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

  • BMC Infectious Diseases, Volume 18, No. 1, Year 2018

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Study Justification:
– The study aims to address the problem of underreporting and underdiagnosis of tuberculosis (TB) cases in Kenya, which contributes to a significant number of missed cases globally.
– Understanding the spatial distribution and patterns of use of TB diagnostic tests can help identify gaps in diagnosis and improve TB case detection.
– The study also aims to assess adherence to guidelines for TB diagnostic tests, specifically the use of Xpert MTB/RIF®, and identify factors that influence its use.
Study Highlights:
– In 2015, 82,313 TB cases were notified in Kenya, with 7.8% being children.
– There were gaps in adherence to guidelines for the use of Xpert® in both children and adults.
– The study found variability in the spatial distribution of TB diagnostic test facilities across the 47 counties in Kenya.
– Retreatment cases, HIV-positive individuals, and patients in the public sector had higher odds of receiving an Xpert® test.
– Private sector and higher-level hospitals had a tendency towards lower odds of using Xpert®.
Study Recommendations:
– Further research is needed to develop strategies that enhance the use of TB diagnostics, including innovations to improve access and overcome local barriers to the adoption of guidelines and technologies.
– Efforts should be made to improve adherence to guidelines for the use of Xpert® in both children and adults.
– Consideration should be given to increasing the availability and distribution of TB diagnostic test facilities, particularly in areas with lower access.
Key Role Players:
– Kenya National TB Programme
– County governments
– Public, private, non-profit non-governmental organizations (NGOs), and faith-based organizations (FBOs)
– Health facilities at different levels of care (primary, secondary, tertiary)
Cost Items for Planning Recommendations:
– Expansion and maintenance of TB diagnostic test facilities
– Training and capacity building for healthcare workers
– Development and implementation of strategies to improve access to diagnostics
– Research and development of innovative approaches to enhance diagnostic use
– Monitoring and evaluation of diagnostic test utilization and adherence to guidelines

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some areas for improvement. The study provides data from the 2015 Kenya National TB program and uses hierarchical regression models to establish determinants of use of Xpert®. The study identifies gaps in guideline adherence and under-use of Xpert® in children. However, the abstract does not provide information on the sample size or representativeness of the data, which could be improved. Additionally, the abstract does not mention any limitations of the study, which should be addressed to strengthen the evidence.

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.

Kenya is administratively divided into 47 counties, that are now largely responsible for health care since devolution in 2013 [29]. Health services are provided by public, private, non-profit non-governmental organisations (NGO) and faith based organisations (FBO). The healthcare system is structured in a hierarchical manner, starting with primary healthcare in the community and complicated cases referred upwards to secondary and tertiary levels of healthcare [29]. According to the Kenyan Master Facility List, there are approximately 10,000 health facilities in the country and just about half are TB treatment sites [30–32]. For this analysis, we aggregated TB health facilities into two: lower level (dispensaries, health centres, and maternity/nursing homes) for primary care; and higher level (county hospitals and national referral facilities) that provide secondary and tertiary referral services. We included patients of all ages who were notified to the Kenya National TB programme and started TB treatment in 2015. We wished to explore the possible influence on use of Xpert MTB/RIF® of variables in three hierarchical levels: county, health facility and individual. Individual level co-variates included: age; gender; HIV status; nutritional status; and type of TB patient i.e. new/transfer-in or retreatment cases (relapse/defaults/treatment failures). An age cut-off of 15yrs for children is used locally and internationally for TB programming [14], therefore “child” here refers to 0-14yr olds. For nutrition status, weight-for-age Z (WAZ) scores were computed for those aged 0-23yr and body mass index (BMI) truncated at -5 to +5SD for those ≥10yr, and patients classified as underweight according to the scores. Those 10-23yr who met criteria of underweight by either criterion were also classified as underweight. Health facility level co-variates included: sector of care (public, private, or FBOs), level of care provided (higher vs lower level) and whether they were an Xpert® site or not. County level co-variates included: poverty; maternal education levels; travel time to nearest health facilities; and availability of Xpert® facilities per 100,000. All co-variates were determined for each of the 47 counties for 2015. These factors were decided upon a priori following review of literature on drivers of use of TB and health care services in general and data availability [21, 22]. The primary outcome of interest was evidence of Xpert® being done in patients who had been started on TB treatment. According to the 2015 Kenyan guidelines, all presumptive paediatric, HIV-infected smear negative, drug resistant (DR) cases, or retreatment cases i.e. relapse (recurrence)/defaults/treatment failures should have had at least one Xpert® assay as part of their diagnostic work up (Additional file 1) [15, 16]. We considered the best data sources for the three levels of variables of interest. For the individual level, de-identified data from the Kenya National TB Programme patients’ treatment register for 2015 were used (patients who were notified and/or started treatment in 2015). TB case definitions were as per Guidance for National TB programmes (Additional file 1) [33]. Health facility level data were from the Kenya Master Health Facility list [30] and Kenya Services Availability and Readiness Assessment Mapping Report (SARAM) 2014 [34]. Health facilities in the national TB register were geocoded using KEMRI-Wellcome Trust’s Kenya Health Facilities Database, which was last updated in 2016 using online digital place-name gazetteers and Global Positioning System (GPS) sources. County level data were from each county governments’ integrated development plans for 2015 [35]. The projected 2015 Kenya gridded population distribution surface at 100m spatial resolution was obtained from the WorldPop project [36, 37]. Stata version 15MP (StataCorp.2017, College Station, TX, USA) and ArcGIS 10.5 (ESRI, Redlands, CA, USA) were used for statistical analysis, and mapping and spatial analysis respectively. We described the proportion of adults and children reported to the TB programme, their socio-demographic characteristics, use of TB diagnostic tests and outcomes. We used the 2015 Kenya National TB programme data to construct maps for each TB case notification rate (CNR) in different counties and linked this with their capacity to perform diagnostic tests (chest x-rays, smear microscopy, Xpert®, culture and line probe assay) from the SARAM report [34]. For adherence to guidelines, we only had data for those who had tests done, and used these patients to describe patterns of use of TB diagnostic tests. Co-variates of theoretical and/or statistical significance were used to build hierarchical logistic regression models to establish determinants of use of Xpert® in adults and children. Possible collinearity was assessed using the variance inflation factor (VIF). Variables with VIF less than 10 were considered for analysis. The models converged at five integration points for complete case analysis, with county and health facility as random effects [38]. Models were built for the 0-14yrs and ≥15yrs separately and a model for the total population, with likelihood ratio tests, exploration for interactions in pre-specified covariates (HIV and nutrition status) and quantile-quantile plots of residuals used to determine best fit as seen in Additional file 2 [38, 39].

Based on the provided information, here are some potential innovations that could improve access to maternal health:

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women with information on prenatal care, nutrition, and maternal health services. These apps can also send reminders for appointments and medication adherence.

2. Telemedicine: Implement telemedicine services to enable pregnant women in remote areas to access prenatal consultations and check-ups through video conferencing with healthcare providers.

3. Community Health Workers: Train and deploy community health workers to provide basic prenatal care, education, and support to pregnant women in underserved areas. These workers can also facilitate referrals to higher-level healthcare facilities when necessary.

4. Transportation Solutions: Develop innovative transportation solutions, such as mobile clinics or ambulances, to ensure that pregnant women can easily access healthcare facilities for prenatal care, delivery, and postnatal care.

5. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to cover the costs of maternal health services, including prenatal care, delivery, and postnatal care.

6. Public-Private Partnerships: Foster collaborations between public and private healthcare providers to improve access to maternal health services. This can involve leveraging private sector resources and expertise to expand healthcare infrastructure and services.

7. Health Information Systems: Implement robust health information systems that enable the collection, analysis, and sharing of maternal health data. This can help identify gaps in service delivery and inform evidence-based decision-making for improving access to maternal health.

8. Capacity Building: Invest in training and capacity building programs for healthcare providers, particularly in underserved areas, to enhance their skills and knowledge in providing quality maternal healthcare.

9. Maternal Health Education: Develop comprehensive maternal health education programs that target women, families, and communities. These programs can raise awareness about the importance of prenatal care, nutrition, and safe delivery practices.

10. Quality Assurance Mechanisms: Establish quality assurance mechanisms, such as accreditation and certification programs, to ensure that healthcare facilities providing maternal health services meet established standards of care.

It is important to note that the specific context and needs of the target population should be considered when implementing these innovations.
AI Innovations Description
Based on the provided description, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Develop strategies to enhance the use of diagnostic tests: Based on the findings of the study, there is under-use and gaps in adherence to guidelines for the use of diagnostic tests, particularly Xpert MTB/RIF. To improve access to maternal health, it is important to develop strategies that promote the use of diagnostic tests, including Xpert MTB/RIF. This could involve training healthcare providers on the importance and proper use of these tests, as well as raising awareness among pregnant women about the availability and benefits of these tests.

2. Implement innovations to improve access: The study highlights the variability in spatial distribution of TB diagnostic facilities across the 47 counties in Kenya. To address this issue, innovative solutions can be developed to improve access to maternal health services. For example, the use of specimen referral systems could be explored, where samples can be collected at primary healthcare facilities and transported to higher-level facilities for testing. This would help overcome geographical barriers and ensure that pregnant women in remote areas have access to timely and accurate diagnostic tests.

3. Overcome barriers to adoption of guidelines and technologies: The study also identifies factors such as sector of care (public, private, or faith-based organizations) and level of care provided (higher vs lower level) that influence the use of Xpert MTB/RIF. To improve access to maternal health, it is important to overcome these barriers and promote the adoption of guidelines and technologies across all healthcare sectors and levels of care. This could involve providing training and support to healthcare providers, as well as addressing any financial or logistical challenges associated with the implementation of new technologies.

Overall, by developing strategies to enhance the use of diagnostic tests, implementing innovations to improve access, and overcoming barriers to adoption of guidelines and technologies, access to maternal health can be significantly improved in Kenya.
AI Innovations Methodology
The study you provided focuses on the variability in the distribution and use of tuberculosis diagnostic tests in Kenya. While the study does not directly address maternal health, it does provide insights into the healthcare system in Kenya, which can be used to inform recommendations for improving access to maternal health. Here are some potential innovations and a brief methodology to simulate their impact on improving access to maternal health:

1. Mobile Clinics: Implementing mobile clinics equipped with essential maternal health services, such as prenatal care, antenatal check-ups, and delivery assistance, can improve access to maternal healthcare in remote or underserved areas. These clinics can travel to different locations, reaching women who may not have easy access to healthcare facilities.

2. Telemedicine: Utilizing telemedicine technology can enable pregnant women to consult with healthcare professionals remotely, reducing the need for physical visits to healthcare facilities. This can be particularly beneficial for women in rural areas who may have limited access to healthcare facilities.

3. Community Health Workers: Training and deploying community health workers who can provide basic maternal health services, education, and support within their communities can help bridge the gap in access to maternal healthcare. These workers can conduct home visits, provide health education, and assist with referrals to healthcare facilities when necessary.

Methodology to simulate the impact of these recommendations:

1. Define the target population: Identify the specific population group that will benefit from the innovations, such as pregnant women in rural areas or underserved communities.

2. Collect baseline data: Gather data on the current access to maternal health services in the target population, including the number of healthcare facilities, distance to the nearest facility, utilization rates, and health outcomes.

3. Develop a simulation model: Create a simulation model that incorporates the innovations being considered, such as mobile clinics, telemedicine, or community health workers. The model should consider factors such as the number and distribution of the innovations, their capacity to provide services, and the potential increase in access to maternal health services.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the impact of the innovations on improving access to maternal health. Vary the parameters, such as the number of mobile clinics or the coverage of telemedicine services, to explore different scenarios.

5. Analyze results: Analyze the simulation results to determine the potential impact of the innovations on access to maternal health services. Assess indicators such as the increase in the number of women accessing prenatal care, the reduction in travel time to healthcare facilities, and improvements in health outcomes.

6. Refine and validate the model: Refine the simulation model based on the analysis of the results and validate it using real-world data. This will help ensure the accuracy and reliability of the model’s predictions.

7. Make recommendations: Based on the simulation results, make recommendations for implementing the innovations that have the highest potential for improving access to maternal health. Consider factors such as feasibility, cost-effectiveness, and sustainability.

It’s important to note that the methodology provided is a general framework and may need to be adapted based on the specific context and available data. Additionally, involving stakeholders, such as healthcare professionals, policymakers, and community members, in the simulation process can help ensure that the recommendations are practical and aligned with the needs of the target population.

Statistics:
Citations: 9
Authors: 9
Identifiers:
DOI: 10.1186/s12879-018-3237-z
Research Areas:
Community Interventions, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants, Technology and Innovations
Study Countries:
Kenya
Study Design:
Cross Sectional Study
Study Approach:
Quantitative
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