetsewe (NC) (29%), Waterberg (LP) (28%), Capricorn (LP) (25%), Zululand (KZN) (21%), Alfred Nzo (EC) (14%), and NM Molema (NW) (13%).
In contrast, five districts reported increases in CFRs for all three mortality indicators. These districts were: Pixley ka Seme (NC), Tshwane (GP), Amajuba (KZN), Johannesburg (GP), and uThungulu (KZN).
Figure 26: Child under 5 years severe acute malnutrition case fatality rate by province, 2014/15
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Map 8: Child under 5 years severe acute malnutrition case fatality rate by district, 2014/15
Child under 5 years severe acute malnutrition case fatality rate by district, 2014/15
Figure 27: Child under 5 years severe acute malnutrition case fatality rate by district, 2014/15
Annual trends: Child under 5 years severe acute malnutrition case fatality rate
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EC A NzoEC AmatholeEC Buffalo CityEC C HaniEC Joe GqabiEC N Mandela BayEC OR TamboEC S BaartmanFS Fezile DabiFS LejweleputswaFS MangaungFS T MofutsanyanaFS Xhariep
GP EkurhuleniGP JohannesburgGP SedibengGP TshwaneGP West RandKZN AmajubaKZN eThekwiniKZN Harry GwalaKZN iLembeKZN UguKZN uMgungundlovuKZN uMkhanyakudeKZN uMzinyathi
KZN uThukelaKZN uThunguluKZN ZululandLP CapricornLP MopaniLP SekhukhuneLP VhembeLP WaterbergMP EhlanzeniMP G SibandeMP NkangalaNC Frances BaardNC JT Gaetsewe
NC NamakwaNC Pixley ka SemeNC ZF MgcawuNW BojanalaNW Dr K KaundaNW NM MolemaNW RS MompatiWC Cape TownWC Cape WinelandsWC Central KarooWC EdenWC OverbergWC West Coast
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Figure 28: Annual trends: Child under 5 years severe acute malnutrition case fatality rate
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Discussion
While it is important to monitor facility-based mortality data, it must be remembered that a high proportion of deaths occur outside of health facilities and are therefore not reported above. Figures from Statistics South Africa suggest that only approximately one-third of deaths from diarrhoea and pneumonia in children are being recorded in the DHIS (see Table 18). This suggests that poor access to health facilities for severely ill children remains an important determinant of child mortality in South Africa.
While deaths that occur outside of health facilities will not be captured through the DHIS, incomplete and poor quality reporting by facilities also play a role. Until recently, more attention was paid to ensuring complete reporting of DHIS indicators by PHC facilities, than to the quality of reporting by hospitals, especially with regard to clinical (as opposed to managerial) indicators. This is especially true of large hospitals; underreporting from these hospitals may result in significant undercounting for the district and province as a whole. It would also appear that some facilities are not using the correct standard data items definitions.
Table 18: Data on deaths from diarrhoeal disease and pneumonia in children under 5 from Vital Registration data and DHIS data
It should be remembered that referral patterns may also skew CFRs as deaths may occur (and be recorded) in the referral district.
Notwithstanding concerns regarding completeness, the DHIS figures suggest that the number of children dying from diarrhoea and pneumonia continues to decline. The fact that this is not seen in the vital registration data may reflect improved vital registration data on cause of death, which allows a higher proportion of deaths to be ascribed to a specific cause.
The decline in deaths due to diarrhoea and pneumonia are likely to reflect three factors. Firstly, a reduction in the prevalence of HIV infection in young children as scale-up of prevention of mother-to-child transmission (PMTCT) has resulted in a substantial reduction in child deaths associated with HIV infection.m
Secondly, immunisation against pneumococcal and rotavirus infection (which respectively account for a high proportion of cases of pneumonia and diarrhoea in children) has contributed to declining mortality. Case control studies of both pneumococcaln and rotaviruso immunisations have shown substantial reductions in the likelihood of admission. High coverage with both vaccines has been achieved and programme effectiveness has been documented.p
The third factor relates to other improvements in child health and other services. These are the most difficult to measure and quantify, but include increased access to preventive and curative health services as indicated by an increase in the average number of visits to PHC facilities by children under 5 years of age. Likewise, the role played by improved access to income support and other basic services is difficult to quantify. The decline in CFRs associated with diarrhoea and pneumonia suggests that the quality of care provided to children in hospitals has also improved, and/or that children are less ill when accessing care and are therefore responding better to standard treatment.
i Statistics South Africa. Mortality and causes of death in South Africa, 2010: Findings from death notification. Pretoria: Statistics South Africa, 2013.
j Statistics South Africa. Mortality and causes of death in South Africa, 2011: Findings from death notification. Pretoria: Statistics South Africa, 2014.
k Statistics South Africa. Mortality and causes of death in South Africa, 2012: Findings from death notification. Pretoria: Statistics South Africa, 2014.
l Statistics South Africa. Mortality and causes of death in South Africa, 2013: Findings from death notification. Pretoria: Statistics South Africa, 2014.
m Kerber KJ, Lawn JE, Johnson LF, Mahy M, Dorrington RE, et al. South African child deaths 1990–2011: Have HIV services reversed the trend enough to meet Millennium Development Goal 4? AIDS. 2013;27(16):2637-48. doi:10.1097/01.aids.0000432987.53271.40.
n Cohen C, von Mollendorf C, Naidoo N, et al. Effectiveness of seven-valent pneumococcal conjugate vaccine against invasive pneumococcal disease in South Africa: A matched case-control study. Presented at the 8th International Symposium on Pneumococci and Pneumococcal Diseases; Iguaçu Falls, Brazil: 11-15 March 2012. Abstract 226.
o Groome MJ, Page N, Cortese MM, Moyes J, Zar HJ, Kapongo CN, et al. Effectiveness of monovalent human rotavirus vaccine against admission to hospital for acute rotavirus diarrhoea in South African children: A case-control study. Lancet Infect Dis. 2014;14:1096-104.
p Madhi SA, Bamford LJ, Ngcobo N. Effectiveness of pneumococcal conjugate vaccine and rotavirus vaccine introduction into the South African public immunisation programme. S Afr Med J. 2014;104(3 Suppl 1):228-34. DOI:10.7196/SAMJ.7597.
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However, it should be remembered that almost all deaths from diarrhoea are preventable, and the number of deaths remains far too high for a middle-income country such as South Africa.
While the number of deaths from diarrhoea and pneumonia appears to be declining, it is of great concern that more deaths from SAM are being reported and that the associated CFR has increased. Identification of children with SAM remains sub-optimal, and it is therefore difficult to know the extent to which increased numbers reflect better identification and reporting of SAM cases, as opposed to a true increase in the number of children with SAM. However, there is an urgent need to improve both identification and management of children with SAM throughout the country, and especially in districts and facilities with high CFRs and/or a high number of cases.
The reason for the increase in number of admissions for all three priority conditions is not immediately apparent. It may be due to a real increase in the number of cases, better access to health services, introduction of more appropriate admission criteria, or to better reporting. A real increase in the number of cases would appear unlikely (the reported incidence of pneumonia and diarrhoea have both declined over the past five years). It therefore is most likely to reflect better reporting of admissions. However, this should be monitored carefully.
While CFRs for diarrhoea and pneumonia have decreased in the majority of districts, the on-going disparities between metro and non-metro and across socio-economic quintiles are extremely concerning (see Table 19). This reflects both difficulties in accessing health facilities and poor quality of care in facilities in rural and disadvantaged districts.
Table 19: Case fatality rates for diarrhoea, pneumonia and SAM in children under 5 by socio-economic quintile, 2014/15
Socio-economic quintile 1 2 3 4 5 TotalAdmissions for diarrhoea (N) 10 464 8 935 7 251 5 028 14 109 45 787Deaths due to diarrhoea (N) 528 351 295 132 207 1 513Diarrhoea case fatality rate (%) 5.0 3.9 4.1 2.6 1.5 3.3Admissions for pneumonia (N) 8 232 9 249 7 212 6 115 17 575 48 383Deaths due to pneumonia (N) 325 339 282 154 311 1 411Pneumonia case fatality rate (%) 3.9 3.7 3.9 2.5 1.8 2.9Admissions for SAM (N) 3 874 3 695 2 962 2 065 3 314 15 910Deaths due to SAM (N) 512 456 429 210 245 1 852SAM case fatality rate (%) 13.2 12.3 14.5 10.2 7.4 11.6
Recommendations
The completeness and quality of data on child deaths and CFRs should be improved. The DHIS data items and indicators should be reviewed, and if necessary, updated. Mechanisms for ensuring that all facilities correctly record the necessary data, and that these are correctly collated and entered into the DHIS, also need to be strengthened.
High coverage of practices and interventions that promote early childhood development are critical in reducing the incidence of diarrhoea, pneumonia and SAM. Practices include improved infant feeding practices, especially exclusive breastfeeding (for six months) followed by introduction of high-quality complementary feeds, while health services need to ensure that all children are fully immunised and receive other routine services.
While falling CFRs suggest that the quality of care may have improved, attention still needs to be paid to ensuring that health care workers are able to identify and manage children correctly using standardised guidelines according to Integrated Management of Childhood Illness (IMCI) at primary health care level and the essential medicines list (EML) standard treatment guidelines at hospital level).
Districts with high CFRs and/or a high number of deaths should be prioritised, and progress should be monitored carefully. District clinical specialist teams are well placed to ensure that deficiencies are identified and addressed. All hospitals must conduct mortality audits and ensure that identified insufficiencies are addressed.
High CFRs in rural and disadvantaged districts underscore the role of socio-economic deprivation and inequity in child mortality. Further gains in reducing child mortality are likely to depend on successfully addressing the underlying social determinants of health, and will require stronger efforts to identify and provide support to the most vulnerable children.
