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Fred Nsubuga
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ABOUT THE FELLOWDr. Fred Nsubuga holds a Bachelor’ degree in Medicine and Surgery (MBChB) from Makerere University College of Health Sciences, a Master of Public Health from the Uganda Christian University Mukono and a post graduate diploma in Public Administration and management from UMI. Fred has over 8 years of experience in management of patients and 5 years in health services management. Prior to joining the Public Health Fellowship Program, Fred worked as the District Health Officer of Buvuma District Local Government, senior medical officer at Buvuma Health Center 1V and medical officer Kitgum region for Marie stopes Uganda. During his fellowship training, Fred was posted to the Uganda National Expanded Program on Immunization (UNEPI) which is responsible for ensuring that the Ugandan population is free of vaccine-preventable diseases. While at UNEPI, Fred was involved in many program activities including supplemental and national immunization campaigns, review of national guidelines, outbreak investigations of vaccine preventable diseases, he led a study that compared static and outreach immunization strategies in Uganda. He also conducted a study that assessed factors that affect immunization data quality in Kabarole district. Fred presented at both national and international conferences and got an award as a second runner up for oral presentation during the AFENET conference in Abuja. As a result of the fellowship program, Fred has enhanced his communication skills, leadership and management, outbreak investigation, and use of Epi Infor software in data analysis. Fred’s future plan is to be a vaccine preventable disease (VPD) epidemiologist. In summary the fellowship program has improved his analytical, leadership, programming, communication and outbreak investigation skills. Achievements at the host site
Program-specific achievements (key deliverables)
Summary of Planned study:Title: Factors that affect immunization data quality in Kabarole district, Uganda, July 2016 Introduction: Reliable and timely immunization data is of great value if it is to inform decisions at all levels and improve program performance. Inadequate data quality may impair our understanding of the true vaccination coverage and may hinder our capability to meet the program objectives. It is therefore important that data quality is regularly assessed to ensure good performance, sound decision making and efficient use of resources. Analysis of national immunization data from January-April 2015 showed that Kabarole district had inconsistent diphtheria, pertussis, tetanus, hepatitis and heamophilus influenzae type b (DPT3-HepBHib) vaccination coverage. The cause of this data discrepancy in this district was not known. This study therefore sought to establish sources of immunization data quality gaps and establish factors that influence immunization data quality in Kabarole district. Methods: This was a cross-sectional mixed methods study that was conducted from 9th– 16th July, 2016 in 49 health centers that provide immunization services in Kabarole District. Data were collected using a structured questionnaire which we administered personally. The verification factor was estimated by dividing the recounted DPT3<1 year by reported DPT3<1 year. The quality of data collection processes was measured using quality indices for the 3 different components (recording practices, storage and reporting practices, monitoring and evaluation). These indices were applied to the different levels of the health care service delivery. Quality Index score was estimated by dividing the total question or observation correctly answered by the total number of answers/ observations of a component as the denominator. Results:The 2015 data quality audit conducted in Kabarole District found that the majority of our respondents were nursing assistants 32% (16/50) and enrolled nurses 26% (13/50). All the health centers’ reports were timely between January and June and from November to December. The timeliness and reporting rates remained above 60% between August and October. The mean health center verification factor was 87±27. Sixty five percent (32/49) of the health centers had consistent data, 27% (13/49) over reported and 4% (2/49) underreported. The factors that affect immunization data quality under the data dimension include; arithmetic errors 20% (10/49) and inability to have a single view of immunization data 53% (26/49). The other items used for tallying immunization data included exercise books and plain papers. Quality index (QI) scores varied at all levels of health service delivery. Mean QI for the 49 health centers that conduct immunization was 61% ±26. The factors that affected the data collection process were: Recording component; omission of tally sheet data into HMIS reports 29% (14/49), irregular update of vaccine and injection material control book (VIMCB) 22% (11/49), storing/ reporting; poor storage practices like lack of designated storage place, lack of files for keeping records, tally sheets not arranged in order, limited of access to records because incharge has moved with the key 6% (3/49), missing tally sheets 27% (13/49), monitoring and evaluation; inability to classify target population according to immunization strategy 100%, catchment area maps not displayed 61% (30/49), graphs showing coverage and drop out rates not displayed 41% (20/49), involvement of the community during planning rare done 4% (2/49). There was a weak positive correlation between the health center verifaction factor and quality index though this was not statistically significant (r=0.014; p=0.92). Conclusion: There were two sources of immunization data quality gaps namely; data dimension and data collection processes. The factors that contributed to inadequate data quality included arithmetic errors, inability to have a single view of immunization data, missing tally sheet, irregular update of VIMCB, lack of designated storage place, lack of files for keeping records, tally sheets not arranged in order, limited access to records because in-charge is away and inability to classify target population according to immunization strategy We recommend that subsequent immunization data quality audits address the two sources of data quality gaps. Lessons learned, key skills/ competenciesFred says, the fellowship program has epitomized to him the importance of team work, leadership and good managerial skills. He says, if these are not well nurtured, achievement of results becomes a nightmare. Key skills/competences acquired, and next steps
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