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Influence of Rurality on HIV Testing Practices Across the United States, 2012–2017

  • Lam Tran
  • Phoebe TranEmail author
  • Liem Tran
Original Paper

Abstract

In the US, HIV testing has been key in the identification of new HIV cases, allowing for the initiation of antiretroviral treatment and a reduction in disease transmission. We consider the influence of living in a rural area (rurality) on HIV testing between different US regions and states as existing work in this area is limited. Using the 2012–2017 Behavioral Risk Factor Surveillance Systems surveys, we explored the independent role of rurality on having ever been tested for HIV and having a recent HIV test at the national, regional, and state levels by calculating average adjusted predictions (AAPs) and average marginal effects (AMEs). Suburban and urban areas had higher odds and AAPs of having ever been tested for HIV and having a recent HIV test compared to rural areas across the US. The Midwest had the lowest AAPs for both having ever been tested for HIV (17.57–20.32%) and having a recent HIV test (37.65–41.14%) compared to other regions. For both questions on HIV testing, regions with the highest AAPs had the greatest rural–urban differences in probabilities and regions with the lowest AAPs had the smallest rural–urban difference in probabilities. The highest rural–urban testing disparities were observed in states with high AAPs for HIV testing. HIV testing estimates were higher in urban compared to rural areas at the national, regional, and state level. This study examines the isolated influence of rurality on HIV testing and identifies specific US areas where future efforts to increase HIV testing should be directed to.

Keywords

HIV testing Rurality BRFSS Logistic regression Average adjusted predictions Average marginal effects 

Notes

Funding

There was no funding for this study.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human or animals participants performed by any of the authors. All data that was used is publicly available and anonymized.

Informed Consent

This is not applicable to the study.

References

  1. 1.
    CDC. Estimated HIV incidence and prevalence in the United States, 2010–2015. HIV Surveill Suppl Rep. 2018;23(1):1.Google Scholar
  2. 2.
    CDC. Estimates of new HIV infections in the United States: CDC HIV/AIDS Facts. 2016; http://www.cdc.gov/hiv/topics/surveillance/resources/factsheets/pdf/incidence.pdf. Accessed 2 April 2018.
  3. 3.
    Kaiser Family Foundation. The HIV/AIDS epidemic in the United States: the basics. 2018; https://www.kff.org/hivaids/fact-sheet/the-hivaids-epidemic-in-the-united-states-the-basics/. Accessed 27 Aug 2018.
  4. 4.
    Secretary’s Minority AIDS Initiative Fund U.S. Statistics; 2018. https://www.hiv.gov/hiv-basics/overview/data-and-trends/statistics. Accessed 27 Aug 2018.
  5. 5.
    CDC. HIV in the United States: at a glance. 2018; https://www.cdc.gov/hiv/statistics/overview/ataglance.html. Accessed 27 Aug 2018.
  6. 6.
    Campsmith ML, Rhodes PH, Hall HI, Green TA. Undiagnosed HIV prevalence among adults and adolescents in the United States at the end of 2006. J Acquir Immune Defic Syndr. 2010;53(5):619–24.Google Scholar
  7. 7.
    CDC. Monitoring selected national HIV prevention and care objectives by using HIV surveillance data—United States and 6 dependent areas, 2016. HIV Surveill Suppl Rep. 2018;23(4).Google Scholar
  8. 8.
    CDC. HIV surveillance report, 2016. 2017; http://www.cdc.gov/hiv/library/reports/hiv-surveillance.html.
  9. 9.
    Marks G, Crepaz N, Janssen RS. Estimating sexual transmission of HIV from persons aware and unaware that they are infected with the virus in the USA. AIDS. 2006;20(10):1447–50.CrossRefGoogle Scholar
  10. 10.
    Rutstein SE, Ananworanich J, Fidler S, et al. Clinical and public health implications of acute and early HIV detection and treatment: a scoping review. J Int AIDS Soc. 2017;20(1):21579.CrossRefGoogle Scholar
  11. 11.
    Eaton LA, Kalichman SC. Changes in transmission risk behaviors across stages of HIV disease among people living with HIV. J Assoc Nurses AIDS Care. 2009;20(1):39–49.CrossRefGoogle Scholar
  12. 12.
    Rosenberg NE, Pilcher CD, Busch MP, Cohen MS. How can we better identify early HIV infections? Curr Opin HIV AIDS. 2015;10(1):61–8.CrossRefGoogle Scholar
  13. 13.
    Smith MK, Rutstein SE, Powers KA, et al. The detection and management of early HIV infection: a clinical and public health emergency. J Acquir Immune Defic Syndr. 2013;63(2):S187–99.CrossRefGoogle Scholar
  14. 14.
    Teeraananchai S, Kerr SJ, Amin J, Ruxrungtham K, Law MG. Life expectancy of HIV-positive people after starting combination antiretroviral therapy: a meta-analysis. HIV Med. 2016;18(4):256–66.CrossRefGoogle Scholar
  15. 15.
    Dieffenbach CW, Fauci AS. Universal voluntary testing and treatment for prevention of HIV transmission. JAMA. 2009;301(22):2380–2.CrossRefGoogle Scholar
  16. 16.
    Granich RM, Gilks CF, Dye C, De Cock KM, Williams BG. Universal voluntary HIV testing with immediate antiretroviral therapy as a strategy for elimination of HIV transmission: a mathematical model. Lancet. 2009;373(9657):48–57.CrossRefGoogle Scholar
  17. 17.
    Holtgrave D. Potential and limitations of a “test and treat” strategy as HIV prevention in the United States. Int J Clin Prac. 2010;64(6):678–84.CrossRefGoogle Scholar
  18. 18.
    Selik RM, Mokotoff ED, Branson B, Owen SM, Whitmore S, Hall HI. Revised surveillance case definition for HIV infection & United States, 2014. Morb Mortal Wkly Rep. 2014;63(3):1–10.Google Scholar
  19. 19.
    Takahashi TA, Johnson KM, Bradley KA. A population-based study of HIV testing practices and perceptions in 4 U.S. states. J Gen Intern Med. 2005;20(7):618–22.CrossRefGoogle Scholar
  20. 20.
    Kaiser Family Foundation. Views and Experiences with HIV testing in the U.S.: survey brief. 2009; https://kaiserfamilyfoundation.files.wordpress.com/2013/01/7926.pdf.
  21. 21.
    Ford CL, Godette DC, Mulatu MS, Gaines TL. Recent HIV testing prevalence, determinants, and disparities among US older adult respondents to the behavioral risk factor surveillance system. Sex Transm Dis. 2015;42(8):405–10.CrossRefGoogle Scholar
  22. 22.
    Ansa BE, White S, Chung Y, Smith SA. Trends in HIV testing among adults in Georgia: analysis of the 2011–2015 BRFSS data. Int J Environ Res Public Health. 2016;13(11):1126.CrossRefGoogle Scholar
  23. 23.
    Van Handel MM, Rose CE, Hallisey EJ, et al. County-level vulnerability assessment for rapid dissemination of HIV or HCV infections among persons who inject drugs, United States. J Acquir Immune Defic Syndr. 2016;73(3):323–31.CrossRefGoogle Scholar
  24. 24.
    Government Accountability Office. Federal and State efforts to identify infected individuals and connect them to care. GAO-09-985 2009; GAO-09-985. https://www.gao.gov/products/GAO-09-985.
  25. 25.
    Mahajan AP, Sayles JN, Patel VA, et al. Stigma in the HIV/AIDS epidemic: a review of the literature and recommendations for the way forward. AIDS. 2008;22(Suppl 2):S67–79.CrossRefGoogle Scholar
  26. 26.
    CAA Test. HIV testing in the United States. 2016; https://www.cdc.gov/nchhstp/newsroom/docs/factsheets/hiv-testing-us-508.pdf.
  27. 27.
    Forrest KYZ, Lin Y. Comparison of health-related factors between rural and urban Pennsylvania residents using behavioral risk factor surveillance system (BRFSS) data. 2010; http://www.rural.palegislature.us/brfss_2010.pdf.
  28. 28.
    Benavides-Torres RA, Wall KM, Núñez Rocha GM, Onofre Rodríguez DJ, Hopson L. Factors associated with lifetime HIV testing in Texas by race/ethnicity. Open AIDS J. 2012;6:232–8.CrossRefGoogle Scholar
  29. 29.
    Hall HI, Li J, McKenna MT. HIV in predominantly rural areas of the United States. J Rural Health. 2005;21(3):245–53.CrossRefGoogle Scholar
  30. 30.
    Ohl ME, Perencevich E. Frequency of human immunodeficiency virus (HIV) testing in urban vs. rural areas of the United States: results from a nationally-representative sample. BMC Public Health. 2011;11:681.CrossRefGoogle Scholar
  31. 31.
    Henderson ER, Subramaniam DS, Chen J. Rural–urban differences in HIV testing among US adults: findings from the behavioral risk factor surveillance system. Sex Transm Dis. 2018; Publish Ahead of Print.Google Scholar
  32. 32.
    Wilson LE, Korthuis T, Fleishman JA, et al. HIV-related medical service use by rural/urban residents: a multistate perspective. AIDS Care. 2011;23(8):971–9.CrossRefGoogle Scholar
  33. 33.
    Kakietek J, Sullivan PS, Heffelfinger JD. You’ve got male: internet use, rural residence, and risky sex in men who have sex with men recruited in 12 U.S. cities. AIDS Educ Prev. 2011;23(2):118–27.CrossRefGoogle Scholar
  34. 34.
    Thompson EL, Mahony H, Noble C, et al. Rural and urban differences in sexual behaviors among adolescents in Florida. J Community Health. 2018;43(2):268–72.CrossRefGoogle Scholar
  35. 35.
    Oser CB, Leukefeld CG, Tindall MS, et al. Rural drug users: factors associated with substance abuse treatment utilization. Int J Offender Ther Comp Criminol. 2011;55(4):567–86.CrossRefGoogle Scholar
  36. 36.
    Pullen E, Oser C. Barriers to substance abuse treatment in rural and urban communities: counselor perspectives. Subst Use Misuse. 2014;49(7):891–901.CrossRefGoogle Scholar
  37. 37.
    CDC. Behavioral risk factor surveillance system survey data. In: Services USDoHaH, ed. Atlanta: Centers for Disease Control and Prevention; 2012.Google Scholar
  38. 38.
    CDC. Behavioral risk factor surveillance system survey data. In: Services USDoHaH, ed. Atlanta: Centers for Disease Control and Prevention; 2014.Google Scholar
  39. 39.
    CDC. Behavioral risk factor surveillance system survey data. In: Services USDoHaH, ed. Atlanta: Centers for Disease Control and Prevention; 2016.Google Scholar
  40. 40.
    CDC. Behavioral risk factor surveillance system 2013 codebook report land-line and cell-phone data. 2014.Google Scholar
  41. 41.
    CDC. Behavioral risk factor surveillance system 2015 codebook report land-line and cell-phone data. 2016; https://www.cdc.gov/brfss/annual_data/2015/pdf/codebook15_llcp.pdf.
  42. 42.
    CDC. LLCP 2017 codebook report overall version data weighted with _LLCPWT behavioral risk factor surveillance system. 2018; https://www.cdc.gov/brfss/annual_data/2017/pdf/codebook17_llcp-v2-508.pdf.
  43. 43.
    Judd SE, Gutierrez OM, Newby PK, et al. Dietary patterns are associated with incident stroke and contribute to excess risk of stroke in black Americans. Stroke. 2013;44(12):3305–11.CrossRefGoogle Scholar
  44. 44.
    CDC. Behavioral risk factor surveillance system comparability of data BRFSS 2017. 2018; https://www.cdc.gov/brfss/annual_data/2017/pdf/compare-2017-508.pdf.
  45. 45.
    CDC. Behavioral risk factor surveillance system survey questionnaire. In: Services USDoHaH, ed. Atlanta: Centers for Disease Control and Prevention; 2012.Google Scholar
  46. 46.
    CDC. Behavioral risk factor surveillance system survey questionnaire. In: Services DoHaH, ed. Atlanta: Centers for Disease Control and Prevention; 2014.Google Scholar
  47. 47.
    CDC. Behavioral risk factor surveillance system survey questionnaire. In: Services USDoHaH, ed. Atlanta: Centers for Disease Control and Prevention; 2016.Google Scholar
  48. 48.
    Dailey AF, Hoots BE, Hall HI, et al. Vital signs: human immunodeficiency virus testing and diagnosis delays—United States. Morb Mortal Wkly Rep. 2017;66:1300–6.CrossRefGoogle Scholar
  49. 49.
    Patel P, Borkowf CB, Brooks JT, Lasry A, Lansky A, Mermin J. Estimating per-act HIV transmission risk: a systematic review. AIDS. 2014;28(10):1509–19.CrossRefGoogle Scholar
  50. 50.
    Koblin BA, Husnik MJ, Colfax G, et al. Risk factors for HIV infection among men who have sex with men. AIDS. 2006;20(5):731–9.CrossRefGoogle Scholar
  51. 51.
    Kwan CK, Rose CE, Brooks JT, Marks G, Sionean C. HIV testing among men at risk for acquiring HIV infection before and after the 2006 CDC recommendations. Public Health Rep. 2016;131(2):311–9.CrossRefGoogle Scholar
  52. 52.
    Purcell DW, Johnson CH, Lansky A, et al. Estimating the population size of men who have sex with men in the United States to obtain HIV and syphilis rates. Open AIDS J. 2012;6:98–107.CrossRefGoogle Scholar
  53. 53.
    Wejnert C, Le B, Rose CE, Oster AM, Smith AJ, Zhu J. HIV infection and awareness among men who have sex with men-20 cities, United States, 2008 and 2011. PLoS ONE. 2013;8(10):e76878.CrossRefGoogle Scholar
  54. 54.
    Bauer HM, Gibson P, Hernandez M, Kent C, Klausner J, Bolan G. Intimate partner violence and high-risk sexual behaviors among female patients with sexually transmitted diseases. Sex Transm Dis. 2002;29(7):411–6.CrossRefGoogle Scholar
  55. 55.
    Cavanaugh CE, Hansen NB, Sullivan TP. HIV sexual risk behavior among low-income women experiencing intimate partner violence: the role of posttraumatic stress disorder. AIDS Behav. 2010;14(2):318–27.CrossRefGoogle Scholar
  56. 56.
    Crosby RA, DiClemente RJ, Wingood GM, et al. Sexual agency versus relational factors: a study of condom use antecedents among high-risk young African American women. Sex Health. 2008;5(1):41–7.CrossRefGoogle Scholar
  57. 57.
    Coker AL. Does physical intimate partner violence affect sexual health? A systematic review. Trauma Violence Abuse. 2007;8(2):149–77.CrossRefGoogle Scholar
  58. 58.
    Dinenno EA, Oster AM, Sionean C, Denning P, Lansky A. Piloting a system for behavioral surveillance among heterosexuals at increased risk of HIV in the United States. Open AIDS J. 2012;6:169–76.CrossRefGoogle Scholar
  59. 59.
    Herbenick D, Reece M, Schick V, Sanders SA, Dodge B, Fortenberry JD. Sexual behavior in the United States: results from a national probability sample of men and women ages 14–94. J Sex Med. 2010;7(Suppl 5):255–65.CrossRefGoogle Scholar
  60. 60.
    Moreno CL. The relationship between culture, gender, structural factors, abuse, trauma, and HIV/AIDS for Latinas. Qual Health Res. 2007;17(3):340–52.CrossRefGoogle Scholar
  61. 61.
    Mosack KE, Randolph ME, Dickson-Gomez J, Abbott M, Smith E, Weeks MR. Sexual risk-taking among high-risk urban women with and without histories of childhood sexual abuse: mediating effects of contextual factors. J Child Sex Abuse. 2010;19(1):43–61.CrossRefGoogle Scholar
  62. 62.
    Simoni JM, Sehgal S, Walters KL. Triangle of risk: urban American Indian women’s sexual trauma, injection drug use, and HIV sexual risk behaviors. AIDS Behav. 2004;8(1):33–45.CrossRefGoogle Scholar
  63. 63.
    Tross S, Hanner J, Hu MC, Pavlicova M, Campbell A, Nunes EV. Substance use and high risk sexual behaviors among women in psychosocial outpatient and methadone maintenance treatment programs. Am J Drug Alcohol Abuse. 2009;35(5):368–74.CrossRefGoogle Scholar
  64. 64.
    Reisen CA, Zea MC, Bianchi FT, Poppen PJ, Shedlin MG, Penha MM. Latino gay and bisexual men’s relationships with non-gay-identified men who have sex with men. J Homosex. 2010;57(8):1004–21.CrossRefGoogle Scholar
  65. 65.
    Albarracin J, Plambeck CR. Demographic factors and sexist beliefs as predictors of condom use among Latinos in the USA. AIDS Care. 2010;22(8):1021–8.CrossRefGoogle Scholar
  66. 66.
    Shedlin MG, Decena CU, Oliver-Velez D. Initial acculturation and HIV risk among new Hispanic immigrants. J Natl Med Assoc. 2005;97(7 Suppl):32S–7S.Google Scholar
  67. 67.
    Duran D, Usman HR, Beltrami J, Alvarez ME, Valleroy L, Lyles CM. HIV counseling and testing among hispanics at CDC-funded sites in the United States, 2007. Am J Public Health. 2010;100(Suppl 1):S152–8.CrossRefGoogle Scholar
  68. 68.
    del Rio C. Latinos and HIV care in the Southeastern United States: new challenges complicating longstanding problems. Clin Infect Dis. 2011;53(5):488–9.CrossRefGoogle Scholar
  69. 69.
    Beer L, Mattson CL, Bradley H, Shouse RL. Trends in ART prescription and viral suppression among HIV-positive young adults in care in the United States, 2009–2013. J Acquir Immune Defic Syndr. 2017;76(1):e1–6.CrossRefGoogle Scholar
  70. 70.
    Kann L, Olsen EO, McManus T, et al. Sexual identity, sex of sexual contacts, and health-risk behaviors among students in grades 9–12-youth risk behavior surveillance, selected sites, United States, 2001–2009. Morb Mortal Wkly Rep. 2011;60(7):1–133.Google Scholar
  71. 71.
    Althoff KN, Gebo KA, Gange SJ, et al. CD4 count at presentation for HIV care in the United States and Canada: are those over 50 years more likely to have a delayed presentation? AIDS Res Ther. 2010;7:45CrossRefGoogle Scholar
  72. 72.
    Brooks JT, Buchacz K, Gebo KA, Mermin J. HIV infection and older Americans: the public health perspective. Am J Public Health. 2012;102(8):1516–26.CrossRefGoogle Scholar
  73. 73.
    Oberoi S, Chaudhary N, Patnaik S, Singh A. Understanding health seeking behavior. J Fam Med Prim Care. 2016;5(2):463–4.CrossRefGoogle Scholar
  74. 74.
    McNamee R. Regression modelling and other methods to control confounding. Occup Environ Med. 2005;62(7):500.CrossRefGoogle Scholar
  75. 75.
    Geng Z, Guo J, Fung W-K. Criteria for confounders in epidemiological studies. J R Stat Soc Ser B. 2002;64(1):3–15.CrossRefGoogle Scholar
  76. 76.
    LaMorte WW. Residual confounding, confounding by indication, & reverse causality. 2016; http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704-EP713_Confounding-EM/BS704-EP713_Confounding-EM4.html.
  77. 77.
    Becher H. The concept of residual confounding in regression models and some applications. Stat Med. 1992;11(13):1747–58.CrossRefGoogle Scholar
  78. 78.
    Wang F, Shin H-C. SAS® model selection macros for complex survey data using PROC SURVEYLOGISTIC/SURVEYREG. Midwest SAS Users Group, Kansas City, KS; 2011. http://www.mwsug.org/proceedings/2011/stats/MWSUG-2011-SA02.pdf.
  79. 79.
    SAS 9.4 [computer program]. Cary, North Carolina 2017.Google Scholar
  80. 80.
    NIDA. Who is at risk for HIV infection and which populations are most affected? 2012; https://www.drugabuse.gov/publications/research-reports/hivaids/who-risk-hiv-infection-which-populations-are-most-affected.
  81. 81.
    NICHD. Who is at risk of HIV/AIDS? 2016; https://www.nichd.nih.gov/health/topics/hiv/conditioninfo/risk.
  82. 82.
    CDC. Populations at greatest risk. 2015; https://www.cdc.gov/hiv/policies/hip/risk.html.
  83. 83.
    Molina J-M, Capitant C, Spire B, et al. On-demand preexposure prophylaxis in men at high risk for HIV-1 infection. N Engl J Med. 2015;373(23):2237–46.CrossRefGoogle Scholar
  84. 84.
    WHO. Men who have sex with men. 2018; https://www.who.int/hiv/topics/msm/en/.
  85. 85.
    CDC. Behavioral risk factor surveillance system 2012 codebook report land-line and cell-phone data. 2013; https://www.cdc.gov/brfss/annual_data/2012/pdf/codebook12_llcp.pdf.
  86. 86.
    Jann B. Predictive margins and marginal effects in Stata. 2013.Google Scholar
  87. 87.
    Williams R. Using stata’s margins command to estimate and interpret adjusted predictions and marginal effects. Stata J. 2012;12(2):308–31.CrossRefGoogle Scholar
  88. 88.
    Stata Statistical Software. Release 15 [computer program]. College Station: StataCorp LLC; 2017.Google Scholar
  89. 89.
    U.S. Census Bureau. Census Bureau Regions and Divisions with State FIPS Codes; 2015. https://www2.census.gov/geo/docs/maps-data/maps/reg_div.txt.
  90. 90.
    CDC. Sexually transmitted disease surveillance 2017. 2018; https://www.cdc.gov/std/stats17/2017-STD-Surveillance-Report_CDC-clearance-9.10.18.pdf.
  91. 91.
    Nusbaum MR, Wallace RR, Slatt LM, Kondrad EC. Sexually transmitted infections and increased risk of co-infection with human immunodeficiency virus. J Am Osteopath Assoc. 2004;104(12):527–35.Google Scholar
  92. 92.
    Peterman TA, Newman DR, Maddox L, Schmitt K, Shiver S. Risk for HIV following a diagnosis of syphilis, gonorrhoea or chlamydia: 328,456 women in Florida, 2000–2011. Int J STD AIDS. 2015;26(2):113–9.CrossRefGoogle Scholar
  93. 93.
    Pathela P, Braunstein SL, Blank S, Schillinger JA. HIV incidence among men with and those without sexually transmitted rectal infections: estimates from matching against an HIV case registry. Clin Infect Dis. 2013;57(8):1203–9.CrossRefGoogle Scholar
  94. 94.
    Page P. Beyond statistical significance: clinical interpretation of rehabilitation research literature. Int J Sports Phys Ther. 2014;9(5):726–36.Google Scholar
  95. 95.
    US Census. Georgia: 2010 population and housing Unit counts. 2012; https://www2.census.gov/library/publications/decennial/2010/cph-2/cph-2-12.pdf.
  96. 96.
    CDC. HIV in the United States by region. 2018; https://www.cdc.gov/hiv/statistics/overview/geographicdistribution.html.
  97. 97.
    Bowlin SJ, Morrill BD, Nafziger AN, Jenkins PL, Lewis C, Pearson TA. Validity of cardiovascular disease risk factors assessed by telephone survey: the behavioral risk factor survey. J Clin Epidemiol. 1993;46(6):561–71.CrossRefGoogle Scholar
  98. 98.
    Klompas M, Cocoros NM, Menchaca JT, et al. State and local chronic disease surveillance using electronic health record systems. Am J Public Health. 2017;107(9):1406–12.CrossRefGoogle Scholar
  99. 99.
    Scribani M, Shelton J, Chapel D, Krupa N, Wyckoff L, Jenkins P. Comparison of bias resulting from two methods of self-reporting height and weight: a validation study. RSM Open. 2014;5(6):1–7.Google Scholar
  100. 100.
    Schneider KL, Clark MA, Rakowski W, Lapane KL. Evaluating the impact of non-response bias in the behavioral risk factor surveillance system (BRFSS). J Epidemiol Community Health. 2012;66(4):290–5.CrossRefGoogle Scholar
  101. 101.

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Authors and Affiliations

  1. 1.Department of BiostatisticsMichigan School of Public HealthAnn ArborUSA
  2. 2.Department of Chronic Disease EpidemiologyYale UniversityNew HavenUSA
  3. 3.Deparment of GeographyUniversity of TennesseeKnoxvilleUSA

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