Converging Epidemics of Diabetes and Tuberculosis...

Converging Epidemics of Diabetes and Tuberculosis: Epidemiology and Public Health Consequences

Julia Critchley, Fiona Pearson, Peijue Huangfu, Daniel Grint, Reinout van Crevel, Susanne Awad, Yoko LaurenceTANDEM team EC FP7 fundingQNRF

Global Burden of TB and of DM

• 10.4 million TB cases• 1/4 global population infected

with LTBI• 1.7 million deaths, 95% of which

occur in LMIC

• 424.9 million with diabetes, 50% undiagnosed

• >90% Type II

• 79% living in LMIC• Increase to 629 million by 2045

• 4 million deaths

Estimated TB incidence 2016

Estimated DM prevalence 2017

Is DM associated with TB?

• Association historically noted and treatment was given in specialist centres (Avicenna, Susutra,

Morton; 1950’s tx centres in UK)

• Evidence synthesised in several distinct reviews (Stevenson 2007; Jeon 2008, Al-Rifai 2017)

• Recently updated - 44 studies from 16 countries

• Prospective: DM ~ 3.6-fold higher TB risk (2.3-5.7)

• Higher in low-income and high-incidence

• Higher in Asia compared to Europe/USA

• Higher for confirmed TB and blood tested DM

Hyperglycaemia / Glycaemic control & TB risk / outcomes

• Limited evidence – e.g.• Leung et al 2008 HK• Lee et al. 2016 Taiwan• Leegard et al 2011 Denmark• Pealing et al 2015 UK

• Some suggestions of higher TB risk among those with poorly controlled DM

• Recent systematic review of 8 studies (Shewade et al. Plos One 2017) could not reach any conclusions

• Association may be non-linear

• Unclear to what extent it might be reversible?

• English primary care data 2010-2015 (>85,000 DM patients, >150,000 controls)

• 24% of TB among DM patients in UK attributable to poor • glycemic control Critchley J, Diabetes Care 2018

TB treatment outcomes amongst those with DM

• 2011 review suggested DM worsens treatment outcomes among TB patients [Baker et al. 2011],recently updated [Huangfu et al 2019, in press IJLTD]

• Included studies: relatively poor in quality (observational data)

• New review; 102 studies in total including 44 reporting on mortality (56,122 individuals with TB-DM and 243,035 with TB)

• Death OR=2.83, 95%CI: 1.45-5.52;

I2=47% among studies that

controlled for key confounders

(age, sex, HIV)

5

Odds Ratio 95% CI

2 month culture +ve 1.88 1.59 - 2.21

Death 1.64 1.29 - 2.08

Relapse 1.98 1.51 - 2.60

Death and Relapse 1.90 1.43 – 2.53

MDR-TB 1.86 1.51 – 2.28

Prevalence of DM in TB patients; TB-DM hotspots ...

• Overall prevalence of DM 15·3% (95% prediction interval 2·5–36·1; I2 99·8%), varying from 0·1% in Latvia to 45·2% in Marshall Islands [200 studies; 2,291,571 people with active tuberculosis

Noubiap et al, Lancet Global Health 2019

• South India. 209 pulmonary TB, ~45 years, BMI ~20• 54% diabetes (OGTT, HbA1c), 21% pre-diabetes• 25% euglycemic Kornfeld et al, Chest 2016

• Kiribati, Pacific. 275 TB cases, ~37 years, BMI 22.5• 37% diabetes (>50% in those >35 years) vs. 18% in matched controls• 55% previously undiagnosed DM

Viney et al, TMIH 2015

• Southern Texas/ Mexico. 233 TB cases, ~44 years, 25% obese• 37% diabetes, ~ 2-3-fold more compared to background population• DM responsible for 25% of TB (versus HIV: 5%)

Restrepo, Bull WHO 2011

Daniel Grint et alBull WHO 2018

Tuesday, 19 May 2015 2nd Progress Meeting

Day 2

Work Package 3 Chair: Jackie Cliff

9.00 – 11.00 WP3 Bioprofiling analysis in TB, TB-DM and DM patients

Introduction: WP Status Update & Relevant Literature: Jackie Cliff (LSHTM)

Selection of platform, location, samples for cross-sectional analysis: Jackie Cliff MLPA gene panel (brief update): Simone Joosten (LUMC)

Immune Assays at SUN: Katharina Ronacher (SUN)

11.00 – 11.30 Coffee Break 11.30 – 12.20 Two open presentations about TANDEM and TB-DM (Indonesian & foreign speaker) 12.20 – 13.30 Lunch & Meeting with National TB and DM Representatives Work Package 4 Chair: Macarena Beigier 13.30 – 15.00 WP4 Cellular basis accounting for the causal link between diabetes

and TB

Introduction: WP Status Overview: Macarena Beigier (MPIIB) Adipocytes and TB: Macarena Beigier (MPIIB) Macrophage studies: Simone Joosten (LUMC) In-vitro studies in Nijmegen: Ekta Lachmandas (RUNMC) Genetic susceptibility and eQTL of TB and DM: Vinod Kumar (UMCG)

15.00 – 15.30 Coffee Break WPs: Where do we stand (break-out in 2 groups) 15.30 – 16.45 Planned vs. actual work progress

Next objectives and action tasks

Indonesia

Peru

Romania

South Africa

• Practical screening algorithms• Characteristics of DM drugs• Glycemic control• Cardiovascular risk management• TB treatment issues• combined HIV, diabetes and TB• ….

Yan Lin, Tony Harries ..

Issued by the TB Union and World Diabetes Foundation

What should be done for a TB patient diagnosed in a TB clinic who is

diagnosed with new DM or who is already receiving treatment for DM?

The following steps should be carried out:

• Glycaemic control should be assessed either by measurement of HbA1c or

measurement of FBG. This assessment can be done in the TB clinic if blood glucose

can be measured. If assessment has to be done at the DM clinic or a general health

clinic, this is best postponed until at least two weeks or even 2 months of TB treatment

have been completed. Guidance for newly diagnosed patients or those already

receiving DM treatment is shown in Table 3.

• Document cigarette smoking status and counsel if still smoking.

• Ask about history of cardiovascular disease (myocardial infarction, stroke, peripheral

arterial disease). If yes, then start/continue low-dose aspirin (75-150 mg once a day).

• After 8 weeks (at the end of the initial intensive phase of TB treatment for drug-

susceptible TB), measure blood pressure and start / increase antihypertensive

medication if systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥

90mm Hg, considering possible drug-interactions with rifampicin.

• After 8 weeks (at the end of the initial intensive phase of TB treatment for drug- susceptible TB), start/continue statin if age > 40 years or there is established

cardiovascular disease.

Table 3: Management of HbA1c or blood glucose at the start of TB treatment

HbA1c or FBG at the start

of

anti-TB treatment

Diagnosed with

new DM

Already receiving

treatment for DM

If HbA1c <8% or FBG < 10.0

mmol/l (180 mg/dl)

No further immediate

action is taken; re-assess

blood glucose levels at 2

months and again at the

end of TB treatment

No further action is

taken; the patient

continues on current

medication for DM

If HbA1c ≥8% but less than

10% or FBG ≥ 10 mmol/l (180 mg/dl) but less than15 mmol/l

(270 mg/dl)

Start metformin 500 mg

once a day, reassess in two weeks and increase the

dose to 500 mg twice a day or refer if blood glucose

levels have not improved

Intensify current glucose

lowering treatment and reassess one - two weeks

later

If HbA1c ≥10% or FBG ≥

15mmol/l (270 mg/dl)

Start metformin 500 mg

twice a day and seek

specialist advice.

Seek specialist advice

and consider the need for

hospital admission for

better glucose control

What is the population impact? - INDIA

INDIA - TB-DM dynamic mathematical model

199014.5% (95% UI: 9.5%-18.2%) TB-

related deaths

11.4% (95% UI: 6.3%-14.4%) TB incidence

2050 42.8% (95% UI: 28.7%-53.1%) TB deaths

33.3% (95% UI: 19.0%-44.1%) TB incidence

effects of DM on TB disease progression and infectiousness

Sensitivity analyses suggested that the impact could be even greater

16/09/2016 9Awad S et al 2019 (in press) Journal of Global Health; Awad et al 2019 (in press) Scientific Reports

Conclusions

• DM increases TB disease risk and risk of poor TB treatment outcomes (mortality)• DM is often common in TB patients, and often undiagnosed• Higher glucose / HbA1c levels – higher risks – reversible?• Need for screening and better clinical management of TB-DM

• Screening TB patients for DM (YES), less clear on screening DM for TB• TWO step screening (using RPG, or a risk score as first step) may be most

cost-effective, screening needs repeating later in TB treatment also• What happens to patients identified with DM at the end of TB treatment?

• POPULATION IMPACT - In India, one in every five TB disease cases could potentially be attributed to DM, and by 2050, one in every three could potentiallybe attributed to DM

• Heterogeneity of both DM and TB-DM, interactions with HIV, other multi-morbidities?

Field staff and patients

[email protected]

http://www.tandem-fp7.eu/

Potential effects of TB-DM interventions

16/09/2016 12

TB Vaccination targeted at people with DM Screening and treating people with TB for DM

• English primary care data 2010-2015 (>85.000 DM patients, >150.000 controls)• Consistently higher risk of infection compared to non-DM, higher risk with higher HbA1c• 24% of TB among DM patients in UK is a result of poor glycemic control

• Critchley J, Diabetes Care 2018

Any infection Hospital after infection Death from infection

No DM

Rising HbA1c

TANDEM: DM prevalence (1856 pulmonary TB)

0

5

10

15

20

25

Indonesia Peru Romania SouthAfrica

crude

ageadjusted

country-data

0

5

10

15

20

25

Indonesia Peru Romania SouthAfrica

crude

ageadjusted

country-data

DM

pre

vale

nce

0

5

10

15

20

25

30

35

40

18-34 35-49 50+ 18-34 35-49 50+ 18-34 35-49 50+ 18-34 35-49 50+

Indonesia Peru South Africa Romania

%

Pink = newGrey = known

73% of DM previously known

(“neglected”)

TANDEM consortium, Ugarte-Gll et al; Clinical Infectious Diseases 2019 and Grint et al Bulletin of WHO 2018

or more progression to active TB?

• 651 DM patients in Indonesia

• Sex- and age-matched TB household contacts

DM patients TB household contacts

Active TB 4.9% 1.2%

Latent TB (IGRA) 38.6% 68.6%

Ratio active/latent 1:8 1:57

Koesoemadinata et al. Tr Royal Soc Trop Med H 2017

Updated systematic review –also increases risk of culture positivity at 2 months, death and failure, relapse, MDR-TB

16/09/2016 25

NOTE: Weights are from random effects analysis

Overall (I-squared = 84.2%, p = 0.000)

Touré, 2007

Pina, 2006

Viswanathan, 2014

Lin, 2014

Alisjahbana, 2007Vasankari, 2007

Dooley, 2009

Reis-Santos, 2013

Alavi-Naini, 2013

Mathew, 2006

Jiménez-Corona, 2013

Tabarsi, 2014

Kitahara, 1994

Moosazadeh, 2014

Reed, 2013Sulaiman, 2013

Chen, 2015

Chiang, 2009

Viswanathan, 2014

Burgielski, 1985

Fielder, 2002

Amnuaiphon, 2009

Study

Ambrosetti, 1997 report

Singla, 2006

Sangral, 2012

Maâlej, 2009

Alo, 2014

Tatar, 2009

Cavanaugh, 2015

Mboussa, 2003

Mi, 2013

Wang, 2009

Ribeiro Macedo, 2013

Kourbatova, 2006

Nandakumar, 2013


Magee, 2014

Centis, 1998 report

Oursler, 2002Centis, 1999 report


Wu, 2015

Orofino Rde, 2011

Bashar, 2001

Chiang, 2015

Uchimura, 2013

Vasankari, 2010

Hasibi, 2008Senegal

Spain

India

Taiwan

IndonesiaFinland

U.S.

Brazil

Iran

Russia

Mexico

Iran

Japan

Iran

R.O.K.Malaysia

China

Taiwan

India

Poland

U.S.

Thailand

Country

Italy

K.S.A

India

Tunisia

Fiji

Turkey

Kiribati

Congo

China

Taiwan

Brazil

Russia

India

Italy

U.S.

Italy

U.S.Italy

Italy

Taiwan

Brazil

U.S.

Taiwan

Japan

Finland

Iran18/86

8/73

1/89

65/182

2/9422/92

6/42

73/703

40/108

8/44

11/363

1/37

3/71

26/140

21/16219/342

9/182

52/241

0/96

38/90

13/22

376/4188

n/N

1/41

1/134

2/23

5/60

3/53

2/78

4/101

8/32

2/97

13/74

39/323

5/20

42/667

4/50

16/151

5/41

8/182/40

3/32

1645/7637

4/14

7/50

79/705

1193/5622

7/26

3/6

TB-DM

6/88

97/1438

1/120

184/565

0/54086/537

20/255

995/17047

35/607

75/1872

36/847

1/67

14/449

123/824

30/49542/914

9/944

137/886

5/149

19/90

29/152

19/290

n/N

43/666

3/384

16/257

0/82

11/335

0/78

2/174

8/100

2/483

11/143

712/12472

87/440

71/2127

19/773

66/1167

49/1059

14/10826/852

29/737

3939/26214

15/294

1/105

61/768

6282/28077

25/205

6/44

TB-nonDM

2.10 (1.76, 2.51)

3.62 (1.36, 9.62)

1.70 (0.79, 3.65)

1.35 (0.08, 21.92)

1.15 (0.81, 1.63)

29.22 (1.39, 613.44)1.65 (0.97, 2.80)

1.96 (0.74, 5.20)

1.87 (1.45, 2.40)

9.61 (5.72, 16.15)

5.32 (2.39, 11.85)

0.70 (0.35, 1.40)

1.83 (0.11, 30.19)

1.37 (0.38, 4.90)

1.30 (0.81, 2.07)

2.31 (1.28, 4.16)1.22 (0.70, 2.13)

5.40 (2.12, 13.81)

1.50 (1.05, 2.15)

0.14 (0.01, 2.49)

2.73 (1.42, 5.27)

6.13 (2.39, 15.70)

1.41 (0.87, 2.27)

Odds ratio (95% CI)

0.36 (0.05, 2.70)

0.95 (0.10, 9.26)

1.43 (0.31, 6.67)

16.35 (0.89, 301.66)

1.77 (0.48, 6.56)

5.13 (0.24, 108.62)

3.55 (0.64, 19.72)

3.83 (1.30, 11.27)

5.06 (0.70, 36.39)

2.56 (1.08, 6.03)

2.27 (1.61, 3.20)

1.35 (0.48, 3.82)

1.95 (1.32, 2.88)

3.45 (1.13, 10.56)

1.98 (1.11, 3.51)

2.86 (1.08, 7.62)

5.37 (1.81, 15.91)1.67 (0.38, 7.31)

2.53 (0.73, 8.77)

1.55 (1.46, 1.66)

7.44 (2.09, 26.51)

16.93 (2.02, 141.78)

1.46 (1.03, 2.08)

0.93 (0.87, 1.00)

2.65 (1.01, 6.94)

6.33 (1.03, 38.98)

100.00

1.91

2.47

0.37

3.89

0.313.24

1.91

4.20

3.29

2.36

2.71

0.37

1.36

3.47

3.043.15

2.00

3.87

0.34

2.81

1.99

3.44

Weight

0.66

0.53

1.03

0.34

1.30

0.31

0.86

1.69

0.68

2.20

3.91

1.77

3.74

1.61

3.09

1.91

1.681.09

1.40

4.56

1.36

0.60

3.89

4.56

1.94

0.78

%

2.10 (1.76, 2.51)

3.62 (1.36, 9.62)

1.70 (0.79, 3.65)

1.35 (0.08, 21.92)

1.15 (0.81, 1.63)

29.22 (1.39, 613.44)1.65 (0.97, 2.80)

1.96 (0.74, 5.20)

1.87 (1.45, 2.40)

9.61 (5.72, 16.15)

5.32 (2.39, 11.85)

0.70 (0.35, 1.40)

1.83 (0.11, 30.19)

1.37 (0.38, 4.90)

1.30 (0.81, 2.07)

2.31 (1.28, 4.16)1.22 (0.70, 2.13)

5.40 (2.12, 13.81)

1.50 (1.05, 2.15)

0.14 (0.01, 2.49)

2.73 (1.42, 5.27)

6.13 (2.39, 15.70)

1.41 (0.87, 2.27)

Odds ratio (95% CI)

0.36 (0.05, 2.70)

0.95 (0.10, 9.26)

1.43 (0.31, 6.67)

16.35 (0.89, 301.66)

1.77 (0.48, 6.56)

5.13 (0.24, 108.62)

3.55 (0.64, 19.72)

3.83 (1.30, 11.27)

5.06 (0.70, 36.39)

2.56 (1.08, 6.03)

2.27 (1.61, 3.20)

1.35 (0.48, 3.82)

1.95 (1.32, 2.88)

3.45 (1.13, 10.56)

1.98 (1.11, 3.51)

2.86 (1.08, 7.62)

5.37 (1.81, 15.91)1.67 (0.38, 7.31)

2.53 (0.73, 8.77)

1.55 (1.46, 1.66)

7.44 (2.09, 26.51)

16.93 (2.02, 141.78)

1.46 (1.03, 2.08)

0.93 (0.87, 1.00)

2.65 (1.01, 6.94)

6.33 (1.03, 38.98)

100.00

1.91

2.47

0.37

3.89

0.313.24

1.91

4.20

3.29

2.36

2.71

0.37

1.36

3.47

3.043.15

2.00

3.87

0.34

2.81

1.99

3.44

Weight

0.66

0.53

1.03

0.34

1.30

0.31

0.86

1.69

0.68

2.20

3.91

1.77

3.74

1.61

3.09

1.91

1.681.09

1.40

4.56

1.36

0.60

3.89

4.56

1.94

0.78

%

1.1 .5 1 2.11 5 10 15

Huangfu et al, 2019 Int J Tub Lung Dis (in press)



Dooley, 2009

Magee, 2016

Study

Reed, 2013

Wang, 2009

Mundra, 2017

Oursler, 2002

U.S.

USA

Country

R.O.K.

Taiwan

India

U.S.

6/42

10/42

n/N

21/162

13/74

./.

8/18

TB-DM

20/255

32/327

n/N

30/495

11/143

./.

14/108

TB-nonDM

2.83 (1.45, 5.52)

6.50 (1.11, 38.20)

1.19 (0.54, 2.63)

ratio (95% CI)

2.18 (1.10, 4.33)

7.60 (1.98, 29.16)

1.30 (0.16, 10.53)

6.70 (1.57, 28.67)

Odds

100.00

10.40

25.17

Weight

27.59

15.09

8.07

13.67

%

2.83 (1.45, 5.52)

6.50 (1.11, 38.20)

1.19 (0.54, 2.63)

ratio (95% CI)

2.18 (1.10, 4.33)

7.60 (1.98, 29.16)

1.30 (0.16, 10.53)

6.70 (1.57, 28.67)

Odds

100.00

10.40

25.17

Weight

27.59

15.09

8.07

13.67

%

1.1 .5 1 2.83 5 10 15


.

.

1: High income




Centis, 1998 report

Centis, 1999 report

Singla, 2006

Chiang, 2009

Uchimura, 2013

Choi, 2014

Chiang, 2015

Barss, 2016

Lee, 2017

Leung, 2017

Subtotal (I-squared = 83.7%, p = 0.000)

2: Upper/Lower-middle/Low

Mboussa, 2003

Morsy, 2003

Anunnatsiri, 2005

Namukwaya, 2011

Orofino Rde, 2011

Sangral, 2012

Jimenez-Corona, 2013

Mi, 2013

Nandakumar, 2013

Tabarsi, 2014

Alo, 2014

Viswanathan, 2014

Viswanathan, 2014

Cavanaugh, 2015

Hongguang, 2015

Delgado-Sanchez, 2015

Abdelbary, 2016

Satung, 2016

Siddiqui, 2016

Workneh, 2016

Ma, 2017

Perez-Navarro, 2017


Study

Italy

Italy

Italy

Italy

Italy

K.S.A.

Taiwan

Japan

R.O.K.

Taiwan

Canada

South Korea

Hong Kong

Congo

Egypt

Thailand

Uganda

Brazil

India

Mexico

China

India

Iran

Fiji

India

India

Kiribati

China

Mexico

Mexico

Thailand

India

Ethiopia

China

Mexico

Country

3/32

5/50

2/41

5/41

2/40

1/134

60/241

1281/5622

./.

101/705

./.

8/238

317/3206

13/32

31/40

4/38

2/2

4/14

4/23

28/363

12/97

74/667

2/37

3/53

4/96

8/89

5/101

15/182

2594/29535

136/2121

73/556

3/37

16/109

19/157

19/184

n/N

TB-DM

33/737

20/773

45/666

61/1059

28/852

7/384

161/886

6578/28077

./.

78/768

./.

31/764

1152/17488

13/100

88/198

11/188

48/148

21/294

20/257

55/847

13/483

148/2127

3/67

12/335

6/149

1/120

4/174

14/944

12131/114249

543/6310

1228/7251

12/150

56/1205

74/1156

21/325

n/N

TB-nonDM

2.21 (0.64, 7.62)

4.18 (1.50, 11.66)

0.71 (0.17, 3.03)

2.27 (0.86, 6.00)

1.55 (0.36, 6.74)

0.40 (0.05, 3.32)

1.49 (1.06, 2.09)

0.96 (0.90, 1.03)

1.78 (1.07, 2.96)

1.48 (1.08, 2.03)

3.02 (1.64, 5.56)

0.82 (0.37, 1.81)

1.56 (1.37, 1.77)

1.52 (1.17, 1.98)

4.58 (1.83, 11.43)

4.31 (1.95, 9.52)

1.89 (0.57, 6.30)

10.36 (0.49, 220.01)

5.20 (1.50, 18.00)

2.49 (0.77, 8.04)

1.20 (0.75, 1.93)

5.10 (2.25, 11.56)

1.67 (1.24, 2.24)

1.22 (0.19, 7.65)

1.62 (0.44, 5.92)

1.04 (0.28, 3.77)

11.75 (1.44, 95.78)

2.21 (0.58, 8.44)

5.97 (2.83, 12.59)

0.81 (0.78, 0.85)

0.73 (0.60, 0.88)

0.74 (0.58, 0.96)

1.01 (0.27, 3.80)

3.53 (1.95, 6.40)

2.01 (1.18, 3.43)

1.67 (0.87, 3.19)

1.90 (1.43, 2.53)

Odds ratio (95% CI)

3.46

4.59

2.68

4.95

2.63

1.40

12.38

15.45

9.84

12.73

8.43

6.42

15.03

100.00

4.37

4.93

3.29

0.78

3.16

3.39

6.55

4.81

7.38

1.84

2.99

3.01

1.49

2.88

5.15

7.99

7.72

7.53

2.93

5.93

6.24

5.65

100.00

Weight

%

2.21 (0.64, 7.62)

4.18 (1.50, 11.66)

0.71 (0.17, 3.03)

2.27 (0.86, 6.00)

1.55 (0.36, 6.74)

0.40 (0.05, 3.32)

1.49 (1.06, 2.09)

0.96 (0.90, 1.03)

1.78 (1.07, 2.96)

1.48 (1.08, 2.03)

3.02 (1.64, 5.56)

0.82 (0.37, 1.81)

1.56 (1.37, 1.77)

1.52 (1.17, 1.98)

4.58 (1.83, 11.43)

4.31 (1.95, 9.52)

1.89 (0.57, 6.30)

10.36 (0.49, 220.01)

5.20 (1.50, 18.00)

2.49 (0.77, 8.04)

1.20 (0.75, 1.93)

5.10 (2.25, 11.56)

1.67 (1.24, 2.24)

1.22 (0.19, 7.65)

1.62 (0.44, 5.92)

1.04 (0.28, 3.77)

11.75 (1.44, 95.78)

2.21 (0.58, 8.44)

5.97 (2.83, 12.59)

0.81 (0.78, 0.85)

0.73 (0.60, 0.88)

0.74 (0.58, 0.96)

1.01 (0.27, 3.80)

3.53 (1.95, 6.40)

2.01 (1.18, 3.43)

1.67 (0.87, 3.19)

1.90 (1.43, 2.53)

Odds ratio (95% CI)

3.46

4.59

2.68

4.95

2.63

1.40

12.38

15.45

9.84

12.73

8.43

6.42

15.03

100.00

4.37

4.93

3.29

0.78

3.16

3.39

6.55

4.81

7.38

1.84

2.99

3.01

1.49

2.88

5.15

7.99

7.72

7.53

2.93

5.93

6.24

5.65

100.00

Weight

%

1.2 .5 1 2 3 5 10 15

DM: worse TB outcome, more resistance

• Slower bacteriological response

• More TB treatment failure

• More TB recurrence

• More early deaths

• More MDR-TB

Tegegne Syst Rev 2018

Baker, BMC Med 2011Faruholt-Jespen, TMIH 2013Reed, Plos One 2010

Huangfu et al SR 2019

Low quality of evidence base:timing and selection for DR testingadjustment for confounders?primary versus acquired?



Subhash, 2003

Fisher-Hoch, 2008

Fisher-Hoch, 2008

Hongguang, 2015

Viswanathan, 2014


Wang, 2001

Salindri, 2016

Study

Reis-Santos, 2013

Chang, 2011

Delgado-Sanchez, 2015

Bashar, 2001

Baghaei, 2016

Perez-Navarro, 2017

Perez-Navarro, 2015

Nissapatorn, 2005

Zhang, 2009

Siddiqui, 2016

Nandakumar, 2013

India

U.S.

Mexico

China

India

Brazil

Taiwan

Georgia

Country

Brazil

Taiwan

Mexico

U.S.

Iran

Mexico

Mexico

Malaysia

China

India

India

19/72

18/401

33/287

2/182

1/95

2/323

TB-DM

5/75

11/36

n/N

4/703

3/60

67/672

18/50

2/62

26/174

15/146

2/241

36/203

0/36

5/667

89/289

31/1041

79/1149

4/944

0/148

44/12472

TB-nonDM

29/376

41/232

n/N

60/17047

1/132

126/1614

10/105

0/64

16/318

5/263

4/1410

163/1938

4/150

5/2127

1.98 (1.51, 2.60)

0.81 (0.45, 1.44)

1.53 (0.85, 2.77)

1.76 (1.15, 2.70)

2.61 (0.47, 14.36)

4.71 (0.19, 116.93)

1.76 (0.42, 7.29)

0.85 (0.32, 2.28)

2.05 (0.93, 4.50)

Odds ratio (95% CI)

1.62 (0.59, 4.47)

6.89 (0.70, 67.71)

1.31 (0.96, 1.79)

5.34 (2.24, 12.76)

5.33 (0.25, 113.30)

3.32 (1.73, 6.37)

5.91 (2.10, 16.61)

2.94 (0.54, 16.15)

2.35 (1.58, 3.48)

0.45 (0.02, 8.47)

3.21 (0.93, 11.11)

100.00

8.70

8.56

10.65

2.14

0.67

2.89

%

4.97

6.52

Weight

4.76

1.27

12.21

5.79

0.74

7.86

4.64

2.14

11.11

0.79

3.56

1.98 (1.51, 2.60)

0.81 (0.45, 1.44)

1.53 (0.85, 2.77)

1.76 (1.15, 2.70)

2.61 (0.47, 14.36)

4.71 (0.19, 116.93)

1.76 (0.42, 7.29)

0.85 (0.32, 2.28)

2.05 (0.93, 4.50)

Odds ratio (95% CI)

1.62 (0.59, 4.47)

6.89 (0.70, 67.71)

1.31 (0.96, 1.79)

5.34 (2.24, 12.76)

5.33 (0.25, 113.30)

3.32 (1.73, 6.37)

5.91 (2.10, 16.61)

2.94 (0.54, 16.15)

2.35 (1.58, 3.48)

0.45 (0.02, 8.47)

3.21 (0.93, 11.11)

100.00

8.70

8.56

10.65

2.14

0.67

2.89

%

4.97

6.52

Weight

4.76

1.27

12.21

5.79

0.74

7.86

4.64

2.14

11.11

0.79

3.56

1.1 .5 1 1.98 3 5 10 15



Jimenez-Corona, 2013

Hung, 2015

Perez-Navarro, 2017

Tipayamongkholgul, 2016

Study

Lee, 2014

Mexico

Taiwan

Mexico

Thailand

Country

Taiwan

41/352

44/165

21/183

20/.

n/N

102/170

TB-DM

66/811

108/595

10/324

79/.

n/N

198/430

TB-nonDM

1.86 (1.51, 2.28)

1.76 (1.11, 2.79)

1.51 (1.04, 2.18)

1.86 (1.10, 3.14)

2.76 (1.66, 4.60)

ratio (95% CI)

1.96 (1.22, 3.15)

Odds

100.00

19.57

30.66

15.22

16.07

Weight

18.48

%

1.86 (1.51, 2.28)

1.76 (1.11, 2.79)

1.51 (1.04, 2.18)

1.86 (1.10, 3.14)

2.76 (1.66, 4.60)

ratio (95% CI)

1.96 (1.22, 3.15)

Odds

100.00

19.57

30.66

15.22

16.07

Weight

18.48

%

1 1.86 3 5

Call to Action

• Extended to become an age-structured deterministic compartmental model

• TB natural history was modulated by specific effects of having concurrent DM

Natural history of TB with

DM

Key assumptions for the DM-on-TB effects

Effect Description Effect size

Effects of DM on TB natural history (TB infection and TB disease)

Effect 1-Susceptibility DM increases susceptibility to TB infection 1.50

Effect 2-Fast progression DM increases the proportion of TB infections entering latent-fast state as opposed to latent-slow state 2.20

Effect 3-Reactivation DM increases the rate of developing TB disease among those with latent TB infection 1.00 (no effect)

Effect 4-Latent reinfection DM increases the susceptibility to TB reinfection among those with latent-slow TB infection 1.00 (no effect)

Effect 5-Smear positivity DM increases the proportion of new PTB# disease cases progressing to SP-PTB* as opposed to SN-PTB

Effect 6-Disease infectiousness

DM increases the infectiousness of PTB (SP-PTB and SN-PTB) for untreated and treated TB disease cases 1.46

Effect 7-TB mortality DM increases the hazard of TB-related mortality for untreated and treated TB disease cases 2.11

Effects of DM on TB treatment outcomes

Effect 8-Treatment failureDM reduces the proportion of successful treatment (through increased risk of treatment failure and MDR-TB)

1.00 (no effect)

Effect 9-RecoveryDM reduces the rate of TB recovery (i.e., prolongs the recovery time) for those who recover naturally or due to treatment

0.82

Effect 10-Cured reinfection DM increases susceptibility to TB reinfection among those treated or recovered from TB disease 1.80

1.25

0.67

DM role was investigated using a conservative approach whereby DM-

on-TB effect was set at its lowest likely value or set at the null value

• TB incidence and mortality rates were projected to decrease from 215 to 116 and from 40.7 to 15.7 per 100,000 population per year

• Annual number of incident and TB deaths cases were projected to decrease from 2.8 to 2.0 million and from 534,000 to 287,000

Model Fitting

DM in India

DM prevalence in India was projected to increase from 8.5% in 2017 to 12.1% in 2050

Proportion of TB incident

and mortality cases attributed to DM

TB incident cases

2017: 21.9% (95% UI: 12.1-26.4%)

2050: 33.3% (95% UI: 19.0%-44.1%)

TB-related deaths

2017: 28.9% (95% UI: 18.9%-34.1%)

2050: 42.8% (95% UI: 28.7%-53.1%)

The burgeoning DM epidemic in India was predicted to become a

leading driver of TB disease incidence and mortality over the coming

decades

Setting effect sizes of some of the effects based on best quality evidence → as much as half of TB disease cases and TB-related deaths could be attributed to DM by 2050

Could DM impact be

Underestimated?

Age-dependence of the TB-DM association also resulted in a larger impact on TB disease incidence and mortality

Age-dependence of the

TB-DM association

TB incident cases were

attributed to DM

2017: 27.9%

2050: 39.2%

TB-related deaths were attributed to DM

2017: 33.3%

2050: 45.41%

TB-DM bi-directionality resulted in a slightly larger impact on TB disease incidence and mortality

Implication of TB-DM

synergy

Different TB disease incidence trajectories over the next few decades resulted in minimal changes in the impact of DM on TB incidence and mortality

TB disease incidence

trajectories

• Exploring the TB-DM synergy implications assuming no change in the susceptibility to TB reinfection resulted in slightly larger impact for DM on TB disease incidence and mortality

• Exploring the TB-DM synergy implications assuming a 35% increase in the susceptibility to TB for reinfection, resulted in a relatively larger impact for DM on TB disease incidence and mortality

Risk of reinfection

rather than protective immunity

If young adults were assumed to be at higher risk of developing latent fast compared to older TB individuals, the impact of DM on TB disease incidence and mortality was reduced

Could DM impact be

Overestimated?

TB incident cases were

attributed to DM

2017: 12.6%

2050: 20.4%

TB-related deaths were

attributed to DM

2017: 17.7%

2050: 28.6%

Conclusions

• Currently one in every five TB disease cases is attributed to DM, and by 2050, one in every three will be attributed to DM

• DM is emerging as a leading driver of TB incidence and mortality in India, and possibly elsewhere

• Only a country-by-country approach, following the concept of “know your epidemic, know your response” for managing TB, has the potential to advance TB efforts towards TB elimination by 2050

• Intervention strategies should target DM patients before onset of TB disease, which is unfortunately more or less overlooked in the current TB strategies/guidance

• Objective 1: Develop a conceptual framework of DM effects on TB natural history and treatment outcomes, and to assess theoretically the implications of the DM effects on TB epidemiology for an improved understanding of the TB-DM synergy

• Objective 2: Quantify the effect of past and future trends in DM on TB incidence, prevalence and related deaths at the population level in a selected TB endemic country.

• Objective 3: Estimate the impact of interventions aimed at controlling TB transmission among people with DM

Clinical management of TB-DM

• TB treatment: longer? Higher dose rifampicin? TDM?

• How to attain glycemic control? Is this useful?

• More toxicity? need to monitor TB-DM more intensively?

• What explains higher mortality? Statins? Aspirin? Smoking cessation?

• Appreciate the heterogeneity of DM and TB-DM

• Need for more studies ‘beyond screening’ for TB-DM • including (pragmatic) clinical trials

• Transient hyperglycaemia – need to repeat screening tests later on in TB treatment

• WHAT HAPPENS AFTER THE END OF TB TREATMENT? [health systems development for multi-morbidity, referral to DM services]

Riza A et al, Lancet Diabetes 2016. Clinical management of concurrent TB and DMVan Crevel et al. IJTLD (in press). Clinical management of combined TB and DM

Effect of HIV on TB-DM?

16/09/2016 48

Oni et al 2017 Faurholt-Jepson 2011

Bailey et al 2016

TANDEM experience

• Variable TB-DM prevalence and phenotype across sites

• DM screening in TB pts: yes. TB screening in DM pts: maybe?

• Cost-effectiveness supports 2 step screening

• Repeated at end of treatment

• Appreciate the heterogeneity of DM / hyperglycaemia

• “Transient hyperglycaemia”: follow-up of patients (on-going, magnitude, effect on TB outcomes, prognosis…)

• Glycemic control: insulin or metformin? intensive monitoring?

• How important is glycemic control for TB outcomes?

• We should consider other treatments for DM pts (CVD risk)?

• Need for studies on clinical management of TB-DM

• Including (pragmatic) clinical trials aiming to improve joint management

Biological Plausibility• It is biologically plausible that the risk of developing DM is higher after TB disease

• Studies have shown IH and/or IGT during the early phases of active TB. Such metabolic states are linked with progression to overt DM amongst 20-50% of individuals after 3 to 5 years.

• TB disease may identify individuals at higher risk of progression to DM, as gestational DM identifies groups at high risk of progression to overt DM

• Changes in body composition during and following acute illness; limited evidence from cohort studies suggests weight re-gain during treatment could increase the proportion of body fat in recovered TB patients thus increasing future DM risk

• However, previous studies find hyperglycaemia observed amongst TB patients is intermittent, reversing after the early, acute phase of disease.

• T2DM is often undiagnosed due to the natural history of disease so misclassification bias could be occurring

• We excluded individuals <1 yr follow-up after TB diagnosis, and those developing DM within 1 year; this made little difference to the results, suggesting risk of DM among former TB patients cannot simply be a treatment effect

• Longer follow-up periods are required to fully tease out temporality

If findings are validated there are important clinical and public health implications: • Continuing health care of those with a TB history, not routinely longitudinally assessed

for DM may need to be amended• Lifetime risk of DM is already very high in many TB endemic countries; it could be highly

cost-effective to screen former TB patients at regular intervals for DM; reducing risks of complications and improving HR-QOL

• TB patients with DM also appear to be more infectious (higher bacterial load, and longer smear positivity) and experience more recurrent TB. This suggests TB patients at risk of, or already with, overt DM may be driving continued TB transmission disproportionately to their number and may require different treatment guidelines

• Whilst findings are likely of most importance for population health in high TB-DM burden countries, they may also be important among high risk groups; for example in the UK it has recently been estimated that 20% of PTB in Asian men can be statistically attributable to DM

• Co-morbid individuals: are more likely to be sputum positive, take longer to become sputum negative, have a higher risk of death or relapse.

Implications

Strengths and Weaknesses

• Cohort design and size allowing for temporal analyses and analyses by sub-type

• Inability to control for confounders due to aggregated data format• Recent analysis of UK CPRD primary care data found no effect modification from ethnicity,

age or duration of DM (Pealing). Similar findings with US NHANES data (Corris)• Low BMI is associated with TB, and high BMI with DM. TB patients also lose weight which is

rapidly regained on treatment, so single measures are not sufficient to adjust. More detailed analyses, potentially treating BMI as a time-varying exposure, are required. However, it is unlikely a 5 fold increased risk would be entirely due to residual confounding

• Loss to follow up due to migration

• Potential for Berksonian bias

• Misclassification bias

TANDEM: TB and Diabetes Mellitus


TANDEM

Aims

• Improve knowledge of the link between TB-DM

• Improve screening and management of DM among TB patients

• Impact on control of TB-DM co-morbidity

Consortium

• Multidisciplinary team of clinicians, epidemiologists and laboratory scientists

• Collaborating centres:• Indonesia• Romania• Peru• South Africa• European labs (Germany, UK,

Netherlands, Romania)

TANDEM data

• Approximately 2000 TB patients screened for DM

• Approx 150 clinical trial of increased glucose monitoring

• Strength of the study is in the breadth of data collected and longitudinal data

Data collected:

• Demographics

• Anthropometric measures

• Clinical data: family history, drugs, comorbidities

• Residential/employment status

• Range of DM glucose markers

• TB sputum smear and culture, CXR

• Smoking/alcohol intake

• Economic / Cost data (incl overheads, patient costs)

• Quality of life

• TB treatment outcomes, changes in hyperglycaemia over time

All newly diagnosed PTB patients (target n=2000)• history of DM

• Accredited Laboratory HbA1c (gold standard)

• POC HbA1c, RPG/RCG, FPG, urine dipstick,

• diabetes “risk scores” (alone/combination)

Screening TB patients for DM and DM patients for TB

6/21/201958

Feasibility, accuracy, and cost of those modalities

Previously known DM patients (target n=2000)• Symptom screen• CXR• OR clinical suspicion of TB • and followed by sputum / culture examination

•Most DM identified in TB patients was pre-existing not newly diagnosed DM

Screening TB patients for DM

Diabetes Screening

Aims

• Estimate prevalence of DM among patients presenting with PTB

• Assess the feasibility and accuracy of screening TB patients for DM and vice versa

Methods

• Crude and age-standardised prevalence estimated with exact binomial CI

• New and previously diagnosed DM included

• DM defined consistently using laboratory determined repeated HbA1c (>6.5%)

DM diagnosis in TB patients: any of these +

RPG >=6.1 mmol/l?

Yes

RPG>=11.1 mmol/l?

Yes:DM Symptoms?

Yes:

DM+

No: Take FBG

NoTake FBG.FBG>=7.0?

Yes:DM+

No: Stop

NoStop

Lab HbA1c >=6.5%

Yes

Repeat lab HbA1c>=6.5%

Yes:DM+

No:FBG >=7.0

NoStop

No: Stop

Yes:DM+

DM risk scoresFindrisc

(Age, BMI, WC, BP medication, Previous high glucose, Physical activity, Diet)

Indian Risk Score

(Age, BMI, WC, Family history, Physical activity)

Oman Risk Score

(Age, Sex, BMI, WC, Family history, Hypertension)

Full TANDEM score

• POC HbA1c

• RPG

• Age

Restricted TANDEM score

• RPG

• Age

• BMI

• Physical activity

Baseline dataIndonesia

(N=728)

Peru

(N=562)

Romania

(N=436)

South Africa

(N=259)

DM Marker N Median N Median N Median N Median

DM Cases 49 17 30 18

Age 728 36.7 562 28.8 436 41.8 259 33.7

Male 415 57.0% 328 58.4% 305 70.3% 166 64.1%

BMI 721 18.1 562 22.0 435 20.2 258 18.4

RPG 726 5.5 558 5.4 407 5.8 258 5.7

FBG 128 4.6 432 4.9 433 5.0 0

POC HbA1c 715 5.8 542 5.8 400 5.5 250 5.5

HbA1c (No DM) 679 5.6 545 5.5 406 5.8 241 5.8

HbA1c (DM Case) 49 10.7 17 7.9 30 6.7 18 6.8

Diagnostic Accuracy

Aims

• Identify the most accurate markers of DM among TB patients

• Are cheap clinical diagnostic risk scores a viable alternative to blood testing?

• Which combination of tests provides the best diagnostic accuracy?

Methods

• New DM cases defined consistently using laboratory HbA1c >6.5%

• Diagnostic accuracy assessed using AUROC, sensitivity and specificity

• Fractional polynomial functions considered for continuous predictors

Available Data

Indonesia (N=737) Peru (N=562) Romania (N=467) South Africa (N=259)


DM Cases 55 17 31 18

Age 737 36.9 562 28.8 467 41.9 259 33.7

BMI 731 18.1 562 22.0 466 20.2 258 18.4

RPG 735 5.5 558 5.4 432 5.8 258 5.7

FBG 132 4.6 446 4.9 463 4.9 0

POC HbA1c 724 5.8 542 5.8 407 5.5 250 5.5

Lab HbA1c (No DM) 682 5.6 545 5.5 436 5.8 241 5.8

Lab HbA1c (DM) 55 10.5 17 7.9 31 6.7 18 6.8

Overall diagnostic accuracyDM Marker AUROC (95% CI) Cut-point Sensitivity (95% CI) Specificity (95% CI)

POC HbA1c + Age 0.89 (0.86 - 0.92)

POC HbA1c 0.88 (0.85 - 0.91) ≥ 6.0 92.3 (0.9 - 100.0) 75.5 (43.1 - 92.6)

≥ 6.5 58.1 (1.6 - 99.2) 91.9 (62.9 - 98.7)

RPG + Age 0.84 (0.80 - 0.88)

RPG 0.82 (0.78 - 0.86) ≥ 6.1 76.0 (29.9 - 95.9) 66.4 (47.9 - 80.9)

FBG 0.77 (0.72 - 0.83) ≥ 6.0 47.3 (0.7 - 99.1) 91.5 (29.5 - 99.6)

Age 0.77 (0.73 - 0.80) ≥ 40 80.4 (44.1 - 95.5) 62.7 (42.3 - 79.4)

Oman Risk Score 0.76 (0.72 - 0.80) ≥ 7 82.7 (31.0 - 98.1) 58.3 (44.1 - 71.3)

Indian Risk Score 0.75 (0.71 - 0.79) ≥ 30 70.8 (38.7 - 90.3) 61.5 (46.1 - 74.9)

Findrisc Score 0.74 (0.70 - 0.79) ≥ 5 46.8 (18.0 - 77.9) 80.3 (62.7 - 90.9)

WHR 0.69 (0.64 - 0.73)

Urine Dipstick 0.68 (0.63 - 0.74) ≥ trace 32.8 (3.1 - 88.2) 98.4 (6.5 - 100.0)

BMI 0.67 (0.62 - 0.72)

Diagnostic accuracy – Stratified by country

DM Marker Cut-Point Sensitivity (%) Specificity (%) Sensitivity (%) Specificity (%)

Indonesia Peru

RPG/POC HbA1c HbA1c ≥6.5% 91.7 96.1 70.6 92.7

TANDEM Score ≥8.1 95.7 87.3 82.4 84.5

POC HbA1c ≥6.5% 91.5 87.6 88.2 81.1

RPG ≥11.1mmol/l 66.7 99.3 35.3 99.6

Romania South Africa

RPG/POC HbA1c HbA1c ≥6.5% 24.0 97.3 22.2 96.6

HbA1c ≥6.0% 40.0 87.7 22.2 90.6

TANDEM Score ≥8.1 50.0 78.4 33.3 90.5

POC HbA1c ≥6.5% 16.7 97.9 33.3 92.7

RPG ≥11.1mmol/l 15.4 98.7 11.1 99.6

FBG ≥ 5.8 mmol/l 46.7 82.4

Distribution of laboratory HbA1c in TB patients newly diagnosed with DM

Diagnostic accuracy by test and site

New DM cases confirmed with repeat HbA1c or FBG following TB treatment

DM Marker Cut-point AUROC Sensitivity (%) Specificity (%)

RPG/POC HbA1c

Combination1

POC HbA1c ≥ 6.0% 0.97 (0.92 - 1.00) 93.3 (79.2 – 99.2) 73.1 (52.2 – 88.4)

POC HbA1c ≥ 6.5% 93.8 (79.2 – 99.2) 100.0 (86.2 – 100.0)

POC HbA1c

≥ 6.2% 0.96 (0.92 – 1.0) 96.9 (83.8 - 99.9) 76.9 (56.4 – 91.0)

≥ 6.5% 93.8 (79.2 - 99.2) 92.3 (74.9 – 99.1)

≥ 7.0% 87.5 (71.0 – 96.5) 100.0 (86.8 – 100.0)

RPG 95.6 (87.6 - 99.1) 37.7 (35.5 - 39.9)

≥ 6.9mmol/l 0.87 (0.78 - 0.93) 87.7 (71.8 - 96.6) 57.7 (36.9- 76.6)

≥ 11.1mmol/l 66.7 (48.2 – 82.0) 100.0 (86.8 – 100.0)

16/09/2016 83Of the initial 121 newly diagnosed DM patients, 59 (48%) had a confirmatory test for DM following TB treatment. Of this 59, 33 had DM status confirmed by this test (56%)

These 59 patients were from Indonesia (25), Peru (3), Romania (22) and South Africa (9).

How to screen in practice? –agreement between POC & Lab HbA1c

Why “Error Grid”? Visually assess disagreement (POC & laboratory tests)

Clinically important difference

Alter clinical management of DM in TB patients

Our cut-points

6.5%: threshold for DM diagnosis

9% : pragmatic assessment of severe hyperglycaemia

New guidelines for TB-DM management 8% : changes in management might be needed (e.g. change

of medication, referral to DM services)

10% : urgent referral, hospitalisation

21/06/2019 84Huangfu et al, IUALTD 2019 (in press)

Results – individual level difference

Variables Mean(POC-Lab)

mean-2SD, mean+2SDP value

Total sample 0.15 -1.56, 1.84 <0.001

Age group <30yrs 0.19 -1.36, 1.73 Ref

30-39yrs 0.27 -1.79, 2.33 0.340

40-49yrs -0.001 -1.54, 1.54 0.017

50-59yrs 0.02 -1.39, 1.43 0.010

60yrs+ 0.13 -1.71, 1.97 0.704

Country Indonesia 0.26 -1.10, 1.62 Ref

Peru 0.55 -1.48, 2.58 <0.001

Romania -0.37 -1.47, 0.74 <0.001

South Africa -0.23 -1.70, 1.25 <0.001

Anaemia Non-anaemia 0.12 -1.58, 1.82 Ref

Mild anaemia 0.11 -1.55, 1.78 0.920

Moderate anaemia 0.20 -1.45, 1.85 0.523

Severe anaemia 1.07 -0.93, 3.06 0.038

Lab HbA1c <5.7 0.37 -1.33, 2.07 Ref

5.7-6.4 -0.11 -1.32, 1.11 0.014

6.5-8.9 -0.60 -2.16, 0.97 0.011

9+ -0.13 -3.09, 2.82 0.020

21/06/2019 85

For example, a person

with POC of 6.5% lab

HbA1c 5.0% - 7.9%

Huangfu et al, IUALTD 2019 (in press)

Pragmatic 2-step Combination

• POC HbA1c testing most accurate (and supported by TB-DM guidelines). However, it is expensive.

• Non-fasting plasma glucose (RPG) is much cheaper but with lower anticipated accuracy.

• Combining these two measurements, using RPG as an initial screen could increase diagnostic accuracy and reduce cost (by about 70% in TANDEM).

The 2-step process:• Every individual is tested with RPG

o RPG ≥11.1mmol/l is determined to be DM. No further test performed

o RPG <6.1mmol/l is determined not to be DM. No further test performed

• Everyone else is then tested with POC HbA1c

o POC HbA1c ≥6.5% is determined to be DM

Cost per case accurately diagnosed TB screening and diagnostic tests (Bandung, Indonesia)

21/06/2019 90

Diagnostic

approach

Unit

cost, US$

N people

screened

Sensitivity /

Specificity

Cost per case

accurately

diagnosed

Incremental

cost

GOLD STANDARD

Repeated HbA1c$29.10 672 1.0 / 1.0 $29.12 $14.57

RPG then GOLD

STANDARD$30.16 671 0.30 / 1.0 $34.84 $33.47

POC RPG $1.06 672 0.93 / 0.76 $1.38

Omani Risk Score

(>30)$0.7 642 0.84 / 0.57 $1.18

POC HbA1c $7.19 665 1.0 / 0.635 $11.02

RPG

POC HbA1c$8.25 671 0.923 /

0.915

$11.53 $10.15

Feasibility of DM screening• Simple point-of-care methods of

screening for DM have good diagnostic accuracy in some sites

• RPG followed by POC HbA1c maximises sensitivity / efficiency

No one-size-fits-all approach

• Diagnostic accuracy depends on undiagnosed DM disease “severity”

Why is there so much heterogeneity by country/region?

• Are national healthcare systems already finding DM cases?

• Delay in TB treatment?

• “Stress hyperglycaemia”

Cost per case accurately diagnosed TB screening and diagnostic tests (Bandung, Indonesia)

21/06/2019 104

Diagnostic

approach

Unit

cost,

US$

N people

screened

Sensitivity /

Specificity

N

needing

confirmat

ory tests

Cost per case

accurately

diagnosed

Incremental

cost

GOLD STANDARD

(smear +culture)$45.66 112 0.56 / 1.0 5 $47.99 $34.01

Symptom Screen $2.07 809 0.91 / 0.55 370 $3.74

CXR $17.51 802 0.91 / 0.90 57 $19.42

Stepped

symptoms then

two ZN smears

£14.98 809 0.273 / 0.998 4 $17.93 $14.20

Stepped

symptoms then

CXR

$23.95 809 0.82 / 0.94 57 $23.95 $20.22

Feasibility of Bi-Directional TB-DM ScreeningScreening for DM among TB

• Prevalence >10% in each setting

• Prevalence >30% in those aged 50+

• Associations with BMI and family history of DM

• 2 step combinations (RPG, HbA1c) may be feasible and most cost-effective

• Heterogeneity between sites

• Repeat screening at end of TB treatment to confirm diagnosis

Screening for TB among DM

• Prevalence of 1% for probably/definite TB

• Prevalence of 3-16% for any previous/current occurrence of TB

• Associations with longer duration of DM and education level

Further work / Unanswered questions• Describe the degree of transient hyperglycaemia & its associations with TB

outcomes and longer term outcomes (where possible) • Co-morbidities• Age• Initial FBG

• Altering cut-points (data driven) in places where algorithms performed less well (Romania)

• HbA1c and FPG – patient characteristics, associations with longer term outcomes

TANDEM experience

• Variable TB-DM prevalence and phenotype across sites

• DM screening in TB pts: yes. TB screening in DM pts: maybe?

• Cost-effectiveness supports 2 step screening

• Repeated at end of treatment

• Appreciate the heterogeneity of DM / hyperglycaemia

• “Transient hyperglycaemia”: follow-up of patients (on-going, magnitude, effect on TB outcomes, prognosis…)

• Glycemic control: insulin or metformin? intensive monitoring?

• How important is glycemic control for TB outcomes?

• We should consider other treatments for DM pts (CVD risk)?

• Need for studies on clinical management of TB-DM

• Including (pragmatic) clinical trials aiming to improve joint management

TANDEM Sites

Indonesia Peru

TANDEM Sites


113

Thanks to all the members of TANDEM, all the field staff and patients

• Bandung: Bachti Alisjabana, Rovina Ruslami, Raspati Koesoemadinata

• Craiova: Mihai Ioana, Nicolae Panduru, Anca Riza

• Lima: Cesar Ugarte-Gill• Stellenbosch: Katharina

Ronacher, Gerhard Walzl, Stephane Malherbe

• Nijmegen: Reinout van Crevel• Otago: Philip Hill, Sue

McAllister• St Georges London: Julia

Critchley, Sarah Kerry, Daniel Grint, Fiona Pearson

• LSHTM: Dave Moore, Ulla Griffiths, Yoko Laurence, Hazel Dockrell

TANDEM Funding

114

TANDEM: Tuberculosis and Diabetes Mellituswww.tandem-fp7.eu


Diagnostic Accuracy

Aims

• Identify the most accurate markers of DM among TB patients

• Are cheap clinical diagnostic risk scores a viable alternative to blood testing?

• Which combination of tests provides the best diagnostic accuracy?

Methods

• New DM cases defined consistently using laboratory HbA1c >6.5%

• Diagnostic accuracy assessed using AUROC, sensitivity and specificity

• Fractional polynomial functions considered for continuous predictors

Available Data

Indonesia (N=737) Peru (N=562) Romania (N=467) South Africa (N=259)


DM Cases 55 17 31 18

Age 737 36.9 562 28.8 467 41.9 259 33.7

BMI 731 18.1 562 22.0 466 20.2 258 18.4

RPG 735 5.5 558 5.4 432 5.8 258 5.7

FBG 132 4.6 446 4.9 463 4.9 0

POC HbA1c 724 5.8 542 5.8 407 5.5 250 5.5

Lab HbA1c (No DM) 682 5.6 545 5.5 436 5.8 241 5.8

Lab HbA1c (DM) 55 10.5 17 7.9 31 6.7 18 6.8

Overall diagnostic accuracyDM Marker AUROC (95% CI) Cut-point Sensitivity (95% CI) Specificity (95% CI)

POC HbA1c + Age 0.89 (0.86 - 0.92)

POC HbA1c 0.88 (0.85 - 0.91) ≥ 6.0 92.3 (0.9 - 100.0) 75.5 (43.1 - 92.6)

≥ 6.5 58.1 (1.6 - 99.2) 91.9 (62.9 - 98.7)

RPG + Age 0.84 (0.80 - 0.88)

RPG 0.82 (0.78 - 0.86) ≥ 6.1 76.0 (29.9 - 95.9) 66.4 (47.9 - 80.9)

FBG 0.77 (0.72 - 0.83) ≥ 6.0 47.3 (0.7 - 99.1) 91.5 (29.5 - 99.6)

Age 0.77 (0.73 - 0.80) ≥ 40 80.4 (44.1 - 95.5) 62.7 (42.3 - 79.4)

Oman Risk Score 0.76 (0.72 - 0.80) ≥ 7 82.7 (31.0 - 98.1) 58.3 (44.1 - 71.3)


Findrisc Score 0.74 (0.70 - 0.79) ≥ 5 46.8 (18.0 - 77.9) 80.3 (62.7 - 90.9)

WHR 0.69 (0.64 - 0.73)


BMI 0.67 (0.62 - 0.72)

Diagnostic accuracy – Stratified by country

DM Marker Cut-Point Sensitivity (%) Specificity (%) Sensitivity (%) Specificity (%)

Indonesia Peru

RPG/POC HbA1c HbA1c ≥6.5% 91.7 96.1 70.6 92.7

TANDEM Score ≥8.1 95.7 87.3 82.4 84.5

POC HbA1c ≥6.5% 91.5 87.6 88.2 81.1

RPG ≥11.1mmol/l 66.7 99.3 35.3 99.6


RPG/POC HbA1c HbA1c ≥6.5% 24.0 97.3 22.2 96.6

HbA1c ≥6.0% 40.0 87.7 22.2 90.6

TANDEM Score ≥8.1 50.0 78.4 33.3 90.5

POC HbA1c ≥6.5% 16.7 97.9 33.3 92.7

RPG ≥11.1mmol/l 15.4 98.7 11.1 99.6

FBG ≥ 5.8 mmol/l 46.7 82.4

Diagnostic accuracy by test and site

Diagnostic accuracy – Romania/RSA

DM Marker AUROC (95% CI) Cut-point Sensitivity (95% CI) Specificity (95% CI)

RPG/POC HbA1c 2-step HbA1c ≥ 6.0 49.2 (36.4 - 62.1) 88.0 (85.4 - 90.3)

HbA1c ≥ 6.5 27.0 (16.6 - 39.7) 94.2 (92.3 - 95.8)

POC HbA1c + Age 0.80 (0.75 - 0.86)

RPG + Age 0.78 (0.72 - 0.84)

Age 0.76 (0.70 - 0.82) ≥ 40 81.5 (70.0 - 90.1) 62.6 (59.0 - 66.1)

Oman Risk Score 0.70 (0.63 - 0.77) ≥ 7 81.5 (70.0 - 90.1) 57.6 (54.0 - 61.2)

POC HbA1c 0.69 (0.62 - 0.76) ≥ 6.0 55.6 (42.5 - 68.1) 71.9 (68.4 - 75.1)

≥ 6.5 27.0 (16.6 - 39.7) 87.7 (85.1 - 90.0)


RPG 0.68 (0.60 - 0.76) ≥ 6.1 65.1 (52.0 - 76.7) 63.6 (60.0 - 67.1)

≥ 6.5 55.6 (42.5 - 68.1) 73.5 (70.1 - 76.6)

FBG 0.66 (0.57 - 0.74) ≥ 6.0 35.9 (24.3 - 48.9) 89.0 (86.4 - 91.3)


Pragmatic 2-step Combination• It is anticipated that POC HbA1c testing will be the most

accurate. However, it is expensive. Non-fasting plasma glucose (RPG) is much cheaper but with lower anticipated accuracy.

• Combining these two measurements, using RPG as an initial screen could increase diagnostic accuracy and reduce cost (by about 70% in TANDEM).

The 2-step process:• Every individual is tested with RPG

o RPG ≥11.1mmol/l is determined to be DM. No further test performed

o RPG <6.1mmol/l is determined not to be DM. No further test performed

• Everyone else is then tested with POC HbA1c

o POC HbA1c ≥6.5% is determined to be DM

Distribution of laboratory HbA1c (new DM)

New and previous DM

Effects of DM on TB treatment outcomes: systematic review and meta-analysis

Peijue Huangfu, Cesar Ugarte

134

Systematic Review of TB outcomes amongst those with DM

• 2011 review (33 studies): suggested diabetes worsens the treatmentoutcomes among TB patients [Baker et al. 2011]

• Included studies: relatively poor in quality (observational data)

• Modest effect size (RR for death or treatment failure - 1.69, 95% CI 1.36 to 2.12), possibly influenced by publication bias, study quality (e.g. little control of confounding; only 4 of 33 studies attempted any adjustment!)

• 2011-2015, many more studies have emerged—some better designed

• Aim: update this systematic review, assess effects of study quality on outcomes and over time

135

Method

• Databases 1980-2015: PubMed, EMBASE and regional databases (e.g.LILACS and WHO Regional libraries, Cochrane Libraries)

• Comprehensive search including terms for outcomes and exposure• TB AND DM

• TB AND Risk factors OR Treatment Outcomes [and synonyms]

• Reduce risk of bias from identifying only studies which mention diabetes in the title or abstract [or MeSH headings]

136

Methods• Outcomes (WHO guidelines):

• 1. Failure and death

• 2. Death

• 3. Relapse

• 4. Recurrence

• 5. Multi-drug resistant TB (MDRTB)

• Inclusion criteria: all cohort and case-control studies (all languages)

• Exclusion criteria: severely ill patients and MDRTB at baseline

• Data extraction:

• Study design

• Study characteristics

• Key treatment outcomes

• Aspects of study quality137

Statistical analyses

• Random effects meta-analysis due to expected heterogeneity

• Subgroup analysesControl of confounding

Study designs

Diabetes definition

138

Study selection process

139

Records screened(n= 12611)

Full-text articles assessed for eligibility

(n=355)

Records excluded(n= 12256)

Studies included in qualitative and

quantitative syntheses(n=90)

Full-text articles excluded (n=267)

• case series (n=4)• review article (n=8)• Letter to editor (n=3)• No DM exposure comparator (n=90)• Editorial (n=2)• No standard TB treatment popn

(n=14)• Conference abstract (n=29)• Not available for abstraction (n=66)• Patients with severe conditions (n=6)• No TB treatment recorded (n=45)

Scre

enin

gEl

igib

ility

Incl

usi

on

Results- 1. Death

140

48 unadjusted studies: diabetes doubled the risk of death among DM-TB patients4 adjusted studies: 4 times the risk of deathamong DM-TB patients


Reed, 2013

Study

Wang, 2009

Oursler, 2002

Dooley, 2009

R.O.K.

Country

Taiwan

U.S.

U.S.

21/162

n/N

13/74

TB-DM

8/18

6/42

30/495

n/N

11/143

TB-nonDM

14/108

20/255

4.04 (2.70, 6.05)

2.18 (1.21, 3.93)

ratio (95% CI)

7.60 (3.22, 17.93)

Odds

6.70 (2.26, 19.85)

6.50 (2.45, 17.27)

100.00

47.02

Weight

22.11

%

13.81

17.05

4.04 (2.70, 6.05)

2.18 (1.21, 3.93)

ratio (95% CI)

7.60 (3.22, 17.93)

Odds

6.70 (2.26, 19.85)

6.50 (2.45, 17.27)

100.00

47.02

Weight

22.11

%

13.81

17.05

11 2 4.04 6 15 30



Chen, 2015

Viswanathan, 2014

Orofino Rde, 2011

Singla, 2006


Chiang, 2015

Morsy, 2003

Study

Mboussa, 2003

Anunnatsiri, 2005



Cavanaugh, 2015

Nandakumar, 2013

Namukwaya, 2011

Choi, 2014

Mi, 2013

Viswanathan, 2014

Centis, 1999 report

Chiang, 2009

Tabarsi, 2014

Centis, 1998 report

Sangral, 2012

Uchimura, 2013


Alo, 2014

China

India

Brazil

K.S.A.

Italy

Taiwan

Egypt

Country

Congo

Thailand

Italy

Mexico

Kiribati

India

Uganda

R.O.K.

China

India

Italy

Taiwan

Iran

Italy

India

Japan

Italy

Fiji

15/182

8/89

4/14

1/134

5/50

101/705

31/40

n/N

13/32

4/38

2/41

TB-DM

28/363

5/101

74/667

2/2

./.

12/97

4/96

2/40

60/241

2/37

5/41

4/23

1281/5622

3/32

3/53

14/944

1/120

21/294

7/384

20/773

78/768

88/198

n/N

13/100

11/188

45/666

TB-nonDM

55/847

4/174

148/2127

48/148

./.

13/483

6/149

28/852

161/886

3/67

61/1059

20/257

6578/28077

33/737

12/335

2.08 (1.58, 2.74)

5.97 (2.83, 12.59)

11.75 (1.44, 95.78)

5.20 (1.50, 18.00)

0.40 (0.05, 3.32)

4.18 (1.50, 11.66)

1.48 (1.08, 2.03)

4.31 (1.95, 9.52)

Odds ratio (95% CI)

4.58 (1.83, 11.43)

1.89 (0.57, 6.30)

0.71 (0.17, 3.03)

1.20 (0.75, 1.93)

2.21 (0.58, 8.44)

1.67 (1.24, 2.24)

10.36 (0.49, 220.01)

1.78 (1.09, 2.92)

5.10 (2.25, 11.56)

1.04 (0.28, 3.77)

1.55 (0.36, 6.74)

1.49 (1.06, 2.09)

1.22 (0.19, 7.65)

2.27 (0.86, 6.00)

2.49 (0.77, 8.04)

0.96 (0.90, 1.03)

2.21 (0.64, 7.62)

1.62 (0.44, 5.92)

100.00

5.02

1.42

3.04

1.41

3.78

7.19

4.79

Weight

4.23

3.16

2.48

%

6.44

2.76

7.28

0.74

6.33

4.67

2.89

2.43

7.09

1.75

4.00

3.26

7.90

3.05

2.87

2.08 (1.58, 2.74)

5.97 (2.83, 12.59)

11.75 (1.44, 95.78)

5.20 (1.50, 18.00)

0.40 (0.05, 3.32)

4.18 (1.50, 11.66)

1.48 (1.08, 2.03)

4.31 (1.95, 9.52)

Odds ratio (95% CI)

4.58 (1.83, 11.43)

1.89 (0.57, 6.30)

0.71 (0.17, 3.03)

1.20 (0.75, 1.93)

2.21 (0.58, 8.44)

1.67 (1.24, 2.24)

10.36 (0.49, 220.01)

1.78 (1.09, 2.92)

5.10 (2.25, 11.56)

1.04 (0.28, 3.77)

1.55 (0.36, 6.74)

1.49 (1.06, 2.09)

1.22 (0.19, 7.65)

2.27 (0.86, 6.00)

2.49 (0.77, 8.04)

0.96 (0.90, 1.03)

2.21 (0.64, 7.62)

1.62 (0.44, 5.92)

100.00

5.02

1.42

3.04

1.41

3.78

7.19

4.79

Weight

4.23

3.16

2.48

%

6.44

2.76

7.28

0.74

6.33

4.67

2.89

2.43

7.09

1.75

4.00

3.26

7.90

3.05

2.87

1.2 .5 1 2.08 3 5 10 15

Results- 2. Failure and death

141

25 studies: 2 times the risk of treatment failure and death among DM-TB patients. 6 adjusted studies: 5 times the risk of treatment failure and death among DM-TB patients


Wang, 2009

Study

Anunnatsiri, 2005

Morsy, 2003

Dooley, 2009

Oursler, 2002

Reed, 2013

Taiwan

Country

Thailand

Egypt

U.S.

U.S.

R.O.K.

13/74

n/N

4/38

31/40

6/42

8/18

TB-DM

21/162

11/143

n/N

11/188

88/198

20/255

14/108

TB-nonDM

30/495

4.96 (3.52, 7.00)

7.60 (3.22, 17.93)

ratio (95% CI)

7.20 (2.16, 23.95)

9.32 (4.22, 20.60)

6.50 (2.45, 17.27)

6.70 (2.26, 19.85)

Odds

2.18 (1.21, 3.93)

100.00

16.12

Weight

8.22

18.87

12.43

10.07

%

34.28

4.96 (3.52, 7.00)

7.60 (3.22, 17.93)

ratio (95% CI)

7.20 (2.16, 23.95)

9.32 (4.22, 20.60)

6.50 (2.45, 17.27)

6.70 (2.26, 19.85)

Odds

2.18 (1.21, 3.93)

100.00

16.12

Weight

8.22

18.87

12.43

10.07

%

34.28

11 2 4.96 8 15 30

Results- 3. Relapse/Recurrence

16/09/2016 142

Diabetes associated with 2 fold increased risk of TB relapse



Wang, 2015

Study


Mboussa, 2003

Bashar, 2001

Pérez-Navarro, 2011

Maâlej, 2009

Zhang, 2009

Lee, 2014

Fisher-Hoch, 2008

Singla, 2006

De Oliveira, 2000

Wada, 2000

El Sahly, 2004

Taiwan

Country

Mexico

Congo

U.S.

Mexico

Tunisia

China

Taiwan

U.S.

K.S.A.

Brazil

Japan

U.S.

279/12688

n/N

41/352

6/32

10/50

./.

4/55

33/165

102/170

23/401

2/130

6/11

7/61

10/42

TB-DM

597/43195

n/N

66/811

9/100

4/105

./.

1/82

9/170

198/430

52/1041

3/367

50/150

4/284

76/302

TB-nonDM

1.80 (1.40, 2.30)

1.60 (1.39, 1.85)

ratio (95% CI)

1.49 (0.99, 2.25)

2.33 (0.76, 7.16)

6.31 (1.87, 21.30)

1.22 (0.83, 1.79)

6.35 (0.69, 58.44)

4.47 (2.07, 9.68)

1.76 (1.23, 2.52)

1.16 (0.70, 1.92)

1.90 (0.31, 11.47)

2.40 (0.70, 8.25)

9.07 (2.57, 32.07)

0.93 (0.44, 1.98)

Odds

100.00

18.39

Weight

12.76

3.94

3.45

13.43

1.18

6.79

13.88

10.85

1.74

3.37

3.24

6.98

%

1.80 (1.40, 2.30)

1.60 (1.39, 1.85)

ratio (95% CI)

1.49 (0.99, 2.25)

2.33 (0.76, 7.16)

6.31 (1.87, 21.30)

1.22 (0.83, 1.79)

6.35 (0.69, 58.44)

4.47 (2.07, 9.68)

1.76 (1.23, 2.52)

1.16 (0.70, 1.92)

1.90 (0.31, 11.47)

2.40 (0.70, 8.25)

9.07 (2.57, 32.07)

0.93 (0.44, 1.98)

Odds

100.00

18.39

Weight

12.76

3.94

3.45

13.43

1.18

6.79

13.88

10.85

1.74

3.37

3.24

6.98

%

1.5 1 1.8 5 10 15

143



Fisher-Hoch, 2008

Chang, 2011

Subhash, 2003

Viswanathan, 2014

Nandakumar, 2013

Study


Fisher-Hoch, 2008

Reis-Santos, 2013

Chen, 2015

Nissapatorn, 2005

Zhang, 2009

Wang, 2001

Perez-Navarro, 2015

Bashar, 2001

U.S.

Taiwan

India

India

India

Country

Brazil

Mexico

Brazil

China

Malaysia

China

Taiwan

Mexico

U.S.

18/401

3/60

19/72

1/95

5/667

TB-DM

n/N

2/323

33/287

4/703

2/182

2/241

36/203

5/75

15/146

18/50

31/1041

1/132

89/289

0/148

5/2127

TB-nonDM

n/N

44/12472

79/1149

60/17047

4/944

4/1410

163/1938

29/376

5/263

10/105

2.04 (1.45, 2.85)

1.53 (0.85, 2.77)

6.89 (0.70, 67.71)

0.81 (0.45, 1.44)

4.71 (0.19, 116.93)

3.21 (0.93, 11.11)

Odds ratio (95% CI)

1.76 (0.42, 7.29)

1.76 (1.15, 2.70)

1.62 (0.59, 4.47)

2.61 (0.47, 14.36)

2.94 (0.54, 16.15)

2.35 (1.58, 3.48)

0.85 (0.32, 2.28)

5.91 (2.10, 16.61)

5.34 (2.24, 12.76)

100.00

11.67

1.93

11.84

1.03

5.22

%

Weight

4.28

14.11

6.85

3.20

3.21

14.63

7.13

6.69

8.21

2.04 (1.45, 2.85)

1.53 (0.85, 2.77)

6.89 (0.70, 67.71)

0.81 (0.45, 1.44)

4.71 (0.19, 116.93)

3.21 (0.93, 11.11)

Odds ratio (95% CI)

1.76 (0.42, 7.29)

1.76 (1.15, 2.70)

1.62 (0.59, 4.47)

2.61 (0.47, 14.36)

2.94 (0.54, 16.15)

2.35 (1.58, 3.48)

0.85 (0.32, 2.28)

5.91 (2.10, 16.61)

5.34 (2.24, 12.76)

100.00

11.67

1.93

11.84

1.03

5.22

%

Weight

4.28

14.11

6.85

3.20

3.21

14.63

7.13

6.69

8.21

1.1 .5 1 2.04 3 5 10 15

Results- 4. MDR-TB

Diabetes associated with 2 fold increased risk of MDR-TB

144


.

.

1: Medical/self-reported

Burgielski, 1985

Kitahara, 1994




Centis, 1998 report

Bashar, 2001

Fielder, 2002

Centis, 1999 report

Oursler, 2002

Mathew, 2006

Pina, 2006

Kourbatova, 2006

Vasankari, 2007

Hasibi, 2008

Amnuaiphon, 2009

Maâlej, 2009

Chiang, 2009

Vasankari, 2010

Orofino Rde, 2011

Sangral, 2012


Sulaiman, 2013

Reis-Santos, 2013


Magee, 2014

Moosazadeh, 2014

Lin, 2014

Wu, 2015

Chiang, 2015


2: Blood glucose

Mboussa, 2003

Singla, 2006

Alisjahbana, 2007

Touré, 2007

Dooley, 2009

Wang, 2009

Tatar, 2009

Uchimura, 2013

Mi, 2013

Alavi-Naini, 2013

Reed, 2013

Nandakumar, 2013

Tabarsi, 2014

Alo, 2014

Viswanathan, 2014

Viswanathan, 2014

Cavanaugh, 2015

Chen, 2015


Study

Poland

Japan

Italy

Italy

Italy

Italy

U.S.

U.S.

Italy

U.S.

Russia

Spain

Russia

Finland

Iran

Thailand

Tunisia

Taiwan

Finland

Brazil

India

Mexico

Malaysia

Brazil

Brazil

U.S.

Iran

Taiwan

Taiwan

Taiwan

Congo

K.S.A

Indonesia

Senegal

U.S.

Taiwan

Turkey

Japan

China

Iran

R.O.K.

India

Iran

Fiji

India

India

Kiribati

China

Country

38/90

3/71

3/32

4/50

1/41

5/41

7/50

13/22

2/40

8/18

8/44

8/73

5/20

22/92

3/6

376/4188

5/60

52/241

7/26

4/14

2/23

11/363

19/342

73/703

39/323

16/151

26/140

65/182

1645/7637

79/705

8/32

1/134

2/94

18/86

6/42

13/74

2/78

1193/5622

2/97

40/108

21/162

42/667

1/37

3/53

0/96

1/89

4/101

9/182

n/N

TB-DM

19/90

14/449

29/737

19/773

43/666

49/1059

1/105

29/152

26/852

14/108

75/1872

97/1438

87/440

86/537

6/44

19/290

0/82

137/886

25/205

15/294

16/257

36/847

42/914

995/17047

712/12472

66/1167

123/824

184/565

3939/26214

61/768

8/100

3/384

0/540

6/88

20/255

11/143

0/78

6282/28077

2/483

35/607

30/495

71/2127

1/67

11/335

5/149

1/120

2/174

9/944

n/N

TB-nonDM

2.73 (1.42, 5.27)

1.37 (0.38, 4.90)

2.53 (0.73, 8.77)

3.45 (1.13, 10.56)

0.36 (0.05, 2.70)

2.86 (1.08, 7.62)

16.93 (2.02, 141.78)

6.13 (2.39, 15.70)

1.67 (0.38, 7.31)

5.37 (1.81, 15.91)

5.32 (2.39, 11.85)

1.70 (0.79, 3.65)

1.35 (0.48, 3.82)

1.65 (0.97, 2.80)

6.33 (1.03, 38.98)

1.41 (0.87, 2.27)

16.35 (0.89, 301.66)

1.50 (1.05, 2.15)

2.65 (1.01, 6.94)

7.44 (2.09, 26.51)

1.43 (0.31, 6.67)

0.70 (0.35, 1.40)

1.22 (0.70, 2.13)

1.87 (1.45, 2.40)

2.27 (1.61, 3.20)

1.98 (1.11, 3.51)

1.30 (0.81, 2.07)

1.15 (0.81, 1.63)

1.55 (1.46, 1.66)

1.46 (1.03, 2.08)

1.83 (1.56, 2.13)

3.83 (1.30, 11.27)

0.95 (0.10, 9.26)

29.22 (1.39, 613.44)

3.62 (1.36, 9.62)

1.96 (0.74, 5.20)

2.56 (1.08, 6.03)

5.13 (0.24, 108.62)

0.93 (0.87, 1.00)

5.06 (0.70, 36.39)

9.61 (5.72, 16.15)

2.31 (1.28, 4.16)

1.95 (1.32, 2.88)

1.83 (0.11, 30.19)

1.77 (0.48, 6.56)

0.14 (0.01, 2.49)

1.35 (0.08, 21.92)

3.55 (0.64, 19.72)

5.40 (2.12, 13.81)

2.65 (1.55, 4.52)

Odds ratio (95% CI)

3.59

1.30

1.35

1.62

0.56

2.01

0.51

2.14

1.00

1.70

2.74

2.94

1.83

4.61

0.68

5.16

0.28

6.56

2.07

1.30

0.93

3.39

4.39

7.96

6.74

4.23

5.26

6.64

9.87

6.64

100.00

6.66

3.45

2.31

6.99

6.99

7.39

2.30

9.11

4.07

8.40

8.22

8.70

2.60

5.89

2.47

2.63

4.71

7.12

100.00

Weight

%

2.73 (1.42, 5.27)

1.37 (0.38, 4.90)

2.53 (0.73, 8.77)

3.45 (1.13, 10.56)

0.36 (0.05, 2.70)

2.86 (1.08, 7.62)

16.93 (2.02, 141.78)

6.13 (2.39, 15.70)

1.67 (0.38, 7.31)

5.37 (1.81, 15.91)

5.32 (2.39, 11.85)

1.70 (0.79, 3.65)

1.35 (0.48, 3.82)

1.65 (0.97, 2.80)

6.33 (1.03, 38.98)

1.41 (0.87, 2.27)

16.35 (0.89, 301.66)

1.50 (1.05, 2.15)

2.65 (1.01, 6.94)

7.44 (2.09, 26.51)

1.43 (0.31, 6.67)

0.70 (0.35, 1.40)

1.22 (0.70, 2.13)

1.87 (1.45, 2.40)

2.27 (1.61, 3.20)

1.98 (1.11, 3.51)

1.30 (0.81, 2.07)

1.15 (0.81, 1.63)

1.55 (1.46, 1.66)

1.46 (1.03, 2.08)

1.83 (1.56, 2.13)

3.83 (1.30, 11.27)

0.95 (0.10, 9.26)

29.22 (1.39, 613.44)

3.62 (1.36, 9.62)

1.96 (0.74, 5.20)

2.56 (1.08, 6.03)

5.13 (0.24, 108.62)

0.93 (0.87, 1.00)

5.06 (0.70, 36.39)

9.61 (5.72, 16.15)

2.31 (1.28, 4.16)

1.95 (1.32, 2.88)

1.83 (0.11, 30.19)

1.77 (0.48, 6.56)

0.14 (0.01, 2.49)

1.35 (0.08, 21.92)

3.55 (0.64, 19.72)

5.40 (2.12, 13.81)

2.65 (1.55, 4.52)

Odds ratio (95% CI)

3.59

1.30

1.35

1.62

0.56

2.01

0.51

2.14

1.00

1.70

2.74

2.94

1.83

4.61

0.68

5.16

0.28

6.56

2.07

1.30

0.93

3.39

4.39

7.96

6.74

4.23

5.26

6.64

9.87

6.64

100.00

6.66

3.45

2.31

6.99

6.99

7.39

2.30

9.11

4.07

8.40

8.22

8.70

2.60

5.89

2.47

2.63

4.71

7.12

100.00

Weight

%

1.2 .5 1 2 3 5 10 15 20

Subgroup analyses: death(medical records/self-reported - equivalent to previously diagnosed diabetes surrogate measures e.g. blood glucose, HbA1c includes previously diagnosed and newly diagnosed DM)

The association was stronger amongst studies using surrogate DM measures

16/09/2016 145NOTE: Weights are from random effects analysis

.

.

1: High incomeBurgielski, 1985Kitahara, 1994Ambrosetti, 1995 reportAmbrosetti, 1996 reportAmbrosetti, 1997 reportCentis, 1998 reportBashar, 2001Fielder, 2002Centis, 1999 reportOursler, 2002Mathew, 2006Pina, 2006Singla, 2006Kourbatova, 2006Vasankari, 2007Dooley, 2009Wang, 2009Chiang, 2009Vasankari, 2010Uchimura, 2013Reed, 2013Magee, 2014Lin, 2014Wu, 2015Chiang, 2015Subtotal (I-squared = 86.4%, p = 0.000)

2: Upper/Lower-middle/LowMboussa, 2003Alisjahbana, 2007Touré, 2007Hasibi, 2008Amnuaiphon, 2009Maâlej, 2009Tatar, 2009Orofino Rde, 2011Sangral, 2012Jiménez-Corona, 2013Mi, 2013Alavi-Naini, 2013Sulaiman, 2013Nandakumar, 2013Reis-Santos, 2013Ribeiro Macedo, 2013Tabarsi, 2014Alo, 2014Viswanathan, 2014Viswanathan, 2014Moosazadeh, 2014Cavanaugh, 2015Chen, 2015Subtotal (I-squared = 71.0%, p = 0.000)

Study

PolandJapanItalyItalyItalyItalyU.S.U.S.ItalyU.S.RussiaSpainK.S.ARussiaFinlandU.S.TaiwanTaiwanFinlandJapanR.O.K.U.S.TaiwanTaiwanTaiwan

CongoIndonesiaSenegalIranThailandTunisiaTurkeyBrazilIndiaMexicoChinaIranMalaysiaIndiaBrazilBrazilIranFijiIndiaIndiaIranKiribatiChina

Country

38/903/713/324/501/415/417/5013/222/408/188/448/731/1345/2022/926/4213/7452/2417/261193/562221/16216/15165/1821645/763779/705

8/322/9418/863/6376/41885/602/784/142/2311/3632/9740/10819/34242/66773/70339/3231/373/530/961/8926/1404/1019/182

n/NTB-DM

19/9014/44929/73719/77343/66649/10591/10529/15226/85214/10875/187297/14383/38487/44086/53720/25511/143137/88625/2056282/2807730/49566/1167184/5653939/2621461/768

8/1000/5406/886/4419/2900/820/7815/29416/25736/8472/48335/60742/91471/2127995/17047712/124721/6711/3355/1491/120123/8242/1749/944

n/NTB-nonDM

2.73 (1.42, 5.27)1.37 (0.38, 4.90)2.53 (0.73, 8.77)3.45 (1.13, 10.56)0.36 (0.05, 2.70)2.86 (1.08, 7.62)16.93 (2.02, 141.78)6.13 (2.39, 15.70)1.67 (0.38, 7.31)5.37 (1.81, 15.91)5.32 (2.39, 11.85)1.70 (0.79, 3.65)0.95 (0.10, 9.26)1.35 (0.48, 3.82)1.65 (0.97, 2.80)1.96 (0.74, 5.20)2.56 (1.08, 6.03)1.50 (1.05, 2.15)2.65 (1.01, 6.94)0.93 (0.87, 1.00)2.31 (1.28, 4.16)1.98 (1.11, 3.51)1.15 (0.81, 1.63)1.55 (1.46, 1.66)1.46 (1.03, 2.08)1.92 (1.55, 2.37)

3.83 (1.30, 11.27)29.22 (1.39, 613.44)3.62 (1.36, 9.62)6.33 (1.03, 38.98)1.41 (0.87, 2.27)16.35 (0.89, 301.66)5.13 (0.24, 108.62)7.44 (2.09, 26.51)1.43 (0.31, 6.67)0.70 (0.35, 1.40)5.06 (0.70, 36.39)9.61 (5.72, 16.15)1.22 (0.70, 2.13)1.95 (1.32, 2.88)1.87 (1.45, 2.40)2.27 (1.61, 3.20)1.83 (0.11, 30.19)1.77 (0.48, 6.56)0.14 (0.01, 2.49)1.35 (0.08, 21.92)1.30 (0.81, 2.07)3.55 (0.64, 19.72)5.40 (2.12, 13.81)2.41 (1.74, 3.33)

Odds ratio (95% CI)

4.752.112.182.550.993.060.903.211.672.663.894.100.792.825.643.063.596.993.128.645.225.327.048.657.04100.00

4.531.004.982.357.631.081.003.792.986.472.077.407.208.078.678.291.163.651.091.177.682.565.17100.00

Weight%

2.73 (1.42, 5.27)1.37 (0.38, 4.90)2.53 (0.73, 8.77)3.45 (1.13, 10.56)0.36 (0.05, 2.70)2.86 (1.08, 7.62)16.93 (2.02, 141.78)6.13 (2.39, 15.70)1.67 (0.38, 7.31)5.37 (1.81, 15.91)5.32 (2.39, 11.85)1.70 (0.79, 3.65)0.95 (0.10, 9.26)1.35 (0.48, 3.82)1.65 (0.97, 2.80)1.96 (0.74, 5.20)2.56 (1.08, 6.03)1.50 (1.05, 2.15)2.65 (1.01, 6.94)0.93 (0.87, 1.00)2.31 (1.28, 4.16)1.98 (1.11, 3.51)1.15 (0.81, 1.63)1.55 (1.46, 1.66)1.46 (1.03, 2.08)1.92 (1.55, 2.37)

3.83 (1.30, 11.27)29.22 (1.39, 613.44)3.62 (1.36, 9.62)6.33 (1.03, 38.98)1.41 (0.87, 2.27)16.35 (0.89, 301.66)5.13 (0.24, 108.62)7.44 (2.09, 26.51)1.43 (0.31, 6.67)0.70 (0.35, 1.40)5.06 (0.70, 36.39)9.61 (5.72, 16.15)1.22 (0.70, 2.13)1.95 (1.32, 2.88)1.87 (1.45, 2.40)2.27 (1.61, 3.20)1.83 (0.11, 30.19)1.77 (0.48, 6.56)0.14 (0.01, 2.49)1.35 (0.08, 21.92)1.30 (0.81, 2.07)3.55 (0.64, 19.72)5.40 (2.12, 13.81)2.41 (1.74, 3.33)

Odds ratio (95% CI)

4.752.112.182.550.993.060.903.211.672.663.894.100.792.825.643.063.596.993.128.645.225.327.048.657.04100.00

4.531.004.982.357.631.081.003.792.986.472.077.407.208.078.678.291.163.651.091.177.682.565.17100.00

Weight%

1.2 .5 1 2 3 5 10 1520

Subgroup analyses: Death(Income level)

The association was slightly stronger amongst low income countries


.

.

1: Medical/self-reported




Centis, 1998 report

Centis, 1999 report

Morsy, 2003

Anunnatsiri, 2005

Chiang, 2009

Orofino Rde, 2011

Sangral, 2012


Choi, 2014

Chiang, 2015


2: Blood glucose

Mboussa, 2003

Singla, 2006

Namukwaya, 2011

Uchimura, 2013

Mi, 2013

Nandakumar, 2013

Tabarsi, 2014

Alo, 2014

Viswanathan, 2014

Viswanathan, 2014

Cavanaugh, 2015

Chen, 2015


Study

Italy

Italy

Italy

Italy

Italy

Egypt

Thailand

Taiwan

Brazil

India

Mexico

R.O.K.

Taiwan

Congo

K.S.A.

Uganda

Japan

China

India

Iran

Fiji

India

India

Kiribati

China

Country

3/32

5/50

2/41

5/41

2/40

31/40

4/38

60/241

4/14

4/23

28/363

./.

101/705

13/32

1/134

2/2

1281/5622

12/97

74/667

2/37

3/53

4/96

8/89

5/101

15/182

n/N

TB-DM

33/737

20/773

45/666

61/1059

28/852

88/198

11/188

161/886

21/294

20/257

55/847

./.

78/768

13/100

7/384

48/148

6578/28077

13/483

148/2127

3/67

12/335

6/149

1/120

4/174

14/944

n/N

TB-nonDM

2.21 (0.64, 7.62)

4.18 (1.50, 11.66)

0.71 (0.17, 3.03)

2.27 (0.86, 6.00)

1.55 (0.36, 6.74)

4.31 (1.95, 9.52)

1.89 (0.57, 6.30)

1.49 (1.06, 2.09)

5.20 (1.50, 18.00)

2.49 (0.77, 8.04)

1.20 (0.75, 1.93)

1.78 (1.09, 2.92)

1.48 (1.08, 2.03)

1.81 (1.43, 2.29)

4.58 (1.83, 11.43)

0.40 (0.05, 3.32)

10.36 (0.49, 220.01)

0.96 (0.90, 1.03)

5.10 (2.25, 11.56)

1.67 (1.24, 2.24)

1.22 (0.19, 7.65)

1.62 (0.44, 5.92)

1.04 (0.28, 3.77)

11.75 (1.44, 95.78)

2.21 (0.58, 8.44)

5.97 (2.83, 12.59)

2.29 (1.38, 3.79)

Odds ratio (95% CI)

3.23

4.50

2.42

4.94

2.37

6.85

3.41

18.81

3.22

3.58

13.74

13.09

19.84

100.00

10.11

4.16

2.30

15.12

10.83

14.42

5.03

7.55

7.59

4.18

7.32

11.38

100.00

Weight

%

2.21 (0.64, 7.62)

4.18 (1.50, 11.66)

0.71 (0.17, 3.03)

2.27 (0.86, 6.00)

1.55 (0.36, 6.74)

4.31 (1.95, 9.52)

1.89 (0.57, 6.30)

1.49 (1.06, 2.09)

5.20 (1.50, 18.00)

2.49 (0.77, 8.04)

1.20 (0.75, 1.93)

1.78 (1.09, 2.92)

1.48 (1.08, 2.03)

1.81 (1.43, 2.29)

4.58 (1.83, 11.43)

0.40 (0.05, 3.32)

10.36 (0.49, 220.01)

0.96 (0.90, 1.03)

5.10 (2.25, 11.56)

1.67 (1.24, 2.24)

1.22 (0.19, 7.65)

1.62 (0.44, 5.92)

1.04 (0.28, 3.77)

11.75 (1.44, 95.78)

2.21 (0.58, 8.44)

5.97 (2.83, 12.59)

2.29 (1.38, 3.79)

Odds ratio (95% CI)

3.23

4.50

2.42

4.94

2.37

6.85

3.41

18.81

3.22

3.58

13.74

13.09

19.84

100.00

10.11

4.16

2.30

15.12

10.83

14.42

5.03

7.55

7.59

4.18

7.32

11.38

100.00

Weight

%

1.2 .5 1 2 3 5 10 15

Subgroup analyses: Failure and death(medical records/self-reported - equivalent to previously diagnosed diabetes surrogate measures e.g. blood glucose, HbA1c includes previously diagnosed and newly diagnosed DM)

146

The association was stronger amongst studies using surrogate DM measures

Subgroup analyses: Failure and death(Income level)

147


.

.

1: High income




Centis, 1998 report

Centis, 1999 report

Singla, 2006

Chiang, 2009

Uchimura, 2013

Choi, 2014

Chiang, 2015


2: Upper/Lower-middle/Low

Mboussa, 2003

Morsy, 2003

Anunnatsiri, 2005

Namukwaya, 2011

Orofino Rde, 2011

Sangral, 2012


Mi, 2013

Nandakumar, 2013

Tabarsi, 2014

Alo, 2014

Viswanathan, 2014

Viswanathan, 2014

Cavanaugh, 2015

Chen, 2015


Study

Italy

Italy

Italy

Italy

Italy

K.S.A.

Taiwan

Japan

R.O.K.

Taiwan

Congo

Egypt

Thailand

Uganda

Brazil

India

Mexico

China

India

Iran

Fiji

India

India

Kiribati

China

Country

3/32

5/50

2/41

5/41

2/40

1/134

60/241

1281/5622

./.

101/705

13/32

31/40

4/38

2/2

4/14

4/23

28/363

12/97

74/667

2/37

3/53

4/96

8/89

5/101

15/182

n/N

TB-DM

33/737

20/773

45/666

61/1059

28/852

7/384

161/886

6578/28077

./.

78/768

13/100

88/198

11/188

48/148

21/294

20/257

55/847

13/483

148/2127

3/67

12/335

6/149

1/120

4/174

14/944

n/N

TB-nonDM

2.21 (0.64, 7.62)

4.18 (1.50, 11.66)

0.71 (0.17, 3.03)

2.27 (0.86, 6.00)

1.55 (0.36, 6.74)

0.40 (0.05, 3.32)

1.49 (1.06, 2.09)

0.96 (0.90, 1.03)

1.78 (1.09, 2.92)

1.48 (1.08, 2.03)

1.48 (1.08, 2.01)

4.58 (1.83, 11.43)

4.31 (1.95, 9.52)

1.89 (0.57, 6.30)

10.36 (0.49, 220.01)

5.20 (1.50, 18.00)

2.49 (0.77, 8.04)

1.20 (0.75, 1.93)

5.10 (2.25, 11.56)

1.67 (1.24, 2.24)

1.22 (0.19, 7.65)

1.62 (0.44, 5.92)

1.04 (0.28, 3.77)

11.75 (1.44, 95.78)

2.21 (0.58, 8.44)

5.97 (2.83, 12.59)

2.71 (1.87, 3.93)

Odds ratio (95% CI)

4.89

6.49

3.78

7.00

3.70

1.98

17.64

22.10

14.26

18.15

100.00

7.79

8.82

5.80

1.34

5.57

5.99

11.91

8.60

13.50

3.21

5.26

5.30

2.59

5.06

9.24

100.00

Weight

%

2.21 (0.64, 7.62)

4.18 (1.50, 11.66)

0.71 (0.17, 3.03)

2.27 (0.86, 6.00)

1.55 (0.36, 6.74)

0.40 (0.05, 3.32)

1.49 (1.06, 2.09)

0.96 (0.90, 1.03)

1.78 (1.09, 2.92)

1.48 (1.08, 2.03)

1.48 (1.08, 2.01)

4.58 (1.83, 11.43)

4.31 (1.95, 9.52)

1.89 (0.57, 6.30)

10.36 (0.49, 220.01)

5.20 (1.50, 18.00)

2.49 (0.77, 8.04)

1.20 (0.75, 1.93)

5.10 (2.25, 11.56)

1.67 (1.24, 2.24)

1.22 (0.19, 7.65)

1.62 (0.44, 5.92)

1.04 (0.28, 3.77)

11.75 (1.44, 95.78)

2.21 (0.58, 8.44)

5.97 (2.83, 12.59)

2.71 (1.87, 3.93)

Odds ratio (95% CI)

4.89

6.49

3.78

7.00

3.70

1.98

17.64

22.10

14.26

18.15

100.00

7.79

8.82

5.80

1.34

5.57

5.99

11.91

8.60

13.50

3.21

5.26

5.30

2.59

5.06

9.24

100.00

Weight

%

1.2 .5 1 2 3 5 10 15

The association was slightly stronger amongst low income countries

Summary of results

• More recent studies continue to support a significant association: diabetes & TB treatment outcome

• If anything, risk of poor TB treatment outcome appears higher than before with this update• A small number of studies suggested better glycaemic control among TB patients may improve

outcomes? [still few studies available]

• Better designed studies (blood screening for DM; appropriate control of confounding) seem to find the strongest associations with poor TB treatment outcomes

• Increasing evidence that diabetes is associated with multi-drug resistant TB?

• Studies still being poorly described and analysed (despite EQUATOR Network / STROBE guidelines): important to start dialogue with National TB Programme Managers

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Discussion• Heterogeneity – high in many forest plots, reduced in some sub-group analyses

• Although there are studies from LMIC, the “higher quality” evidence comes more from developed countries with lower TB incidence

• Control of confounding: not adjusted or many studies may be over-adjusted

• Neglected competing risk: longer term follow-up studies mostly used regression modelling rather than survival analyses (TB relapse or recurrence cannot compete with death!)

• Few studies reported on “diabetes variables” (e.g. BMI, glycaemic control)

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Acknowledgements

• Co-authors (Cesar Ugarte Gill, Julia Critchley, Fiona Pearson, Johnathan Golub)

• This publication was made possible by NPRP grant number 7-627-3-167 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. The funder had no role in the design, conduct, or analysis of the study.

• It was also partially supported by grants from the TANDEM (TB and DM) EC FP7 consortium

• Support with translation from: Royusuke Omori, Varvara Bashkirova, Anna Kolodziejczyk, Yuki Nitta.

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Research Questions

1. Is there an increased risk of TB among DM patients and is the association bidirectional?

2. If any associations are seen between TB and DM are they limited to specific disease subtypes?

The Health Improvement Network

• To answer such questions large data sets or long term follow-up is needed• THIN is a very large set of UK primary care health data• Contains data from 479 practices with a total of 9.1 million patients

• 1/3 are actively registered; the rest are historically active but have left their practice or died

Institute ofHealth&Society

Data & Methods

• Exposure and reference cohorts were constructed in order

to carry out a set of retrospective cohort analyses

Active THIN Population

• Data are organised in relational files separated by contributing GP (four standardised and one linked file per practice)

• Data is interpreted using ancillary look up tables and dictionaries. Medical events use the Read system and prescriptions a Multi-lexical and BNF code

Linked by patient ID

Linked by patient ID

Linked by practice ID

Patient file

Data on demographics

Therapy file

Data on prescriptions

Medical file

Data on medical eventsAdditional Health Data (AHD) file

Data on prevention, lifestyle and

diagnostics

Drug codes

Encrypted Multilex

code linked to

generic medicine

description

DosageDosage code

linked to

dosage string

Medical codes

Linked to

description of sign,

symptom,

procedure etc

Anonymised comments

Linked by text ID

Pack size

Free text

linked to pack

size code

Postcode Variable

Indicators

Variables linked to patient ID

derived from patients’

ward/location.

AHD codes

Description of codes

and context of

dependant variables

Research file

Patient demographics and selected

completeness of recording by year. Linked

by practice ID

Staff roles linked

by staff ID

Consult

Can be linked to therapy, AHD and

medical events. Data on location,

time and length of consultation

Practice file

Contains dates of last collection, Vision date, AMR,

computerisation etc

The Health Improvement Network

Methods

• Dynamic exposure and reference cohorts constructed using data from 2003-2009• Date of entry taken as date of first exposure or reference condition episode• Date of exit taken as date of subsequent outcome episode, txr date, date of death or end of

study period

• Key variables defined using a sensitive combination of Read codes from multiple fields of interest (265 codes for TB and 479 for DM)

• Individuals with missing data excluded (randomly patterned <1%)• Data were checked for over-dispersion (strong assumption) and IRRs were calculated

using negative binomial regression adjusted for age, sex and smoking status• An adjustment was made to IRRs to account for the potential confounding effect of

ethnicity

Data validation checks

• Consults and prescriptions are comparable to UK level• Deaths reported are as expected for practice demographic structure and are

comparative to national stats prior to study start date

• ID is unique• Either male or female• DOB is present and in correct format (≤ registration date, ≤ first data collection,age is not implausibly large)

• Individuals GP registration date is present and in correct format (≤ practice lastcollection date and/or txr date and/or death date)

• If not labelled as txr’d/dead has no txr out/death date• If labelled as txr’d or dead there is a corresponding date which is > DOB and

registration, ≤ last data collection date

• Has at least one health record

Sex, Age, SES and Smoking distributionVariable % of % of % of Categories TB PTB EPTBSex Male 49.3 53.9 43.7

Female 50.7 46.1 56.3Age 0-15 7.2 4.2 6.7Group 16-30 19 14.9 17.4

31-45 19.3 16.4 23.846-60 20.3 20.7 21.161-75 23.2 29.8 22.676+ 10.7 13.8 8.5

Townsend Quintile1 14.1 13.2 15.12 14.6 14.7 13.53 18.6 17.6 21.54 21.8 23.7 22.05 22.9 22.9 21.1

Smoker Yes 26.5 26.4 25.2Past 40.9 49.2 40.8No 32.6 24.4 34.0

Variable % of % of % of Categories DM T1DM T2DMSex Male 54.9 55.2 55.1

Female 45.1 44.8 44.9Age 0-15 1.3 6.1 0.1Group 16-30 3.9 12.6 1.5

31-45 13.1 20.1 11.546-60 28.5 23.9 29.961-75 35.7 26.7 38.776+ 17.4 10.6 18.3

Townsend Quintile1 20.3 20.4 20.12 19.8 19.4 19.93 20.1 20.3 20.24 19.3 19.4 19.45 14.5 14.5 14.6

Smoker Yes 27.7 29.5 28.1Past 53.3 47.8 54.9No 19.0 22.8 17.0

Mean age of entry into DM cohorts was 60 years and into TB cohorts 50 years Average f/up for each cohort was 4 years

Risk of TB amongst people with DMExposure Outcome

condition condition Adjusted IRR (95% CI)1 P-value

DM TB 1.50 (1.27-1.76) <0.001

T1DM TB 1.455 (1.104-1.917) 0.008

T2DM TB 1.536 (1.296-1.822) <0.001

DM PTB 1.237 (0.934-1.638) 0.137

T1DM PTB 1.304 (0.787-2.160) 0.303

T2DM PTB 1.243 (0.923-1.673) 0.152

DM EPTB 1.434 (0.993-2.071) 0.055

T1DM EPTB 2.088 (1.190-3.664) 0.010

T2DM EPTB 1.388 (0.934-2.062) 0.105 1Adjusted for age, sex, region, Townsend score and smoking status

Adjustment for ethnicity as an “external confounder”

• In the UK TB is highly concentrated among South Asian and Black Afro-Caribbean ethnic minorities, who also have highest risks of T2DM

• Degree of confounding=𝑐𝑜𝑛𝑓𝑜𝑢𝑛𝑑𝑒𝑑𝑅𝑅

𝑡𝑟𝑢𝑒𝑅𝑅=

100−𝑝1 +𝑅𝑅𝑠𝑝1

100−𝑝0 +𝑅𝑅𝑠𝑝0

• p1—% with “BAC or SA ethnicity” in the exposed group; p0—percentage of subjects with “BAC or SA ethnicity” in the unexposed group

• RR for “BAC or SA ethnicity” & outcome (TB or DM) e.g. approx. 6 for DM

• IRRs 1.95- to 2.43-fold by residual confounding by ethnicity

• IRR 5.65 2.33 (95%CI 2.14-2.53)-2.90 (95%CI 2.66-3.16)

16/09/2016 158

Summary• Significant associations between TB and DM after controlling for key confounders

were found (as previously)• The magnitude of risk identified (1.3 fold) is lower but in line with recent studies from

developed countries

• Lower findings likely reflect low TB burden and a strong primary health care system with good glycaemic control and management of DM co-morbidities

• A substantial increase in risk of DM among individuals with prior TB (2-5 fold) has been identified.

• Although bi-directionality has been speculated, as far as we are aware, no other prospective cohort studies have been able to quantify this

• Further studies in high TB incidence countries are needed to confirm our finding and its importance

• Probably difficult to carry out in practice, due to the large sample size and length of f/up required. Alternatively further analysis of similarly sized datasets could be completed

Contributing factors

• Host immunity• T-cells, B-cells, macrophages,

• neutrophils …

• Neuropathy, microangiopathy, wound healing

• Obesity

• More S aureus carriage

• Glycosuria

• More health-care associated infections• More hospitalisation, more instrumentation

• poor glycemic control?:

• more infections, more admissions, higher mortality

Casquiero et al 2012

Converging Epidemics of Diabetes and Tuberculosis...

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