Epidemiology • Volume 26, Number 1, January 2015
Letters
AD are expected to comprise a larger fraction of state populations than they did in 2010 (eTable 2, http://links.lww. com/EDE/A845). Even within age–sex–race– education strata, the experience of the CHAP population might not generalize to state-specific populations or to different points in time. Uncertainty in state population projections contributes additional uncertainty to our estimates. Nonetheless, these limitations are minor compared with the magnitude of the estimated trajectories that portend a substantial increase in the burden of AD on state populations. Jennifer Weuve Liesi E. Hebert Paul A. Scherr Denis A. Evans Rush Institute for Healthy Aging Rush University Medical Center Chicago, IL [email protected]
REFERENCES 1. Banaszak-Holl J, Fendrick AM, Foster NL, et al. Predicting nursing home admission: estimates from a 7-year follow-up of a nationally representative sample of older Americans. Alzheimer Dis Assoc Disord. 2004;18:83–89. 2. Smith GE, Kokmen E, O’Brien PC. Risk factors for nursing home placement in a population-based dementia cohort. J Am Geriatr Soc. 2000;48:519–525. 3. Arrighi HM, Neumann PJ, Lieberburg IM, Townsend RJ. Lethality of Alzheimer disease and its impact on nursing home placement. Alzheimer Dis Assoc Disord. 2010;24: 90–95. 4. Hebert LE, Weuve J, Scherr PA, Evans DA. Alzheimer disease in the United States (2010–2050) estimated using the 2010 census. Neurology. 2013;80:1778–1783. 5. Evans DA, Bennett DA, Wilson RS, et al. Incidence of Alzheimer disease in a biracial urban community: relation to apolipoprotein E allele status. Arch Neurol. 2003;60: 185–189. 6. Bienias JL, Beckett LA, Bennett DA, Wilson RS, Evans DA. Design of the Chicago Health and Aging Project (CHAP). J Alzheimers Dis. 2003;5:349–355. 7. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology. 1984;34:939–944.
e6 | www.epidem.com
Limitations of Indicators of HIV Case Finding To the Editors: he Centers for Disease Control and Prevention recommends routine human immunodeficiency virus (HIV) screening in health care settings.1 To evaluate HIV case finding, which is the process of identifying HIV-infected persons who have not been diagnosed, programs use 2 outcome measures–median CD4 count at diagnosis and proportion of late diagnoses.2–4 It is expected that an improvement in case finding would result in an increasing median CD4 count at diagnosis and a decreasing proportion of late diagnoses. However, despite continuing efforts, the 2 measures remain stable. A recent review article reported a minimal rise of 1.5 cells/ mm3/year in the CD4 count at entry into care and a negligible change in the proportion of late diagnoses in 11 high-income countries from 1992 to 2011.5 When 2 commonly used indicators remain stable over a long period in so many countries despite continuing efforts to expand HIV testing, we should reexamine how well the indicators measure case finding. Here, we use hypothetical data to show the limitations of these 2 indicators. (Detailed methods are presented in the eAppendix, http://links. lww.com/EDE/A844). As shown in the Table, the HIV case detection rate, stratified by duration of infection, is identical across the 3 communities. However, Community A, with an emerging epidemic, has the highest median CD4 count at diagnosis (418 cells/mm3), lowest proportion of late diagnoses (33.0%) and lowest crude
T
Submitted 06 August 2014; accepted 25 August 2014. Supplemental digital content is avail able through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). This content is not peer-reviewed or copy-edited; it is the sole responsibility of the author. Copyright © 2014 by Lippincott Williams & Wilkins ISSN: 1044-3983/15/2601-0141 DOI: 10.1097/EDE.0000000000000202
case detection rate (15.8%); Community C, with a declining epidemic, has the lowest median CD4 count at diagnosis (331 cells/mm3), highest proportion of late diagnoses (39.7%) and highest crude case detection rate (16.6%). All 3 communities have an identical adjusted case detection rate (16.2%) and an identical case detection rate among new infections (25.0%). Using hypothetical data, we demonstrate the limitations of median CD4 count at diagnosis and proportion of late diagnoses. Despite an identical case detection rate stratified by duration of infection, an emerging epidemic would see a higher median CD4 count at diagnosis and a lower proportion of late diagnoses, and a declining epidemic would see a lower median CD4 count at diagnosis and a higher proportion of late diagnoses. Real-world data also show the same phenomenon, eg, Eastern European countries with emerging epidemics had a lower proportion of late diagnoses than Western European countries.6 Median CD4 count at diagnosis and proportion of late diagnoses are measuring the distribution of duration of infection among persons who are newly diagnosed, not case finding. Since a change in case finding usually happens across the board, the change would have little effect on the 2 measures. An improvement in case finding would increase not only the case detection rate among early infections but also late infections, leaving the distribution of duration of infection among the newly diagnosed unchanged.7 We propose 2 new measures for HIV case finding: adjusted case detection rate and case detection rate among new infections. These 2 case detection rates directly measure HIV case finding and are not affected by HIV incidence or distribution of duration of infection. The 2 new indicators cannot be directly measured, but must be estimated based on the distribution of duration of infection among the undiagnosed and newly diagnosed, which is not available using existing methods. With recent advances in estimating HIV incidence, we may soon be able to produce these 2 estimates, or at least the case © 2014 Lippincott Williams & Wilkins
© 2014 Lippincott Williams & Wilkins 900 811 733 661 596 538 485 437 393 38 5,593 418 33.0 15.8 16.2 25.0
Undiagnosed (U) 1,200 882 796 719 648 585 527 475 428 385 6,644
Total (N)a 25.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 90.0 15.8
Case Detection Rate (%) (R)b 250 60 55 51 47 43 40 36 33 346 962
Newly Diagnosed (D)
379 36.0 16.2 16.2 25.0
750 690 635 584 537 494 455 418 385 38 4,987
Undiagnosed (U) 1,000 750 690 635 584 537 494 455 418 385 5,948
Total (N)a 25.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 90.0 16.2
Case Detection Rate (%) (R)b
Community B (Stable Epidemic)
200 49 47 44 42 39 37 35 33 346 871
Newly Diagnosed (D)
600 569 537 507 478 451 425 400 376 39 4,382 331 39.7 16.6 16.2 25.0
Undiagnosed (U)
800 618 584 551 520 490 462 435 409 385 5,253
Total (N)a
25.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 90.0 16.6
Case Detection Rate (%) (R)b
Community C (Declining Epidemic)
b
N = D + U. R = D/N × 100. c Sum may not equal total due to rounding. d Proportion of late diagnoses: number of late diagnoses (≥9 years in duration of infection) divided by the total number of new diagnoses. e Crude case detection rate: number of new diagnoses in a year divided by the sum of the number of undiagnosed at the beginning of the year and the number of new infections in the year. f Adjusted case detection rate: a weighted average of duration of infection-specific case detection rates, where the weights are the proportions of persons in the corresponding groups of the standard population, total population of the undiagnosed in the 3 communities. g Case detection rate among new infections: number of newly diagnosed new infections (
Letters
AD are expected to comprise a larger fraction of state populations than they did in 2010 (eTable 2, http://links.lww. com/EDE/A845). Even within age–sex–race– education strata, the experience of the CHAP population might not generalize to state-specific populations or to different points in time. Uncertainty in state population projections contributes additional uncertainty to our estimates. Nonetheless, these limitations are minor compared with the magnitude of the estimated trajectories that portend a substantial increase in the burden of AD on state populations. Jennifer Weuve Liesi E. Hebert Paul A. Scherr Denis A. Evans Rush Institute for Healthy Aging Rush University Medical Center Chicago, IL [email protected]
REFERENCES 1. Banaszak-Holl J, Fendrick AM, Foster NL, et al. Predicting nursing home admission: estimates from a 7-year follow-up of a nationally representative sample of older Americans. Alzheimer Dis Assoc Disord. 2004;18:83–89. 2. Smith GE, Kokmen E, O’Brien PC. Risk factors for nursing home placement in a population-based dementia cohort. J Am Geriatr Soc. 2000;48:519–525. 3. Arrighi HM, Neumann PJ, Lieberburg IM, Townsend RJ. Lethality of Alzheimer disease and its impact on nursing home placement. Alzheimer Dis Assoc Disord. 2010;24: 90–95. 4. Hebert LE, Weuve J, Scherr PA, Evans DA. Alzheimer disease in the United States (2010–2050) estimated using the 2010 census. Neurology. 2013;80:1778–1783. 5. Evans DA, Bennett DA, Wilson RS, et al. Incidence of Alzheimer disease in a biracial urban community: relation to apolipoprotein E allele status. Arch Neurol. 2003;60: 185–189. 6. Bienias JL, Beckett LA, Bennett DA, Wilson RS, Evans DA. Design of the Chicago Health and Aging Project (CHAP). J Alzheimers Dis. 2003;5:349–355. 7. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology. 1984;34:939–944.
e6 | www.epidem.com
Limitations of Indicators of HIV Case Finding To the Editors: he Centers for Disease Control and Prevention recommends routine human immunodeficiency virus (HIV) screening in health care settings.1 To evaluate HIV case finding, which is the process of identifying HIV-infected persons who have not been diagnosed, programs use 2 outcome measures–median CD4 count at diagnosis and proportion of late diagnoses.2–4 It is expected that an improvement in case finding would result in an increasing median CD4 count at diagnosis and a decreasing proportion of late diagnoses. However, despite continuing efforts, the 2 measures remain stable. A recent review article reported a minimal rise of 1.5 cells/ mm3/year in the CD4 count at entry into care and a negligible change in the proportion of late diagnoses in 11 high-income countries from 1992 to 2011.5 When 2 commonly used indicators remain stable over a long period in so many countries despite continuing efforts to expand HIV testing, we should reexamine how well the indicators measure case finding. Here, we use hypothetical data to show the limitations of these 2 indicators. (Detailed methods are presented in the eAppendix, http://links. lww.com/EDE/A844). As shown in the Table, the HIV case detection rate, stratified by duration of infection, is identical across the 3 communities. However, Community A, with an emerging epidemic, has the highest median CD4 count at diagnosis (418 cells/mm3), lowest proportion of late diagnoses (33.0%) and lowest crude
T
Submitted 06 August 2014; accepted 25 August 2014. Supplemental digital content is avail able through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). This content is not peer-reviewed or copy-edited; it is the sole responsibility of the author. Copyright © 2014 by Lippincott Williams & Wilkins ISSN: 1044-3983/15/2601-0141 DOI: 10.1097/EDE.0000000000000202
case detection rate (15.8%); Community C, with a declining epidemic, has the lowest median CD4 count at diagnosis (331 cells/mm3), highest proportion of late diagnoses (39.7%) and highest crude case detection rate (16.6%). All 3 communities have an identical adjusted case detection rate (16.2%) and an identical case detection rate among new infections (25.0%). Using hypothetical data, we demonstrate the limitations of median CD4 count at diagnosis and proportion of late diagnoses. Despite an identical case detection rate stratified by duration of infection, an emerging epidemic would see a higher median CD4 count at diagnosis and a lower proportion of late diagnoses, and a declining epidemic would see a lower median CD4 count at diagnosis and a higher proportion of late diagnoses. Real-world data also show the same phenomenon, eg, Eastern European countries with emerging epidemics had a lower proportion of late diagnoses than Western European countries.6 Median CD4 count at diagnosis and proportion of late diagnoses are measuring the distribution of duration of infection among persons who are newly diagnosed, not case finding. Since a change in case finding usually happens across the board, the change would have little effect on the 2 measures. An improvement in case finding would increase not only the case detection rate among early infections but also late infections, leaving the distribution of duration of infection among the newly diagnosed unchanged.7 We propose 2 new measures for HIV case finding: adjusted case detection rate and case detection rate among new infections. These 2 case detection rates directly measure HIV case finding and are not affected by HIV incidence or distribution of duration of infection. The 2 new indicators cannot be directly measured, but must be estimated based on the distribution of duration of infection among the undiagnosed and newly diagnosed, which is not available using existing methods. With recent advances in estimating HIV incidence, we may soon be able to produce these 2 estimates, or at least the case © 2014 Lippincott Williams & Wilkins
© 2014 Lippincott Williams & Wilkins 900 811 733 661 596 538 485 437 393 38 5,593 418 33.0 15.8 16.2 25.0
Undiagnosed (U) 1,200 882 796 719 648 585 527 475 428 385 6,644
Total (N)a 25.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 90.0 15.8
Case Detection Rate (%) (R)b 250 60 55 51 47 43 40 36 33 346 962
Newly Diagnosed (D)
379 36.0 16.2 16.2 25.0
750 690 635 584 537 494 455 418 385 38 4,987
Undiagnosed (U) 1,000 750 690 635 584 537 494 455 418 385 5,948
Total (N)a 25.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 90.0 16.2
Case Detection Rate (%) (R)b
Community B (Stable Epidemic)
200 49 47 44 42 39 37 35 33 346 871
Newly Diagnosed (D)
600 569 537 507 478 451 425 400 376 39 4,382 331 39.7 16.6 16.2 25.0
Undiagnosed (U)
800 618 584 551 520 490 462 435 409 385 5,253
Total (N)a
25.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 90.0 16.6
Case Detection Rate (%) (R)b
Community C (Declining Epidemic)
b
N = D + U. R = D/N × 100. c Sum may not equal total due to rounding. d Proportion of late diagnoses: number of late diagnoses (≥9 years in duration of infection) divided by the total number of new diagnoses. e Crude case detection rate: number of new diagnoses in a year divided by the sum of the number of undiagnosed at the beginning of the year and the number of new infections in the year. f Adjusted case detection rate: a weighted average of duration of infection-specific case detection rates, where the weights are the proportions of persons in the corresponding groups of the standard population, total population of the undiagnosed in the 3 communities. g Case detection rate among new infections: number of newly diagnosed new infections (
