Variability in distribution and use of tuberculosis diagnostic tests in Kenya: a cross-sectional survey.

BACKGROUND: Globally, 40% of all tuberculosis (TB) cases, 65% paediatric cases and 75% multi-drug resistant TB (MDR-TB) cases are missed due to underreporting and/or under diagnosis. A recent Kenyan TB prevalence survey found that a significant number of TB cases are being missed here. Understanding spatial distribution and patterns of use of TB diagnostic tests as per the guidelines could potentially help improve TB case detection by identifying diagnostic gaps. METHODS: We used 2015 Kenya National TB programme data to map TB case notification rates (CNR) in different counties, linked with their capacity to perform diagnostic tests (chest x-rays, smear microscopy, Xpert MTB/RIF®, culture and line probe assay). We then ran hierarchical regression models for adults and children to specifically establish determinants of use of Xpert® (as per Kenyan guidelines) with county and facility as random effects. RESULTS: In 2015, 82,313 TB cases were notified and 7.8% were children. The median CNR/100,000 amongst 0-14yr olds was 37.2 (IQR 20.6, 41.0) and 267.4 (IQR 202.6, 338.1) for ≥15yr olds respectively. 4.8% of child TB cases and 12.2% of adult TB cases had an Xpert® test done, with gaps in guideline adherence. There were 2,072 microscopy sites (mean microscopy density 4.46/100,000); 129 Xpert® sites (mean 0.31/100,000); two TB culture laboratories and 304 chest X-ray facilities (mean 0.74/100,000) with variability in spatial distribution across the 47 counties. Retreatment cases (i.e. failures, relapses/recurrences, defaulters) had the highest odds of getting an Xpert® test compared to new/transfer-in patients (AOR 7.81, 95% CI 7.33-8.33). Children had reduced odds of getting an Xpert® (AOR 0.41, CI 0.36-0.47). HIV-positive individuals had nearly twice the odds of getting an Xpert® test (AOR 1.82, CI 1.73-1.92). Private sector and higher-level hospitals had a tendency towards lower odds of use of Xpert®. CONCLUSIONS: We noted under-use and gaps in guideline adherence for Xpert® especially in children. The under-use despite considerable investment undermines cost-effectiveness of Xpert®. Further research is needed to develop strategies enhancing use of diagnostics, including innovations to improve access (e.g. specimen referral) and overcoming local barriers to adoption of guidelines and technologies.

Accuracy of giant African pouched rats for diagnosing tuberculosis: comparison with culture and Xpert® MTB/RIF.

SETTING: Enhanced tuberculosis (TB) case finding using detection rats in Tanzania. OBJECTIVES: To assess the diagnostic accuracy of detection rats compared with culture and Xpert® MTB/RIF, and to compare enhanced case-finding algorithms using rats in smear-negative presumptive TB patients. DESIGN: A fully paired diagnostic accuracy study in which sputum of new adult presumptive TB patients in Tanzania was tested using smear microscopy, 11 detection rats, culture and Xpert. RESULTS: Of 771 eligible participants, 345 (45%) were culture-positive for Mycobacterium tuberculosis, and 264 (34%) were human immunodeficiency virus (HIV) positive. The sensitivity of the detection rats was up to 75.1% (95%CI 70.1-79.5) when compared with culture, and up to 81.8% (95%CI 76.0-86.5) when compared with Xpert, which was statistically significantly higher than the sensitivity of smear microscopy. Corresponding specificity was 40.6% (95%CI 35.9-45.5) compared with culture. The accuracy of rat detection was independent of HIV status. Using rats for triage, followed by Xpert, would result in a statistically higher yield than rats followed by light-emitting diode fluorescence microscopy, whereas the number of false-positives would be significantly lower than when using Xpert alone. CONCLUSION: Although detection rats did not meet the accuracy criteria as standalone diagnostic or triage testing for presumptive TB, they have additive value as a triage test for enhanced case finding among smear-negative TB patients if more advanced diagnostics are not available.

The potential of a multiplex high-throughput molecular assay for early detection of first and second line tuberculosis drug resistance mutations to improve infection control and reduce costs: a decision analytical modeling study.

BACKGROUND: Molecular resistance detection (MRD) of resistance to second-line anti-tuberculous drugs provides faster results than phenotypic tests, may shorten treatment and allow earlier separation among patients with and without second-line drug resistance. METHODS: In a decision-analytical model we simulated a cohort of patients diagnosed with TB in a setting where drug resistant TB is highly prevalent and requires initial hospitalization, to explore the potential benefits of a high-throughput MRD-assay for reducing potential nosocomial transmission of highly resistant strains, and total costs for diagnosis of drug resistance, treatment and hospitalization. In the base case scenario first-line drug resistance was diagnosed with WHO-endorsed molecular tests, and second-line drug resistance with culture and phenotypic methods. Three alternative scenarios were explored, each deploying high-throughput MRD allowing either detection of second-line mutations in cultured isolates, directly on sputum, or MRD with optimized markers. RESULTS: Compared to a base case scenario, deployment of high-throughput MRD reduced total costs by 17-21 %. The period during which nosocomial transmission may take place increased by 15 % compared to the base case if MRD had currently reported suboptimal sensitivity and required cultured isolates; increased by 7 % if direct sputum analysis were possible including in patients with smear-negative TB, and reduced by 24 % if the assay had improved markers, but was still performed on cultured isolates. Improved clinical sensitivity of the assay (additional markers) by more than 35 % would be needed to avoid compromising infection control. CONCLUSIONS: Further development of rapid second-line resistance testing should prioritize investment in optimizing markers above investments in a platform for direct analysis of sputum.

Risk factors for excess mortality and death in adults with tuberculosis in Western Kenya.

OBJECTIVES: To evaluate excess mortality and risk factors for death during anti-tuberculosis treatment in Western Kenya. METHODS: We abstracted surveillance data and compared mortality rates during anti-tuberculosis treatment with all-cause mortality from a health and demographic surveillance population to obtain standardised mortality ratios (SMRs). Risk factors for excess mortality were obtained using a relative survival model, and for death during treatment using a proportional hazards regression model. RESULTS: The crude mortality rate during anti-tuberculosis treatment was 18.0 (95%CI 16.8-19.2) per 100 person-years. The age and sex SMR was 8.8 (95%CI 8.2-9.4). Excess mortality was greater in human immunodeficiency virus (HIV) positive TB patients (excess hazard ratio [eHR] 2.1, 95%CI 1.5-3.1), and lower in patients who were female or started treatment in a later year. Mortality was high in patients with unknown HIV status (HR 2.9, 95%CI 2.2-3.8) or, if HIV-positive, not on antiretroviral treatment (ART; HR 3.3, 95%CI 2.5-4.5) or not known to be on ART (HR 2.8, 95%CI 2.1-3.7). The attributable fraction of incomplete uptake of HIV testing and ART on mortality was 31% (95%CI 15-45) compared to HIV-positive patients on ART. CONCLUSION: Increasing the uptake of HIV testing and ART would further reduce mortality during anti-tuberculosis treatment by an estimated 31%.

Measuring socio-economic data in tuberculosis prevalence surveys.

Addressing social determinants in the field of tuberculosis (TB) has received great attention in the past years, mainly due to the fact that worldwide TB incidence has not declined as much as expected, despite highly curative control strategies. One of the objectives of the World Health Organization Global Task Force on TB Impact Measurement is to assess the prevalence of TB disease in 22 high-burden countries by active screening of a random sample of the general population. These surveys provide a unique opportunity to assess socio-economic determinants in relation to prevalent TB and its risk factors. This article describes methods of measuring the socio-economic position in the context of a TB prevalence survey. An indirect measurement using an assets score is the most feasible way of doing this. Several examples are given from recently conducted prevalence surveys of the use of an assets score, its construction, and the analyses of the obtained data.

Modelling local and global effects on the risk of contracting Tuberculosis using stochastic Markov-chain models.

For some diseases, the transmission of infection can cause spatial clustering of disease cases. This clustering has an impact on how one estimates the rate of the spread of the disease and on the design of control strategies. It is, however, difficult to assess such clustering, (local effects on transmission), using traditional statistical methods. A stochastic Markov-chain model that takes into account possible local or more dispersed global effects on the risk of contracting disease is introduced in the context of the transmission dynamics of tuberculosis. The model is used to analyse TB notifications collected in the Asembo and Gem Divisions of Nyanza Province in western Kenya by the Kenya Ministry of Health/National Leprosy and Tuberculosis Program and the Centers for Disease Control and Prevention. The model shows evidence of a pronounced local effect that is significantly greater than the global effect. We discuss a number of variations of the model which identify how this local effect depends on factors such as age and gender. Zoning/clustering of villages is used to identify the influence that zone size has on the model's ability to distinguish local and global effects. An important possible use of the model is in the design of a community randomised trial where geographical clusters of people are divided into two groups and the effectiveness of an intervention policy is assessed by applying it to one group but not the other. Here the model can be used to take the effect of case clustering into consideration in calculating the minimum difference in an outcome variable (e.g. disease prevalence) that can be detected with statistical significance. It thereby gauges the potential effectiveness of such a trial. Such a possible application is illustrated with the given time/spatial TB data set.

Evaluation of TB and HIV services prior to introducing TB-HIV activities in two rural districts in western Kenya.

SETTING: Health facilities providing tuberculosis (TB) treatment in two districts in rural western Kenya with a high TB and human immunodeficiency virus (HIV) burden. OBJECTIVE: To evaluate TB and HIV/acquired immune-deficiency syndrome (AIDS) services at the facilities and identify barriers to providing quality diagnostic HIV testing and counseling (DTC) and HIV treatment for TB patients in anticipation of the introduction of TB-HIV collaborative services. METHODS: We performed a standard interview with health workers responsible for TB care, inspected the facilities and collected service delivery data. A self-administered questionnaire on training attended was given to all health workers. Results were shared with stakeholders and plans for implementation were developed. RESULTS: Of the 59 facilities, 58 (98%) provided TB treatment, 19 (32%) offered sputum microscopy and 24 (41%) HIV testing. Most facilities (72%) advised HIV testing only if TB patients were suspected of having AIDS. Barriers identified included unaccommodating TB clinic schedules and lack of space, which was an obstacle to holding confidential discussions. The need to refer for HIV testing and/or HIV care was a perceived barrier to recommending these services. Activities implemented following the assessment aimed 1) to provide HIV testing and cotrimoxazole prophylaxis at all TB treatment clinics, 2) to increase availability of HIV treatment services, and 3) to address structural needs at each facility. CONCLUSION: This evaluation identified barriers to the implementation of HIV testing and care services within facilities providing TB treatment.