Projected demographic composition of the United States population of people living with diagnosed HIV

ABSTRACT

The transformation of HIV from a fatal disease to lifelong disease has resulted in an HIV-infected population that is growing and aging, placing new and increasing demands on public programs and health services. We used National HIV Surveillance System and US census data to project the demographic composition of the population of people living with diagnosed HIV (PLWDH) in the United States through 2045. The input parameters for the projections include: (1) census projections, (2) number of people with an existing HIV diagnosis in 2013, (3) number of new HIV diagnoses in 2013, and (4) death rate within the PLWDH population in 2013. Sex-, risk group-, and race-specific projections were estimated through an adapted Leslie Matrix Model for age-structured populations. Projections for 2013–2045 suggest that the number of PLWDH in the U.S. will consistently grow, from 917,294 to 1,232,054, though the annual growth rate will slow from 1.8% to 0.8%. The number of PLWDH aged 55 years and older will increase from 232,113 to 470,221. The number of non-Hispanic (NH) African Americans/Blacks and Hispanics is projected to consistently grow, shifting the racial/ethnic composition of the US PLWDH population from 32 to 23% NH-White, 42 to 38% NH-Black, and 20–32% Hispanic between 2013 and 2045. Given current trends, the composition of the PLWDH population is projected to change considerably. Public health practitioners should anticipate large shifts in the age and racial/ethnic structure of the PLWDH population in the United States.

KEYWORDS:

• HIV
• United States
• demography
• aging

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. For all sub-groups in 2008–2013, we estimated the annual diagnosis and mortality rates, according to these equations: Then we used linear regression to estimate trends in estimated new diagnosis rates, using these equations:

We used results from the regression models to predict the estimated diagnosis rate and mortality rate in 2013.