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The 7th EAHSC Quality of Routine Health Management Information System Data in Tanzania: a case for outpatient and antenatal care data
               Susan F. Rumisha1, Emanuel Peter1, Irene R. Mremi1,2, Patrick K. Tungu1, Victor Mwingira1, Doris Mbata1, Sia Malekia1 & Leonard E.G. Mboera2
Presenter; Emanuel Peter B.Pharm, MPH, (PhD candidate)
1National Institute for Medical Research, Dar es Salaam, Tanzania
2SACIDS-Foundation for One Health, Sokoine University of Agriculture, Morogoro, Tanzania
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Background 1/2
Routine Health Management Information System (HMIS)
A set of health services, epidemiological, administrative and financial data collected at health facilities
Quality health information
Monitoring, evaluating and improving the delivery of health care services and programmes
Low-and-middle income countries
have challenges with data quality, including incomplete records, inconsistency and untimely reporting
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Background 2/2
Underperformance of HMIS in Sub-Saharan
low knowledge, computation errors, negligence, inadequate staff and skills, low motivation, lack of incentives
Low utilisation of the generated data by decision and policy makers
Tanzania
Several studies have reported challenges in HMIS. However, few studies have focused on verification and data quality of the system in recent years
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Objective
To determine the quality of routine HMIS focusing on outpatient (OPD) and antenatal care (ANC) services in Tanzania
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Method 1/3
Design: A cross sectional study design combined quantitative and qualitative approaches
Sampling: Multistage sampling of regions and districts from eight zones
Random sampling of health centers, dispensaries except Regional hosp. & district hosp.
Sample size
1-3 regions per zones
1 district per region
1 regional or district hosp.
2-4 health centers
10-20% of dispensaries
Total size was 115 health facilities in 11 districts
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Method 1/2
Figure 1: Map of Tanzania indicating study districts
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Methods 3/3
Data collection
Documents, system and database reviews between
In-depth interview with staff
Done between Oct. and Nov. 2017
5 Indicators from OPD and 8 from ANC were used.
Data source
Registers, Tally sheets, Monthly reports at HF
Monthly reports at District level
Electronic source (DHIS2)
Data analysis
By data availability, completeness, accuracy, usability and accessibility
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Results 1/6
I: Availability
Majority 58.26% (n=67) of health facilities were dispensaries, 31.3% (n=36)% health centres and 0.43% (n=12) were hospitals
Owned by government (56.5%), by faith-based organizations (27%) and private institutions (16.5%).
About 114 HMIS tools for OPD, 108 for ANC were reviewed
About 99% of HFs had OPD tools and 95% had had tools for ANC
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Results 2/5
District wise performance
Less than half of the expected data were found in Dodoma (median=45%, IQR: ) and Kinondoni (median=46%, IQR: ) districts
Data were mostly found in rural districts with about 75% available in Hai (median=0.75, IQR: ), 74% in Igunga (median=0.74, IQR: )
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10. Results 3/6
Table 1: Status of data tools availability by different attributes (n = 45)
Attribute
Categories
Median
%(n)
IQR
p-value
Year
2014
0.8 (10)
(0[0],1 [12])
<0.001§
Registers
0.9
(0.2,1)
Tally Sheets
0.4
(0,1)
Report forms
0.8
2015
1.0(12)
(0.5[6],1[12])
1.0
(0.8,1)
(0.3,1)
2016
(0.83[10],1[12])
(0.9,1)
2017*
1.0(9)
(0.78[7],1[9])
(0.6,1)

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Results 4/6
II. Data accuracy
Table 2: Data accuracy as indicated by difference ratio for the ANC service area
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Results 5/6
II. Data accuracy
Table 3: Data accuracy as indicated by difference ratio for the OPD service area
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Results 6/6
III. Completeness
Non-adherence to recording procedure
Empty cells
For OPD
-Height and weight, diagnosis/results cells most common left blank
-Sex and age occasionally left blank
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Conclusion
Notable improvement of HMIS data quality observed
To make HMIS an effective tool the persisting challenges need attention.
Electronic tools for receiving reports at the district level
SOPs for the Management of Routine Health Information
Job aids with simple instructions could be developed and utilized
A culture of use of evidence for decision making should be emphasized at all levels of the health system
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Acknowledgement
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Thank you
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