National Institute for Medical Research 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 National Institute for Medical Research www.nimr.or.tz

National Institute for Medical Research www.nimr.or.tz 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 National Institute for Medical Research www.nimr.or.tz

National Institute for Medical Research www.nimr.or.tz 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 National Institute for Medical Research www.nimr.or.tz

National Institute for Medical Research www.nimr.or.tz Objective To determine the quality of routine HMIS focusing on outpatient (OPD) and antenatal care (ANC) services in Tanzania National Institute for Medical Research www.nimr.or.tz

National Institute for Medical Research www.nimr.or.tz 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 National Institute for Medical Research www.nimr.or.tz

National Institute for Medical Research www.nimr.or.tz Method 1/2 Figure 1: Map of Tanzania indicating study districts National Institute for Medical Research www.nimr.or.tz

National Institute for Medical Research www.nimr.or.tz Methods 3/3 Data collection Documents, system and database reviews between 2014-2017 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 National Institute for Medical Research www.nimr.or.tz

National Institute for Medical Research www.nimr.or.tz 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 National Institute for Medical Research www.nimr.or.tz

National Institute for Medical Research www.nimr.or.tz Results 2/5 District wise performance Less than half of the expected data were found in Dodoma (median=45%, IQR: 0.25-0.51) and Kinondoni (median=46%, IQR: 0.41-0.5) districts Data were mostly found in rural districts with about 75% available in Hai (median=0.75, IQR: 0.67-0.82), 74% in Igunga (median=0.74, IQR: 0.56-0.81) National Institute for Medical Research www.nimr.or.tz

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)

National Institute for Medical Research www.nimr.or.tz Results 4/6 II. Data accuracy Table 2: Data accuracy as indicated by difference ratio for the ANC service area National Institute for Medical Research www.nimr.or.tz

National Institute for Medical Research www.nimr.or.tz Results 5/6 II. Data accuracy Table 3: Data accuracy as indicated by difference ratio for the OPD service area National Institute for Medical Research www.nimr.or.tz

National Institute for Medical Research www.nimr.or.tz 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 National Institute for Medical Research www.nimr.or.tz

National Institute for Medical Research www.nimr.or.tz 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 National Institute for Medical Research www.nimr.or.tz

National Institute for Medical Research Acknowledgement National Institute for Medical Research www.nimr.or.tz

National Institute for Medical Research www.nimr.or.tz Thank you National Institute for Medical Research www.nimr.or.tz