Using quality improvement to improve the utilisation of GeneXpert testing at five lab hubs in Northern Uganda

Karamagi, Esther; Nturo, Joseph; Donggo, Pamela; Kyobutungi, Isabel; Aloyo, Judith; Sensalire, Simon; Rahimzai, Mirwais

doi:10.1136/bmjoq-2017-000201

Cited by 4 publications

(4 citation statements)

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“…Measuring direct yield would be the first step to assess the outcome of this QI-ACF intervention [20]. Monthly adjusted notification data from DHIS2 and data on GeneXpert utilization in Northern Uganda [12] indicated that case notifications substantially increased during the intervention period as compared to historical baseline cases in intervention and non-intervention sites. The positive improvements in case notification in intervention sites over the intervention period attributed to QI-ACF imply that replicating the same model could lead to increased numbers of TB cases diagnosed.…”

Section: Discussionmentioning

confidence: 99%

“…Quality improvement (QI) is an approach which identifies and addresses failures in the health system that result in poor patient outcomes by actively engaging health care workers in analyzing gaps in care processes, testing changes to close the gaps, and monitoring the results from implementing the tested changes [11]. In diverse settings, including Uganda, QI has been shown to improve health outcomes, including GeneXpert utilization for detection of TB [11, 12]. Given the different contexts and approaches for TB response, successful interventions need to be documented and compared to inform policies and practice.…”

Section: Introductionmentioning

confidence: 99%

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Improving TB case notification in northern Uganda: evidence of a quality improvement-guided active case finding intervention

Karamagi

Sensalire

Muhire

et al. 2018

BMC Health Serv Res

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BackgroundStrategies to identify and treat undiagnosed prevalent cases that have not sought diagnostic services on their own, are necessary to treat TB in patients earlier and interrupt transmission. Late presentation for medical services of symptomatic patients require special efforts to detect early and notify TB in high risk populations. An intervention that combined quality improvement with facility-led active case finding (QI-ACF) was implemented in 10 districts of Northern Uganda with the highest TB burden to improve case notification among populations at highest risk of TB.MethodsUsing QI-ACF intervention approach in 48 facilities, we; 1) targeted key vulnerable populations, 2) engaged district and facility teams in TB systems strengthening, 3) conducted systematic screening and diagnosis in vulnerable groups (people living with HIV, fishing communities, and prisoners), and 4) trained health workers on national x-ray diagnosis guidelines for smear-negative patients. Facility-led QI-ACF meant that health care providers identified the target population, mobilized and massively screened suspects, and addressed gaps in documentation. Chest X-ray diagnosis was promoted for smear-negative TB among those suspects whose sputum examination was negative. The effect of the intervention on case notification was then assessed separately over the post intervention period.ResultsOver all TB case notification in the intervention districts increased from 171 to 223 per 100,000 population between the baseline months of October–December 2016 and end line month of April–June 2017. TB patient contacts had the majority of TB positive cases identified during active case finding (40, 6.1%). Fishing communities had the highest TB positivity rate at 6.8%. Prisoners accounted for the lowest number of TB positive cases at 34 (2.3%).ConclusionTargeting should be applied at all levels of TB intervention to improve yield: targeting districts and facilities with the lowest rates of case notification and targeting index patient contacts, HIV clients, and fishing communities. Screening tools are useful to guide health workers to identify presumptive cases. Efforts to improve availability of x-ray for TB diagnosis contributed to almost half of the new cases identified. Having all HIV patients who were eligible for viral load provide sputum for TB screening proved easy to implement.Electronic supplementary materialThe online version of this article (10.1186/s12913-018-3786-2) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving TB case notification in northern Uganda: evidence of a quality improvement-guided active case finding intervention

Karamagi

Sensalire

Muhire

et al. 2018

BMC Health Serv Res

Self Cite

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“…Although some notable work has been accomplished to apply QI concepts and methods to TB care [36], [37], [38], [39], [40], [41], [42], these efforts have remained limited in scale and with minimal attention paid to capacity building for ongoing QI implementation, leaving their sustainability beyond the few facilities or districts in which they are implemented an open question. Experiences from HIV QI implementation in settings with workforce shortages and frequent staff rotations point to the importance of developing models, curricula, and standards whose scalability is planned from the outset, and whose intended targets for QI capacity building span facility, district, and national cadres, as well as the public and private sectors [43].…”

Section: Investment In Capacity Building and System Strengtheningmentioning

confidence: 99%

Implementing quality improvement in tuberculosis programming: Lessons learned from the global HIV response

Ikeda

Basenero

Murungu

et al. 2019

Journal of Clinical Tuberculosis and Other Mycobacterial Diseas

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The quality of care and treatment for tuberculosis (TB) is a major barrier in global efforts to end TB as a global health emergency. Despite a growing recognition of the need to measure, assure, and improve quality of TB services, implementation of quality improvement (QI) activities remains limited. Applying principles of systems thinking, continuous measurement, and root cause analysis, QI represents a proven approach for identifying and addressing performance gaps in healthcare delivery, with demonstrated success in low- and middle-income settings in the areas of HIV/AIDS, maternal, newborn, and child health, and infection control, among others. Drawing from lessons learned in the development of QI programming as part of the global response to HIV, we review key enablers to implementation that may assist NTPs in turning aspirations of high-quality service delivery into action. Under the umbrella of a formal quality management (QM) program, NTPs’ attention to planning and coordination, commitment to tracking key processes of care, investment in QI capacity building, and integration of TB QI activities within efforts to advance universal health coverage provide a framework to sustainably implement QI activities.

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“…5 As a result, tuberculosis programmes are accumulating but not using the data being collected to drive decision making. This becomes apparent when one considers the various challenges still hindering tuberculosis programmes today, including gross under-utilisation of instruments, 3,6,7,8 high unsuccessful test and error rates (loss of tests), 9,10 cartridge stock-outs, instrument breakdowns, and lack of adequate module replacements and maintenance of instruments. 11 There is a dire need to build capacity in health data analytical skills amongst staff within national tuberculosis programmes (NTPs) in order to bolster the usage of data.…”

Section: Introductionmentioning

confidence: 99%

Driving the usage of tuberculosis diagnostic data through capacity building in low- and middle-income countries

Gous¹,

Nyaruhirira²,

Cunningham³

et al. 2020

Afr J Lab Med

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Background Connectivity platforms collect a wealth of data from connected GeneXpert instruments, with the potential to provide valuable insights into the burden of disease and effectiveness of tuberculosis programmes. The challenge faced by many countries is a lack of training, analytical skills, and resources required to understand and translate this data into patient management and programme improvement. Objective We describe a novel training programme, the tuberculosis Data Fellowship, designed to build capacity in low- and middle- income countries for tuberculosis data analytics. Methods The programme consisted of classroom and remote training plus mentorship over a 12-month period. The focus was on skills development in Tableau software, followed by training in exploration, analysis, and interpretation of GeneXpert tuberculosis data across five key programme areas: patient services, programme monitoring, quality of testing, inventory management, and disease burden. Results The programme was piloted in six countries (Bangladesh, Ethiopia, Ghana, Malawi, Mozambique) in July 2018 and Nigeria in September 2018; 20 participants completed the training. A number of key outputs have been achieved, such as improved instrument utilisation rates, decreased error rates, and improved instrument management. Conclusion The training programme empowers local tuberculosis programme staff to discover and fix critical inefficiencies, provides high-level technical and operational support to the tuberculosis programme, and provides a platform for continued sharing of insights and best practices between countries. It supports the notion that connectivity can increase efficiencies and clinical benefits with better data for decision making, if coupled with commensurate capacity building in data analysis and interpretation.

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Using quality improvement to improve the utilisation of GeneXpert testing at five lab hubs in Northern Uganda

Cited by 4 publications

References 1 publication

Improving TB case notification in northern Uganda: evidence of a quality improvement-guided active case finding intervention

Improving TB case notification in northern Uganda: evidence of a quality improvement-guided active case finding intervention

Implementing quality improvement in tuberculosis programming: Lessons learned from the global HIV response

Driving the usage of tuberculosis diagnostic data through capacity building in low- and middle-income countries

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