The authors have declared that no competing interests exist.
Analyzed the data: FL AL SS. Contributed reagents/materials/analysis tools: FL AL SS RJW. Wrote the paper: FL AL SS RJW.
In the wake of a national economic downturn, the state of California, in 2009–2010, implemented budget cuts that eliminated state funding of HIV prevention and testing. To mitigate the effect of these cuts remaining federal funds were redirected. This analysis estimates the impact of these budget cuts and reallocation of resources on HIV transmission and associated HIV treatment costs.
We estimated the effect of the budget cuts and reallocation for California county health departments (excluding Los Angeles and San Francisco) on the number of individuals living with or at-risk for HIV who received HIV prevention services. We used a Bernoulli model to estimate the number of new infections that would occur each year as a result of the changes, and assigned lifetime treatment costs to those new infections. We explored the effect of redirecting federal funds to more cost-effective programs, as well as the potential effect of allocating funds proportionately by transmission category. We estimated that cutting HIV prevention resulted in 55 new infections that were associated with $20 million in lifetime treatment costs. The redirection of federal funds to more cost-effective programs averted 15 HIV infections. If HIV prevention funding were allocated proportionately to transmission categories, we estimated that HIV infections could be reduced below the number that occurred annually before the state budget cuts.
Reducing funding for HIV prevention may result in short-term savings at the expense of additional HIV infections and increased HIV treatment costs. Existing HIV prevention funds would likely have a greater impact on the epidemic if they were allocated to the more cost-effective programs and the populations most likely to acquire and transmit the infection.
The HIV epidemic continues to be a major public health problem in the United States. Nearly 1.2 million persons are living with the disease
In the United States, HIV prevention programs are primarily funded by the federal, state and local governments and are administered by state and local health departments. In fiscal year 2007, 58% of prevention funding ($337 million) was provided by the federal government, 35% ($205 million) by state and local governments, and 7% ($39 million) by non-governmental entities, such as foundations and pharmaceutical and diagnostic companies
In 2009, an estimated 107,138 persons were living with HIV in California, and 4,981 were newly diagnosed with HIV. Men who have sex with men (MSM) accounted for 73% of HIV prevalence and almost 80% of new diagnoses. Historically, the state of California has allocated a substantial amount of state funds to HIV prevention
Historically, HIV prevention funds in California were allocated to 58 county and 3 city health departments (Berkeley, Long Beach, and Pasadena), for a total of 61 local health jurisdictions. However, complete data were not available for Los Angeles and San Francisco which were thus excluded from this analysis. For the years preceding the budget cut, or what we refer to as the “pre-cut” timeframe, we considered 59 local health jurisdictions, including 56 counties and the 3 cities. In the “post-cut” timeframe, we considered the subset of the 59 local health jurisdictions that continued to receive state-administered HIV prevention funds, again excluding Los Angeles and San Francisco.
The California Department of Public Health’s Office of AIDS provided annual epidemiologic, budgetary and program data for the state’s fiscal years 2005–2006 through 2009–2010. Data included HIV prevalence and the annual number of new HIV cases diagnosed, by transmission category, including MSM, injecting drug users (IDU) and high-risk heterosexuals (HET), defined as heterosexual contact with a person known to have, or to be at high risk for, HIV infection
We used a Bernoulli model to estimate, by transmission category, the annual rate at which HIV-infected individuals transmit the disease to uninfected persons and the annual risk of infection for uninfected persons
Parameter | Value (bounds for sensitivity analyses) | Source |
HIV prevalence in California | ||
HET | 1.0% (0.75–1.25%) |
|
IDU | 5.9% (4.0–10.2%) |
|
MSM | 19.1% (12.8–25.35%) |
|
Per-act HIV transmission probability |
||
Vaginal receptive | 0.08% (0.06–0.11%) |
|
Vaginal insertive | 0.04% (0.01–0.14%) |
|
Anal receptive | 1.4% (0.2–2.5%) |
|
Anal insertive | 0.7% (0%–1.3%) |
|
Contaminated needle sharing | 0.30% (0.24%–0.65%) |
|
Annual number of sexual partners | ||
HET | 1.21 (1–20) |
|
IDU | 3 (1–20) |
|
MSM | 3.5 (1–20) |
|
Annual number of sex acts all partners |
||
HET | 70 (26–365) | |
IDU | 70 (26–365) | |
MSM | 70 (26–365) | |
Annual number of intravenous injections (all partners) | 200 (100–300) | |
Proportion of protected sex acts for undiagnosed HIV-infected individuals and uninfected individuals who do not participate in risk reduction | ||
HET | 20% (10–50%) |
|
IDU | 20% (10–50%) |
|
MSM | 55% (40–80%) |
|
Proportion of needle sharing acts among all injections for IDU who do not receive anyprevention interventions | 15% (5–25%) | |
Proportion of new diagnoses among positives notified of test results | 60% (40–80%) | |
Reduction in unprotected sex for aware HIV-infected individuals compared with unaware | 53% (45–60%) |
|
Intervention effect size of HIV education and risk reduction for positive clients |
27% (0–40%) | |
Intervention effect size of HIV education and risk reduction for negative clients |
12% (0–20%) | |
Condom efficacy for per-act transmission | 80% (65–95%) |
|
Reduction in per-act transmissibility for HIV-infected individuals who achieve viralload suppression | 96% (50–100%) | |
Reduction in needle sharing infectivity for infected IDU who achieve viral load suppression | 50% (0–90%) |
|
Proportion of diagnosed HIV-infected persons who are linked to care | 77% (60–85%) |
|
Proportion of HIV-infected persons linked to care who are retained in care | 66% (50–80%) |
|
Proportion of HIV-infected persons, retained in care, who have started ART | 88% (70–90%) |
|
Proportion of HIV-infected persons who started ART who are adherent to ART (i.e., achieveviral load suppression) | 77% (60–85%) |
|
HIV lifetime treatment cost (2009 $) | 367,134 (184,000–550,000) |
|
Proportion of protected sex acts for HIV-positive aware w/o participating in risk reduction |
||
HET | 73% | Calculated |
IDU | 73% | Calculated |
MSM | 85% | Calculated |
In the Bernoulli model, we assumed HET and IDU females engaged in vaginal receptive sex, while HET and IDU males engaged in vaginal insertive sex. We also considered transmission via contaminated needle sharing for IDU. For MSM, we assumed 50% of their sex acts were insertive anal and 50% were receptive anal.
We assumed every individual in a particular transmission category had the same number of annual sex acts. The annual number of sex acts for HET was reported in the National Survey of Family Growth (NSFG) and the National Survey of Sexual Health and Behavior (NSSHB) for HET. We assumed IDU and MSM had the same annual number of sex acts as HET.
The effect sizes of risk reduction for HIV-infected and uninfected at-risk individuals were estimated by the percent reduction in unprotected sex acts (unprotected vaginal sex or anal sex). We included behavioral studies that reported the reduction in number (or percent) of unprotected sex acts. We took the median values of the reviewed studies in which the reported reduction in unprotected sex acts between intervention and control groups was statistically significant.
The proportion of protected sex acts among HIV-positive aware persons who do not receive risk reduction was calculated from the proportion of protected sex for unaware HIV positive persons and the reduction in unprotected sex for aware HIV-infected persons. That is, the proportion of protected sex acts for aware HIV-infected persons = 1-(1- proportion of protected sex acts for unaware HIV-positive persons)×(1- reduction in unprotected sex acts for aware HIV-infected persons).
We considered testing and partner services, and HIV education and risk reduction in our analysis because reasonably robust data exist on their efficacy and the programmatic data on their service provision were complete. HIV testing and partner services diagnose and notify HIV-infected individuals of their infection. HIV-infected individuals, once diagnosed, have been found to reduce the proportion of their sex acts that are unprotected by condoms by 53%
In California, the state HIV prevention budget reductions started in fiscal year 2008–2009, and were followed by a more drastic cut in fiscal year 2009–2010. In fiscal year 2008–2009, $4.6 million in temporary state funding ended for HIV prevention in low-prevalence jurisdictions. This cut was followed in fiscal year 2009–2010 by the elimination of the rest of the $11.4 million state funding for HIV prevention. To explore the impact of the combined state budget cuts on HIV prevention, we defined fiscal year 2009–2010 as our post-cut timeframe, and compared it with the years before any of the state budget cuts began: fiscal year 2005–2006 through 2007–2008. To get a stable and robust representation of the budget and services before any cuts occurred, we averaged the values from fiscal year 2005–2006 through 2007–2008. We referred to this as the pre-cut timeframe.
In the base case analysis, we compared the pre-cut and post-cut budget and allocation strategies during each timeframe with respect to the number of individuals served, the reported number of diagnoses, and the estimated number of additional HIV cases compared with HIV cases during the pre-cut timeframe in the 59 jurisdictions. We applied lifetime HIV treatment costs to the additional cases of HIV to estimate the financial impact of the budget cuts. We used an HIV lifetime treatment cost of $367,134, in 2009 U.S. dollars
To understand the potential impact of different budget allocation strategies we assessed three additional hypothetical scenarios. First, using the 2009–2010 budget, we compared outcomes under the actual 2009–2010 budget allocation to testing and risk reduction programs (that is, the proportion of total funding allocated to testing compared with risk reduction programs), to those that would have been expected had the same amount of funding instead been distributed according to the pre-cut allocation to testing and risk reduction programs. In the second scenario, we applied the 2009–2010 budget and the 2009–2010 allocation to testing and risk reduction programs, and within each program type, we allocated services to each transmission category proportionate to that group’s contribution to all living cases of HIV in the funded jurisdictions. For instance, if MSM comprised 75% of all those infected with HIV in the areas under consideration, we allocated 75% of services to MSM. In the third scenario, we allocated the entire pre-cut HIV prevention budget to the 59 original jurisdictions, but this time we allocated to programs under the post-cut allocation, and we allocated to transmission categories proportionate to each group’s contribution to all living cases of HIV in the 59 jurisdictions.
In each scenario, to determine the number of tests performed and clients served by risk reduction programs, we divided the total amount allocated to each prevention program by the cost per person tested or client served, as reported by the state for fiscal year 2009–2010. To determine the number of positive test results, we multiplied the number tested by the HIV sero-positive rate by transmission category reported among those tested in 2009–2010. To estimate the number of new diagnoses, we multiplied the number of positive tests by 60%
We performed both one-way and probabilistic sensitivity analyses on a variety of parameters, including HIV prevalence in California among the three transmission categories, the per-act transmission probabilities, the self-reported sexual behaviors that inform the Bernoulli model, and the effect of behavioral and biomedical interventions on transmission probabilities. We tested the impact of each parameter on the main outcome, the estimated number of new infections associated with the budget cuts. The probabilistic analysis provided 95% confidence intervals around each estimate of new HIV infections associated with our base case and analytic scenarios. We applied the point estimates and confidence intervals to estimates of lifetime HIV treatment costs to determine the range of costs associated with each scenario. Distributions applied to each parameter in the probabilistic analysis are described in
During the pre-cut timeframe, the HIV prevention budget for the 59 jurisdictions was on average $21.8 million, 91% provided by the state and 9% provided by the federal government (
Pre-cut |
Post-cut: Fiscal year 2009–2010 | |||
Program data | Change from pre-cut |
|||
Total prevention budget ($) | 21,849,923 | 5,860,723 | −15,989,200 | (−73) |
Federal funding | 1,923,529 | 5,860,723 | 3,937,194 | (205) |
State funding | 19,926,394 | 0 | −19,926,394 | (−100) |
Funded prevention agencies | 143 |
36 | −107 | (−75) |
Funded local health jurisdictions | 59 | 15 | −44 | (−75) |
HIV prevalence: | 54,635 |
59,908 | – | – |
15 jurisdictions funded in FY0910 | 47,328 |
51,959 | – | – |
44 jurisdictions not funded in FY0910 | 7,307 |
7,949 | – | – |
|
||||
Budget ($) | 4,024,634 | 3,160,148 | −864,486 | (−21) |
Federal funding | 479,574 | 3,160,148 | 2,680,574 | (559) |
State funding | 3,545,060 | 0 | −3,545,060 | (−100) |
Number of persons served | 85,636 | 53,545 | −32,091 | (−37) |
Number of tests performed | 83,968 | 53,001 | −30,967 | (−37) |
HET | 59,567 | 35,976 | −23,591 | (−40) |
IDU | 7,586 | 4,216 | −3,371 | (−44) |
MSM | 16,815 | 12,809 | −4,006 | (−24) |
Number of HIV-positive clients notified of test result | 813 | 465 | −348 | (−43) |
HET | 254 | 117 | −138 | (−54) |
IDU | 46 | 18 | −28 | (−62) |
MSM | 512 | 331 | −182 | (−35) |
Sero-positive rate: all transmission categories | 0.97% | 0.88% | – | – |
HET | 0.43% | 0.32% | – | – |
IDU | 0.61% | 0.42% | – | – |
MSM | 3.05% | 2.58% | – | – |
|
||||
Budget ($) | 17,825,289 | 2,700,575 | −15,124,714 | (−85) |
Federal funding | 1,443,956 | 2,700,575 | 1,256,619 | (87) |
State funding | 16,381,333 | $0 | −16,381,333 | (−100) |
Number of unique positive clients served | 2,884 | 1,100 | −1,784 | (−62) |
HET | 1,362 | 560 | −802 | (−59) |
IDU | 135 | 45 | −90 | (−66) |
MSM | 1,387 | 494 | −893 | (−64) |
Number of unique negative clients served | 8,900 | 2,286 | −6,614 | (−74) |
HET | 5,765 | 1,459 | −4,306 | (−75) |
IDU | 1,090 | 245 | −845 | (−77) |
MSM | 2,045 | 582 | −1,463 | (−72) |
We assumed average values from fiscal year 2005–2006 to fiscal year 2007–2008 for the pre-cut scenario.
Estimated.
Number of funded local prevention agencies was only available in fiscal year 2007–2008.
HIV prevalence for selected jurisdictions in California in fiscal year 2007–2008.
During the pre-cut years, 143 agencies received HIV prevention funds; afterwards, 36 agencies received funds. During the pre-cut years, more than 75% of the budget was allocated to risk reduction programs. Afterwards, about 50% went to risk reduction and 50% to testing.
During the pre-cut years, 83,968 tests were performed and an estimated 813 persons (sero-positive rate of 0.97%) were notified of a positive HIV diagnosis annually. Afterwards, the number of tests performed dropped to 53,001, and 465 persons (sero-positive rate of 0.88%) were notified of a positive HIV diagnosis. Seventy-one percent of the tests were provided to HET, 9% to IDU, and 20% to MSM in the pre-cut timeframe; post-cut, 68% of tests were provided to HET, 8% to IDU, and 24% to MSM. Among the positives notified of test results, 63% were MSM, 31% were HET and 6% were IDU in the pre-cut timeframe; post-cut, 71% were MSM, 25% were HET and 4% were IDU.
An average of 11,784 unique clients was served by risk reduction programs annually in pre-cut timeframe, including 2,884 (24%) positive clients and 8,900 (76%) negative clients. Post-cut, the number of unique risk reduction clients decreased to 3,386, including 1,100 (32%) positive clients and 2,286 (68%) negative clients. During the pre-cut years, 47% of HIV-positive risk reduction clients were HET, 5% were IDU, and 48% were MSM; while 65% of HIV-negative clients were HET, 12% were IDU, and 23% were MSM. Those proportions remained the about same following the cuts.
Based on Bernoulli models, we were able to estimate the effect of HIV prevention interventions on annual transmission and acquisition rates among MSM, HET and IDU (
Without intervention | With intervention | Effectiveness: reduction in transmission rate or risk of infection (%) | ||
|
||||
Transmission rate | Unaware infected individuals | Aware infected individuals | ||
HET Male | 0.04569 | 0.01838 | 0.02731 | (60) |
HET Female | 0.02306 | 0.00924 | 0.01382 | (60) |
IDU Male | 0.12619 | 0.06830 | 0.05789 | (46) |
IDU Female | 0.10495 | 0.05972 | 0.04524 | (43) |
MSM | 0.31589 | 0.14230 | 0.17359 | (55) |
|
||||
Transmission rate | Aware infected individuals who had not received risk reduction services | Aware infected individuals who had received risk reduction services | ||
HET Male | 0.02746 | 0.02305 | 0.00441 | (16) |
HET Female | 0.01380 | 0.01158 | 0.00223 | (16) |
IDU Male | 0.08736 | 0.08321 | 0.00415 | (5) |
IDU Female | 0.07454 | 0.07245 | 0.00208 | (3) |
MSM | 0.21243 | 0.18704 | 0.02540 | (12) |
|
||||
Risk of infection | Uninfected individuals who had notreceived risk reduction services | Uninfected individuals who had receivedrisk reduction services | ||
HET Male | 0.00046 | 0.00042 | 0.00004 | (9) |
HET Female | 0.00023 | 0.00021 | 0.00002 | (9) |
IDU Male | 0.00789 | 0.00765 | 0.00024 | (3) |
IDU Female | 0.00657 | 0.00644 | 0.00012 | (2) |
MSM | 0.07261 | 0.06745 | 0.00517 | (7) |
Based on these calculations of annual transmission and incidence rates, we estimated that 55 additional HIV infections would occur in connection with the first year of the state’s budget cut (
Base case: post-cut allocation, actual allocation in FY0910 | Analytic allocation scenarios | |||
Scenario 1: FY0910 budget, pre-cut programmatic allocation, and services to transmission categories proportionate to each group’s pre-cut allocation | Scenario 2: FY0910 budget, FY0910 programmatic allocation, and services to transmission categories proportionate to each group’s contribution to HIV prevalence | Scenario 3: Pre-cut budget, FY0910 programmatic allocation, and services to transmission categories proportionate to each group’s contribution to HIV prevalence | ||
Total prevention budget ($) | 5,860,723 (100%) | 5,860,723 (100%) | 5,860,723 (100%) | 21,849,923 (100%) |
Testing and partner services | 3,160,148 (54%) | 1,079,512 (18%) | 3,160,148 (54%) | 11,781,651 (54%) |
HIV education and risk reduction | 2,700,575 (46%) | 4,781,211 (82%) | 2,700,575 (46%) | 10,068,273 (46%) |
Number of local health jurisdictions funded | 15 | 59 | 15 | 59 |
Number of tests performed | 53,001 | 18,105 | 53,001 | 197,888 |
HET (% of 993,600 at risk HET) | 35,976 (3.6%) | 12,844 (1.3%) | 6,879 (0.7%) | 26,864 (2.7%) |
IDU (% of 178,678 at risk IDU) | 4,216 (2.4%) | 1,636 (0.9%) | 6,868 (3.8%) | 26,020 (14.6%) |
MSM (% of 401,592 at risk MSM) | 12,809 (3.2%) | 3,626 (0.9%) | 39,253 (9.8%) | 145,005 (36%) |
Number of positives notified of test result | 465 | 175 | 1,267 | 4,664 |
HET | 117 | 55 | 29 | 112 |
IDU | 18 | 10 | 42 | 166 |
MSM | 331 | 110 | 1,196 | 4,386 |
Number of new diagnoses (60% new diagnosis rate |
279 | 105 | 760 | 2,798 |
HET | 70 | 33 | 18 | 67 |
IDU | 11 | 6 | 25 | 100 |
MSM | 198 | 66 | 717 | 2,631 |
Number of unique risk reduction clients | 3,386 | 5,995 | 3,386 | 2,798 |
HIV-positive clients | 1,100 | 1,467 | 1,100 | 4,101 |
HET | 560 | 693 | 146 | 544 |
IDU | 45 | 69 | 140 | 569 |
MSM | 494 | 706 | 815 | 2,988 |
HIV-negative clients | 2,286 | 4,527 | 2,286 | 8,523 |
HET | 1,459 | 2,933 | 303 | 1,131 |
IDU | 245 | 555 | 290 | 1,182 |
MSM | 582 | 1,040 | 1,693 | 6,210 |
Estimated number of infections associated with the budget cuts (95% CI) | 55.0 (19.1, 108.8) | 70.5 (23.5, 142.0) | −47.6 (−112.8, −7.9) | −466.1 (−997, −135) |
Percent change from the 2,874 annual new diagnoses in pre-cut years | 1.91% | 2.45% | −1.66% | −16.22% |
Expected life-time treatment cost attributable to budget cuts in HIV prevention: $ in million (95% CI) | 20.2 (6.0, 42.4) | 25.9 (7.3, 55.8) | −17.5 (−44.3, −2.6) | −171.1 (−387, −43.5) |
Estimated number of infections associated with the budget cuts by program and transmission category | ||||
Testing and partner services | 21.5 | 45.4 | −68.3 | −405.4 |
HET | 1.7 | 2.5 | 2.8 | 1.8 |
IDU | 0.9 | 1.1 | 0.1 | −3.7 |
MSM | 18.9 | 41.8 | −71.2 | −403.5 |
Risk reduction for positive | 25.6 | 19.7 | 18.5 | −39.3 |
HET | 2.7 | 2.2 | 4.0 | 2.7 |
IDU | 0.3 | 0.2 | 0.0 | −1.4 |
MSM | 22.7 | 17.3 | 14.5 | −40.7 |
Risk reduction for negative | 7.9 | 5.4 | 2.1 | −21.4 |
HET | 0.1 | 0.1 | 0.2 | 0.1 |
IDU | 0.2 | 0.1 | 0.1 | 0.0 |
MSM | 7.6 | 5.2 | 1.8 | −21.5 |
For scenario 1, we estimated that, based on the relative cost-effectiveness of testing compared with risk reduction programs, the redirection of a greater proportion of prevention funding to testing over risk reduction, compared with programmatic allocations in pre-cut timeframe, averted 15 infections that otherwise would have occurred (
We presented the results of one-way sensitivity analysis in a tornado graph (
We plotted the input parameters whose change to either the lower or the upper bound resulted in a change of 10% or more in the additional number of new infections associated with the first year of budget cuts. The shadow bar corresponds to the lower bound and the dotted bar corresponds to the upper bound value associated with a particular parameter. For example, if the annual number of sex acts for MSM was 365, the expected number of new infections associated with the first year of the budget cut would increase 236% to 183, from the baseline estimate of 55. If the annual number of sex acts for MSMs was 26, the expected number of new infections associated with the first year budget cuts would decrease by 55% to 25, from the baseline estimate.
HIV prevention funding for California’s 59 local health departments outside of Los Angeles and San Francisco declined 70%, by almost $16 million in fiscal year 2009–2010. As a result, an estimated 348 fewer persons with HIV were diagnosed and 8,000 fewer clients were served by risk reduction programs. We estimated that 55 more HIV infections occurred because of the first year of budget cuts, generating $20 million in lifetime treatment costs to the health care system, and indicating that California’s pre-cut HIV prevention funding generated more in medical care savings than the cost of prevention programs.
This study systematically analyzed the effects of HIV prevention programs related to testing and risk reduction on annual transmission and acquisition rates among MSM, HET and IDU. It found that MSM, whose transmission and acquisition rates are highest, also achieve the largest reductions in new HIV cases when they are served by prevention programs. For all transmission categories, the diagnosis of a new infection led to the greatest reduction in annual transmission rates, followed by risk reduction for persons living with HIV. This is because among HIV-infected but undiagnosed individuals, awareness of HIV infection has been shown to increase condom use to a greater extent and for a more sustained period than receiving risk reduction services alone
Our findings have been generally supported by other studies
Findings of the sensitivity analysis indicate that the main outcome, the expected number of new infections associated with the cuts, was sensitive to biological and behavioral parameters for MSM, such as their annual number of sex acts, per-act transmission probabilities of anal sex, proportion of all sex acts protected by condom, and number of partners. Our sensitivity analysis underscores the important role of MSM in the HIV epidemic in California (and many other parts of the United States), and the need for more accurate data on their sexual behaviors. HIV prevalence did not play a key role in this analysis, because when resources were focused on 15 jurisdictions with 87% of the new diagnoses reported in all 59 jurisdictions, the positivity rate among those tested declined slightly. In general, however, HIV prevalence is likely to be an important factor in the cost-effective targeting of prevention resources both because of a theoretically higher positivity rate among those tested and because of a greater likelihood of exposure to HIV among uninfected individuals.
Our analysis is subject to several limitations. Our estimate of the effect of the budget cut does not provide a complete picture for the State of California because the analysis does not consider San Francisco and Los Angeles counties, where half of all Californians living with HIV/AIDS reside. However, the budget and programmatic data for San Francisco and Los Angeles were not sufficiently complete to analyze the impact of the budget cuts in those jurisdictions. We believe our overall findings – that the most efficient allocation of HIV prevention funds in the 59 studied jurisdictions would focus on MSM, with testing prioritized over risk reduction, is likely to hold true for Los Angeles and San Francisco, given that 87% of the living cases of HIV and 81% of new diagnoses in these two counties are among MSM. Our analysis only captures the first generation of transmission by those assumed to become infected with HIV as a consequence of the service reductions, making our estimates conservative. We assumed HIV prevention services provided in California achieve the same level of efficacy reported in published studies. In reality, the delivery and effectiveness of programs likely varies across jurisdictions. Reductions in HIV prevention services, particularly those designed to decrease risky sexual behaviors, could have resulted in increases in other sexually transmitted diseases or unintended pregnancy. The data available to us did not allow us to examine those potential effects.
Most of our analytic scenarios did not take into account possible barriers, including cost, to expanding testing programs or reaching greater numbers of MSM (36% of the MSM at risk for HIV infection). To the extent that these barriers exist, our estimates of new HIV infections averted from budget reallocation are too high. The estimates of annual transmission rates, and reductions in transmission rates associated with prevention services, based on Bernoulli process models, rely on self-reported behavioral data, which are subject to recall and social desirability bias, and other uncertain inputs. Parameter uncertainty is reflected in the results of the probabilistic sensitivity analysis, which provides wide intervals around the base case estimates of new cases associated with the budget cuts and corresponding lifetime HIV treatment costs. The probabilistic sensitivity analysis and the scenario analyses do, however, point to relatively more efficient allocation decisions. Although we did not perform a formal budget optimization analysis, our exploration of various budget scenarios suggest where the California state and local health departments avoided additional increases in HIV infections following the severe budget cut, as well as how additional infections might have been prevented. Our analysis did not examine the impact of the simultaneous state cuts to HIV care and treatment programs, which may as well have resulted in additional infections.
Changes in federal allocation of HIV prevention funding
One estimate suggests that achieving National HIV/AIDS Strategy prevention goals by 2015 will require an additional annual investment of $420 million
(DOCX)
We would like to thank Annette Ladan from Quantitative Sciences and Data Management Branch at the Centers for Disease Control and Prevention, Atlanta, Georgia for her statistical support for data analysis.
The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.