Implementation of Xpert MTB/RIF in Uganda: Missed Opportunities to Improve Diagnosis of Tuberculosis

Hanrahan, Colleen F.; Haguma, Priscilla; Ochom, Emmanuel; Kinera, Irene; Cobelens, Frank; Cattamanchi, Adithya; Davis, J. Lucian; Katamba, Achilles; Dowdy, David W.

doi:10.1093/ofid/ofw068

Cited by 56 publications

(60 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our study confirms that Xpert utilization was poor among community health centers in Uganda linked to [8,9]. A study in Malawi evaluating the impact of Xpert roll-out on TB diagnosis and treatment showed only a small contribution to case detection.…”

Section: Discussionsupporting

confidence: 80%

Challenges with scale-up of GeneXpert MTB/RIF® in Uganda: a health systems perspective

Nalugwa

Shete

Nantale

et al. 2020

BMC Health Serv Res

Self Cite

View full text Add to dashboard Cite

Background: Many high burden countries are scaling-up GeneXpert® MTB/RIF (Xpert) testing for tuberculosis (TB) using a hub-and-spoke model. However, the effect of scale up on reducing TB has been limited. We sought to characterize variation in implementation of referral-based Xpert TB testing across Uganda, and to identify health system factors that may enhance or prevent high-quality implementation of Xpert testing services. Methods: We conducted a cross-sectional study triangulating quantitative and qualitative data sources at 23 community health centers linked to one of 15 Xpert testing sites between November 2016 and May 2017 to assess health systems infrastructure for hub-and-spoke Xpert testing. Data sources included a standardized site assessment survey, routine TB notification data, and field notes from site visits. Results: Challenges with Xpert implementation occurred at every step of the diagnostic evaluation process, leading to low overall uptake of testing. Of 2192 patients eligible for TB testing, only 574 (26%) who initiated testing were referred for Xpert testing. Of those, 54 (9.4%) were Xpert confirmed positive just under half initiated treatment within 14 days (n = 25, 46%). Gaps in required infrastructure at 23 community health centers to support the huband-spoke system included lack of refrigeration (n = 14, 61%) for sputum testing and lack of telephone/mobile communication (n = 21, 91%). Motorcycle riders responsible for transporting sputum to Xpert sites operated variable with trips once, twice, or three times a week at 10 (43%), nine (39%) and four (17%) health centers, respectively. Staff recorded Xpert results in the TB laboratory register at only one health center and called patients with positive results at only two health centers. Of the 15 Xpert testing sites, five (33%) had at least one non-functioning module. The median number of tests per day was 3.57 (IQR 2.06-4.54), and 10 (67%) sites had error/invalid rates > 5%. Conclusions: Although Xpert devices are now widely distributed throughout Uganda, health system factors across the continuum from test referral to results reporting and treatment initiation preclude effective implementation of Xpert testing for patients presenting to peripheral health centers. Support for scale up of innovative technologies should include support for communication, coordination and health systems integration.

show abstract

Section: Discussionsupporting

confidence: 80%

Challenges with scale-up of GeneXpert MTB/RIF® in Uganda: a health systems perspective

Nalugwa

Shete

Nantale

et al. 2020

BMC Health Serv Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…Xpert have failed to improve the efficiency of TB testing. Together these data suggest that current methods for decentralizing Xpert testing using a hub-and spoke model for decentralization fail to increase both uptake of Xpert testing and improvements in process metrics such as time to treatment initiation for TB patients, thereby contributing to the low impact of Xpert in high-burden settings.Our findings are in line with other data from Uganda reporting overall low utilization rates for Xpert testing, with only about 20% of eligible patients at community health centers receiving Xpert, and less than 2% being referred for Xpert testing as a first-line test[4,11]. A study in Malawi evaluating the impact of Xpert roll-out on TB diagnosis and treatment showed only a small contribution to case detection.…”

supporting

confidence: 90%

Challenges with Scale-up of GeneXpert MTB/RIF® inUganda: a Health Systems Perspective

Nalugwa

Shete

Nantale

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Many high burden countries are scaling-up GeneXpert MTB/RIF (Xpert) testing for tuberculosis (TB) using a hub-and-spoke model. However, the effect of scale up on reducing TB has been limited. We sought to characterize variation in implementation of referral-based Xpert TB testing across Uganda, and to identify health system factors that may enhance or prevent high-quality implementation of Xpert testing services. Methods: We conducted a cross-sectional study triangulating quantitative and qualitative data sources at 23 community health centers linked to one of 15 Xpert testing sites between November 2016 and May 2017 to assess health systems infrastructure for hub-and-spoke Xpert testing. Data sources included a standardized site assessment survey, routine TB notification data, and field notes from site visits. Results: Challenges with Xpert implementation occurred at every step of the diagnostic evaluation process, leading to low overall uptake of testing. Of 2,192 patients eligible for TB testing, only 574 (26%) who initiated testing were referred for Xpert testing. Of those, 54 (9.4%)were Xpert confirmed positive just under half initiated treatment within 14 days (n=25, 46%). Gaps in required infrastructure at 23 community health centers to support the hub-and-spoke system included lack of refrigeration (n=14, 61%) for sputum testing and lack of telephone/mobile communication (n=21, 91%). Motorcycle riders responsible for transporting sputum to Xpert sites operated variable with trips once, twice, or three times a week at 10 (43%), nine (39%) and four (17%) health centers, respectively. Staff recorded Xpert results in the TB laboratory register at only one health center and called patients with positive results at only two health centers. Of the 15 Xpert testing sites, five (33%) had at least one non-functioning module. The median number of tests per day was 3.57 (IQR 2.06-4.54), and 10 (67%) sites had error/invalid rates >5%. Conclusions: Although Xpert devices are now widely distributed throughout Uganda, health system factors across the continuum from test referral to results reporting and treatment initiation preclude effective implementation of Xpert testing for patients presenting to peripheral health centers. Support for scale up of innovative technologies should include support for communication, coordination and health systems integration.

show abstract

“…22 Data were collected at 18 rural and urban health facilities from 2012–2014, selected to be representative of both geography and health system level: one national referral center (not included in this analysis), four regional referral centers, nine district hospitals, and four sub-district health centers. Xpert had been implemented in seven sites (plus the national referral center) at the time of evaluation, whereas the remaining 10 sites had laboratories capable of performing sputum smear microscopy.…”

Section: Methodsmentioning

confidence: 99%

“…22 Laboratory results and treatment data for 100 consecutive patients who provided sputum samples were also abstracted at each site in order to establish linkages between test results and treatment initiation. In order not to underestimate the cost-effectiveness of Xpert in diagnosing smear-negative TB, we assumed that Xpert tests run without a specific reason recorded were performed for the purpose of diagnosing TB over other reasons (e.g., treatment monitoring).…”

Section: Methodsmentioning

confidence: 99%

Higher cost of implementing Xpert<SUP>®</SUP> MTB/RIF in Ugandan peripheral settings: implications for cost-effectiveness

Hsiang

Little

Haguma

et al. 2016

int j tuberc lung dis

View full text Add to dashboard Cite

SUMMARY Setting Initial cost-effectiveness evaluations of Xpert MTB/RIF (Xpert) for tuberculosis (TB) diagnosis have not fully accounted for realities of implementation in peripheral settings. Objective We evaluated costs and diagnostic outcomes of Xpert testing implemented at various healthcare levels in Uganda. Design We collected empirical cost data from five health centers utilizing Xpert for TB diagnosis, employing an ingredients approach. We reviewed laboratory and patient records to assess outcomes in these sites and ten sites without Xpert. We also estimated incremental cost-effectiveness of Xpert testing; our primary outcome was incremental cost of Xpert testing per newly detected TB case. Results The mean unit cost of an Xpert test was US$21 based on a mean monthly volume of 54 tests per site, though unit cost varied widely (US$16–58) and was primarily determined by testing volume. Total diagnostic costs were 2.4-fold higher in Xpert clinics compared to non-Xpert clinics, though Xpert only increased diagnoses by 12%. Diagnostic costs of Xpert averaged US$119 per newly detected TB case but were as high as US$885 in the lowest-volume center. Conclusion Xpert testing can detect TB cases at reasonable cost but may double diagnostic budgets for relatively small gains, with cost-effectiveness deteriorating with lower testing volumes.

show abstract

Implementation of Xpert MTB/RIF in Uganda: Missed Opportunities to Improve Diagnosis of Tuberculosis

Cited by 56 publications

References 16 publications

Challenges with scale-up of GeneXpert MTB/RIF® in Uganda: a health systems perspective

Challenges with scale-up of GeneXpert MTB/RIF® in Uganda: a health systems perspective

Challenges with Scale-up of GeneXpert MTB/RIF® inUganda: a Health Systems Perspective

Higher cost of implementing Xpert<SUP>®</SUP> MTB/RIF in Ugandan peripheral settings: implications for cost-effectiveness

Contact Info

Product

Resources

About