1. TRANSFORMING TABULAR DATA INTO VISUALIZATIONS
INTRAHEALTH INTERNATIONAL: STRENGTHENING HEALTH SYSTEMS & IMPROVING SERVICES IN TANZANIA USING DATA
TRANSFORMING TABULAR DATA INTO VISUALIZATIONS

2. OUTLINE BACKGROUND PROBLEM SOLUTION PROCESS CHALLENGES
WHERE WE ARE HEADED
PROPOSED WAY FORWARD

3. IntraHealth Tanzania portfolio 2011-2016
1.. Tanzania HIV Prevention Project (THPP)
2. TZ/Family Planning outreach Program
3. Better Immunization Data registry
4. Public Sector Systems Strengthening (PS3)
5. Tanzania Health Network Program
6. Tanzania Open data collaborative (dLab)
7. Tohara Plus
Technical program areas: HIV/AIDs , HRH, ICT, FP, GBV/VAC
Donors: PEPFAR/CDC, DFID, USAID, BMGF, PEPFAR/MCC

4. THPP Goal
Reduce the transmission of new HIV infections by supporting the MOHCDGEC/NACP to expand, strengthen and sustain the provision of optimum quality HTC, GBV/VAC and VMMC services for HIV prevention

5. PROBLEM: TRACKING CLIENTS DATA
Clients service data is tracked using tabular format.
This provides a very limited perspective on clients services.

6. SOLUTION 1. Graphs make it easy to view all data and make comparisons
GBV
HTC
VMMC

7. SOLUTION 2. Geospatial visualization help us better understand
Project coverage
the impact we make
Better ways to manage our resources
Adding a geospatial component to the tabular data is a necessity in understanding;
The distribution of clients in a region relative to the location of the nearest Health facility working with IntraHealth,
Finding the percentage of a local population that is being served by IntraHealth through VMMC and other Projects.
Assessing where unserved or underserved populations are located and identify additional health resources they need.

8. SOLUTION ON REACHING GAPS IDENTIFIED
A detailed geotagged Health Facilities list
Shapefiles
Population Data
Broken down to lowest administrative level such as a village
Program Data
HFR and NBS provide the data listed,
IH has the Program Data
Once these are combined (supported by dLab) we can then have the visualizated
Program Data

9. PROCESS UNDERTAKEN
Joining project data with population and geospatial data from the National Bureau of Statistics (NBS) and geotagged health facilities lists to have more complete picture.
Matched the health facilities to their geospatial coordinates paints a clear picture of where IntraHealth facilities are clustered.
Overlayed that information on a map that shows district level data.

12. Joining data from disparate sources can be challenging.
CHALLENGES
Joining data from disparate sources can be challenging.
Solution: Revisit data sharing processes and work with partners to have common standards and cleaning methods. Additional capacity building.
IntraHealth’s data has varied aggregated age bands, which keeps on changing overtime as per donors requirements.
Solution: Longer term approach is to address this for all data consumers by partnerships with government to provide de-identified, disaggregated data.
hence this precludes a direct comparison between services offered to certain population subsets year.

13. Locating populations that are underserved and reallocate resources.
WHERE WE ARE HEADED
Moving forward with understanding IntraHealth’s coverage compared to the total population.
Granular population (denominator) for advanced analysis.
Locating populations that are underserved and reallocate resources.
Incorporate resource allocation datasets.
Targeted outreach sensitization campaigns to reach the right places and right people.
Data will drive evidence-based advocacy and policy making.
Impact will be improvement in services at the lower-performing facilities.
and to the target number of beneficiaries, thereby allowing IntraHealth to quantitatively assess the performance of each facility;
, e.g. through increased staffing, extended facility hours, or services at new locations;

14. WAY FORWARD
Improve future data collection: Data use project needs complete critical analysis on how future data collection strategies could be tailored to create a more robust product in the next iteration.
Scale-up data partnerships for open data: Continue to intensify advocacy activities with respective ministries to ensure public data are collected in raw formats (not- dissagregated).

15. “It always seems impossible until it’s done” Thank you for listening!
Nelson Mandela
Thank you for listening!