sexual encounters) have found no HIV transmissions to an HIV-negative partner occurred when the HIV-positive person was virally suppressed.
These were both heterosexual and homosexual sexual encounters with- out the use of condoms or pre-exposure prophylaxis (PrEP), a drug that people at high risk for HIV take daily to lower their chances of getting infected.95-98 The CDC has therefore stated that people who take ART daily as prescribed and are able to maintain a suppressed viral load “have effectively no risk” of transmitting HIV to their sexual partner.99
Background and Descriptive Epidemiology
cancer to the expected rate among a similar group without cancer. As expected, women diagnosed with localized disease had much better sur- vival (98.9%) than those diagnosed with regional (85.2%) and distant disease (26.9%).
Although the 5-year survival data are encouraging, they represent only the short-term consequences of the disease. The ultimate effect of breast cancer can be assessed solely by examining mortality rates.
With an age-adjusted mortality rate of 21.2 per 100,000 women during 2010–2014, breast cancer ranked second only to lung cancer among female cancer deaths.
Time
Age-adjusted incidence rate of breast cancer among all women increased from 105.0 per 100,000 women in 1975 to 130.6 per 100,000 women in 2014.6 This increase was quite steep during the 1980s but has attenuated in recent years for Black women and has stabilized for White women (see FIGURE 5-13). Since 1975, mortality rates have fallen by 37% among White women and only 5% among Black women.6 These mortality trends vary by age at diagnosis. Among White women, mortality rates declined by 56% among those under the age of 50 years but only by 32% for those age 50 and older. Among Black women, mortality rates declined by 30%
among those under the age of 50 years but increased by 4% among those age 50 and older.
Person
Many individual characteristics, such as age, race, socioeconomic level, and religion, are associated with the risk of breast cancer.104 Breast cancer incidence rates rise sharply with age. The increase is very steep from age 40 through 79 years and then drops off (see FIGURE 5-14). Incidence rates are highest among White women, intermediate among Black and His- panic women, and lowest among Asian and American Indian women.
Rates are also higher among Jewish women and among women of high socioeconomic status. Various reproductive characteristics also influence a woman’s risk of breast cancer.105 For example, a younger age at men- arche and older age at menopause are associated with a higher risk, and surgical removal of both ovaries is associated with a lower risk.
Place
Incidence rates are highest in North America, western and northern Europe, and Oceania, and lowest in Africa and Asia.22 However, inci- dence rates in many African and Asian countries are currently increas- ing because of changes in reproductive patterns and other risk factors (see the following section) and increases in breast cancer awareness and screening mammography.
Examples: Three Important Causes of Morbidity in the United States 139
FIGURE 5-13 Breast cancer incidence and mortality for White and Black women, 1975–2014, United States.
0
19751977 1979 1981 19831985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 20112013 20
60 80 100 120 140
White women Black women
White women Black women 160
Rate per 100,000
Year of Diagnosis Incidence
45 40 35 30 25
Rate per 100,000
20 15 5 0
1975 1980 1985 1990 1995
Year of Death Mortality
2000 2005 2010 2014
Modified from Howlader N, Noone AM, Krapcho M, Miller D, Bishop K, Kosary CL, et al. (eds). SEER Cancer Statistics Review, 1975-2014. cancer.gov. https://seer.cancer.gov/csr/1975_2014/. Published April 2017.
Accessed April 2017.
Examples: Three Important Causes of Morbidity in the United States 141
FIGURE 5-14 Age-specific breast cancer incidence rates, 2010–2014.
300 250 200 150 100 50 0
15–1 9
20–24 25–29 30–34 35–39 40–44 45–49 50–54 Age at Diagnosis
Rate per 100,000
55–59 60–64 65–69 70–7 4
75–79 80–84 85+
Data from Howlader N, Noone AM, Krapcho M, Miller D, Bishop K, Kosary CL, et al. (eds). SEER Cancer Statistics Review, 1975-2014. cancer.gov. https://seer.cancer.gov/csr/1975_2014/. Published April 2017.
Accessed April 2017.
FIGURE 5-15 Age-adjusted breast cancer death rates by state, 2011–2015.
AK
HI WA
ID
MT
WY
CO
ND
MN
IA
MO
AR
LA
MS AL GA
FL SC TN NC
KY IL IN
WI
MI OH
PA
WV VA
NY ME VT
NH MA RI CT NJ DE MD DC SD
NE
KS
OK
TX AZ NM
NV UT OR
CA
22.26 – 28.91 19.96 – 20.34 21.78 – 22.25 18.87 – 19.95 20.35 – 21.77
Age-adjusted death rates per 100,000 quantile interval
15.86 – 18.86 U.S. rate: 20.87
From Noone AM, Howlader N, Krapcho M, Miller D, Brest A, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin KA (eds). SEER Cancer Statistics Review, 1975-2015, National Cancer Institute.
Bethesda, MD, https://seer.cancer.gov/csr/1975_2015/, based on November 2017 SEER data submission, posted to the SEER web site, April 2018.
Death rates also vary dramatically within the United States (see FIGURE 5-15). They are highest in the District of Columbia, Louisiana, Mississippi, Oklahoma, and Ohio and lowest in Massachusetts, Connecti- cut, Maine, North Dakota, and Hawaii. These mortality rates are adjusted for age differences between the states, and so the geographic variation is, in part, due to differences in stage at diagnosis and access to care.
Discussion
Because the epidemiological data are so consistent, many of the character- istics described earlier are considered “established” risk factors for breast cancer106 (see TABLE 5-10). Most of these characteristics also fit into the
TABLE 5-10 Risk Factors for Breast Cancer
Characteristic High-risk group
Gender Female
Age Old
Country of birth North America, western and northern
Europe, Australia, and New Zealand
Place of residence in United States Northeast, Pacific Coast
Mother, sister, or daughter with breast cancer Yes
Personal history of breast cancer Yes
Personal history of endometrium, ovary, or colon cancer Yes
History of atypical hyperplasia or breast densities Yes
Certain inherited genetic mutations Yes
High doses of radiation to chest Yes
Bone density (postmenopausal) High
Socioeconomic status High
Breast cancer at ≥ 35 years of age White
Breast cancer at < 35 years of age Black
Religion Ashkenazi Jewish heritage
Reproductive history No full-term pregnancies
Age at first full-term birth > 30 years at first birth
History of breastfeeding Never breastfed
Age at menarche and menopause < 12 years at menarche and > 55 years
at menopause
Endogenous estrogen or testosterone levels High
Oral contraceptives Recent use
Hormone replacement therapy Recent and long-term use
Obesity (for postmenopausal breast cancer) Yes
Alcohol consumption Daily drinking
Height Tall
Adapted from Breast Cancer Facts and Figures 2017-2018. cancer.org. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and -statistics/breast-cancer-facts-and-figures/breast-cancer-facts-and-figures-2017-2018.pdf. Jemal A, Bray F, Center M, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin. 2011;62:169-190.
well-accepted hypothesis that circulating sex hormones, such as estrogen and andogen, play a crucial role in some types of breast cancer develop- ment.107,108 For example, the low rates among women in Japan and other Asian countries can be accounted for by low serum levels of these hormones.
Furthermore, the association with family history and Jewish religion may be partly explained by two gene alterations (BRCA1 and BRCA2) that predis- pose a woman to breast and ovarian cancer. These genetic alterations in BRCA1 and BRCA2 are extremely rare in the general population but occur slightly more often in certain ethnic or geographically isolated groups, such as those of Ashkenazi Jewish descent (about 2%).109,110
Unfortunately, the “established” risk factors cannot account for all cases of breast cancer, and many of these factors are not personally modifiable, such as age, family history, height, and age at menarche.
Furthermore, many established risk factors for breast cancer are spe- cifically associated with certain subtypes of breast cancer that express the estrogen receptor (ER+/luminal breast cancer); less is known about risk factors for ER− or basal-like breast cancers. Clearly, more research is needed to determine additional risk factors for breast cancer. For example, hypotheses have been postulated involving exposure to elec- tromagnetic fields111 and environmental estrogens.112 The environmen- tal estrogens, termed xenoestrogens, are a diverse group of chemicals, including pesticides, plasticizers, and detergents, that have been released into the environment in great quantities.113 Although hypotheses about electromagnetic fields and xenoestrogens as risk factors for breast can- cer are biologically plausible because exposure to these items is thought
Examples: Three Important Causes of Morbidity in the United States 143
to influence a woman’s estrogen levels, recently completed studies have found mixed results.114-118 Further research on the correlation between specific environmental exposures during different critical windows of mammary gland development and breast cancer risk will be crucial.
Chemopreventive agents are drugs used to inhibit or delay can- cer.100 Tamoxifen and raloxifene are approved chemopreventive agents in healthy women at high risk for breast cancer.119,120 Aromatase inhibitors are a class of drug undergoing clinical trials to examine whether they may also be helpful chemopreventive agents among high-risk postmeno- pausal women.106 Aromatase inhibitors lower estrogen levels by stopping an enzyme in fat tissue from changing other hormones into estrogen.
They lower estrogen levels in women whose ovaries are not making estrogen and thus are effective only in postmenopausal women. Cur- rently, these drugs are approved to prevent only breast cancer recurrence, but early clinical trial results for prevention are promising.121,122
Although breast cancer cannot yet be prevented, survival may be improved through early diagnosis via clinical breast examination and mammography. For women at average risk of breast cancer, current guidelines from the American Cancer Society recommend that those 40 to 44 years of age have the option to begin annual mammography, those 45 to 54 years should undergo annual mammography, and those 55 years of age or older may transition to mammography every 2 years or continue with annual mammograms.106 In contrast, the U.S. Preventive Services Task Force recommends that women age 40 up to 49 have the option to begin screening mammography every 2 years, and women age 50 to 74 should screen every 2 years. For women 75 and older, no rec- ommendation is provided given current evidence is insufficient to assess the balance of benefits and harms of screening mammography in this group.123 Descriptive data on screening mammography can be used to monitor the public response to these often confusing recommendations.
In fact, use of mammography among women over 40 years of age more than doubled from 1987 through 2015.5(p267)
Summary
Descriptive epidemiology involves the analysis of disease patterns by per- son, place, and time. Personal characteristics include age, gender, race and ethnicity, and socioeconomic status. Place, which is usually defined in geopolitical units, such as countries, encompasses the physical envi- ronment (such as water and air), biological environment (such as flora and fauna), and social environment (such as cultural traditions). Time trends are examined for short- and long-term changes (ranging from days to decades) as well as cyclical fluctuations.
The principal reasons for describing disease rates by person, place, and time are (1) to assess the health status of a population, (2) to generate
hypotheses about causal factors for disease, and (3) to plan and evaluate public health programs.
Descriptive analyses can also be used to identify disease clusters, outbreaks and epidemics. A disease cluster is the occurrence of cases of disease close together in space, time, or both space and time. A disease outbreak or epidemic is the occurrence of disease in excess of what would normally be expected. Even though the terms are synonymous, a disease outbreak often describes a localized rather than a widespread epidemic.
The Ebola virus outbreaks in Africa have been among the most devastat- ing infectious disease outbreaks in recent times.
The analysis of U.S. mortality rates by age reveals numerous pat- terns, including higher death rates from unintentional injuries among the young than among older individuals, higher death rates from motor vehicle accidents and homicides among Blacks than among Whites, and lower death rates from heart disease and lung cancer among females than among males. Although mortality data have many advantages, including complete reporting and easy access, they also have several disadvantages, including inaccurate information on cause of death and insufficient data on serious nonfatal diseases. Thus, epidemiologists rely on other data sources to learn about descriptive patterns of import- ant fatal and nonfatal illnesses, such as childhood lead poisoning, HIV/
AIDS, and breast cancer. For example, the NHANES, the primary data source on the descriptive epidemiology of childhood lead poisoning in the United States, reveals continuing racial disparities in children’s blood lead levels, despite significant decreases in all groups over time.
Similarly, descriptive statistics on HIV from the CDC and the WHO show a lower prevalence in the United States than in other parts of the world, especially in sub-Saharan Africa. In addition, breast cancer data from the National Cancer Institute’s Surveillance, Epidemiology and End Results program indicate that breast cancer is the most commonly diagnosed cancer among U.S. women and that rates are highest among White women, women of high socioeconomic status, and women resid- ing in the Northeast.
References
1. MacMahon B, Trichopoulos D. Epidemiology:
Principles and Methods. 2nd ed. Boston, MA: Little, Brown and Co; 1996.
2. Porta M. A Dictionary of Epidemiology. 6th ed. New York, NY: Oxford University Press; 2014.
3. Yoon SS, Fryar CD, Carroll MD. Hypertension prevalence and control among adults: United States, 2011–2014. NCHS data brief, no 220. Hyattsville, MD: National Center for Health Statistics; 2015.
4. Heymann DL (ed.). Control of Communicable Diseases Manual. 20th ed. Washington, DC: American Public Health Association; 2015.
5. National Center for Health Statistics. Health, United States, 2016, With Chartbook on Long-term Trends in Health. Hyattsville, MD; 2017.
6. Howlader N, Noone AM, Krapcho M, Miller D, Bishop K, Kosary CL, et al. (eds). SEER Cancer Statistics Review, 1975–2014. cancer.gov. https://seer.cancer .gov/csr/1975_2014/. Published April 2017. Accessed April 2017.
7. HIV/AIDS Surveillance Report: Diagnoses of HIV Infection in the United States and Dependent Areas, 2015. cdc.gov. https://www.cdc.gov/hiv/pdf/library /reports/surveillance/cdc-hiv-surveillance 145 References
-report-2015-vol-27.pdf. Published November 2016.
Accessed April 2017.
8. Olsson AC, Gustavsson P, Kromhout H, Peters S, Vermeulen R, Bruske I, et al. Exposure to diesel motor exhaust and lung cancer risk in a pooled analysis from case-control studies in Europe and Canada. Am J Respir Crit Care Med.
2011;183:941-948.
9. Humes K, Jones N, Ramirez R. Overview of Race and Hispanic Origin: 2010. census.gov. www.census.gov /prod/cen2010/briefs/c2010br-02.pdf. Issued March 2011. Accessed September 2017.
10. Ríos M, Romero F, Ramirez, R. Race Reporting Among Hispanics: 2010. census.gov. www.census .gov/population/www/documentation/twps0102 /twps0102.pdf. Issued March 2014. Accessed September 2017.
11. National Center for Health Statistics. Health, United States, 2015, With Special Feature on Racial and Ethnic Health Disparities. Hyattsville, MD: U.S.
Government Printing Office; 2016.
12. Wade WC. The Titanic: End of a Dream. New York, NY: Rawson Wade; 1979.
13. Chetty R, Stepner M, Abraham S, Lin S, Scuderi B, Turner N, et al. The association between income and life expectancy in the United States, 2001–2014.
JAMA. 2016;315:1750-1766.
14. Davis RL, Robertson DM (eds.). Textbook of Neuropathology. 2nd ed. Baltimore, MD: Williams and Wilkins; 1991.
15. Merrill RM, Lyon JL. Cancer incidence among Mormons and non-Mormons in Utah (United States), 1995–1999. Prev Med. 2005;40:535-541.
16. Blair A. Occupation. In: Harras A, Edwards BK, Blot WJ, Ries LAG (eds.). Cancer Rates and Risks. NIH pub no 96–691. Bethesda, MD: National Cancer Institute; 1996.
17. Kaplan RM, Kronick RG. Marital status and longevity in the United States population. J Epidemiol Commun H. 2006;60:760-765.
18. McIntosh JH, Berman K, Holliday FM, Byth K, Chapman R, Piper DW. Some factors associated with mortality in perforated peptic ulcer: a case-control study. J Gastroen Hepatol. 1996;11:82-87.
19. Ebrahim S, Wannamethee G, McCallum A, Walker M, Shaper AG. Marital status, change in marital status, and mortality in middle-aged British men.
Am J Epidemiol. 1995;142:834-842.
20. Goldman N, Korenman S, Weinstein R. Marital status and health among the elderly. Soc Sci Med.
1995;40:1717-1730.
21. Cotter C, Sturrock HJ, Hsiang MS, Liu J, Phillips AA, Hwang J, et al. The changing epidemiology of malaria elimination: new strategies for new challenges. Lancet. 2013;382:900-911.
22. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65:87-108.
23. Bosch FX, Ribes J, Cleries R, Diaz M. Epidemiology of hepatocellular carcinoma. Clin Liver Dis.
2005;9:191–211.
24. Jorgensen N, Carlsen E, Nermoen I, et al. East- West gradient in semen quality in the Nordic-Baltic area: a study of men from the general population in Denmark, Norway, Estonia and Finland. Hum Reprod. 2002;17:2199-2208.
25. Hayes RB. Prostate. In: Harras A, Edwards BK, Blot WJ, Ries LAG (eds.). Cancer Rates and Risks. NIH pub no 96–691. Bethesda, MD: National Cancer Institute; 1996.
26. Katanoda K, Hori M. Incidence rate for breast cancer in Japanese in Japan and in the United States from the Cancer Incidence in Five Continents. Jpn J Clin Oncol. 2016;46:883.
27. Fraser DW, Tsai TR, Orenstein W, et al. and the Field Investigation Team. Legionnaires’ disease:
description of an epidemic of pneumonia. New Engl J Med. 1977;297:1189-1197.
28. Schwartz J. What are people dying of on high air pollution days? Environ Res. 1994;64:26-35.
29. Schwartz J, Dockery DW. Increased mortality in Philadelphia associated with daily air pollution concentrations. Am Rev Respir Dis. 1992;145:600-604.
30. Van der Palen J, Doggen CJ, Beaglehorn R.
Variation in the time and day of onset of myocardial infarction and sudden death. New Zealand Med J.
1995;108:332-334.
31. Cohen MC, Rohtla KM, Lavery CE, Muller JE, Mittleman MA. Meta-analysis of the morning excess of acute myocardial infarction and sudden cardiac death. Am J Cardio. 1997;79:1512-1516.
32. Willich SN, Lowel H, Lewis M, Hormann A, Arntz HR, Keil U. Weekly variation of acute myocardial infarction. Increased Monday risk in the working population. Circulation. 1994;90:87-93.
33. Carvalho JS, Mavrides E, Shinebourne EA, Campbell S, Thilaganathan B. Improving the effectiveness of routine prenatal screening for major congenital heart defects. Heart. 2002;88:387-391.
34. Roueche B. Eleven Blue Men and Other Narratives of Medical Detection. New York, NY: Berkeley Publishing Co; 1953.
35. Lagakos S, Wessen B, Zelen M. An analysis of contaminated well water and health effects in Woburn, Massachusetts. J Am Stat Assoc. 1986;
81:583-595.
36. Rothman KJ. A sobering start for the cluster busters’
conference. Am J Epidemiol. 1990;132:S6-S13.
37. Neutra RR. Counterpoint from a cluster buster. Am J Epidemiol. 1990;132:1-8.
38. Disease outbreaks. who.int. www.who.int/topics /disease_outbreaks/en/. Accessed February 20, 2017.
39. Outbreak. Epidemiology Glossary. cdc.gov. www .cdc.gov/reproductivehealth/data_stats/glossary .html#O. Updated January 21, 2015. Accessed February 23, 2017.
40. Response to Measles Outbreaks in Measles Mortality Reduction Settings: Immunization, Vaccines and Biologicals. NCBI.gov. www.ncbi.nlm.nih.gov/books /NBK143963/.
41. Overview of Influenza Surveillance in the United States. cdc.gov. www.cdc.gov/flu/weekly/overview .htm. Updated October 13, 2016. Accessed February 23, 2017.
42. Epidemic intelligence - systematic event detection. who.
int. www.who.int/csr/alertresponse/epidemicintelligence /en. Accessed February 24, 2017.
43. Responding to an Infectious Disease Outbreak:
Progress Between SARS and Pandemic Influenza H1N1. Public Health Agency Canada. www .phac-aspc.gc.ca/ep-mu/rido-iemi/index-eng.php.
Updated April 11, 2012. Accessed March 2, 2017.
44. Reingold AL. Outbreak investigations: a perspective.
Emerg Infect Dis. 1998;4:21-27.
45. Lesson 6: Investigating an outbreak. In: Principles of Epidemiology in Public Health Practice. 3rd ed. cdc.
gov. www.cdc.gov/ophss/csels/dsepd/ss1978/ss1978 .pdf. Updated May 2012. Accessed February 26, 2017.
46. Surveillance for Foodborne Disease Outbreaks, United States, 2014: Annual Report. cdc.gov. www.cdc.gov /foodsafety/pdfs/foodborne-outbreaks-annual -report-2014–508.pdf. Published 2016. Accessed February 19, 2017.
47. Lesson 6: Investigating an outbreak. In: Principles of Epidemiology in Public Health Practice. 3rd ed. cdc .gov. www.cdc.gov/ophss/csels/dsepd/ss1978/ss1978 .pdf. Updated May 2012. Accessed February 26, 2017.
48. World Health Organization. Ebola haemorrhagic fever in Sudan, 1976. Bull WHO. 1978;56:247-270.
49. World Health Organization. Ebola haemorrhagic fever in Zaire, 1976. Bull WHO. 1978;56:271-293.
50. Brès P. The epidemic of Ebola haemorrhagic fever in Sudan and Zaire, 1976: introductory note. Bull WHO. 1978;56:245.
51. Shears P, O’Dempsey TJ. Ebola virus disease in Africa: epidemiology and nosocomial transmission.
J Hosp Infect. 2015;90:1-9.
52. Frieden TR, Damon I, Bell BP, Kenyon T, Nichol S.
Ebola 2014—new challenges, new global response and responsibility. N Engl J Med. 2014;371:1177-1180.
53. Piot P. No Time to Lose: A Life in Pursuit of Deadly Viruses. New York, NY: W. W. Norton & Co.; 2012.
54. World Health Organization. Ebola Outbreak Monthly Update, November 2016. www.afro.who.int/en/disease
-outbreaks/outbreak-news/4790-ebola-outbreak -monthly-update-november-2015.html. Accessed March 5, 2017.
55. Situation Report, 10 June 2016. who.int. apps.who .int/iris/bitstream/10665/208883/1/ebolasitrep _10Jun2016_eng.pdf?ua=1. Accessed March 5, 2017.
56. Busting the myths about Ebola is crucial to stop the transmission of the disease in Guinea. who.int. who.
int/features/2014/ebola-myths/en/. Published April 23, 2014. Accessed March 3, 2017.
57. Böl G. Risk communication in times of crisis: pitfalls and challenges in ensuring preparedness instead of hysterics. EMBO Reports. 2016;17:1-9.
58. Levi J, Segal L, Lieberman D, May K, St. Laurent R.
Outbreaks: Protecting Americans from Infectious Diseases, 2014. healthyamericans.org. healthyamericans.org /assets/files/Final%20Outbreaks%202014%20Report .pdf. Accessed on March 6, 2017.
59. Halabi SF, Gostin LO, Crowley JS (eds.). Global Management of Infectious Disease After Ebola.
Oxford, UK: Oxford University Press; 2017:26-27.
60. Henao-Restrepo A, Camacho A, Longini IM, Watson CH, Edmunds WH, Egger M, et al. Efficacy and effectiveness of an rVSV-vectored vaccine in preventing Ebola virus disease: final results from the Guinea ring vaccination, open-label, cluster-randomised trial (Ebola Ça Suffit!). Lancet. 2016;389:505-518.
61. Successful Ebola vaccine will be fast-tracked for use. BBC News. December 23, 2016. http://www.bbc .com/news/world-africa-38414060. Accessed March 24, 2017.
62. Pickett JP (exec. ed.). The American Heritage Dictionary of the English Language. 4th ed. Boston, MA: Houghton Mifflin Co.; 2000.
63. Tsugane S. Salt, salted food intake, and risk of gastric cancer: epidemiologic evidence. Cancer Science.
2005;96:1-6.
64. Zahm S. Non-Hodgkin’s lymphoma. In: Harras A, Edwards BK, Blot WJ, Ries LAG (eds.). Cancer Rates and Risks. NIH pub no 96–691. Bethesda, MD:
National Cancer Institute; 1996.
65. Escobedo LG, Peddicord JP. Smoking prevalence in U.S. birth cohorts: the influence of gender and education. Am J Public Health. 1996;86:231-236.
66. Alter MJ, Francis DP. Hepatitis B virus transmission between homosexual men: a model of the acquired immune deficiency syndrome (AIDS). In: Ma P, Armstrong D (eds.). The Acquired Immune Deficiency Syndrome and Infections of Homosexual Men. New York, NY: Yorke Medical Books; 1984.
67. Healthy People 2020. healthypeople.gov. http://
www.healthypeople.gov/2020/topicsobjectives2020 /default.aspx. Accessed September 2017.
68. Annual Estimates of the Resident Population by Sex, Race, and Hispanic Origin for the United States, States, 147 References