- Email: [email protected]
Opportunistic screening for type 2 diabetes in primary care
Correspondence
2
3
4
Gandhi NR, Nunn P, Dheda K, et al. Multidrug-resistant and extensively drugresistant tuberculosis: a threat to global control of tuberculosis. Lancet 2010; 375: 1830–43. Singla R, Sarin R, Khalid UK, et al. Seven-year DOTS-Plus pilot experience in India: results, constraints and issues. Int J Tuberc Lung Dis 2009; 13: 976–81. Morankar S, Deshmukh D. Socio-cultural aspects of tuberculosis among women: implications for delivery of health services. Pune: Foundation for Research in Community Health, 2001. Uppal R, Sarkar U, Giriyappanavar CR, Kacker V. Antimicrobial drug use in primary health care. J Clin Epidemiol 1993; 46: 671–73.
Where is diabetes in The Lancet‘s tuberculosis Series? We commend The Lancet for raising the profile of tuberculosis in its dedicated Series, naming it rightly as a global public health crisis and calling for a radical reframing of tuberculosis (May 22, p 1755).1 However, we are disappointed to find no discussion on the increasing overlap of tuberculosis with non-communicable diseases. A review2 of the convergence of the epidemics of tuberculosis and diabetes mellitus highlighted growing evidence that diabetes is an important risk factor for active tuberculosis: the incidence of tuberculosis is two to five times higher in patients with diabetes than in those without. With rising rates of obesity and diabetes, particularly in low-income and middleincome countries where high rates of tuberculosis remain, there is concern that diabetes poses a threat to global tuberculosis control. The Lancet‘s tuberculosis Series recognises the need to improve integration of HIV/AIDS and tuberculosis services and promotes broader health-system strengthening to aid tuberculosis control.3,4 We suggest that specific recognition of the importance of diabetes to tuberculosis disease control is valuable for policy and research priorities, particularly in developing countries where systems www.thelancet.com Vol 376 August 28, 2010
for non-communicable disease surveillance and management are largely non-existent. Improved clinician awareness of overlaps between tuberculosis and diabetes would aid diagnosis, as would greater coordination of tuberculosis and diabetes services. Consideration of how lessons learned from management of tuberculosis as a chronic illness could be applied to diabetes management in resourcepoor settings is worthy of greater exploration, aiming to improve diabetes chronic disease management and consequently outcomes of both diseases.5 We declare that we have no conflicts of interest.
*S L Bailey, P Godfrey-Faussett [email protected] Brighton and Sussex Medical School, Brighton BN1 9PX, UK (SLB); and London School of Hygiene and Tropical Medicine, London, UK (PGF) 1
2
3
4
5
Das P, Horton R. Tuberculosis—time to accelerate progress. Lancet 2010; 375: 1755–57. Dooley KE, Chaisson RE. Tuberculosis and diabetes mellitus: convergence of two epidemics. Lancet Infect Dis 2009; 9: 737–46. Harries AD, Zachariah R, Corbett EL, et al. The HIV-associated tuberculosis epidemic—when will we act? Lancet 2010; 375: 1906–19. Atun R, Weil DE, Eang MT, Mwakyusa D. Health-system strengthening and tuberculosis control. Lancet 2010; 375: 2169–78. Harries AD, Jahn A, Zachariah R, Enarson D. Adapting the DOTS framework for tuberculosis control to the management of noncommunicable diseases in sub-Saharan Africa. PLoS Med 2008; 5: e124.
Opportunistic screening for type 2 diabetes in primary care Richard Kahn and colleagues (April 17, p 1365)1 used a mathematical model to show that screening for type 2 diabetes is cost-effective when started at the age of 30–45 years and repeated every 3–5 years. They conclude that the cost per quality-adjusted life-year would be improved if screening was done opportunistically and by risk assessment before glucose testing. They state that there are no clinical
trials against which to validate their model. In the Diabscreen study,2 an opportunistic screening programme for type 2 diabetes in patients aged 45–75 years in primary care in the Netherlands, we used the family practice electronic medical record (EMR) for risk assessment before glucose testing. Risk was marked in the EMR. In 1 year, physicians succeeded in starting stepwise fasting glucose testing during usual care in 39% of the patients. First response rate was 90%. The screening yield was much higher in high-risk than in low-risk patients (number needed to screen 37 vs 233). Obesity was the best predictor of undiagnosed diabetes (odds ratio 3·2). This finding is in line with one of the American Diabetes Association’s recommendations to screen all adults aged 45 years and older with a body-mass index of 25 or greater.3 Although not a trial, our clinical findings clearly show that opportunistic screening in primary care is feasible. Middle-aged and older adults at high risk, especially those with obesity, can be targeted effectively. An EMR can be most helpful for identification of highrisk patients and also in supporting repeated screening, but this requires universal access and continuity of patient registration. We declare that we have no conflicts of interest.
*Erwin P Klein Woolthuis, Wim J C de Grauw, Chris van Weel [email protected] Radboud University Nijmegen Medical Centre, Department of Primary and Community Care, PO Box 9101, 6500 HB Nijmegen, Netherlands 1
2
3
Kahn R, Alperin P, Eddy D, et al. Age at initiation and frequency of screening to detect type 2 diabetes: a cost-effectiveness analysis. Lancet 2010; 375: 1365–74. Klein Woolthuis EP, de Grauw WJ, van Gerwen WH, et al. Yield of opportunistic targeted screening for type 2 diabetes in primary care: the diabscreen study. Ann Fam Med 2009; 7: 422–30. American Diabetes Association. Standards of medical care in diabetes—2010. Diabetes Care 2010; 33(suppl 1): S11–61.
Science Photo Library
1
683
Correspondence
Population of England in mid-2009 (millions)
All six regions (population 36·3 million)
London and West Midlands (population 13·2 million)
Before
Before (B)
After
Northeast, northwest, southeast, southwest (population 23·1 million)
After (A)
Ratio A:B
Before (B)
After (A)
Ratio A:B 1·7:1
<25 years
15·9
16/345 (5%)
97/603 (16%)
2/93 (2%)
55/179 (31%)
1·9:1
14/252 (5%)
42/424 (10%)
25–64 years
27·4
31/273 (11%)
91/795 (11%)
14/89 (16%)
36/273 (13%)
3·1:1
17/184 (9%)
55/522 (11%)
2·8:1
8·4
99/406 (24%)
119/556 (21%)
49/118 (42%)
31/139 (22%)
1·2:1
50/288 (17%)
88/417 (21%)
1·4:1
≥65 years
Before=before H1N1 pandemic, 2008 baseline. After=after first wave of H1N1 pandemic, August and September, 2009.
Table: Comparison of protective response (haemagglutination inhibition titre of 1:32 or more), and ratio of number of samples analysed, by age-group for two regional subdivisions
Like-with-like comparisons? Elizabeth Miller and colleagues (March 27, p 1100)1 compared preexisting immunity to influenza A H1N1 in 2008 with protective response after the UK’s first wave of the disease in 2009. I have some concerns about the comparability of the populations studied in the “before” and “after” groups, when data are broken down according to the six regions that submitted timely sera for August and September, 2009. (1) Samples for 2009, unlike 2008, included residual sera from chemical pathology laboratories. (2) Relative to population, both age extremes were over-represented in “before” sera (table). (3) The ratio of “after” to “before” sera was similar by age-group across regions, but strikingly different between age-groups, putting before-and-after comparisons on a different footing by age (table). (4) For London and the West Midlands, two regions where H1N1 hit hardest,1 protective response in the oldest age-group was significantly lower in “after” sera (table; χ²=11·0). To June 30, 2009, 75% of 4878 UKacquired confirmed H1N1 infections were in people younger than 25 years.2 The Health Protection Agency estimated 100 000 incident H1N1 cases in the week to July 23, 2009, so that cumulative cases could have been about 200 000. If at least 65% of England’s H1N1 cases remained in those younger than 25 years, with as many asymptomatic as clinical 684
cases, we would expect 140 000 of 35 900 000 infection-related responses in people aged 25 years or older, or four per 1000. Weekly doubling would have to continue for another 3 weeks (but did not) for their protective response to reach 2·5%. Similar arguments put the likely increase as 3% to 13% for those younger than 25 years (table). Immune responses by individuals aged 25 years and older were little altered by England’s first wave of H1N1. But, differentially sourced samples confound age comparisons, as sera in those aged 65 years and older warn. Serosurveillance should have robust protocols for sample acquisition. Many more sera from those aged 25 years and older were needed to reveal a modest effect on their protective response (of one to three per 100 samples). I am grateful to Prof Miller for providing the regional 2008 baseline data by age-group. I write in a personal capacity, but am a statistician member of the Scientific Pandemic Influenza Advisory Committee.
Sheila M Bird [email protected] MRC Biostatistics Unit, Cambridge CB2 0SR, UK 1
2
Miller E, Hoschler K, Hardelid P, Stanford E, Andrews N, Zambon M. Incidence of 2009 pandemic influenza A H1N1 infection in England: a cross-sectional serological study. Lancet 2010; 375: 1100–108. Bird SM. Missing swine flu data for patients admitted to hospital. Straight Statistics 2009; published online Sept 30. http://www. straightstatistics.org/article/missing-swineflu-data-patients-admitted-hospital (accessed July 12, 2010).
Authors’ reply We agree with Sheila Bird that there was over-representation of sera in both age extremes in the baseline: this was specified in our protocol. The number tested by age was not designed
to reflect the age breakdown of the population but to ensure sufficient numbers of samples in each age-group to define how the prevalence of crossreactive antibody increased with age. This is clearly outlined in our paper. However, where population-based incidence estimates are presented, these are weighted for population size. To account for the difference in age distribution across time points, all seroincidence estimates were presented by age-group, and when results for larger age-groups (<15 years and ≥65 years) were shown, these were age-standardised to the age distribution of the baseline. This is clearly highlighted in our paper. We agree that with much larger sample sizes in those aged 25 years or older, we might have detected small changes in antibody prevalence and potentially avoided the negative point estimates of the difference in prevalence between successive time points that we showed in some agegroups in some regions. However, given the need to generate data quickly and the inevitable limitations on testing capacity, the source of samples and numbers tested needed to take account of operational reality. In her last point, Bird seems to misunderstand why there was a need for seroepidemiology studies of H1N1 in the first place. It is precisely because the relation between the number of clinical cases presenting to health care and the number of infections in the population was unknown that seroepidemiology data were urgently needed. Our study showed that the number of infections in children in www.thelancet.com Vol 376 August 28, 2010
2
3
4
Gandhi NR, Nunn P, Dheda K, et al. Multidrug-resistant and extensively drugresistant tuberculosis: a threat to global control of tuberculosis. Lancet 2010; 375: 1830–43. Singla R, Sarin R, Khalid UK, et al. Seven-year DOTS-Plus pilot experience in India: results, constraints and issues. Int J Tuberc Lung Dis 2009; 13: 976–81. Morankar S, Deshmukh D. Socio-cultural aspects of tuberculosis among women: implications for delivery of health services. Pune: Foundation for Research in Community Health, 2001. Uppal R, Sarkar U, Giriyappanavar CR, Kacker V. Antimicrobial drug use in primary health care. J Clin Epidemiol 1993; 46: 671–73.
Where is diabetes in The Lancet‘s tuberculosis Series? We commend The Lancet for raising the profile of tuberculosis in its dedicated Series, naming it rightly as a global public health crisis and calling for a radical reframing of tuberculosis (May 22, p 1755).1 However, we are disappointed to find no discussion on the increasing overlap of tuberculosis with non-communicable diseases. A review2 of the convergence of the epidemics of tuberculosis and diabetes mellitus highlighted growing evidence that diabetes is an important risk factor for active tuberculosis: the incidence of tuberculosis is two to five times higher in patients with diabetes than in those without. With rising rates of obesity and diabetes, particularly in low-income and middleincome countries where high rates of tuberculosis remain, there is concern that diabetes poses a threat to global tuberculosis control. The Lancet‘s tuberculosis Series recognises the need to improve integration of HIV/AIDS and tuberculosis services and promotes broader health-system strengthening to aid tuberculosis control.3,4 We suggest that specific recognition of the importance of diabetes to tuberculosis disease control is valuable for policy and research priorities, particularly in developing countries where systems www.thelancet.com Vol 376 August 28, 2010
for non-communicable disease surveillance and management are largely non-existent. Improved clinician awareness of overlaps between tuberculosis and diabetes would aid diagnosis, as would greater coordination of tuberculosis and diabetes services. Consideration of how lessons learned from management of tuberculosis as a chronic illness could be applied to diabetes management in resourcepoor settings is worthy of greater exploration, aiming to improve diabetes chronic disease management and consequently outcomes of both diseases.5 We declare that we have no conflicts of interest.
*S L Bailey, P Godfrey-Faussett [email protected] Brighton and Sussex Medical School, Brighton BN1 9PX, UK (SLB); and London School of Hygiene and Tropical Medicine, London, UK (PGF) 1
2
3
4
5
Das P, Horton R. Tuberculosis—time to accelerate progress. Lancet 2010; 375: 1755–57. Dooley KE, Chaisson RE. Tuberculosis and diabetes mellitus: convergence of two epidemics. Lancet Infect Dis 2009; 9: 737–46. Harries AD, Zachariah R, Corbett EL, et al. The HIV-associated tuberculosis epidemic—when will we act? Lancet 2010; 375: 1906–19. Atun R, Weil DE, Eang MT, Mwakyusa D. Health-system strengthening and tuberculosis control. Lancet 2010; 375: 2169–78. Harries AD, Jahn A, Zachariah R, Enarson D. Adapting the DOTS framework for tuberculosis control to the management of noncommunicable diseases in sub-Saharan Africa. PLoS Med 2008; 5: e124.
Opportunistic screening for type 2 diabetes in primary care Richard Kahn and colleagues (April 17, p 1365)1 used a mathematical model to show that screening for type 2 diabetes is cost-effective when started at the age of 30–45 years and repeated every 3–5 years. They conclude that the cost per quality-adjusted life-year would be improved if screening was done opportunistically and by risk assessment before glucose testing. They state that there are no clinical
trials against which to validate their model. In the Diabscreen study,2 an opportunistic screening programme for type 2 diabetes in patients aged 45–75 years in primary care in the Netherlands, we used the family practice electronic medical record (EMR) for risk assessment before glucose testing. Risk was marked in the EMR. In 1 year, physicians succeeded in starting stepwise fasting glucose testing during usual care in 39% of the patients. First response rate was 90%. The screening yield was much higher in high-risk than in low-risk patients (number needed to screen 37 vs 233). Obesity was the best predictor of undiagnosed diabetes (odds ratio 3·2). This finding is in line with one of the American Diabetes Association’s recommendations to screen all adults aged 45 years and older with a body-mass index of 25 or greater.3 Although not a trial, our clinical findings clearly show that opportunistic screening in primary care is feasible. Middle-aged and older adults at high risk, especially those with obesity, can be targeted effectively. An EMR can be most helpful for identification of highrisk patients and also in supporting repeated screening, but this requires universal access and continuity of patient registration. We declare that we have no conflicts of interest.
*Erwin P Klein Woolthuis, Wim J C de Grauw, Chris van Weel [email protected] Radboud University Nijmegen Medical Centre, Department of Primary and Community Care, PO Box 9101, 6500 HB Nijmegen, Netherlands 1
2
3
Kahn R, Alperin P, Eddy D, et al. Age at initiation and frequency of screening to detect type 2 diabetes: a cost-effectiveness analysis. Lancet 2010; 375: 1365–74. Klein Woolthuis EP, de Grauw WJ, van Gerwen WH, et al. Yield of opportunistic targeted screening for type 2 diabetes in primary care: the diabscreen study. Ann Fam Med 2009; 7: 422–30. American Diabetes Association. Standards of medical care in diabetes—2010. Diabetes Care 2010; 33(suppl 1): S11–61.
Science Photo Library
1
683
Correspondence
Population of England in mid-2009 (millions)
All six regions (population 36·3 million)
London and West Midlands (population 13·2 million)
Before
Before (B)
After
Northeast, northwest, southeast, southwest (population 23·1 million)
After (A)
Ratio A:B
Before (B)
After (A)
Ratio A:B 1·7:1
<25 years
15·9
16/345 (5%)
97/603 (16%)
2/93 (2%)
55/179 (31%)
1·9:1
14/252 (5%)
42/424 (10%)
25–64 years
27·4
31/273 (11%)
91/795 (11%)
14/89 (16%)
36/273 (13%)
3·1:1
17/184 (9%)
55/522 (11%)
2·8:1
8·4
99/406 (24%)
119/556 (21%)
49/118 (42%)
31/139 (22%)
1·2:1
50/288 (17%)
88/417 (21%)
1·4:1
≥65 years
Before=before H1N1 pandemic, 2008 baseline. After=after first wave of H1N1 pandemic, August and September, 2009.
Table: Comparison of protective response (haemagglutination inhibition titre of 1:32 or more), and ratio of number of samples analysed, by age-group for two regional subdivisions
Like-with-like comparisons? Elizabeth Miller and colleagues (March 27, p 1100)1 compared preexisting immunity to influenza A H1N1 in 2008 with protective response after the UK’s first wave of the disease in 2009. I have some concerns about the comparability of the populations studied in the “before” and “after” groups, when data are broken down according to the six regions that submitted timely sera for August and September, 2009. (1) Samples for 2009, unlike 2008, included residual sera from chemical pathology laboratories. (2) Relative to population, both age extremes were over-represented in “before” sera (table). (3) The ratio of “after” to “before” sera was similar by age-group across regions, but strikingly different between age-groups, putting before-and-after comparisons on a different footing by age (table). (4) For London and the West Midlands, two regions where H1N1 hit hardest,1 protective response in the oldest age-group was significantly lower in “after” sera (table; χ²=11·0). To June 30, 2009, 75% of 4878 UKacquired confirmed H1N1 infections were in people younger than 25 years.2 The Health Protection Agency estimated 100 000 incident H1N1 cases in the week to July 23, 2009, so that cumulative cases could have been about 200 000. If at least 65% of England’s H1N1 cases remained in those younger than 25 years, with as many asymptomatic as clinical 684
cases, we would expect 140 000 of 35 900 000 infection-related responses in people aged 25 years or older, or four per 1000. Weekly doubling would have to continue for another 3 weeks (but did not) for their protective response to reach 2·5%. Similar arguments put the likely increase as 3% to 13% for those younger than 25 years (table). Immune responses by individuals aged 25 years and older were little altered by England’s first wave of H1N1. But, differentially sourced samples confound age comparisons, as sera in those aged 65 years and older warn. Serosurveillance should have robust protocols for sample acquisition. Many more sera from those aged 25 years and older were needed to reveal a modest effect on their protective response (of one to three per 100 samples). I am grateful to Prof Miller for providing the regional 2008 baseline data by age-group. I write in a personal capacity, but am a statistician member of the Scientific Pandemic Influenza Advisory Committee.
Sheila M Bird [email protected] MRC Biostatistics Unit, Cambridge CB2 0SR, UK 1
2
Miller E, Hoschler K, Hardelid P, Stanford E, Andrews N, Zambon M. Incidence of 2009 pandemic influenza A H1N1 infection in England: a cross-sectional serological study. Lancet 2010; 375: 1100–108. Bird SM. Missing swine flu data for patients admitted to hospital. Straight Statistics 2009; published online Sept 30. http://www. straightstatistics.org/article/missing-swineflu-data-patients-admitted-hospital (accessed July 12, 2010).
Authors’ reply We agree with Sheila Bird that there was over-representation of sera in both age extremes in the baseline: this was specified in our protocol. The number tested by age was not designed
to reflect the age breakdown of the population but to ensure sufficient numbers of samples in each age-group to define how the prevalence of crossreactive antibody increased with age. This is clearly outlined in our paper. However, where population-based incidence estimates are presented, these are weighted for population size. To account for the difference in age distribution across time points, all seroincidence estimates were presented by age-group, and when results for larger age-groups (<15 years and ≥65 years) were shown, these were age-standardised to the age distribution of the baseline. This is clearly highlighted in our paper. We agree that with much larger sample sizes in those aged 25 years or older, we might have detected small changes in antibody prevalence and potentially avoided the negative point estimates of the difference in prevalence between successive time points that we showed in some agegroups in some regions. However, given the need to generate data quickly and the inevitable limitations on testing capacity, the source of samples and numbers tested needed to take account of operational reality. In her last point, Bird seems to misunderstand why there was a need for seroepidemiology studies of H1N1 in the first place. It is precisely because the relation between the number of clinical cases presenting to health care and the number of infections in the population was unknown that seroepidemiology data were urgently needed. Our study showed that the number of infections in children in www.thelancet.com Vol 376 August 28, 2010