Several published studies have provided evidence that the amount of resources allocated for the prevention of sexually transmitted diseases (STDs) does indeed have an effect on the incidence of STDs at the population level.1–6 For example, an analysis of state-level gonorrhea case rates and federal funding for STD and human immunodeficiency virus (HIV) prevention from 1981 to 1998 indicated that higher levels of prevention spending in a given year were associated with lower reported rates of gonorrhea in subsequent years.1 Because the estimated direct medical cost of STDs (including HIV) in the United States is almost US $20 billion annually,7 reductions in the incidence of STDs can yield substantial health and financial benefits.
Several published studies have provided evidence that the activities of Disease Intervention Specialists (DIS) can reduce the population-level burden of STDs.4,8–12 The DIS perform STD prevention activities such as partner notification, which involves interviewing people diagnosed with STDs, obtaining information about their sex partners, and locating these partners so that they can be referred for testing and treatment.4,13,14 The DIS have been described as the backbone of state and local STD prevention programs and perform a range of other activities in addition to partner notification, including education and risk reduction counseling, and provider and community outreach and engagement.13,14
The purpose of this report was to describe methods for state and local STD prevention programs in the United States to estimate the potential impact of increases or decreases in their budget, in terms of changes in the incidence of STDs and in the direct medical costs of STDs. We also illustrated how programs can estimate intermediate outcomes of changes in their budget, such as in terms of changes in DIS services provided. Finally, we provided an example of the application of these methods for a hypothetical state STD prevention program.
We proposed 2 distinct approaches to estimate the potential impact of changes in STD prevention resources on STD incidence (Fig. 1). The first approach (the historical formula approach) was based on the observed relationship between state-level gonorrhea case rates and state-level STD prevention funding over an 18-year period.1 The second approach (the DIS approach) was based on the observed association between DIS activities and subsequent gonorrhea case rates at the population-level in several settings.4 Either or both of these approaches could be used, depending on factors such as the needs of the user and the data available to the user.
In addition to estimating the change in STD incidence arising from a budget change, we also described how to estimate changes in (1) the direct medical costs of STDs and (2) the provision of STD services, such as STD patient interviews. Finally, to illustrate the application of our methods, we estimated the impact of a change in budget for a hypothetical, state-level STD program. Table 1 provides a summary of the parameter values we applied. A technical appendix provides additional details.
Estimating the Effect of Funding Changes on STD Burden, Method 1 (Historical Formula Approach)
This approach was based on an analysis of state-level gonorrhea case rates and federal funding for STD prevention from 1981 to 1998.1 This analysis of historical data indicated that each additional dollar of prevention funding per capita (updated to 2016 dollars) in a given year was associated with subsequent reductions in the rate of reported cases of gonorrhea of 16%, after controlling for a range of factors associated with state-level gonorrhea rates.1 To put this estimate in perspective, we note that allocations of federal funds distributed by the Centers for Disease Control and Prevention to states for STD prevention averaged US $0.29 per capita in 2015, with a range of US $0.17 to US $0.52.25 We assumed this reduction of 16% could be applied not only for gonorrhea but also for syphilis and chlamydia.
If the change in prevention funding is ΔX, and the overall population in the jurisdiction of the STD program is P, the estimated change in the STI incidence rate (for gonorrhea, syphilis, and chlamydia) as a result of the change in funding can be estimated as −0.16(ΔX/P). For example, if there is a decrease in funding, ΔX will be negative and this expression will be positive, indicating STD incidence rates after the decrease in funding would be higher than if there had been no change in funding.
When there are changes in the delivery of STD prevention services, the resulting change in STD incidence can become more pronounced over time as a new equilibrium is reached.26 To account for the phasing-in of changes in STD incidence over time after a change in prevention funding, we assumed that the change in STD incidence in year 2 would be 1 + β times that of year 1, that the change in year 3 would be 1+ β + β2 times that of year 1, that the change in year 4 would be 1 + β + β2 + β3 times that of year 1, and so on, as described in more detail in the technical appendix. A value of 0.7 was applied for β (Table 1), based on the regression model used in the analysis of state-level gonorrhea rates and federal funding for STD prevention.1
Estimating the Effect of Funding Changes on STD Burden, Method 2 (DIS Approach)
This approach adapted a published estimate of the change in STD incidence rates that can be expected following a change in the provision of DIS services.4 Specifically, an analysis of a decade of historical records of rates of reported gonorrhea cases and partner notification services in New York State (excluding New York City) indicated that each 10% increase in DIS activities (eg, number of index patients interviewed, or number of partners provided epidemiologic treatment) could reduce gonorrhea case rates by 2% to 6%.4 We applied the lower of these 2 estimates of impact (2%), because this 2% estimate is more conservative and is also consistent with results from ecological analyses conducted in the 1970s and 1980s, which suggested that the scaling up of DIS activities can reduce population-level gonorrhea incidence,10–12 as described in the technical appendix. We assumed this 2% estimate could be applied not only for gonorrhea but also for syphilis and chlamydia.
For this exercise, we assumed the entire change in STD prevention resources would be applied to the DIS workforce, such that the number of DIS would be increased or decreased, depending on whether the budget is increased or decreased. We also assumed that the percentage change in DIS activities would be approximately equal to the percentage change in number of DIS used by the STD program.
To calculate the change in the number of DIS that would result from a change in budget, programs can use their own data regarding the annual cost per DIS, or can apply a national estimate of US $73,600. This national estimate reflects a salary of US $45,677 (based on the federal general schedule level 9, step 3 salary as of January 201615), multiplied by 1.61 to account for benefits,16 and rounded to the nearest US $100). When applying this national estimate, the change in DIS (ΔDIS) due to a change in funding of $X (ΔX) can be calculated as ΔDIS = ΔX/$73,600. For example, if there is an increase in funding, ΔX will be positive and ΔDIS will be positive, indicating an increase in DIS.
The percentage change in DIS activities (%DIS) can be approximated as %DIS = ΔDIS/N, where N is the number of DIS used by the STD program before the change in budget. For consistency, in the event of a budget decrease, the decrease in DIS should not exceed 100%. Finally, the percentage change in STD incidence rates attributable to the change in budget can be estimated as -%DIS/5, where the division by 5 is applied because each 10% change in DIS activities is associated with a 2% change in STD incidence rates. The STD incidence rate that results from applying this percentage change can be interpreted as the new equilibrium and can be phased in over time following the same relative trajectory as calculated above for the historical formula approach (see technical appendix, http://links.lww.com/OLQ/A213).
Estimating the Change in Direct Medical Costs of STDs
The change in the direct medical costs due to the change in STD incidence was estimated as follows. First, the direct medical costs of syphilis, gonorrhea, and chlamydia were approximated by multiplying the change in the estimated number of infections of syphilis, gonorrhea, and chlamydia by US $770, US $230, and US $210, respectively. These three values represent the average lifetime cost per new infection of syphilis, gonorrhea, and chlamydia, respectively.7
We also estimated the change in the number and cost of STD-attributable HIV infections. To do so, we assumed that the probability of an STD-attributable HIV infection per STD infection was 0.0105, 0.0005, and 0.0005 for syphilis, gonorrhea, and chlamydia, respectively.19,20 A lifetime cost of US $351,000 per HIV infection was applied.18 All costs were updated to 2016 dollars, and future costs were discounted to present value at 3% annually.
Estimating the Effect of Funding Changes on Intermediate Outcomes
Programs might also want to know the effect of budget changes on intermediate outcomes, such as DIS activities performed. We described methods to estimate the change in the number of index STD patient interviews conducted. Programs can apply similar methods to examine other intermediate outcomes of interest.
For this exercise, we assumed the change in the number of DIS can be calculated as ΔDIS = ΔX/$73,600, as described above. We then estimated the change in the number of index STD patient interviews conducted each year due to the change in the number of DIS. The key piece of information needed for this estimation was the number of index patient interviews that the average DIS performs in a year. Programs without data on the number of index patient interviews conducted per year can approximate this value based on their number of reported STD cases, multiplied by published estimates of the average percentage of these cases that are interviewed (Table 1). Alternatively, programs can apply a literature-based estimate that each DIS can perform about 400 index patient interviews per year.22,23,27 Using the estimate of 400, the change in the number of STD patient interviews per year can be expressed as 400ΔDIS.
Description of Hypothetical State Used for Illustration of Results
To illustrate the methods described above, we calculated the impact of a reduction in STD prevention funding of US $200,000 in a hypothetical state of 6.5 million people. This hypothetical state was constructed to have an approximately average share (one 50th) of the nation’s population and reported number of STD cases. For example, the number of reported cases for 2015 in this state was assumed to be 1000 for syphilis, 8000 for gonorrhea, and 30,500 for chlamydia, calculated as the national number of reported cases in 201528 divided by 50 and rounded to the nearest 500. The state was assumed to have 15 DIS before the reduction in budget.
The results below describe the estimated impact of the US $200,000 budget cut for the hypothetical state.
Estimating the Effect of Funding Changes on STD Burden, Method 1 (Historical Formula Approach)
The relative change in STDs was calculated as −0.16(ΔX/P), or −0.16(−200,000/6,500,000), or 0.49%. That is, in the first year of the budget cut, STD incidence rates were estimated to be 0.49% higher than they would have been in the absence of the budget cut. When assuming that the budget cut becomes permanent and would lead to a new equilibrium STD incidence rate at year 10, the percentage increase in STD incidence (relative to a scenario of no budget cut) was 0.84% in year 2, 1.37% in year 5, 1.57% in year 9, and 1.59% in year 10 (Table 2A).
Estimating the Effect of Funding Changes on STD Burden, Method 2 (DIS Approach)
The percentage change in DIS was calculated as (ΔX/$73,600)/N, or –(200,000/73,600)/15, or −18.1%. The relative change in STDs was calculated as −(−18.1%)/5, or 3.62%. That is, STD incidence was estimated to be 3.62% higher than it would have been in the absence of a budget cut. When we assumed this 3.62% increase would correspond to the new equilibrium STD incidence rate in year 10 after the funding cut, the percentage increase in STD incidence due to the budget cut was 1.12% in year 1, 1.90% in year 2, 3.10% in year 5, and 3.58% in year 9 (Table 2B).
Estimating the Change in Direct Medical Costs of STDs
In both approaches, the total funding reduction would amount to US $2 million over the 10 years, or about $1.76 million when discounted to year 1. Changes in the costs of STDs were calculated using the cost per infection estimates in Table 1.
Based on the historical formula approach, in the 10 years after the budget cut there would be an estimated cumulative 146 additional syphilis infections, 2106 additional gonorrhea infections, 7250 additional chlamydia infections, and 6.2 additional STD-attributable HIV infections, with additional direct medical costs totaling US $3.7 million (Table 2A).
Based on the DIS approach, in the 10 years after the budget cut there would be an estimated cumulative 332 additional syphilis infections, 4786 additional gonorrhea infections, 16,473 additional chlamydia infections, and 14.1 additional STD-attributable HIV infections, with additional direct medical costs totaling US $8.4 million (Table 2B).
Estimating the Effect of Funding Changes on Provision of Services
The US $200,000 budget cut for this state resulted in an estimated loss of 2.72 DIS, (ΔDIS = −200,000/73,600), and an estimated reduction of 1087 in the number of STD index cases interviewed, calculated as −2.72*400.
In this article, we described methods that STD programs can use to estimate the potential effects of changes in their STD prevention budgets. Although there is no way to predict with any certainty and precision the impact of changes in the amount of funding allocated for STD prevention, the methods we proposed can provide general approximations of how a change in STD prevention funding might affect the level of STD prevention services provided (in terms of DIS activities), STD incidence rates, and the direct medical cost burden of STDs. We have developed a spreadsheet-based tool, available from the corresponding author upon reasonable request, to facilitate the application of these methods.
Although STD prevention programs can avert considerable medical costs, these cost savings rarely accrue to the STD programs. Instead, the beneficiaries of the averted costs are usually the payers of health care services, such as health insurance companies and government-funded health insurance programs, such as Medicaid.2,29,30 Programs with data on how the medical costs of STIs and HIV are apportioned across various payers could conduct additional analyses from a range of different perspectives, such as that of the “state” versus “other” payers.
Many factors affect the population-level burden of STDs, including but not limited to sexual and health-seeking behaviors, sexual network and mixing characteristics, and social determinants of health, such as poverty, racism, income inequality, and access to quality health care.31–34 Most of these factors are beyond the control of STD programs. The study on which our historical formula approach is based attempted to control for these factors, and our approach assumes these factors are constant over time. Thus, the predicted effects of changes in the STD prevention budget should be interpreted as compared to an “all else equal” scenario of no budget change. For example, suppose our approach were used to estimate that STD incidence rates will be 5% lower as a result of an increase in the STD prevention budget. This result should not be interpreted to mean that STD rates will necessarily decrease by 5%, but rather STD rates will be an estimated 5% lower than what they would have been without the increase in budget.
There are three main strengths to the methods we propose. First, the data requirements needed to generate these estimates are minimal, and programs can apply national-level data in place of local data if necessary. Second, we proposed 2 distinct methods to estimate the impact of changes in STD prevention funding on STD incidence rates. Both methods yielded results that showed an increase in direct medical costs that exceeded the budget reductions (the estimates ranged from 210% to 478% of the cumulative budget reductions over the 10-year period). Third, both of these methods are data-based, making use of published studies that examined the impact of STD prevention activities at the population level over long time frames.1,4
The limitations of our approach are numerous and substantial. At best, these methods provide rough approximations of the potential impact of changes in STD prevention funding. The actual impact of changes in prevention funding could be notably different from these approximations. Both approaches use linear approximations and do not account for decreasing or increasing returns to scale, and thus are better suited for assessing relatively small changes in funding, such as 5%, than relatively large changes, such as 50%. The projections generated by both approaches do not account for population growth, and changes in the population at risk for STIs would affect the expected number of STIs. The change in the number of DIS was approximated by dividing the change in budget by the average cost (salary plus benefits) per DIS; this approximation ignored other DIS-related costs, such as transportation (mileage) costs and personnel costs for the support and supervision of DIS.35
For simplicity, we proposed that changes in STD program services could be described by focusing entirely on changes in DIS activities. The programmatic changes in response to budget changes might be more varied. The STD programs at both the state and local level use different staffing models, intervention, and service mixes, and may have varying abilities to cut particular expenditures in response to budget cuts. A survey conducted in late 2013 to early 2014 found that local health departments used a variety of strategies to accommodate budget reductions in fiscal years 2011 to 2012, including closing STD clinics, reducing hours at STD clinics, increasing fees and copays, and reducing partner services.36 However, 42% of surveyed local health departments experiencing budget cuts reduced partner services, suggesting that DIS activities are frequently curtailed when budgets are decreased.36
The studies on which we based our assumptions of program impact used gonorrhea case rates as the primary outcome measures; our application of these studies assumed similar proportional effects on syphilis and chlamydia. Further, these source studies used gonorrhea data from the 1990s and early 2000s, and might not reflect the current epidemiology of gonorrhea or the potential effects of current STD prevention programs. Finally, our direct medical cost estimates are subject to uncertainty, particularly in the probability and cost of STD-attributable HIV infections. The technical appendix describes approaches to address the uncertainty in the key parameter values, and the spreadsheet-based tool we have developed can be used to generate a range of predicted outcomes in addition to the base-case point estimates.
The STD prevention program directors and other personnel are at times asked to provide information about the impact of their programs and to provide estimates of the effects of potential changes in the amount of funding allocated to their program. The methods we proposed, though subject to important limitations, can allow STD prevention personnel to calculate evidence-based responses to such inquiries.
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