AHSPPR FY 2013/14 highlights. Population denominators NBS has not yet published official projections However, we have Census 2012 data for: – Regions

AHSPPR FY 2013/14 highlights

Population denominators

• NBS has not yet published official projections• However, we have Census 2012 data for:– Regions and LGAs– Specific age groups (U1, U5, WRA)

• We also have official inter-censal growth rates for all regions (Census 2012, p2)

• We therefore used these to provide “best estimate” denominators pending the publication of official projections

HEALTH STATUS INDICATORS

Indicator Baseline (2008)

Latest data (source)

Target (2015)

Life expectancy at birth (yrs) F52 M 51 F62 M60 F62 M59

Neonatal mortality rate (per 1,000 live births)

32 26 (TDHS 2010)21.4 (UN 2012)

19

Infant mortality rate (per 1,000 live births)

58 45 (Census 2012) 50

U5 mortality rate (per 1,000 live births)

94 81 (TDHS 2010)54 (UN 2012)

48

Health status indicators

5

529

578

454 432

265

0

100

200

300

400

500

600

700

TDHS 1996 TDHS 2005 TDHS 2010 Census 2012 HSSP III Target2015

The trend in the Maternal Mortality per 100,000 Live Births

Indicator Baseline (2008)

Latest data (source) Target (2015)

% U5 severely underweight 3.70% TBD 2.00%

% U5 severely stunted 38% 42% (TDHS 2010)35% (NPS 2011)

20%

Total Fertility Rate 5.7 5.2 (Census 2012) Trend

Health status indicators

HEALTH SERVICE INDICATORS

Malaria ARI Pneumonia Diarrhoeal Diseases

Urinary tract Infection

05

10152025303540

Year 2011 Year 2012 Year 2013Malaria ARI Pneumonia Diarrhoeal

Diseases Urinary tract

Infection

05

101520253035404550

Year 2011 Year 2012 Year 2013

Malaria ARI Diarrheal Diseases

Pneumonia Intestinal worms

0

5

10

15

20

25

30

35


. Malaria ARI Diarrheal Diseases

Pneumonia Intestinal worms

0

5

10

15

20

25

30

35

40


Top five outpatient (OPD) diagnoses trends 2011 to 2013 using HMIS and SPDs

<5 Years 5 and Above

Health Management Information System (HMIS)


Sentinel panel Districts (SPDs)

Malaria ARI Pneumonia Diarrheal Diseases

Anaemia0

5

10

15

20

25

30

35

40

45


Malaria ARI Pneumonia Diarrheal Diseases

Anaemia0

5

10

15

20

25

30

35

40

45

50


Top five causes of admission (IPD diagnoses); HMIS and SPDs 2011 to 2013



Health Management Information System (HMIS)

Sentinel panel Districts (SPDs)


Pneumonia Anaemia0

5

10

15

20

25

30

35

40



Pneumonia Anaemia0

5

10

15

20

25

30

35

40

45


Malaria, severe HIV-AIDS Anaemia Pneumonia TB0

5

10

15

20

25


Malaria, severe HIV-AIDS Anaemia Pneumonia TB0

5

10

15

20

25

30

35

40


Top FIVE causes of deaths for persons aged under five and 5 years and above, HMIS (and SPD)

<5 Years 5 and Above Health Management Information

System (HMIS)

Sentinel Panel Districts (SPDs)


Malaria Pneumonia Anaemia Perinatal conditions

Diarrheal Diseases

0

5

10

15

20

25

30

35

40


Malaria Pneumonia Anaemia Perinatal conditions

Diarrheal Diseases

0

5

10

15

20

25

30

35

40

45


user pc

change years to 11,12,13

Conclusion

• No significant change in the proportions for the top three OPD diagnosis in three consecutive years. SPD data suggest reduction in the proportion of diagnosis of malaria in both under fives and five and years and above

• Malaria was consistently the leading cause of admission over the last three years, and by a great margin. Proportion of malaria among U5 decreased in 2013 compared with 2012 and 2011 (HMIS).

• Malaria, pneumonia and anaemia accounted for two thirds of reported U5 deaths in 2013 while HIV/AIDS, Malaria and TB account for 45% of deaths among 5 years and above

Per capita OP attendances, 2011 - 13

Target = 1.0

user pc

weka target

Mwanza 0.70 Geita 3.6 Simiyu 0.29

Shinyanga 0.57

Tabora 0.45

Singida 0.76

Dodoma 0.42

Iringa 0.71 =

Morogoro 0.91

Manyara 0.27

DDSM

0.66

Pwani 0.86

Lindi 0.74

Mtwara 0.66Ruvuma 0.43

Njombe 0.66

Mbeya 0.50

Rukwa 0.62

Katavi 0.74

Kigoma 1.54

Kilimanjaro 0.70

Arusha 0.54

Mara 0.76

Kagera 0.48

Tanga 0.90

DSM 0.69

Regional Per Capita OP attendances, all ages,

2013

National Average 0.65

0 – 0.39

0.4 – 0. 59

0.6 – 0.79

0.8 – 1.0

> 1.0

Key

Year 2008 Year 2011 Year 2012 Year 2013 Target 20150

20

40

60

80

100

120

91

98 99

84 85

92

101104

92

858588

9289

85

DPT3 Measles TT2

DTP3, Measles and TT2 vaccination coverage, 2011-13

Mwanza 81% Geita 68 Simiyu 107%

Shinyanga 96%

Tabora 87%

Singida 70%

Dodoma 59%

Iringa 77% =

Morogoro 98%

Manyara 71%

DDSM

0.66

Pwani 80%

Lindi 49%

Mtwara 52%Ruvuma 86%

Njombe 166%

Mbeya 97%

Rukwa 105%

Katavi 53%

Kigoma 73%

Kilimanjaro 51%

Arusha 78%

Mara 108%

Kagera 103%

Tanga 86%

DSM 74%

Regional TT2 vaccination

coverage, 2013

National Average 89%

40 – 59%

60 – 89%

90 – 100%

> 100%

Key0 – 39%

ANC early booking, 2011-13

Baseline (2008) 2011 2012 2013 Target (2015)0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

14%

45% 42%

35%

80%

Note: 2011 < 16 weeks; 2012 and 2013 < 12 weeks

Mwanza 40% Geita 34 Simiyu 29%

Shinyanga 17%

Tabora 23%

Singida 33%

Dodoma 11%

Iringa 74% =

Morogoro 107%

Manyara 24%

DDSM

0.66

Pwani 16%

Lindi 15%


Njombe 38%

Mbeya 49%

Rukwa 60%

Katavi 68%

Kigoma 45%

Kilimanjaro 22%

Arusha 23%

Mara 37%

Kagera 24%

Tanga 40%

DSM 13%

Regional ANC 1st visit before 12 weeks,

2013


0 – 39%

40 – 49%

50 – 78%

80 – 100%

> 100%

Key

Health facility deliveries, 2011-13

Baseline (TDHS 2004/05)

2011 2012 2013 Target (2015)0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

47%

62% 63% 61%

80%

Mwanza 75% Geita 57% Simiyu 46%

Shinyanga 66%

Tabora 71%

Singida 60%

Dodoma 59%

Iringa 74% =

Morogoro 66%

Manyara 32%

DDSM

0.66

Pwani 85%

Lindi 59%


Njombe 68%

Mbeya 68%

Rukwa 100%

Katavi 73%

Kigoma 57%

Kilimanjaro 55%

Arusha 57%

Mara 56%

Kagera 45%

Tanga 46%

DSM 55%

Regional facility deliveries, 2013


0 – 39%

40 – 59%

60 – 79%

80 – 100%

> 100%

Key

Family planning coverage, 2011-13

Baseline (TDHS 2004/05)

2011 2012 2013 Target (2015)0%

10%

20%

30%

40%

50%

60%

70%

20%

44% 42% 43%

60%

Mwanza 31% Geita 18% Simiyu 21%

Shinyanga 32%

Tabora 21%

Singida 57%

Dodoma 82%

Iringa 44% =

Morogoro 37%

Manyara 31%

DDSM

0.66

Pwani 69%

Lindi 71%


Njombe 68%

Mbeya 41%

Rukwa 43%

Katavi 40%

Kigoma 48%

Kilimanjaro 54%

Arusha 40%

Mara 41%

Kagera 38%

Tanga 61%

DSM 38%

Regional FP coverage, 2013


0 – 39%

40 – 59%

60 – 79%

80 – 100%

Key

ART coverage, 2011-13

2011 2012 20130%

10%

20%

30%

40%

50%

60%

70%

80%72%

63%67%

13%

23%29%

15+ <15

2003/04 2007/08 2011/120.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

7

5.85.3

Survey Year

HIV

Pre

vale

nce

(%)

HIV prevalence.

TB and leprosy indicators Indicator Baseline

(2008) 2011 2012 2013 Target

(2015) TB notification rate per 100,000 population 163 140 142 142 no Tuberculosis treatment success rate (%) 84.7 89 88 89 no The proportion of leprosy cases diagnosed and successful completed treatment pauci-bacillary (cohort registered receding yea) -%

95 93 95 96 97

The proportion of leprosy cases diagnosed and successful completed treatment multi-bacillary (cohort registered preceding 2 year) -%

92 95 94 93 95

HEALTH SYSTEMS INDICATORS

Per capita public spending, 2011/12 – 2013/14

2010/11

2011/12

2012/13

2013/14

2014/15

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

29,722 28,086 28,738

32,743 34,660

14,741 12,658 11,639 12,363 12,509

Per capita budget, TZS

nominal real

2010/11 2011/12 2012/13 2013/14 -

5,000

10,000

15,000

20,000

25,000

30,000

22,414 24,416 23,660

28,523

11,116 11,004 9,582

10,770

Per capita actual, TZS

nominal real

Mwanza 1.8% Geita 1.6% Simiyu 1.5%

Shinyanga 2.4%

Tabora 13.2% Singida

29.8%Dodoma 12.6%

Iringa 9.3% =

Morogoro 9.7%

Manyara 3.3%

DDSM

0.66

Pwani 15.8%

Lindi 4.7%

Mtwara 3%Ruvuma 9.9%

Njombe 9.7%

Mbeya 26.4%

Rukwa 12.2%

Katavi 13.8%

Kigoma 8.1%

Kilimanjaro

20.1%

Arusha 5.2%

Mara 2.7%

Kagera 1.3%

Tanga 14.1%

DSM 0%

Regional CHF coverage, 2013

National Average 8.7%

0 – 19%

20 – 39%

40 – 79%

80 – 100%

> 100%

Key

Mwanza7 Geita 3.1% Simiyu 2.5%

Shinyanga 4,9%

Tabora 2.9%

Singida 5.5

Dodoma 6.9

Iringa 11.3 =

Morogoro 7.9

Manyara 7.3%

DDSM

0.66

Pwani 9.6

Lindi 8.3%

Mtwara 6.5Ruvuma 7.2%

Njombe 10.9%

Mbeya 10.1

Rukwa 4.7%

Katavi 2.5%

Kigoma 3.3%

Kilimanjaro

14.8

Arusha 8.6

Mara 6

Kagera 5.2

Tanga 6.7

DSM 13

Human Resource (AMO, MO, Nurses/Nurse Midwife Laboratory staff) Per 10,000

Population by Region 2013


0 – 4.9%

5.0 – 6.9%

7.0 - 9.9%

>10

Key

Percentage of facilities with continuous availability of Tracer medicines, Jan-June 2014

Albendazo

le

Amoxycil

lin

Artemeth

er/Lu

merfan

trine o

ral

Depo-prove

ra

Disposab

le syr

inge

Ergometr

ineMRDT

Normal

saline

Oral re

hydrati

on

Pentav

alent v

accin

e0.65

0.7

0.75

0.8

0.85

0.9

83.1%

80.4%

84.4%82.6%

77.7%

80.7%

75.3%

82.7%

76.5%

88.4%

Mwanza 7.1 Geita 5.8 Simiyu 6.6

Shinyanga 6.7

Tabora 8.1

Singida 8.1

Dodoma 7.2

Iringa 8,1

Morogoro 8.5

Manyara 8.1

DDSM

0.66

Pwani 6.8

Lindi 8.1

Mtwara 7.2Ruvuma 7.8

Njombe 8.4

Mbeya 8.1

Rukwa 8.5

Katavi 8.2

Kigoma 7.1

Kilimanjaro

7.8

Arusha 8

Mara 7.8

Kagera 8

Tanga 8.4

DSM 7.4

Mean number of tracers available

January – June 2013


Challenges • Unsatisfactory quality of HMIS data– under-reporting and delayed reporting from

health facilities– Insufficient capacity for data

analysis/summarization at health facility level• Lack of reliable population denominators • Duplication of data collection through use of

parallel reporting systems• Inadequate data dissemination and use

Way forward• Strengthening of supportive supervision and mentoring of regions

and councils• Quarterly analysis of HMIS data and review by the M&E TWG to

identify data problems/issues and find out solution• Implement data quality audit activities• Establish a way for regular communication with regions to feed

back and discuss the identified data quality issues• Harmonization of reporting systems for all programmes to prevent

duplications and improve quality• Implement activities that will improve data dissemination and use• Strengthen capacity for data collection, compilation at HF level

and use of DHIS database

Documents

AHSPPR FY 2013/14 highlights. Population denominators NBS has not yet published official projections However, we have Census 2012 data for: – Regions