2E
ORAL ABSTRACTS: INFECTIOUS DISEASE CONTROL
Jane J. Kim, PhD
Harvard T.H. Chan School of Public Health
Associate Professor
Center for Health Decision Science
Purpose: Retaining people living with HIV in regular medical care is critical for improving HIV-related outcomes. Currently, retention is considered a binary state: a patient is either “in care” or “out of care”. However, engagement in care is an evolving process, with periods of more or less frequent care encounters. The goal of this analysis is to characterize HIV patient care status not as fixed at a single point in time, but as longitudinal care trajectories.
Methods: The study population consisted of all HIV-infected patients ≥18 years-old seen at a public, hospital-based clinic in Minneapolis, MN between 2008-2013. Care patterns were based on clinic visit and laboratory test data from 2008-2014, ensuring ≥1 year of observation time for all patients. Clinic data was merged with surveillance records to account for HIV care at other clinics, out-of-state relocation, and mortality. Patient-specific HIV care trajectories were constructed in six-month intervals starting from first observed clinical encounter until death, relocation, or the end of 2014. Care trajectories were binary-valued, with a value of 1 in intervals where a patient had at least one clinical encounter and 0 otherwise. Latent class analysis was used to identify care trajectory classes, described by the probability of having a clinical encounter in each six-month interval. The number of care classes was chosen to minimize the Bayesian information criterion (BIC).
Results: Dividing the study population into 5 care trajectory classes minimized BIC, resulting in the following care trajectories (Figure): (1) consistent care over time; (2) initial attrition with a return to care; (3) slow attrition; (4) fast attrition; and (5) moderate attrition. Over half the study population was consistently in care (Class 1). Viral suppression differed across classes, with patients in the fast (Class 4) and moderate (Class 5) attrition classes being 2.7 (95%CI: 2.3-3.1) and 2.4 (95%CI: 2.0-2.8) times more likely to have a detectable viral load at last test, respectively, relative to Class 1. Patients in these classes were also the most likely to change provider or relocate out-of-state.
Conclusions: We demonstrated the feasibility of characterizing retention in HIV care in terms of longitudinal care trajectories. Five intuitive HIV care trajectories emerged, including four distinct patterns of suboptimal care. These results could be used to target patient retention efforts.
Method: We developed a dynamic, compartmental model of HIV transmission that includes 27 compartments defined by disease status and HIV care continuum stages. The model includes the racial/ethnic groups black, Hispanic/Latino and white/other. We compared the 5-year HIV cumulative incidence from 2016 through 2020 under the baseline assumption of 52% VLS among diagnosed with cumulative incidence over the same period when the 80% VLS goal was reached. We structured the model so that progress toward reaching the VLS goal began in 2016 and the goal was achieved in all racial-ethnic groups by 2020. We reported the reductions in incidence for each group.
Result: Over the 5-year period, from 2016 through 2020, HIV incidence dropped by 19% overall, from an estimated 172,362 cases to 140,300 cases when 80% VLS was achieved among all diagnosed with HIV. Incidence reductions by race/ethnicity were 21% for blacks, 25% for Hispanic/Latinos and 14% for white/others.
Conclusion: Achieving by 2020 the goal of 80% VLS among people diagnosed with HIV showed the largest benefits for blacks and Hispanics/Latinos, groups that historically have experienced disproportionate HIV incidence. Understanding how people diagnosed with HIV can best achieve and maintain VLS will be important.
Purpose:
In 2014, 27% of boys and girls aged 13-15 had completed the three-dose human papillomavirus (HPV) vaccine series currently recommended in the US, leaving coverage far short of the Healthy People 2020 objective of 80% for both sexes. We explore the cost-effectiveness of increasing HPV vaccination coverage for both sexes toward and beyond this goal.
Method:
We created a dynamic transmission model of HPV infection in the United States. The model incorporated individuals compartmentalized by gender, age, sexual activity levels, and vaccination status. Data from the clinical literature on HPV infection and vaccine effectiveness were used to parameterize the model. To simplify, we assume 3 doses are needed for efficacy. The model was calibrated to HPV prevalence in the US using NHANES data from 2003-2012 and cancer prevalence from SEER. We assumed no costs associated with increased HPV coverage other than the costs of the additional vaccination. The primary outcomes are HPV-related costs and HPV-related quality-adjusted life-years (QALYs) lost from various male and female cancers, genital warts, and recurrent respiratory papillomatosis. We explored population-level outcomes for an open population of 100,000 individuals between the ages of 12 to 75 over a 50-year time horizon. We explored the cost-effectiveness of scenarios increasing levels of adolescent vaccination from 2014 3-dose vaccination levels (27%) up to 100% 3-dose coverage with the new 9-valent HPV vaccine. We took a societal perspective.
Result:
Expanding adolescent vaccination coverage would lead to better health outcomes at higher costs. Current HPV vaccine coverage has an incremental cost-effectiveness ratio (ICER) of $5,000/QALY when compared to no vaccination. Expanding vaccination beyond current levels (from 27% to 28%) has an ICER of $15,000/QALY. Expanding to 80% coverage of adolescents (from 79%) would have an ICER of $54,000/QALY mainly because the marginal benefits of increased vaccination coverage decline as vaccination coverage increases. Increasing adolescent 3-dose vaccination coverage to 100% (from 99%) would have an ICER of $91,000/QALY.
Figure
Conclusion:
The population-level health benefits of HPV vaccination would be substantially greater under scenarios of 80% vaccine coverage compared to current coverage levels. Interventions to increase HPV vaccine coverage could potentially be cost-effective, particularly when coverage levels are below the Healthy People 2020 objective of 80%.
Method: We employed a microsimulation model of cervical carcinogenesis to evaluate, for women who go onto develop cancer, the age at which they acquire the causal HPV infection. Model outcomes included the proportion and cumulative number of causal HPV infections by age, stratified by HPV genotype (i.e., HPV-16 vs non-HPV-16 related cancers). Uncertainty in the natural history was captured by the maximum and minimum values across a sample of 50 “good-fitting” parameter sets identified during calibration.
Result: In the absence of primary (i.e., HPV vaccination) or secondary (i.e., screening) prevention, our model projected that among all cervical cancers, 42% (range: 30-52%) and 57% (range: 47-68%) of women acquired their causal HPV infection by age 20 and 24, respectively. Importantly, the age distribution was contingent on HPV genotype. For example, when we stratified cancers attributed to HPV-16 (a genotype included in the HPV vaccines), we found that nearly 48% (range: 25-82%) of women had already acquired their causal HPV infection by age 20 years. Conversely, the proportion of causal HPV infections acquired by age 20 was reduced to 36% (range: 32-41%) for cancers caused by non-HPV-16 genotypes.
Conclusion: According to our model, nearly half of women who develop invasive cervical cancer related to HPV-16 have already acquired their HPV infection by age 20, suggesting diminishing opportunities to prevent the causal HPV infection as women age. Our model-based explorations can provide important insights into the natural history of the causal HPV infection and can supplement the use of surrogate endpoints in vaccine efficacy studies to effectively guide policy decisions for implementation.
Method:
We used a validated microsimulation model that considered HCV transmission, natural history of HCV progression, treatment with oral DAAs, and simulation of prison and general population dynamics. The baseline population in the model represented the United States population as a whole in 2015, stratified by age, gender, prevalence of HCV, health states defined by METAVIR fibrosis scores, range of HCV genotype, treatment experiences, and injection drug use. We estimated the long-term disease outcomes, both inside and outside the prisons, per 10,000 prisoners with chronic hepatitis C who had in-prison access to DAAs. We compared these with outcomes if infected prisoners were released to the community without treatment, where they could transmit HCV to others. We assumed a portion of community dwelling individuals could access treatment post-release. All persons who were successfully treated, either inside or outside prisons, could re-acquire HCV. We ran our model for 30 years to estimate the long-term outcomes.
Result: Compared with no treatment, availability of DAAs for treatment per 10,000 infected prisoners would prevent 400–600 new infections; 90% of these would occur after release. In addition, availability of HCV treatment in prisons would also prevent approximately 700 cases of decompensated cirrhosis, 800 hepatocellular carcinomas, 70 liver transplantations, and 1,100 liver-related deaths. Among deaths averted, 60% would occur outside of prisons.
Conclusion:
Providing HCV treatment with oral DAAs to prisoners would reduce ongoing HCV transmission and HCV-associated diseases; the majority of the benefits would be accrued outside the prisons, in the community. If public funding for HCV elimination efforts were to focus on prisons, the greatest beneficiaries will be community healthcare systems. Supplementing current prison budgets with extra resources would be a strategic way to improve health outcomes across the society.
Purpose: Targeted delivery of specific interventions may be an effective approach for improving TB control in high-burden settings. In Southern Africa, individuals previously treated for TB who develop TB again (recurrent TB), contribute substantially to the TB burden and might thus be especially attractive for targeted interventions. The purpose of this study was to estimate the impact of interventions to actively detect and prevent recurrent TB among previously treated people on TB incidence and mortality in a high-burden setting.
Methods: We developed a transmission dynamic model of a TB/HIV co-epidemic among individuals with and without a history of TB treatment in a high-incidence community in suburban Cape Town, South Africa. The model was calibrated to local demography, TB and HIV prevalence, TB case notifications and TB control program data. The interventions modeled were (1) targeted active TB case finding (TACF) alone and (2) in combination with lifelong secondary isoniazid preventive therapy (2°IPT), both among individuals who previously completed TB treatment.
Results: Our model projects that a combination of annual TACF and 2°IPT among individuals who have previously completed TB treatment would lead to a 12.8% (95% Uncertainty Interval: 9.5%, 16.1%) reduction in incident cases of active TB and a 19.7% (16.2%, 23.2%) reduction in TB deaths compared to the projected status-quo over a 10 year period following the introduction of these interventions (Figures A-B). Implementing this targeted strategy would require screening 36 (25, 48) thousand individuals and provision of 2.25 (1.51, 3.04) million IPT doses over 10 years (Figures C-D). We project that during this 10-year time period, the targeted case finding and prevention strategy described above reduces the expected number of individuals who would require treatment for active TB (Figure E).
Conclusions: Our results suggest that in a high TB burden setting, active case finding and secondary preventive therapy targeted to individuals with previous complete TB treatment could accelerate declines in TB incidence and mortality. While promising, further work is needed to understand the cost and resource demands of these targeted strategies in order to determine the cost-effectiveness of these interventions.
