Diabetes mellitus prevalence in tuberculosis patients ...

Transactions of the Royal Society of Tropical Medicine and Hygiene Advance Access published April 26, 2015

ORIGINAL ARTICLE

Trans R Soc Trop Med Hyg doi:10.1093/trstmh/trv030

Diabetes mellitus prevalence in tuberculosis patients and the background population in Guinea-Bissau: a disease burden study from the capital Bissau Thorny L. Haraldsdottira,b,*, Frauke Rudolfa,b, Morten Bjerregaard-Andersena,c,d, Luis Carlos Joaquı´ma,e,†, Kirstine Stochholmf, Victor F. Gomesa, Henning Beck-Nielsend, Lars Ostergaardb, Peter Aabya,c and Christian Wejsea,b,g

†

Deceased

*Corresponding author: Tel: +46 738979839; E-mail: [email protected]

Received 3 January 2015; revised 22 March 2015; accepted 23 March 2015 Background: Data regarding the association between diabetes mellitus (DM) and tuberculosis (TB) in Africa are scare. We did a DM screening survey among TB patients and non-TB controls in Guinea-Bissau. Methods: The study was conducted at the Bandim Health Project (BHP) in the capital Bissau. From July 2010 to July 2011, newly diagnosed TB cases were identified through a TB notification system. Concurrently, non-TB controls were selected randomly from the BHP’s demographic surveillance database and visited at home. Participants were tested using fasting blood glucose (FBG) measurements. DM was diagnosed as FBG ≥7 mmol/l. Our survey was linked to the patient database at the only existing Diabetes Clinic in Bissau. Results: TB patients (n¼110) were older than the controls (n¼572) (35 vs 31 years; p¼0.02), more often male (55% vs 37%; p,0.001) and had a lower body mass index (18.7 vs 24.2 kg/m2; p,0.001). The prevalence of DM was 2.8% (3/107) for TB patients and 2.1% (11/531) for controls (p¼0.64). Excluding two controls already receiving anti-diabetic treatment, the prevalence of DM was 2.8% (3/107) vs 1.7% (9/529) (p¼0.44). Conclusions: The prevalence of DM was low, also among TB patients. No association between DM and TB was found. Keywords: Association, Diabetes mellitus, Disease burden study, Sub-Saharan Africa, Tuberculosis

Introduction The global burden of tuberculosis (TB) and diabetes mellitus (DM) is immense. WHO estimated that in 2013, 9 million people had active TB and that 1.5 million died from TB.1 The International Diabetes Federation (IDF) estimated that 382 million adults had DM, causing 5.1 million deaths in 2013, and the burden is expected to increase.2 An association between the two diseases was suggested decades ago.3 There has been renewed interest in the link between the two as the DM epidemic is growing globally and 70% of DM patients live in developing countries where TB is often endemic.4 International TB recommendations now also

take DM into consideration, and vice versa, due to recent concerns raised over the convergence of the two epidemics.5,6 A number of studies have found an association between DM and TB, but little is known about the nature of the association, although several pathways have been suggested.7 Previous studies have shown an increased TB risk in DM patients due to impaired cell-mediated immunity, as phagocyte and T cell functions are decreased.8 DM patients with TB are more infectious, due to greater sputum bacterial concentration,9 as well as longer sputum smear and culture conversion time.10 DM makes TB management more difficult, but also the other way around, as the chronic stimulation of the inflammatory system by TB negatively affects

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a Bandim Health Project, Apartado 861, 1004 Bissau Codex, Guinea-Bissau; bDepartment of Infectious Diseases, Aarhus University Hospital, Aarhus, Denmark; cResearch Center for Vitamins and Vaccines (CVIVA), Statens Serum Institute, Copenhagen, Denmark; dDepartment of Endocrinology, Odense University Hospital, Odense, Denmark; eThe National Diabetes Association (ANDD), Bissau, Guinea-Bissau; f Department of Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, Denmark; gGloHAU, Center for Global Health, Department of Public Health, Aarhus University, Denmark

T. L. Haraldsdottir et al.

DM treatment.7 Baker et al. performed a large meta-analysis and found a relative risk of dying during TB treatment of 1.89 (CI 1.52– 2.36), if the patient had concomitant DM.11 In 2010, an expert group concluded that data from lowincome countries were limited.12 Harries et al. thus pointed out that African studies are lacking almost entirely, raising uncertainty about the strength of the DM-TB association in sub-Saharan Africa.12 Thus, we conducted a population-based DM prevalence study in Guinea-Bissau, where the TB incidence is high,13 but DM data are very scarce. The objective was to estimate the prevalence of DM and impaired fasting glucose (IFG) in adult TB patients and non-TB controls.

Inclusion and exclusion criteria

Materials and methods

Clinical procedures

The study was conducted in a well-defined area consisting of six sub-urban areas in Bissau, the capital of Guinea-Bissau, where the Bandim Health Project (BHP) has been conducting epidemiological and clinical research since 1978 (www.bandim.org), using an extensive health and demographic surveillance system (HDSS). The study area is a low-income setting. Only half of the houses have access to electricity, 27% have a refrigerator and only 10% have direct water access (unpublished data). All 102 000 individuals living in the study area are registered with ID number, age, sex and ethnic group. Censuses are performed at regular intervals, and information on pregnancies, births, mortality and migration is collected on a daily basis. Since 1996, a notification system has registered all known TB cases receiving treatment at one of the three health centers or the TB hospital in the study area.13 All eligible TB patients were invited to be included in the TB cohort. Since 2008, a DM notification system has registered DM cases in the study area and linked them with the patient registration at the local Diabetes Clinic (unpublished data). Informed written consent, or a fingerprint if the subject was illiterate, was obtained from all participants. DM patients were referred to the local Diabetes Clinic for free consultation and treatment.

TB cohort From July 2010 until July 2011, all newly diagnosed adult (≥15 years) TB patients registered by our notification system were invited in for inclusion in the present DM study. The age cut-off concerning adulthood was defined at 15 years, which is in accordance with previous studies and common hospital procedures.14–16 Full clinical history and examination were obtained. The diagnosis of pulmonary TB was based on: 1. microscopic detection of acidfast bacilli (smear positive TB) or 2. chest radiography plus relevant signs, symptoms and chest radiography changes after ineffective antibiotic treatment (smear-negative TB).

Control cohort Presumed healthy controls (non-TB controls) were identified by random selection of approximately 200 houses in the BHP area, which were visited during the same period. The control cohort was a random sample of the entire adult population, i.e., nonmatched. As detailed in the sample size calculation, we expected to enroll at least 435 individual controls in the random houses.

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Random blood glucose (RBG) was measured using Glucoflex-R strips (National Diagnostic Products, Sidney, Australia); this is an effective and accurate tool, measuring glucose levels of 1–44 mmol/l. Glucoflex-R strips are convenient in low-resource settings as they are affordable, do not require batteries, cleaning, or careful handling and are easy to use. When read visually by trained persons, a very high correlation with laboratory values is shown. For individuals with a RBG ≥7 mmol/l, the diagnosis of DM was subsequently confirmed by measuring fasting blood glucose (FBG) on two occasions, with an ACCU-CHEK Active glucometer (Roche Diagnostics, Indianapolis, IN, USA). The FBG testing took place at the local Diabetes Clinic. Prior to the study, our staff had been trained in using Glucoflex-R strips and ACCU-CHEK glucometers. The code keys of the ACCU-CHEK meters were replaced and a control test was conducted every time a new test strip box was opened (the glucometers were therefore regularly calibrated). The principal investigator ensured correct handling of the tests, by regular visits to the health centres, TB hospital and Diabetes Clinic. The weight of the participants was measured when they were barefoot and with minimal clothing. An analogue bathroom weight manually calibrated before each session was used for the controls and a digital bathroom scale for the TB patients. A roll-up tape measure was used to assess height. Body mass index (BMI) was calculated as weight/height2 (kg/m2). Underweight was defined as BMI ,18.5 kg/m2 and overweight as BMI ≥25 kg/m2. Mid-upper arm circumference was measured on the non-dominant arm, using a non-stretchable measuring tape at the mid-point between shoulder and elbow (TALC, Herts, UK). A short questionnaire regarding religion, ethnicity, smoking, alcohol intake and pregnancy was filled in.

HIV test Human immunodeficiency virus (HIV) status was obtained locally for all TB patients, using Determine HIV-1/2 (Alere Inc., MA, USA). Positive results were confirmed with the SD Bioline HIV 1/2 3.0 test (Standard Diagnostics Inc., Korea).

DM definitions All participants were tested, irrespective of prior DM diagnosis, and only classified as having DM if the diagnosis was confirmed by our study or if the patient was registered at the Diabetes Clinic.


Study population

We invited all pulmonary TB patients that were diagnosed at one of three health centers in the study area or the nearby TB hospital to participate. TB patients below 15 years of age were excluded. Among the controls, all adults (≥15 years) registered as living in the randomly selected houses and present at the visit were asked to participate. We excluded controls with TB symptoms (coughing .2 weeks) or prior anti-TB treatment (treated with TB in the past or registered in the TB cohort). Residents in houses where a TB patient had been living during the last 3 years were also excluded.

Transactions of the Royal Society of Tropical Medicine and Hygiene

Screened individuals with an RBG ≥7.0 mmol/l were regarded as having suspected DM. Individuals with two fasting FBG ≥7.0 mmol/l were considered DM patients, while those in the 6.1–6.9 mmol/l range were considered having IFG. If the two FBG measurements were discordant, average of measurements was used. If a person only had one fasting measure, the DM diagnosis was based on that.

Statistical analysis

Sample size Prior to the study, we hypothesised a DM prevalence of 4% in the general population,18 and a three-fold higher prevalence among TB patients.19 With a two-sample approach at a 5% significance level and 80% power, we would then need to enrol 435 non-TB controls and 145 TB patients during a 1-year study period. Assuming that an average of two to three persons in each house were eligible for inclusion and testing, we would then need to visit 200 houses, corresponding to approximately 3% of all houses in the study area.

Results TB patients During the enrolment period, 159 adults living in the BHP study area were diagnosed with TB; 128 were included in the TB cohort

Figure 1. Flowchart of enrolment, screening and diabetes mellitus (DM)/impaired fasting glucose (IFG) diagnosis among newly diagnosed TB patients in the Bandim Health Project study area. a Moved outside the study area (n¼1), could not participate (n¼2), lost (n¼1), hospitalised for more than 1 month (n¼3), more than 1 month since TB diagnosis (n¼17).

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Data were double entered using dBase 5.0 (dataBased Inc, Vestal, NY, USA). Statistical analyses were performed using Stata 11.0 (Stata Corporation, College Station, TX, USA). The analyses were non-matched, i.e., we considered the survey a DM burden study in a group of TB patients and random non-TB controls. Comparisons were performed using the x2 test for categorical variables, while the Student’s t test was used to compare mean values. As the risk of DM is affected by age, sex and BMI,17 we conducted a secondary analysis adjusting for these potential confounders, using logistical regression and expressed as odds ratio (OR).

In another secondary analysis, we excluded patients with DM already followed at the Diabetes Clinic.


and 110 were screened for DM (Figure 1). Out of the 110 TB patients screened for DM, 71.8% (79/110) had smear-positive TB. The sputum grade was reported +1 in 19% (15/79) of the smear positive TB patients, +2 in 39% (31/79), +3 in 41% (32/79) and unknown in 1% (1/79). A total of 87% (69/79) had sputum converted after 2 months of treatment. Ten percent (11/110) had previously had TB. Ninety-six percent (106/110) were HIV tested, and 22.6% (24/106) were found to be HIV-infected.

Non-TB controls

Comparison between non-TB controls and background population By comparing with the HDSS database, the identified non-TB controls had similar mean age as the general adult population in the

Figure 2. Flowchart of enrolment, screening and diabetes mellitus (DM)/impaired fasting glucose (IFG) diagnosis among random non-TB controls in the Bandim Health Project study area. a Cough .2 weeks.

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Using the HDSS database and selecting a pre-defined random sample of 3% of houses in the study area, 196 random houses were identified, a number sufficiently close to the 200 random houses needed. However, eight houses were not found or had collapsed since the last census, while in another 11 houses nobody was at home or nobody lived there. Furthermore, in six houses the inhabitants refused to participate, while six houses were excluded due to TB case(s) in the household within the last 3 years. There

was no apparent clustering or selective drop-out of the nonincluded houses, which were evenly spread geographically. Prior to inclusions, we therefore ended with 165 randomly selected houses, with a total of 671 adult residents agreeing to participate (an average four adults per house). Of these, eight individuals were excluded after the initial interview due to previous history of TB and 54 individuals due to current possible TB infection. A further 37 were excluded due to incomplete data (Figure 2). Thus, a total of 572 non-TB controls therefore remained for DM screening. Compared with TB patients, non-TB controls screened for DM were more often female (63% vs 45%; p,0.001), younger (31 vs 35 years; p¼0.02) and overweight (35.3% vs 4.5%; p,0.001) (Table 1).


Table 1. Comparison of background characteristics of TB patients vs non-TB controls with random blood glucose (RBG) screened TB patients n¼110

p-value

359 (63)

,0.001 0.06

339 (59) 177 (31) 56 (10) 0.02 196 (34) 63 (11) 45 (8) 60 (11) 48 (8) 59 (10) 14 (3) 22 (4) 65 (11)

Adjustment for age, sex and BMI As a secondary analysis, we examined the correlation between DM and TB status by adjusting the analysis for known confounders such as age (,30, 31–50, .51 years), sex and BMI (,18, 18–25, .25 kg/m2). The adjusted OR was 0.88 (CI 0.17–4.58).

Linkage with Diabetes Clinic 0.02

155 (27) 326 (57) 91 (16) 0.21 156 (27) 243 (43) 173 (30)

Study participants were linked to the registration at the local Diabetes Clinic, using the HDSS. One additional DM case (in the TB group) was identified in this way, though he had only been diagnosed with DM after completion of our study and was therefore not considered a DM patient in this report. Two non-TB controls, diagnosed by us with DM, were already registered and in treatment at the Diabetes Clinic. If excluding those two patients, the DM prevalence was 2.8% (3/107) in TB patients and 1.7% (9/529) in controls (p¼0.44).

24.2 (23.8–24.6) ,0.001 2.3

Discussion

35.3

Main findings

294 (291–298) ,0.001 30 (5.2) 0.22 14 (2.5) 0.22

Cells are n (%) or mean (CI) unless otherwise specified. BMI: body mass index; MUAC: mid upper arm circumference. a Smoking was defined as anyone responding yes to the question: ‘do you smoke’? b Drinking everyday.

BHP study area (31 years). No major differences were found in ethnic background either. However, a slightly higher proportion of females were found among non-TB controls, compared with the overall adult background population (63% vs 55%).

FBG and DM diagnosis Of the 110 TB patients screened for DM with RBG, 24 (21.8%) were considered DM suspects, compared with 155 (27.0%) of the 572

Using blood glucose measurements, we found a DM prevalence of 2.8% vs 2.1% and an IFG prevalence of 1.9% vs 1.5% for TB patients and non-TB controls, respectively. If we excluded participants already in anti-diabetic treatment at the Diabetes Clinic prior to our study, the DM prevalence was 2.8% vs 1.7%, respectively.

Consistency with previous findings The DM prevalence in Guinea-Bissau was lower than we expected, as the overall DM prevalence has previously been estimated to 3–4%.2,18 However, a recent survey on the burden of DM and metabolic syndrome among young twins and singleton controls also found a low prevalence.15 Most of the DM cases were not registered at the local Diabetes Clinic, and were therefore only identified through our survey. As in other African settings, this indicates that DM is vastly underdiagnosed in Guinea-Bissau.17 Compared with neighbouring countries, a study in Guinea found a DM prevalence of 3.4% among 388 randomly selected TB patients.20 A study from Senegal found an overall DM prevalence of 18%, though using a lower DM cut-off.21 The DM burden for the entire African region was estimated to 4.9% in 2013, though with large inter-country variations.17

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Female sex 49 (45) Age group 15–30 years 52 (47) 31–50 years 42 (38) ≥51 years 16 (15) Ethnicity Pepel 32 (29) Fula 15 (14) Mandinga 11 (10) Mandjaco 10 (9) Mancanha 11 (10) Balanta 11 (10) Bijagos 7 (6) Saracole 0 (0) Other 13 (12) Religion Muslim 28 (26) Christian 52 (47) Other 30 (27) Education None (0–4 years) 39 (36) Primary 40 (36) (5–10 years) Secondary 31 (28) (.10 years) BMI 18.7 (18.2–19.3) % Underweight 34.5 (BMI ,18.5 kg/m2) % Overweight (BMI 4.5 ≥25 kg/m2) MUAC 243 (237–250) Smokinga 9 (8.2) Alcohol abuseb 5 (4.6)

Controls n¼572

controls. Among the DM suspects, 3 TB patients and 41 community controls did not have an FBG measurement and were therefore censored from the analysis. The average time span between the two FBG measurements was 18 days, as some participants did not appear for the second FBG test and had to be re-invited. The median time span was 5 days. Of the total number of DM suspects, 72% had two FBG measurements available, while 28% only had one. The prevalence of DM was 2.8% (3/107) in TB patients and 2.1% (11/531) for controls (p¼0.64). The prevalence of IFG was 1.9% (2/107) in TB patients and 1.5% (8/531) for controls (p¼0.78) (Table 2). Mean FBG tended to be higher for TB patients than controls, 6.6 vs 5.6 mmol/l, respectively (p¼0.09).


Table 2. Comparison of glucose results among TB patients vs non-TB controls Controls

p-value

n¼110 86 (78) 24 (22)

n¼572 417 (73) 155 (27)

0.25

n¼21 16 (76) 2 (10) 3 (14) n¼107 2 (1.9) 3 (2.8)

n¼114 95 (83) 8 (7) 11 (10) n¼531 8 (1.5) 11 (2.1)

0.69 0.52 0.78 0.64

4.7 (4.3–5.2) 4.9 (4.8–5.1) 0.38 6.6 (4.5–8.7) 5.6 (5.2–5.9) 0.09

The table includes comparison of RBG, FBG, actual IFG and DM cases and also mean RBG and FBG levels. Cells are n (%) or mean (CI) unless otherwise specified. DM: diabetes mellitus; IFG: impaired fasting glucose; FBG: fasting blood glucose; RBG: random blood glucose.

Our main results are consistent with Leegaard et al. who found no association between TB and dysglycemia in Denmark. 22 A population-based study from Australia found that DM was associated with only a modest TB risk.23 Eighteen studies screening for DM among TB patients yielded large variations in the DM burden.24 The lowest prevalence was observed in India and Nigeria, where no differences were found between TB patients and controls.24 Yet, other studies have indeed shown an association between TB and DM. An analysis of three prospective cohort studies revealed that people with DM have an approximately three-fold higher risk of developing active TB, compared with people without DM.19 A review showed a statistically significant TB risk, varying between 1.5- and 7.8-fold, for those with DM.25 The only largescale study from Africa used the World Health Survey, comparing symptoms of TB with self-reported DM, and found an OR of 1.96 (1.23–3.12).26 Faurholt-Jepsen et al. recently identified a two times higher prevalence of DM in TB patients, compared with healthy controls in Mwanza, Tanzania.27 Interestingly, the TB-DM association appears stronger in settings with a higher DM burden. Thus, a population-based Mexican study reported a 6.8-fold higher TB rate among DM patients, due to both reactivated TB and increased TB susceptibility.28 The overall DM prevalence in the Mexican study was 5.3%. In Bissau, HIV seems a rather more important driving force behind TB, with a background burden of 8%16 and 23% of TB cases being HIV infected. It should be noted that HIV may be an independent risk factor for DM.29

Strengths and weaknesses As one of the first in Africa, our study provides important data on the DM-TB association in a low-income West African setting. The

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Screened by RBG RBG , 7.0 mmol/l RBG ≥ 7.0 mmol/l (DM suspects) Tested with FBG FBG ≤ 6.0 mmol/l (normal) FBG 6.1–6.9 mmol/l (IFG) FBG ≥ 7.0 mmol/l (DM) IFG and DM prevalence IFG DM Glucose levels Random mmol/l Fasting mmol/l

TB patients

study was conducted within a well-described population, which increases its internal validity. Finally, our study area benefitted from a DM notification system, equivalent to that of the TB notification system, to which participants could be linked via the HDSS. Our study, however, has several limitations. The most important one was the relatively small sample size concerning TB patients. The number of TB patients in the BHP area has dropped considerably over the last few years,13 which affected recruitment, and we were thus not able to enroll the targeted number of TB patients. The DM prevalence among non-TB controls was also lower than expected. Although we only managed to include 165 random houses, the number of individual non-TB controls exceeded what was anticipated, as we included more than three controls in each house. However, we cannot exclude that a difference in DM burden below what we have stipulated did in fact exist. Thus, based on the numbers actually reached, we would only expect to show a statistical difference (80% power) if the true difference in DM burden between TB cases vs controls was at least four-fold. Several studies report an excess TB risk associated with DM well above that.25,28 We only included controls that were at home at the time of the afternoon visit. Compared with the overall background population, our control sample was quite representative, though a slightly higher proportion of women was found. No evidence of a clustering effect among the included controls was observed, while more women encountered during house-to-house surveys have previously been reported.14–16 Also, those included may have been of lower socio-economic status, since individuals with a job would most likely not be at home at that time. It is not known if DM is more prevalent among people in the lower or higher socioeconomic groups in Guinea-Bissau; therefore it remains unclear if this has over or underestimated the DM prevalence. Importantly, our study did not match TB cases and controls on age, sex or other characteristics, but simply compared the DM burden between TB patients and a random selection of the adult population in the study area. A higher proportion of females were therefore observed among the non-TB controls, who were also younger than the TB patients. This could be considered a limitation, as it may have made detecting a difference between the two groups more difficult. However, our adjusted analysis found no difference between the two groups. We chose a random sample of non-TB controls, as this gave us a better impression of the overall DM burden in the community (data currently not available from Guinea-Bissau). We only included TB patients that were diagnosed and treated at a health care facility. However, the burden of undiagnosed TB may be considerable in Bissau,13,14 raising the question of how representative our TB cases were. Thus, we do not have data on the DM burden among TB patients not treated at the health centers. Though the TB diagnostic capabilities in Bissau area are limited, we do, however, believe that the majority of TB suspects coming for consultation have been correctly classified, as this has been a focus area.30 Compared with a previous TB trial from Bissau, the TB patients included in the present study were quite similar in important baseline characteristics.31 All controls were screened for TB using cough .2 weeks as an exclusion criterion. Though not entirely sensitive, particularly for HIV-infected TB patients, we believe this method ruled out active pulmonary TB in most cases.


Implications and recommendations As recommended by WHO,6 all TB patients in Bissau are currently screened for DM before starting anti-TB treatment. Stratified analyses should be done to investigate how concomitant DM affects TB treatment outcomes. Based on our data, DM was not associated with TB in Guinea-Bissau, even when adjusting for age, sex and BMI. Yet, our study was not longitudinal and some caution in the interpretation is therefore warranted. It would therefore, as a minimum, be recommended to screen known DM patients for TB symptoms such as chronic cough, and subsequently refer TB suspects to proper evaluation with sputum smear, culture and x-ray.6 Finally, a DM notification system has been established at the BHP, which will make it possible to conduct long-term prospective follow-up of DM patients, including screening of TB development.

Authors’ contributions: TLH, FR, CW, KS and MB-A designed the study protocol; TLH and LCJ conducted the diabetes screening among TB patients and controls; VFG, HB-N, LO and PA co-designed the study and offered advice and support throughout the process. All authors read and approved the final manuscript. TLH is the guarantor of the study. Acknowledgements: The authors would like to thank all the participants, as well as the staff of the TB section at the BHP and at the Diabetes Clinic for their important contribution. We thank the linguistic coach at Aarhus University Hospital, Marianne Godt Hansen, for proof reading and improvement of the English language of the manuscript. We dedicate this article to the memory of Dr. Luis Carlos Joaquı´m, who sadly passed away during the writing of this paper. He was devoted to enhancing diabetes awareness in Guinea-Bissau and this project would not have been possible without his efforts. Funding: This work was supported by the EDCTP, Aase and Ejner Danielsens Foundation, The A.P. Møller Foundation for the Advancement in Medical Science and the Oticon Foundation. The INDEPTH network supported the study with a seed grant for evaluating TB risk factors in the HDSS study area. TLH received a scholarship grant from the Clinical Institute of Aarhus University. Competing interests: None declared. Ethical approval: This study was approved by the Ethical Committee of Guinea-Bissau and the Danish Central Ethical Committee.

References 1 WHO. Tuberculosis Global Report. Geneva: World Health Organization; 2014. 2 IDF. IDF Diabetes Atlas. Brussels: International Diabetes Federation; 2013. 3 Nichols GP. Diabetes among young tuberculous patients; a review of the association of the two diseases. Am Rev Tuberc 1957;76: 1016–30. 4 Harries AD, Billo N, Kapur A. Links between diabetes mellitus and tuberculosis: should we integrate screening and care? Trans R Soc Trop Med Hyg 2009;103:1–2. 5 IDF. International Diabetes Federation, International Union against Tuberculosis and Lung Disease; The looming co-epidemic of TB-diabetes: A call to action. Brussels: International Diabetes Federation; 2014. 6 WHO. International Union against Tuberculosis and Lung Disease, World Health Organization; Collaborative framework for care and control of tuberculosis and diabetes. Geneva: World Health Organization; 2011. 7 Young F, Critchley JA, Johnstone LK, Unwin NC. A review of co-morbidity between infectious and chronic disease in Sub Saharan Africa: TB and diabetes mellitus, HIV and metabolic syndrome, and the impact of globalization. Global Health 2009;5:9. 8 Dooley KE, Chaisson RE. Tuberculosis and diabetes mellitus: convergence of two epidemics. Lancet Infect Dis 2009;9:737–46. 9 Singla R, Khan N, Al-Sharif N et al. Influence of diabetes on manifestations and treatment outcome of pulmonary TB patients. Int J Tuberc Lung Dis 2006;10:74–9.

Conclusions

10 Guler M, Unsal E, Dursun B et al. Factors influencing sputum smear and culture conversion time among patients with new case pulmonary tuberculosis. Int J Clin Pract 2007;61:231–5.

The prevalence of DM and IFG in Guinea-Bissau was low, both in TB patients and non-TB controls and we did not observe an association between DM and pulmonary TB in this setting.

11 Baker MA, Harries AD, Jeon CY et al. The impact of diabetes on tuberculosis treatment outcomes: a systematic review. BMC Med 2011;9:81.

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All participants were tested, irrespective of prior DM diagnosis, and only classified as having DM if the diagnosis was confirmed by our study or if the patient was already treated at the Diabetes Clinic. Post-prandial glucose values were not available. We chose not to use self-reported DM as an outcome as we considered this information to be less reliable. There is a small risk that DM patients who were receiving anti-diabetic treatment at the time of inclusion, and therefore had normal glucose levels, were registered as not having DM. To minimise this, we linked our data to the local Diabetes Clinic, which follows many DM patients from the study area, but no additional DM cases were found. We also conducted a separate statistical analysis excluding DM patients already treated at the Diabetes Clinic. Ideally, all participants should have been FBG screened. However, we considered it too difficult to screen all with FBG. We have previously conduced FBG screening surveys in Bissau,15 and they are considerably more demanding as they require at least two visits, i.e., one for advising and then a re-visit to carry out the test. Of those with an RBG ≥7 mmol/l, 13% of the TB patients and 27% of the non-TB controls did not have an FBG measurement available and were thus censored. Most likely, this has underestimated the DM prevalence, as they were a selected group with elevated RBG. FBG was only performed in those with RBG ≥7 mmol/l. As Glucoflex-R strips do only report glucose values of 4 and 7 mmol/l, there is a risk that some IFG cases have been missed. We aimed for two FBG in order to diagnose DM; we did, however, not exclude patients with only one FBG, as this was an epidemiological study. Two FBG were available in most cases, though 28% did not appear at the Diabetes Clinic for the second FBG. Due to the non-TB control sample being non-matched, we conducted a multivariate analysis, adjusting for age, sex and BMI. Since our sample size was quite small, such adjustment may have caused over fitting of the statistical model. We therefore chose to limit our secondary analysis to only these three potential confounders.


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13 Lemvik G, Rudolf F, Vieira F et al. Decline in overall, smear-negative and HIV-positive TB incidence while smear-positive incidence stays stable in Guinea-Bissau 2004–2011. Trop Med Int Health 2014;19:1367–76.

23 Dobler CC, Flack JR, Marks GB. Risk of tuberculosis among people with diabetes mellitus: an Australian nationwide cohort study. BMJ Open 2012;2:e000666.

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