Screening tuberculosis patients for diabetes mellitus in a routine program setting in Kampala, Uganda: a cross-sectional study

Introduction

Although significant progress has been achieved in tuberculosis (TB) care and prevention during the past decades, TB remains a major public health problem and is responsible for more deaths than any other single infectious disease worldwide¹. In 2017 globally, 10.0 million people developed TB and 1.6 million died from it¹. Uganda is among the 30 high TB and human immunodeficiency virus (HIV) burden countries, with an estimated TB prevalence of 253 per 100,000 population (95% CI: 196-317) and 40% HIV co-infection rate^2,3.

Along with socio-economic development, urbanization, dietary and lifestyle changes, the prevalence of diabetes mellitus (DM) is escalating in most low- and middle-income countries. Available data suggests that an estimated 425 million people worldwide live with DM and by 2045 this number will grow to 629 million⁴. Although estimates for sub-Saharan Africa are based on limited data with substantial uncertainty, DM appears to be increasing rapidly in many urban centres in the region⁵. In Uganda, some risk factors for DM, such as obesity, were greater among persons of a high socio-economic status in rural areas, though the prevalence of DM in the adult population of the entire country was relatively low (1.4% in a nationwide cross-sectional survey in 2014)^6,7.

DM is a well-known risk factor for TB and increases its risk 2-3-fold^8,9. Systematic reviews and meta-analyses suggest that DM patients with TB experience worse treatment outcomes compared with patients without DM, with delayed sputum smear conversion, increased risk of drug resistance, treatment failure, death and relapse after successful completion of anti-TB treatment^8–12. On the other hand, TB patients with previously known DM are at an increased risk of hyperglycemia or worse glycaemic control^13,14. Therefore, early detection of DM among TB patients and proper management of both conditions are well known measures to improve treatment outcomes in these patients.

To address this problem in low- and middle-income countries, the World Health Organization (WHO) and the International Union Against Tuberculosis and Lung Disease (The Union) launched a ‘Collaborative Framework for the Care and Control of Diabetes and Tuberculosis’ in 2011, and recommended bi-directional screening for TB and DM within routine health services¹⁵.

The Union, together with national authorities, implemented the first bi-directional screening of DM and TB within routine health services in China and India, where it was found to be feasible in these settings^16–18. In Uganda, there is no policy on routine screening for DM among TB patients. Whether a similar approach can be implemented in a resource limited setting with a high HIV prevalence remained unknown. To further understand this, a project was implemented from 2016 to 2017 to screen TB patients for DM, supported by the World Diabetes Foundation (WDF). Objectives of this study were to: a) assess whether DM screening can be implemented among TB patients in a routine programmatic health setting in Uganda; b) identify challenges for DM screening among TB patients and solutions to overcome them; and c) identify institutional and health service related factors that may have an impact on DM screening among TB patients.

Methods

DM screening project

Setting and sites. Kampala is the capital city of Uganda and had an estimated population of 1,607,182 in 2017. In the same year, the Kampala Capital City Authority (KCCA) notified a total of 7,260 TB cases, resulting in a case notification rate of 452/100,000 population³. Treatment success was 84.6%. HIV prevalence was 6.9% nationwide (Uganda Population-based HIV Impact Assessment UPHIA 2016–2017)³.

Health services in the city are provided by the KCCA. Persons with presumed TB are investigated in health centres and diagnosed TB patients receive care in TB clinics. DM services are available mainly in specialised DM clinics at tertiary level health facilities.

Ten health centres, both public and private, urban and rural, in Kampala were selected based on their location in the five administrative divisions of KCCA and representative of the health delivery system in Kampala. These were high volume health facilities and selected from different communities based on their ability to manage sufficient numbers of TB patients registered in the previous year and a daily TB patient visits ≥20/day, availability of diagnostic and treatment facilities within the same catchment area and willingness of the staff to participate in this study.

Project training, screening algorithm and monitoring. The National TB and Non-Communicable Disease (NCD) Programs’ staff, Kampala Capital City Authority (KCCA) and The Union Uganda Office staff attended an inception meeting and training of trainers in February-March 2016. Participants reviewed the association between DM and TB, the need for screening and agreed to use the screening algorithm shown in Figure 1. Cascade training sessions for 40 staff from the selected health centres were then conducted. The Union and KCCA officials performed quarterly monitoring, compiled periodic reports and provided overall supportive supervision for the activities. At the same time, they recorded any challenge encountered during the implementation.

Figure 1. Algorithm for diabetes mellitus (DM) screening among tuberculosis patients in Kampala, Uganda.

Patients. Patients aged 15 years and over who were seeking health services in the health centres and were newly registered with any form of TB between 1^st April 2016 to 31^st December 2017 were included in the screening project.

Blood glucose measurement in TB patients. A blood sample was collected for random blood glucose (RBG) using capillary blood immediately after the TB registration in the health centres. A OneTouch glucometer (model SN SAFTM7K8, Lifescan, Johnson & Johnson, New Brunswick, USA) was used with a detection value ranging from 1.7 to 19.1mmol/l. Patients with an RBG value ≥6.1mmol/l were invited to return on the following day, after an overnight fast of at least 10 hours, for measurement of fasting blood glucose (FBG). The FBG cut-off thresholds were based on the World Health Organization (WHO) recommendations as follows: i) FBG ≥7.0 mmol/l (≥126 mg/dl) indicates presence of DM; ii) FBG of 6.1 – 6.9 mmol/l (from 110 mg/dl to less than 126 mg/dl) indicates impaired glucose tolerance; and iii) FBG <6.1 mmol/l (<110 mg /dl) indicates normal blood glucose levels. Health workers provided information on the importance of fasting before returning for a confirmatory test (FBG) to the patients through health education. Reminder phone calls were also provided to all patients who screened positive for RBG to return for confirmatory test.

Results

The characteristics of the selected health centres are described in Table 1. Of the 4,590 TB patients notified, 4,016 (88.0%) were screened with RBG¹⁹. Of those with RBG ≥6.1mmol/l, 1,093 (83.3%) were screened with FBG. Of the 92 TB patients diagnosed with DM, 66 (71.7%) were newly diagnosed DM and were referred to DM services. All DM patients arrived at the DM clinics and received DM care except four patients who died shortly after TB registration. The prevalence of DM in the screened TB patients was 2.3% (Table 2).

The number of registered TB patients in reporting periods one, two, three and four were 372, 1,576, 1,364 and 1,278, respectively. The proportion of patients screened with RBG (number of screened with RBG/number of registered TB patients) were 83.1%, 83.8%, 91.6% and 89.0%, respectively, over the four reporting periods, but the absolute number of TB patients screened with RBG in the first reporting period was 7.7%. Of the number of TB patients with RBG ≥6.1mmol/l, the proportion of screened with FBG (number of screened with FBG/number of RBG ≥6.1mmol/l) were 81.4%, 89.8%, 95.7% and 63.9% over the four reporting periods.

The proportion of patients screened with RBG and FBG varied from 78.9% to 94.5%, and from 58.2% to 100.0%, respectively, among the health centres (Table 3). The proportion of patients screened with RBG was significantly higher in the health centres located in peri-urban areas compared with the centres in urban areas (OR 1.50, 95% CI 1.12-1.99, P=0.006); and in those without DM services compared with those with DM services (OR 1.35, 95% CI 1.11-1.63, P=0.002) (Table 4). Of the patients with RBG ≥6.1mmol/l, the proportion screened with FBG was significantly higher in private health centres (OR 2.16, 95% CI 1.26-3.69, P=0.005) and those without DM services (OR 1.96, 95% CI 1.44-2.67, P<0.001) than that in public health centres and in those with DM services.

A total of 92 TB patients were diagnosed with DM, accounting for a prevalence of 2.3%. The prevalence of DM in TB patients was higher in the private health centres (OR 2.26, 95% CI 1.40-3.62, P=0.001) and in those with DM services (OR 0.60, 95% CI 0.39-0.93, P=0.022).

Discussion

This is the first study to report on screening TB patients for DM in a primary health care setting in Uganda. We included both public and private facilities in urban, peri-urban and rural parts of Kampala to assess the feasibility of integrating DM screening into routine TB service.

The overall prevalence of DM in the screened TB patients was 2.3% (92/4,016), which was higher than that in general population and our finding mirrored the findings of previous studies^6,17,18,20, although it was lower than that reported by a study among hospitalized TB patients at a large referral hospital in an urban area of Uganda²¹. However, using only FBG to diagnose DM can underestimate the prevalence of DM by as much as 50% when compared with the gold standard test, the oral glucose tolerance test, which is more accurate but unavailable at primary health care level²². In addition, some of the facilities raised the FBG screening threshold to 7.0 mmol/l during the first 6–8 months due to misinterpretation of the screening algorithm. We therefore believe that the actual prevalence of DM among TB patients in Uganda might be higher than we have observed in this study. Of the 92 DM patients detected, 66 (71.7%) had not been diagnosed before this study. This strongly demonstrated the need for integrating DM screening into routine TB service^5,23.

In the first six months, a small number of TB patients were screened with RBG, due to the long engagement of stakeholders followed by the cascade training. One health centre (Nsambya) did not send staff to attend the training and did not screen any patients in the first reporting period due to staff turn-over, a situation that should have been identified by training organizers, and this challenge required KCCA attention. The number of screened TB patients increased rapidly over the rest of the reporting periods, suggesting that the procedure was gradually accepted by staff, and we echo a similar finding in China¹⁷. The proportion of patients screened with FBG declined over the last six-month period due to gluco-strips stock-out at the government medical stores. It was reassuring that the health centre staff largely followed the screening algorithm, although there were certain differences. Kisugu Health Centre ensured FBG screening for everyone with RBG ≥ 6.1mmol/l due to strong commitment of the staff. On the contrary, Naguru Health Centre managed to screen only 58.2% of the patients who needed an FBG test even though they had the same number of health workers, although they did have a slightly higher number of TB patient visits. It is necessary to understand reasons for missed opportunities in the DM screening cascade.

Private health centres screened higher proportions of patients with FBG and, subsequently, identified more DM patients than public facilities. Possible reasons for this could be that patients with a higher socio-economic status sought care in private health centres, which have well-motivated staff thanks to better remuneration. Similarly, staff in peri-urban and rural facilities screened higher proportions of patients with RBG and FBG than in urban areas. Murchison Bay is a health centre in peri-urban area operated by the Ministry of Internal Affairs with prisoners being the main source of patients. Given that most patients were confined in prison, they were available for RBG and FBG. In urban health centres there is more frequently congestion and longer waiting times than in rural centres. This may have caused some patients to miss their FBG tests.

An intriguing finding that also requires a further study was that health centres with available DM services at time of the study, a feature that tends to reflect better health services, did not screen more patients with RBG and FBG compared with those without DM services. This was unexpected and not in line with the findings of other studies¹⁶. Low screening at Naguru Health Centre could have contributed to this finding.

There were some service-related barriers identified in the project implementation: 1) lack of a sustainable supply of glucose testing strips, which caused insufficient screening for RBG and FBG; 2) lack of community support mechanisms to help trace patients who failed to return for FBG testing; 3) insufficient number of staff in the health centres caused by staff turn-over; 4) routine DM service was not available in some health centres.

This study had several limitations. Diagnosis of DM relied on FBG alone and we were unable to include oral glucose-tolerance tests, a more accurate test, as part of the screening cascade. This may have resulted in an underestimated prevalence of DM in TB patients²¹. Blood samples for RBG and FBG were taken from capillary blood and measured with glucometers without quality assurance, rather than using venous blood specimens and tested with biochemical analyser. This may have caused a testing bias²⁴. RBG or FBG were measured only once immediately after TB registration, without any confirmatory test at the end of anti-TB treatment, and glycosylated haemoglobin (HbA1c) tests were not carried out, which is influenced less by infection²⁵. Therefore, we do not know whether any TB patient may have been diagnosed as having DM due to infection-induced hyperglycaemia. However, a previous study suggested that taking one blood sample for FBG immediately after TB diagnosis would be appropriate for majority of TB patients¹⁴. We did not collect individual patient data, including socio-demographic data, and perform multivariate analysis and were unable to identify and control potential confounding factors. Finally, the selected health centers were only located in Kampala due to financial limitations and transportation barriers. This might have limited the representative value.

The strength of this study is that we implemented DM screening among a large number of TB patients in routine health service using mainly existing resources. Only training, meetings, support supervision, mentorship and provision of glucometers were supported. There were no staff or patient incentives. This is a key point for sustainability and scale up of integration of DM and TB services. Some challenges identified in the study need government attention and this indicates the need for collaboration between TB and non-communicable disease programs.

Conclusion

This study revealed that prevalence of DM in TB patients is higher than that in the general population in Uganda. Screening TB patients for DM in a primary health care setting in Kampala with high HIV-associated TB epidemic appeared feasible. RBG and FBG are practical tools to be used for screening in routine program settings even though they may underestimate the actual situation. This study identified certain service-related factors that may be barriers and need to be addressed by relevant authorities. Inclusion of bi-directional screening in both TB and DM clinics could be considered. Every year, 44,000–47,000 persons with TB are registered in Uganda and if all were screened for DM, at least 1,748 additional persons with DM could be detected and enrolled to DM care. As early diagnosis and management for the two diseases will improve treatment outcomes and well-being of people, this is an important public health intervention.

Data availability