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

IndicatorBaseline (2008) Latest data (source) Target (2015) Life expectancy at birth (yrs) F52 M 51 F62 M60F62 M59 Neonatal mortality rate (per 1,000 live births) 3226 (TDHS 2010) 21.4 (UN 2012) 19 Infant mortality rate (per 1,000 live births) 5845 (Census 2012)50 U5 mortality rate (per 1,000 live births) 9481 (TDHS 2010) 54 (UN 2012) 48 Health status indicators

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

IndicatorBaseline (2008) Latest data (source)Target (2015) % U5 severely underweight3.70%TBD2.00% % U5 severely stunted38%42% (TDHS 2010) 35% (NPS 2011) 20% Total Fertility Rate (Census 2012) Trend Health status indicators

HEALTH SERVICE INDICATORS

Top five outpatient (OPD) diagnoses trends 2011 to 2013 using HMIS and SPDs <5 Years 5 and Above Health Management Information System (HMIS) <5 Years 5 and Above Sentinel panel Districts (SPDs)

Top five causes of admission (IPD diagnoses); HMIS and SPDs 2011 to 2013 <5 Years 5 and Above <5 Years 5 and Above Health Management Information System (HMIS) Sentinel panel Districts (SPDs)

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) <5 Years 5 and Above

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, Target = 1.0

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 0.66DDSM 0.66 Pwani 0.86 Lindi 0.74 Mtwara 0.66 Ruvuma 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 – – – – 1.0 > 1.0 Key

DTP3, Measles and TT2 vaccination coverage,

Mwanza 81% Geita 68 Simiyu 107% Shinyanga 96% Tabora 87% Singida 70% Dodoma 59% Iringa 77% = Morogoro 98% Manyara 71% DDSM 0.66DDSM 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% Key 0 – 39%

ANC early booking, 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 0.66DDSM 0.66 Pwani 16% Lindi 15% Mtwara 22% Ruvuma 56% Njombe 38% Mbeya 49% Rukwa 60% Katavi 68% Kigoma 45% Kilimanjaro 22% Arusha 23% Mara 37% Kagera 24% Tanga 40% DSM 13% Regional ANC 1 st visit before 12 weeks, 2013 National Average 35% 0 – 39% 40 – 49% 50 – 78% 80 – 100% > 100% Key

Health facility deliveries,

Mwanza 75% Geita 57% Simiyu 46% Shinyanga 66% Tabora 71% Singida 60% Dodoma 59% Iringa 74% = Morogoro 66% Manyara 32% DDSM 0.66DDSM 0.66 Pwani 85% Lindi 59% Mtwara 48% Ruvuma 78% 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 National Average 61% 0 – 39% 40 – 59% 60 – 79% 80 – 100% > 100% Key

Family planning coverage,

Mwanza 31% Geita 18% Simiyu 21% Shinyanga 32% Tabora 21% Singida 57% Dodoma 82% Iringa 44% = Morogoro 37% Manyara 31% DDSM 0.66DDSM 0.66 Pwani 69% Lindi 71% Mtwara 69% Ruvuma 78% 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 National Average 43% 0 – 39% 40 – 59% 60 – 79% 80 – 100% Key

ART coverage,

HIV prevalence.

TB and leprosy indicators

HEALTH SYSTEMS INDICATORS

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

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 0.66DDSM 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

Mwanza 7 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 0.66DDSM 0.66 Pwani 9.6 Lindi 8.3% Mtwara 6.5 Ruvuma 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 National Average – 4.9% 5.0 – 6.9% % >10 Key

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

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 0.66DDSM 0.66 Pwani 6.8 Lindi 8.1 Mtwara 7.2 Ruvuma 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 National Average 7.7

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