Modeling the risk of aquatic species invasion spread through boater movements and river connections

Aquatic invasive species (AIS) are one of the greatest threats to the functioning of aquatic ecosystems worldwide. Once an invasive species has been introduced to a new region, many governments develop management strategies to reduce further spread. Nevertheless, managing AIS in a new region is challenging because of the vast areas that need protection and limited resources. Spatial heterogeneity in invasion risk is driven by environmental suitability and propagule pressure, which can be used to prioritize locations for surveillance and intervention activities. To better understand invasion risk across aquatic landscapes, we developed a simulation model to estimate the likelihood of a waterbody becoming invaded with an AIS. The model included waterbodies connected via a multilayer network that included boater movements and hydrological connections. In a case study of Minnesota, we used zebra mussels (Dreissena polymorpha) and starry stonewort (Nitellopsis obtusa) as model species. We simulated the impacts of management scenarios developed by stakeholders and created a decision-support tool available through an online application provided as part of the AIS Explorer dashboard. Our baseline model revealed that 89% of new zebra mussel invasions and 84% of new starry stonewort invasions occurred through boater movements, establishing it as a primary pathway of spread and offering insights beyond risk estimates generated by traditional environmental suitability models alone. Our results highlight the critical role of interventions applied to boater movements to reduce AIS dispersal.

Variation in local Ryan White HIV/AIDS program service use and impacts on viral suppression: informing quality improvement efforts

The U.S. Ryan White HIV/AIDS Program (RWHAP) funds comprehensive services for people living with HIV to support viral suppression (VS). We analyzed five years of RWHAP data from the Minneapolis-St. Paul region to (1) assess variation and (2) evaluate the causal effect of each RWHAP service on sustained VS by race/ethnicity. Sixteen medical and support services were included. Descriptive analyses assessed service use and trends over time. Causal analyses used generalized estimating equations and propensity scores to adjust for the probability of service use. Receipt of AIDS Drug Assistance Program and financial aid consistently showed higher probabilities of sustained VS, while food aid and transportation aid had positive impacts on VS at higher levels of service encounters; however, the impact of services could vary by race/ethnicity. For example, financial aid increased the probability of sustained VS by at least 3 percentage points for white, Hispanic and Black/African American clients, but only 1.6 points for Black/African-born clients. This study found that services addressing socioeconomic needs typically had positive impacts on viral suppression, yet service use and impact of services often varied by race/ethnicity. This highlights a need to ensure these services are designed and delivered in ways that equitably serve all clients.

Diagnosed Gonorrhea Among Privately Insured Women: Analysis of United States Claims Data

Background: Gonorrhea incidence in the United States has risen by nearly 50% in the last decade, while screening rates have increased. Gonorrhea sequelae rates could indicate whether increased gonorrhea incidence is due to better screening. We estimated the association of gonorrhea diagnosis with pelvic inflammatory disease (PID), ectopic pregnancy (EP), and tubal factor infertility (TFI) in women and detected changes in associations over time. Materials and Methods: This retrospective cohort study included 5,553,506 women aged 18-49 tested for gonorrhea in the IBM MarketScan claims administrative database from 2013-2018 in the United States. We estimated incidence rates and hazard ratios (HRs) of gonorrhea diagnosis for each outcome, adjusting for potential confounders using Cox proportional hazards models. We tested the interaction between gonorrhea diagnosis and the initial gonorrhea test year to identify changes in associations over time. Results: We identified 32,729 women with a gonorrhea diagnosis (mean follow-up time in years: PID = 1.73, EP = 1.75, TFI = 1.76). A total of 131,500 women were diagnosed with PID, 64,225 had EP, and 41,507 had TFI. Women with gonorrhea diagnoses had greater incidence per 1000 person-years for all outcomes (PID = 33.5, EP = 9.4, TFI = 5.3) compared to women without gonorrhea diagnoses (PID = 13.9, EP = 6.7, TFI = 4.3). After adjustment, HRs were higher in women with a gonorrhea diagnosis vs. those without [PID = 2.29 (95% confidence interval, CI: 2.15-2.44), EP = 1.57, (95% CI: 1.41-1.76), TFI = 1.70 (95% CI: 1.47-1.97)]. The interaction of gonorrhea diagnosis and test year was not significant, indicating no change in relationship by initial test year. Conclusion: The relationship between gonorrhea and reproductive outcomes has persisted, suggesting a higher disease burden.

Adaptive COVID-19 Mitigation Strategies: Tradeoffs between Trigger Thresholds, Response Timing, and Effectiveness

Background. To support proactive decision making during the COVID-19 pandemic, mathematical models have been leveraged to identify surveillance indicator thresholds at which strengthening nonpharmaceutical interventions (NPIs) is necessary to protect health care capacity. Understanding tradeoffs between different adaptive COVID-19 response components is important when designing strategies that balance public preference and public health goals. Methods. We considered 3 components of an adaptive COVID-19 response: 1) the threshold at which to implement the NPI, 2) the time needed to implement the NPI, and 3) the effectiveness of the NPI. Using a compartmental model of SARS-CoV-2 transmission calibrated to Minnesota state data, we evaluated different adaptive policies in terms of the peak number of hospitalizations and the time spent with the NPI in force. Scenarios were compared with a reference strategy, in which an NPI with an 80% contact reduction was triggered when new weekly hospitalizations surpassed 8 per 100,000 population, with a 7-day implementation period. Assumptions were varied in sensitivity analysis. Results. All adaptive response scenarios substantially reduced peak hospitalizations relative to no response. Among adaptive response scenarios, slower NPI implementation resulted in somewhat higher peak hospitalization and a longer time spent under the NPIs than the reference scenario. A stronger NPI response resulted in slightly less time with the NPIs in place and smaller hospitalization peak. A higher trigger threshold resulted in greater peak hospitalizations with little reduction in the length of time under the NPIs. Conclusions. An adaptive NPI response can substantially reduce infection circulation and prevent health care capacity from being exceeded. However, population preferences as well as the feasibility and timeliness of compliance with reenacting NPIs should inform response design.

Highlights

This study uses a mathematical model to compare different adaptive nonpharmaceutical intervention (NPI) strategies for COVID-19 management across 3 dimensions: threshold when the NPI should be implemented, time it takes to implement the NPI, and the effectiveness of the NPI.All adaptive NPI response scenarios considered substantially reduced peak hospitalizations compared with no response.Slower NPI implementation results in a somewhat higher peak hospitalization and longer time spent with the NPI in place but may make an adaptive strategy more feasible by allowing the population sufficient time to prepare for changing restrictions.A stronger, more effective NPI response results in a modest reduction in the time spent under the NPIs and slightly lower peak hospitalizations.A higher threshold for triggering the NPI delays the time at which the NPI starts but results in a higher peak hospitalization and does not substantially reduce the time the NPI remains in force.

Longitudinal Trajectories of Alcohol Use in Vietnamese Adults with Hazardous Alcohol Use and HIV

A three-armed drinking cessation trial in Vietnam found that both a brief and intensive version of an intervention effectively reduced hazardous drinking in people living with HIV. We used group-based trajectory modeling (GBTM) to assess the extent to which findings may vary by latent subgroups distinguished by their unique responses to the intervention. Using data on drinking patterns collected over the 12 months, GBTM identified five trajectory groups, three of which were suboptimal ["non-response" (17.2%); "non-sustained response" (15.7%), "slow response" (13.1%)] and two optimal ["abstinent" (36.4%); "fast response" (17.6%)]. Multinomial logistic regression was used to determine that those randomized to any intervention arm were less likely to be in a suboptimal trajectory group, even more so if randomized to the brief (vs. intensive) intervention. Older age and higher baseline coping skills protected against membership in suboptimal trajectory groups; higher scores for readiness to quit drinking were predictive of it. GBTM revealed substantial heterogeneity in participants' response to a cessation intervention and may help identify subgroups who may benefit from more specialized services within the context of the larger intervention.

The illusion of personal health decisions for infectious disease management: disease spread in social contact networks

Close contacts between individuals provide opportunities for the transmission of diseases, including COVID-19. While individuals take part in many different types of interactions, including those with classmates, co-workers and household members, it is the conglomeration of all of these interactions that produces the complex social contact network interconnecting individuals across the population. Thus, while an individual might decide their own risk tolerance in response to a threat of infection, the consequences of such decisions are rarely so confined, propagating far beyond any one person. We assess the effect of different population-level risk-tolerance regimes, population structure in the form of age and household-size distributions, and different interaction types on epidemic spread in plausible human contact networks to gain insight into how contact network structure affects pathogen spread through a population. In particular, we find that behavioural changes by vulnerable individuals in isolation are insufficient to reduce those individuals' infection risk and that population structure can have varied and counteracting effects on epidemic outcomes. The relative impact of each interaction type was contingent on assumptions underlying contact network construction, stressing the importance of empirical validation. Taken together, these results promote a nuanced understanding of disease spread on contact networks, with implications for public health strategies.

Barriers, opportunities, and potential costs of expanding HIV support services

Experiencing housing instability, food insecurity, and financial stress can negatively impact retention in care and treatment adherence for people living with HIV. Expanding services that support socioeconomic needs could help improve HIV outcomes. Our objective was to investigate barriers, opportunities, and costs of expanding socioeconomic support programs. Semi-structured interviews were conducted with organizations serving U.S. Ryan White HIV/AIDS Program clients. Costs were estimated from interviews, organization documents, and city-specific wages. Organizations reported complex patient, organization, program, and system challenges as well as several opportunities for expansion. The average one-year per-person cost for engaging new clients was $196 for transportation, $612 for financial aid, $650 for food aid, and $2498 for short-term housing (2020 USD). Understanding potential expansion costs is important for funders and local stakeholders. This study provides a sense of magnitude for costs to scale-up programs to better meet socioeconomic needs of low-income patients living with HIV.

Evaluation of Strategies to Improve Uptake of Expedited Partner Therapy for Chlamydia trachomatis Treatment in Minnesota: A Decision Analytic Model

Background. Despite the established effectiveness of expedited partner therapy (EPT) in partner treatment of bacterial sexually transmitted infections (STI), the practice is underutilized. Objective. To estimate the relative effectiveness of strategies to increase EPT uptake (numbers of partners treated for chlamydia). Methods. We developed a care cascade model of cumulative probabilities to estimate the number of partners treated under strategies to increase EPT uptake in Minnesota. The care cascade model used data from clinical trials, population-based studies, and Minnesota chlamydia surveillance as well as in-depth interviews of health providers who regularly treat STI patients and a statewide survey of health providers across Minnesota. Results. Several strategies could improve EPT uptake among providers, including facilitating treatment payment (additional 1,932 partners treated) and implementing electronic health record reminders (additional 1,755 partners treated). Addressing concerns about liability would have the greatest effect, resulting in 2,187 additional partners treated. Conclusions. Providers expressed openness to offering EPT under several scenarios, which reflect differences in knowledge about EPT, its legality, and potential risks to patients. While addressing concerns about provider liability would have the greatest effect on number of partners treated, provider education and procedural changes could make a substantial impact.

Highlights

Addressing provider concerns about expedited partner therapy (EPT) legality and its potential risks would result in the most partners treated for chlamydia.EPT alerts and electronic EPT prescriptions may also streamline partner treatment.Provider education about the legality of EPT and its potential risks and training in counseling patients on EPT could also increase uptake.

An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example

Decision models can combine information from different sources to simulate the long-term consequences of alternative strategies in the presence of uncertainty. A cohort state-transition model (cSTM) is a decision model commonly used in medical decision making to simulate the transitions of a hypothetical cohort among various health states over time. This tutorial focuses on time-independent cSTM, in which transition probabilities among health states remain constant over time. We implement time-independent cSTM in R, an open-source mathematical and statistical programming language. We illustrate time-independent cSTMs using a previously published decision model, calculate costs and effectiveness outcomes, and conduct a cost-effectiveness analysis of multiple strategies, including a probabilistic sensitivity analysis. We provide open-source code in R to facilitate wider adoption. In a second, more advanced tutorial, we illustrate time-dependent cSTMs.

A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example

In an introductory tutorial, we illustrated building cohort state-transition models (cSTMs) in R, where the state transition probabilities were constant over time. However, in practice, many cSTMs require transitions, rewards, or both to vary over time (time dependent). This tutorial illustrates adding 2 types of time dependence using a previously published cost-effectiveness analysis of multiple strategies as an example. The first is simulation-time dependence, which allows for the transition probabilities to vary as a function of time as measured since the start of the simulation (e.g., varying probability of death as the cohort ages). The second is state-residence time dependence, allowing for history by tracking the time spent in any particular health state using tunnel states. We use these time-dependent cSTMs to conduct cost-effectiveness and probabilistic sensitivity analyses. We also obtain various epidemiological outcomes of interest from the outputs generated from the cSTM, such as survival probability and disease prevalence, often used for model calibration and validation. We present the mathematical notation first, followed by the R code to execute the calculations. The full R code is provided in a public code repository for broader implementation.

The Role of HIV Partner Services in the Modern Biomedical HIV Prevention Era: A Network Modeling Study

BACKGROUND

HIV partner services can accelerate the use of antiretroviral-based HIV prevention tools (antiretroviral therapy [ART] and preexposure prophylaxis [PrEP]), but its population impact on long-term HIV incidence reduction is challenging to quantify with traditional partner services metrics of partner identified or HIV screened. Understanding the role of partner services within the portfolio of HIV prevention interventions, including using it to efficiently deliver antiretrovirals, is needed to achieve HIV prevention targets.

METHODS

We used a stochastic network model of HIV/sexually transmitted infection transmission for men who have sex with men, calibrated to surveillance-based estimates in the Atlanta area, a jurisdiction with high HIV burden and suboptimal partner services uptake. Model scenarios varied successful delivery of partner services cascade steps (newly diagnosed "index" patient and partner identification, partner HIV screening, and linkage or reengagement of partners in PrEP or ART care) individually and jointly.

RESULTS

At current levels observed in Atlanta, removal of HIV partner services had minimal impact on 10-year cumulative HIV incidence, as did improving a single partner services step while holding the others constant. These changes did not sufficiently impact overall PrEP or ART coverage to reduce HIV transmission. If all index patients and partners were identified, maximizing partner HIV screening, partner PrEP provision, partner ART linkage, and partner ART reengagement would avert 6%, 11%, 5%, and 18% of infections, respectively. Realistic improvements in partner identification and service delivery were estimated to avert 2% to 8% of infections, depending on the combination of improvements.

CONCLUSIONS

Achieving optimal HIV prevention with partner services depends on pairing improvements in index patient and partner identification with maximal delivery of HIV screening, ART, and PrEP to partners if indicated. Improving the identification steps without improvement to antiretroviral service delivery steps, or vice versa, is projected to result in negligible population HIV prevention benefit.

Cost-effectiveness of Fecal Microbiota Transplantation for First Recurrent Clostridioides difficile Infection

BACKGROUND

Both the American College of Gastroenterology and the Infectious Diseases Society of America (IDSA)/Society for Healthcare Epidemiology of America 2021 Clostridioides difficile infection (CDI) guidelines recommend fecal microbiota transplantation (FMT) for persons with multiple recurrent CDI. Emerging data suggest that FMT may have high cure rates when used for first recurrent CDI. The aim of this study was to assess the cost-effectiveness of FMT for first recurrent CDI.

METHODS

We developed a Markov model to simulate a cohort of patients presenting with initial CDI infection. The model estimated the costs, effectiveness, and cost-effectiveness of different CDI treatment regimens recommended in the 2021 IDSA guidelines, with the additional option of FMT for first recurrent CDI. The model includes stratification by the severity of initial infection, estimates of cure, recurrence, and mortality. Data sources were taken from IDSA guidelines and published literature on treatment outcomes. Outcome measures were quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs).

RESULTS

When FMT is available for first recurrent CDI, the optimal cost-effective treatment strategy is fidaxomicin for initial nonsevere CDI, vancomycin for initial severe CDI, and FMT for first and subsequent recurrent CDI, with an ICER of $27 135/QALY. In probabilistic sensitivity analysis at a $100 000 cost-effectiveness threshold, FMT for first and subsequent CDI recurrence was cost-effective 90% of the time given parameter uncertainty.

CONCLUSIONS

FMT is a cost-effective strategy for first recurrent CDI. Prospective evaluation of FMT for first recurrent CDI is warranted to determine the efficacy and risk of recurrence.

A Statewide Mixed-Methods Study of Provider Knowledge and Behavior Administering Expedited Partner Therapy for Chlamydia and Gonorrhea

BACKGROUND

Expedited partner therapy (EPT) refers to the practice of having patients diagnosed with chlamydia or gonorrhea deliver medication directly to their partner(s) to treat them presumptively for infection. Although EPT facilitates timely treatment and prevents reinfection, it remains underused. We used findings from key informant interviews to design and implement a statewide survey to estimate knowledge and utilization of EPT and to identify barriers and facilitators to EPT among Minnesota providers.

METHODS

From November to December 2020, we carried out 15 interviews with health providers who currently provide EPT and coded interviews by recurring themes. We then conducted a statewide online survey on sexually transmitted infection treatment and barriers to EPT, from December 2020 to March 2021. We disseminated the survey to all licensed Minnesota health providers, and those who reported treating bacterial sexually transmitted infections in the past year were included in the study.

RESULTS

Interview themes included the importance of direct provision of partner medication, administrative/pharmacy barriers to treatment, inclusive EPT eligibility, and patient counseling. Of the 623 health providers who completed the online survey, only 70% thought EPT was legal and only 37% currently offer EPT. Of those who did not provide EPT, 78% said they would under certain circumstances. Barriers included concerns about safety/liability of prescribing without a medical examination, administrative concerns about prescriptions, and patient acceptance.

CONCLUSIONS

Given that over a quarter of respondents did not know expedited partner therapy (EPT)'s legal status, improving provider education may increase EPT provision. More research is needed on system-level barriers and patient acceptance of solutions identified in this study.

Cost-Effectiveness of HIV Retention and Re-engagement Interventions in High-Income Countries: A Systematic Literature Review

Engagement in lifelong HIV care is critical for both patient and public health, yet there are limited resources to invest in improving HIV outcomes. We systematically reviewed evidence on the cost-effectiveness of retention and re-engagement interventions. We searched five databases for peer-reviewed studies published between 2010 and 2020. We assessed reporting and methods quality, extracted data on target populations, interventions, and cost-effectiveness, and evaluated overall strength of evidence. Eleven studies met inclusion criteria, and eight had moderate-high quality. Cost-effectiveness estimates ranged from cost-saving to over $1,000,000/quality-adjusted life year (QALY) gained. Of the 73 cost-effectiveness ratios reported, 64% were < $100,000/QALY gained. Interventions were more likely to be cost-effective when targeted to high-risk groups, implemented in locations where baseline retention levels were low, and when used in combination with other high-impact HIV interventions (such as prevention). Overall, existing evidence is moderately strong that retention and/or re-engagement interventions can be cost-effective in high-income countries.

Cost-Effectiveness of Interventions to Improve HIV Pre-exposure Prophylaxis Initiation, Adherence, and Persistence Among Men Who Have Sex With Men

BACKGROUND

To help achieve Ending the HIV Epidemic (EHE) goals of reducing new HIV incidence, pre-exposure prophylaxis (PrEP) use and engagement must increase despite multidimensional barriers to scale-up and limitations in funding. We investigated the cost-effectiveness of interventions spanning the PrEP continuum of care.

SETTING

Men who have sex with men in Atlanta, GA, a focal jurisdiction for the EHE plan.

METHODS

Using a network-based HIV transmission model, we simulated lifetime costs, quality-adjusted life years (QALYs), and infections averted for 8 intervention strategies using a health sector perspective. Strategies included a status quo (no interventions), 3 distinct interventions (targeting PrEP initiation, adherence, or persistence), and all possible intervention combinations. Cost-effectiveness was evaluated incrementally using a $100,000/QALY gained threshold. We performed sensitivity analyses on PrEP costs, intervention costs, and intervention coverage.

RESULTS

Strategies averted 0.2%-4.2% new infections and gained 0.0045%-0.24% QALYs compared with the status quo. Initiation strategies achieved 20%-23% PrEP coverage (up from 15% with no interventions) and moderate clinical benefits at a high cost, while adherence strategies were relatively low cost and low benefit. Under our assumptions, the adherence and initiation combination strategy was cost-effective ($86,927/QALY gained). Sensitivity analyses showed no strategies were cost-effective when intervention costs increased by 60% and the strategy combining all 3 interventions was cost-effective when PrEP costs decreased to $1000/month.

CONCLUSION

PrEP initiation interventions achieved moderate public health gains and could be cost-effective. However, substantial financial resources would be needed to improve the PrEP care continuum toward meeting EHE goals.

Optimal timing and effectiveness of COVID-19 outbreak responses in China: a modelling study

Background

In January 2020, an outbreak of atypical pneumonia caused by a novel coronavirus, SARS-CoV-2, was reported in Wuhan, China. On Jan 23, 2020, the Chinese government instituted mitigation strategies to control spread. Most modeling studies have focused on projecting epidemiological outcomes throughout the pandemic. However, the impact and optimal timing of different mitigation approaches have not been well-studied.

Methods

We developed a mathematical model reflecting SARS-CoV-2 transmission dynamics in an age-stratified population. The model simulates health and economic outcomes from Dec 1, 2019 through Mar 31, 2020 for cities including Wuhan, Chongqing, Beijing, and Shanghai in China. We considered differences in timing and duration of three mitigation strategies in the early phase of the epidemic: city-wide quarantine on Wuhan, travel history screening and isolation of travelers from Wuhan to other Chinese cities, and general social distancing.

Results

Our model estimated that implementing all three mitigation strategies one week earlier would have averted 35% of deaths in Wuhan (50% in other cities) with a 7% increase in economic impacts (16-18% in other cities). One week’s delay in mitigation strategy initiation was estimated to decrease economic cost by the same amount, but with 35% more deaths in Wuhan and more than 80% more deaths in the other cities. Of the three mitigation approaches, infections and deaths increased most rapidly if initiation of social distancing was delayed. Furthermore, social distancing of working-age adults was most critical to reducing COVID-19 outcomes versus social distancing among children and/or the elderly.

Conclusions

Optimizing the timing of epidemic mitigation strategies is paramount and involves weighing trade-offs between preventing infections and deaths and incurring immense economic impacts. City-wide quarantine was not as effective as city-wide social distancing due to its much higher daily cost than social distancing. Under typical economic evaluation standards, the optimal timing for the full set of control measures would have been much later than Jan 23, 2020 (status quo).

Supplementary Information

The online version contains supplementary material available at (10.1186/s12889-022-12659-2).

Occupational Segregation And Hypertension Inequity: The Implication Of The Inverse Hazard Law Among Healthcare Workers

In the United States (US), Black-particularly Black female-healthcare workers are more likely to hold occupations with high job demand, low job control with limited support from supervisors or coworkers and are more vulnerable to job loss than their white counterparts. These work-related factors increase the risk of hypertension. This study examines the extent to which occupational segregation explains the persistent racial inequity in hypertension in the healthcare workforce and the potential health impact of workforce desegregation policies. We simulated a US healthcare workforce with four occupational classes: health diagnosing professionals (i.e., highest status), health treating professionals, healthcare technicians, and healthcare aides (i.e., lowest status). We simulated occupational segregation by allocating 25-year-old workers to occupational classes with the race- and gender-specific probabilities estimated from the American Community Survey data. Our model used occupational class attributes and workers' health behaviors to predict hypertension over a 40-year career. We tracked the hypertension prevalence and the Black-white prevalence gap among the simulated workers under the staus quo condition (occupational segregation) and the experimental conditions in which occupational segregation was eliminated. We found that the Black-white hypertension prevalence gap became approximately one percentage point smaller in the experimental than in the status quo conditions. These findings suggest that policies designed to desegregate the healthcare workforce may reduce racial health inequities in this population. Our microsimulation may be used in future research to compare various desegregation policies as they may affect workers' health differently.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41996-022-00098-5.

Retention in Care, Mortality, Loss-to-Follow-Up, and Viral Suppression among Antiretroviral Treatment-Naïve and Experienced Persons Participating in a Nationally Representative HIV Pre-Treatment Drug Resistance Survey in Mexico

We describe associations of pretreatment drug resistance (PDR) with clinical outcomes such as remaining in care, loss to follow-up (LTFU), viral suppression, and death in Mexico, in real-life clinical settings. We analyzed clinical outcomes after a two-year follow up period in participants of a large 2017–2018 nationally representative PDR survey cross-referenced with information of the national ministry of health HIV database. Participants were stratified according to prior ART exposure and presence of efavirenz/nevirapine PDR. Using a Fine-Gray model, we evaluated virological suppression among resistant patients, in a context of competing risk with lost to follow-up and death. A total of 1823 participants were followed-up by a median of 1.88 years (Interquartile Range (IQR): 1.59–2.02): 20 (1%) were classified as experienced + resistant; 165 (9%) naïve + resistant; 211 (11%) experienced + non-resistant; and 1427 (78%) as naïve + non-resistant. Being ART-experienced was associated with a lower probability of remaining in care (adjusted Hazard Ratio(aHR) = 0.68, 0.53–0.86, for the non-resistant group and aHR = 0.37, 0.17–0.84, for the resistant group, compared to the naïve + non-resistant group). Heterosexual cisgender women compared to men who have sex with men [MSM], had a lower viral suppression (aHR = 0.84, 0.70–1.01, p = 0.06) ART-experienced persons with NNRTI-PDR showed the worst clinical outcomes. This group was enriched with women and persons with lower education and unemployed, which suggests higher levels of social vulnerability.

Impact of university re-opening on total community COVID-19 burden

BACKGROUND

University students have higher average number of contacts than the general population. Students returning to university campuses may exacerbate COVID-19 dynamics in the surrounding community.

METHODS

We developed a dynamic transmission model of COVID-19 in a mid-sized city currently experiencing a low infection rate. We evaluated the impact of 20,000 university students arriving on September 1 in terms of cumulative COVID-19 infections, time to peak infections, and the timing and peak level of critical care occupancy. We also considered how these impacts might be mitigated through screening interventions targeted to students.

RESULTS

If arriving students reduce their contacts by 40% compared to pre-COVID levels, the total number of infections in the community increases by 115% (from 3,515 to 7,551), with 70% of the incremental infections occurring in the general population, and an incremental 19 COVID-19 deaths. Screening students every 5 days reduces the number of infections attributable to the student population by 42% and the total COVID-19 deaths by 8. One-time mass screening of students prevents fewer infections than 5-day screening, but is more efficient, requiring 196 tests needed to avert one infection instead of 237.

INTERPRETATION

University students are highly inter-connected with the surrounding off-campus community. Screening targeted at this population provides significant public health benefits to the community through averted infections, critical care admissions, and COVID-19 deaths.

Predicting the Effectiveness of Endemic Infectious Disease Control Interventions: The Impact of Mass Action versus Network Model Structure

BACKGROUND

Analyses of the effectiveness of infectious disease control interventions often rely on dynamic transmission models to simulate intervention effects. We aim to understand how the choice of network or compartmental model can influence estimates of intervention effectiveness in the short and long term for an endemic disease with susceptible and infected states in which infection, once contracted, is lifelong.

METHODS

We consider 4 disease models with different permutations of socially connected network versus unstructured contact (mass-action mixing) model and heterogeneous versus homogeneous disease risk. The models have susceptible and infected populations calibrated to the same long-term equilibrium disease prevalence. We consider a simple intervention with varying levels of coverage and efficacy that reduces transmission probabilities. We measure the rate of prevalence decline over the first 365 d after the intervention, long-term equilibrium prevalence, and long-term effective reproduction ratio at equilibrium.

RESULTS

Prevalence declined up to 10% faster in homogeneous risk models than heterogeneous risk models. When the disease was not eradicated, the long-term equilibrium disease prevalence was higher in mass-action mixing models than in network models by 40% or more. This difference in long-term equilibrium prevalence between network versus mass-action mixing models was greater than that of heterogeneous versus homogeneous risk models (less than 30%); network models tended to have higher effective reproduction ratios than mass-action mixing models for given combinations of intervention coverage and efficacy.

CONCLUSIONS

For interventions with high efficacy and coverage, mass-action mixing models could provide a sufficient estimate of effectiveness, whereas for interventions with low efficacy and coverage, or interventions in which outcomes are measured over short time horizons, predictions from network and mass-action models diverge, highlighting the importance of sensitivity analyses on model structure.

HIGHLIGHTS

• We calibrate 4 models-socially connected network versus unstructured contact (mass-action mixing) model and heterogeneous versus homogeneous disease risk-to 10% preintervention disease prevalence.• We measure the short- and long-term intervention effectiveness of all models using the rate of prevalence decline, long-term equilibrium disease prevalence, and effective reproduction ratio.• Generally, in the short term, prevalence declined faster in the homogeneous risk models than in the heterogeneous risk models.• Generally, in the long term, equilibrium disease prevalence was higher in the mass-action mixing models than in the network models, and the effective reproduction ratio was higher in network models than in the mass-action mixing models.

Addressing Pediatric HIV Pretreatment Drug Resistance and Virologic Failure in Sub-Saharan Africa: A Cost-Effectiveness Analysis of Diagnostic-Based Strategies in Children ≥3 Years Old

Improvement of antiretroviral therapy (ART) regimen switching practices and implementation of pretreatment drug resistance (PDR) testing are two potential approaches to improve health outcomes for children living with HIV. We developed a microsimulation model of disease progression and treatment focused on children with perinatally acquired HIV in sub-Saharan Africa who initiate ART at 3 years of age. We evaluated the cost-effectiveness of diagnostic-based strategies (improved switching and PDR testing), over a 10-year time horizon, in settings without and with pediatric dolutegravir (DTG) availability as first-line ART. The improved switching strategy increases the probability of switching to second-line ART when virologic failure is diagnosed through viral load testing. The PDR testing strategy involves a one-time PDR test prior to ART initiation to guide choice of initial regimen. When DTG is not available, PDR testing is dominated by the improved switching strategy, which has an incremental cost-effectiveness ratio (ICER) of USD 579/life-year gained (LY), relative to the status quo. If DTG is available, improved switching has a similar ICER (USD 591/LY) relative to the DTGstatus quo. Even when substantial financial investment is needed to achieve improved regimen switching practices, the improved switching strategy still has the potential to be cost-effective in a wide range of sub-Saharan African countries. Our analysis highlights the importance of strengthening existing laboratory monitoring systems to improve the health of children living with HIV.

Age-related decreases in relapses among adults with relapsing-onset multiple sclerosis

BACKGROUND

Relapsing-onset multiple sclerosis (MS) typically starts in early- to mid-adulthood, yet the trajectory of disease activity over the subsequent lifetime remains poorly defined. Previous studies have not quantified the age-specific portion of decreases in annualized relapse rates (ARR).

OBJECTIVE

The aim of this article is to determine, under a range of disease-related assumptions, the age-specific component of decreases in ARR over time among adults with relapsing-onset MS.

METHODS

We used a simulation modeling approach to examine a range of assumptions about changes in ARR due to age versus disability status. Scenarios included variations in initial ARR and rate of worsening on the Expanded Disability Status Scale. Model parameters were developed through analysis of MS patients in British Columbia, Canada, and literature review.

RESULTS

We found a substantial age-specific decrease in ARR in all simulated scenarios, independent of disability worsening. Under a range of clinically plausible assumptions, 88%-97% of the decrease was attributed to age and 3%-13% to disability. The age-specific decrease ranged from 22% to 37% per 5 years for a wide range of initial ARR (0.33-1.0).

CONCLUSION

Decreases in ARR were due mostly to age rather than disability status. To facilitate informed decision making in MS, it is important to quantify the dynamic relationship between relapses and age.

Informing Medication Discontinuation Decisions among Older Adults with Relapsing-Onset Multiple Sclerosis

BACKGROUND

For older adults with relapsing-onset multiple sclerosis (MS), limited information is available to inform if, or when, disease-modifying drugs (DMDs) may be safely discontinued.

OBJECTIVE

The aim of this study was to project the outcomes of DMD discontinuation among older adults with relapsing-onset MS.

METHODS

We projected the 10-year outcomes of discontinuation of a DMD (interferon-β, fingolimod, or natalizumab) among older adults (aged 55 or 70 years) who were relapse-free for 5 or more years and had not reached an Expanded Disability Status Scale (EDSS) score of 6. Outcomes included the percentage of people who had at least one relapse or reached EDSS 6, and quality-adjusted life-years (QALYs), which incorporated both relapses and disability. We used a simulation modeling approach. With increased age, relapses decreased and the effectiveness of DMDs for disability outcomes also decreased.

RESULTS

We found lower projected benefits for DMD continuation at 70 years of age than at 55 years of age. Compared with discontinuation, the projected benefit of DMD continuation ranged from 0.007 to 0.017 QALYs at 55 years of age and dropped to 0.002-0.006 at 70 years of age. The annual projected benefits of DMD continuation (0.1-3.0 quality-adjusted life-days) were very low compared with typical patient preferences regarding treatment burden.

CONCLUSION

The benefits of DMDs may not be substantial among older adults with relapsing-onset MS. Direct clinical evidence remains limited and the decision of whether to discontinue a DMD should also take into account patient preferences. It is important to gain a better understanding of how age-related changes in the trajectory of relapsing-onset MS affect treatment effectiveness among older adults.

Cost-effectiveness analysis of pre-ART HIV drug resistance testing in Kenyan women

BACKGROUND

The prevalence of pre-treatment drug resistance (PDR) to non-nucleoside reverse-transcriptase inhibitor (NNRTI) agents is increasing in sub-Saharan Africa, which may decrease the effectiveness of efavirenz-based antiretroviral therapy (ART) programs. However, due to recent safety concerns, there has been hesitancy to replace efavirenz-based ART with dolutegravir in women of reproductive potential. Our objective was to evaluate whether PDR testing for women not initiating dolutegravir-based ART would be a cost-effective strategy to address the challenges posed by PDR.

METHODS

We developed an HIV drug resistance model that simulates the emergence and transmission of resistance mutations, calibrated to the Kenyan epidemic. We modeled three care strategies for PDR testing among women not initiating dolutegravir-based ART: no PDR testing, PDR testing with a low-cost point mutation assay, known as oligonucleotide ligation assay (OLA), and PDR testing with consensus sequencing. Using a health sector perspective, this model was used to evaluate the health outcomes, lifetime costs, and cost-effectiveness under each strategy over a 15-year time horizon starting in 2019.

FINDINGS

OLA and CS PDR testing were projected to have incremental cost-effectiveness ratios (ICER) of $10,741/QALY gained and $134,396/QALY gained, respectively, which are not cost-effective by national income standards. Viral suppression rates among women at 12 months after ART initiation were 87·8%, 89·0%, and 89·3% with no testing, OLA testing, and CS testing, respectively. PDR testing with OLA and CS were associated with a 0.5% and 0.6% reduction in incidence rate compared to no PDR testing. Initial PDR prevalence among women was 13.1% in 2019. By 2034, this prevalence was 17·6%, 17·4%, and 17·3% with no testing, OLA testing, and CS testing, respectively.

INTERPRETATION

PDR testing for women is unlikely to be cost-effective in Kenya whether one uses a low-cost assay, such as OLA, or consensus sequencing.

FUNDING

National Institutes of Health, Gilead Sciences.

Cost-effectiveness of Treatment Regimens for Clostridioides difficile Infection: An Evaluation of the 2018 Infectious Diseases Society of America Guidelines

BACKGROUND

In 2018, the Infectious Diseases Society of America (IDSA) published guidelines for diagnosis and treatment of Clostridioides (formerly Clostridium) difficile infection (CDI). However, there is little guidance regarding which treatments are cost-effective.

METHODS

We used a Markov model to simulate a cohort of patients presenting with an initial CDI diagnosis. We used the model to estimate the costs, effectiveness, and cost-effectiveness of different CDI treatment regimens recommended in the recently published 2018 IDSA guidelines. The model includes stratification by the severity of the initial infection, and subsequent likelihood of cure, recurrence, mortality, and outcomes of subsequent recurrences. Data sources were taken from IDSA guidelines and published literature on treatment outcomes. Outcome measures were discounted quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs).

RESULTS

Use of fidaxomicin for nonsevere initial CDI, vancomycin for severe CDI, fidaxomicin for first recurrence, and fecal microbiota transplantation (FMT) for subsequent recurrence (strategy 44) cost an additional $478 for 0.009 QALYs gained per CDI patient, resulting in an ICER of $31 751 per QALY, below the willingness-to-pay threshold of $100 000/QALY. This is the optimal, cost-effective CDI treatment strategy.

CONCLUSIONS

Metronidazole is suboptimal for nonsevere CDI as it is less beneficial than alternative strategies. The preferred treatment regimen is fidaxomicin for nonsevere CDI, vancomycin for severe CDI, fidaxomicin for first recurrence, and FMT for subsequent recurrence. The most effective treatments, with highest cure rates, are also cost-effective due to averted mortality, utility loss, and costs of rehospitalization and/or further treatments for recurrent CDI.

A Multidimensional Array Representation of State-Transition Model Dynamics

Cost-effectiveness analyses often rely on cohort state-transition models (cSTMs). The cohort trace is the primary outcome of cSTMs, which captures the proportion of the cohort in each health state over time (state occupancy). However, the cohort trace is an aggregated measure that does not capture information about the specific transitions among health states (transition dynamics). In practice, these transition dynamics are crucial in many applications, such as incorporating transition rewards or computing various epidemiological outcomes that could be used for model calibration and validation (e.g., disease incidence and lifetime risk). In this article, we propose an alternative approach to compute and store cSTMs outcomes that capture both state occupancy and transition dynamics. This approach produces a multidimensional array from which both the state occupancy and the transition dynamics can be recovered. We highlight the advantages of the multidimensional array over the traditional cohort trace and provide potential applications of the proposed approach with an example coded in R to facilitate the implementation of our method.

Proactive Vs Reactive Therapeutic Drug Monitoring of Infliximab in Crohn's Disease: A Cost-Effectiveness Analysis in a Simulated Cohort

BACKGROUND

Therapeutic drug monitoring (TDM) is increasingly performed for Infliximab (IFX) in patients with Crohn's disease (CD). Reactive TDM is a cost-effective strategy to empiric IFX dose escalation. The cost-effectiveness of proactive TDM is unknown. The aim of this study is to assess the cost-effectiveness of proactive vs reactive TDM in a simulated population of CD patients on IFX.

METHODS

We developed a stochastic simulation model of CD patients on IFX and evaluated the expected health costs and outcomes of a proactive TDM strategy compared with a reactive strategy. The proactive strategy measured IFX concentration and antibody status every 6 months, or at the time of a flare, and dosed IFX to a therapeutic window. The reactive strategy only did so at the time of a flare.

RESULTS

The proactive strategy led to fewer flares than the reactive strategy. More patients stayed on IFX in the proactive vs reactive strategy (63.4% vs 58.8% at year 5). From a health sector perspective, a proactive strategy was marginally cost-effective compared with a reactive strategy (incremental cost-effectiveness ratio of $146,494 per quality-adjusted life year), assuming a 40% of the wholesale price of IFX. The results were most sensitive to risk of flaring with a low IFX concentration and the cost of IFX.

CONCLUSIONS

Assuming 40% of the average wholesale acquisition cost of biologic therapies, proactive TDM for IFX is marginally cost-effective compared with a reactive TDM strategy. As the cost of infliximab decreases, a proactive monitoring strategy is more cost-effective.

A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling

The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and shareability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research question, with model sharing and transparency relegated to being secondary goals. Consequently, code is often neither well documented nor systematically organized in a comprehensible and shareable approach. Moreover, many decision modelers are not formally trained in computer programming and may lack good coding practices, further compounding the problem of model transparency. To address these challenges, we propose a high-level framework for model-based decision and cost-effectiveness analyses (CEA) in R. The proposed framework consists of a conceptual, modular structure and coding recommendations for the implementation of model-based decision analyses in R. This framework defines a set of common decision model elements divided into five components: (1) model inputs, (2) decision model implementation, (3) model calibration, (4) model validation, and (5) analysis. The first four components form the model development phase. The analysis component is the application of the fully developed decision model to answer the policy or the research question of interest, assess decision uncertainty, and/or to determine the value of future research through value of information (VOI) analysis. In this framework, we also make recommendations for good coding practices specific to decision modeling, such as file organization and variable naming conventions. We showcase the framework through a fully functional, testbed decision model, which is hosted on GitHub for free download and easy adaptation to other applications. The use of this framework in decision modeling will improve code readability and model sharing, paving the way to an ideal, open-source world.

HIV Care Trajectories as a Novel Longitudinal Assessment of Retention in Care

Consistent engagement in care is associated with positive health outcomes among people living with HIV (PLWH). However, traditional retention measures ignore the evolving dynamics of engagement in care. To understand the longitudinal patterns of HIV care, we analyzed medical records from 2008 to 2015 of PLWH ≥ 18 years-old receiving care at a public, hospital-based HIV clinic (N = 2110). Using latent class analysis, we identified five distinct care trajectory classes: (1) consistent care (N = 1281); (2) less frequent care (N = 270); (3) return to care after initial attrition (N = 192); (4) moderate attrition (N = 163); and (5) rapid attrition (N = 204). The majority of PLWH in Class 1 (73.9%) had achieved sustained viral suppression (viral load ≤ 200 copies/mL at last test and > 12 months prior) by study end. Among the other care classes, there was substantial variation in sustained viral suppression (61.1% in Class 2 to 3.4% in Class 5). Care trajectories could be used to prioritize re-engagement efforts.

Dynamics of Drug Resistance: Optimal Control of an Infectious Disease

Antimicrobial resistance is a significant public health threat. In the U.S. alone, 2 million people are infected and 23,000 die each year from antibiotic resistant bacterial infections. In many cases, infections are resistant to all but a few remaining drugs. We examine the case where a single drug remains and solve for the optimal treatment policy for an SIS infectious disease model incorporating the effects of drug resistance. The problem is formulated as an optimal control problem with two continuous state variables, the disease prevalence and drug's "quality" (the fraction of infections that are drug-susceptible). The decision maker's objective is to minimize the discounted cost of the disease to society over an infinite horizon. We provide a new generalizable solution approach that allows us to thoroughly characterize the optimal treatment policy analytically. We prove that the optimal treatment policy is a bang-bang policy with a single switching time. The action/inaction regions can be described by a single boundary that is strictly increasing when viewed as a function of drug quality, indicating that when the disease transmission rate is constant, the policy of withholding treatment to preserve the drug for a potentially more serious future outbreak is not optimal. We show that the optimal value function and/or its derivatives are neither C ¹ nor Lipschitz continuous suggesting that numerical approaches to this family of dynamic infectious disease models may not be computationally stable. Furthermore, we demonstrate that relaxing the standard assumption of constant disease transmission rate can fundamentally change the shape of the action region, add a singular arc to the optimal control, and make preserving the drug for a serious outbreak optimal. In addition, we apply our framework to the case of antibiotic resistant gonorrhea.

"Time Traveling Is Just Too Dangerous" but Some Methods Are Worth Revisiting: The Advantages of Expected Loss Curves Over Cost-Effectiveness Acceptability Curves and Frontier

BACKGROUND

Cost-effectiveness acceptability curves (CEACs) and the cost-effectiveness acceptability frontier (CEAF) are the recommended graphical representations of uncertainty in a cost-effectiveness analysis (CEA). Nevertheless, many limitations of CEACs and the CEAF have been recognized by others. Expected loss curves (ELCs) overcome these limitations by displaying the expected foregone benefits of choosing one strategy over others, the optimal strategy in expectation, and the value of potential future research all in a single figure.

OBJECTIVES

To revisit ELCs, illustrate their benefits using a case study, and promote their adoption by providing open-source code.

METHODS

We used a probabilistic sensitivity analysis of a CEA comparing 6 cerebrospinal fluid biomarker test-and-treat strategies in patients with mild cognitive impairment. We showed how to calculate ELCs for a set of decision alternatives. We used the probabilistic sensitivity analysis of the case study to illustrate the limitations of currently recommended methods for communicating uncertainty and then demonstrated how ELCs can address these issues.

RESULTS

ELCs combine the probability that each strategy is not cost-effective on the basis of current information and the expected foregone benefits resulting from choosing that strategy (ie, how much is lost if we recommended a strategy with a higher expected loss). ELCs display how the optimal strategy switches across willingness-to-pay thresholds and enables comparison between different strategies in terms of the expected loss.

CONCLUSIONS

ELCs provide a more comprehensive representation of uncertainty and overcome current limitations of CEACs and the CEAF. Communication of uncertainty in CEA would benefit from greater adoption of ELCs as a complementary method to CEACs, the CEAF, and the expected value of perfect information.

Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial

Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling. It has the capacity to perform microsimulation models more efficiently than software commonly used for decision modeling, incorporate statistical analyses within decision models, and produce more transparent models and reproducible results. However, no clear guidance for the implementation of microsimulation models in R exists. In this tutorial, we provide a step-by-step guide to build microsimulation models in R and illustrate the use of this guide on a simple, but transferable, hypothetical decision problem. We guide the reader through the necessary steps and provide generic R code that is flexible and can be adapted for other models. We also show how this code can be extended to address more complex model structures and provide an efficient microsimulation approach that relies on vectorization solutions.

Nonidentifiability in Model Calibration and Implications for Medical Decision Making

BACKGROUND

Calibration is the process of estimating parameters of a mathematical model by matching model outputs to calibration targets. In the presence of nonidentifiability, multiple parameter sets solve the calibration problem, which may have important implications for decision making. We evaluate the implications of nonidentifiability on the optimal strategy and provide methods to check for nonidentifiability.

METHODS

We illustrate nonidentifiability by calibrating a 3-state Markov model of cancer relative survival (RS). We performed 2 different calibration exercises: 1) only including RS as a calibration target and 2) adding the ratio between the 2 nondeath states over time as an additional target. We used the Nelder-Mead (NM) algorithm to identify parameter sets that best matched the calibration targets. We used collinearity and likelihood profile analyses to check for nonidentifiability. We then estimated the benefit of a hypothetical treatment in terms of life expectancy gains using different, but equally good-fitting, parameter sets. We also applied collinearity analysis to a realistic model of the natural history of colorectal cancer.

RESULTS

When only RS is used as the calibration target, 2 different parameter sets yield similar maximum likelihood values. The high collinearity index and the bimodal likelihood profile on both parameters demonstrated the presence of nonidentifiability. These different, equally good-fitting parameter sets produce different estimates of the treatment effectiveness (0.67 v. 0.31 years), which could influence the optimal decision. By incorporating the additional target, the model becomes identifiable with a collinearity index of 3.5 and a unimodal likelihood profile.

CONCLUSIONS

In the presence of nonidentifiability, equally likely parameter estimates might yield different conclusions. Checking for the existence of nonidentifiability and its implications should be incorporated into standard model calibration procedures.

Cost-Effectiveness of Antibiotic Prophylaxis Strategies for Transrectal Prostate Biopsy in an Era of Increasing Antimicrobial Resistance

OBJECTIVES

To determine the optimal antibiotic prophylaxis strategy for transrectal prostate biopsy (TRPB) as a function of the local antibiotic resistance profile.

METHODS

We developed a decision-analytic model to assess the cost-effectiveness of four antibiotic prophylaxis strategies: ciprofloxacin alone, ceftriaxone alone, ciprofloxacin and ceftriaxone in combination, and directed prophylaxis selection based on susceptibility testing. We used a payer's perspective and estimated the health care costs and quality-adjusted life-years (QALYs) associated with each strategy for a cohort of 66-year-old men undergoing TRPB. Costs and benefits were discounted at 3% annually. Base-case resistance prevalence was 29% to ciprofloxacin and 7% to ceftriaxone, reflecting susceptibility patterns observed at the Minneapolis Veterans Affairs Health Care System. Resistance levels were varied in sensitivity analysis.

RESULTS

In the base case, single-agent prophylaxis strategies were dominated. Directed prophylaxis strategy was the optimal strategy at a willingness-to-pay threshold of $50,000/QALY gained. Relative to the directed prophylaxis strategy, the incremental cost-effectiveness ratio of the combination strategy was $123,333/QALY gained over the lifetime time horizon. In sensitivity analysis, single-agent prophylaxis strategies were preferred only at extreme levels of resistance.

CONCLUSIONS

Directed or combination prophylaxis strategies were optimal for a wide range of resistance levels. Facilities using single-agent antibiotic prophylaxis strategies before TRPB should re-evaluate their strategies unless extremely low levels of antimicrobial resistance are documented.

Evaluating empirical contact networks as potential transmission pathways for infectious diseases

Networks are often used to incorporate heterogeneity in contact patterns in mathematical models of pathogen spread. However, few tools exist to evaluate whether potential transmission pathways in a population are adequately represented by an observed contact network. Here, we describe a novel permutation-based approach, the network k-test, to determine whether the pattern of cases within the observed contact network are likely to have resulted from transmission processes in the network, indicating that the network represents potential transmission pathways between nodes. Using simulated data of pathogen spread, we compare the power of this approach to other commonly used analytical methods. We test the robustness of this technique across common sampling constraints, including undetected cases, unobserved individuals and missing interaction data. We also demonstrate the application of this technique in two case studies of livestock and wildlife networks. We show that the power of the k-test to correctly identify the epidemiologic relevance of contact networks is substantially greater than other methods, even when 50% of contact or case data are missing. We further demonstrate that the impact of missing data on network analysis depends on the structure of the network and the type of missing data.

Potential Impact of Integrating HIV Surveillance and Clinic Data on Retention-in-Care Estimates and Re-Engagement Efforts

Retention in care is essential to the health of people living with HIV and also for their communities. We sought to quantify the value of integrating HIV surveillance data with clinical records for improving the accuracy of retention-in-care estimates and the efficiency of efforts to re-engage out-of-care patients. Electronic medical records (EMRs) of HIV+ patients ≥18 years old from a public, hospital-based clinic in Minneapolis, MN between 2008 and 2014 were merged with state surveillance data on HIV-related laboratory tests, out-of-state relocation, and mortality. We calculated levels of retention and estimated the number of required case investigations to re-engage patients who appeared to be out of care over the study period with and without surveillance data integration. Retention was measured as the proportion of years in compliance with Health Resources and Services Administration (HRSA) guidelines (two clinical encounters >90 days apart annually) and the proportion of patients experiencing a gap in care >12 months. With data integration, retention estimates improved from an average HRSA compliance of 70.3% using EMR data alone to 77.5% with surveillance data, whereas the proportion of patients experiencing a >12-month gap in care decreased from 45.0% to 34.4%. If case investigations to re-engage patients were initiated after a 12-month gap in care, surveillance data would avoid 330 (29.3%) investigations over the study period. Surveillance data integration improves the accuracy of retention-in-care estimates and would avert a substantial number of unnecessary case investigations for patients who appear to be out of care but, in fact, receive care elsewhere or have died.

Prevalence of Inappropriate Antibiotic Prescriptions Among US Ambulatory Care Visits, 2010-2011

IMPORTANCE

The National Action Plan for Combating Antibiotic-Resistant Bacteria set a goal of reducing inappropriate outpatient antibiotic use by 50% by 2020, but the extent of inappropriate outpatient antibiotic use is unknown.

OBJECTIVE

To estimate the rates of outpatient oral antibiotic prescribing by age and diagnosis, and the estimated portions of antibiotic use that may be inappropriate in adults and children in the United States.

DESIGN, SETTING, AND PARTICIPANTS

Using the 2010-2011 National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey, annual numbers and population-adjusted rates with 95% confidence intervals of ambulatory visits with oral antibiotic prescriptions by age, region, and diagnosis in the United States were estimated.

EXPOSURES

Ambulatory care visits.

MAIN OUTCOMES AND MEASURES

Based on national guidelines and regional variation in prescribing, diagnosis-specific prevalence and rates of total and appropriate antibiotic prescriptions were determined. These rates were combined to calculate an estimate of the appropriate annual rate of antibiotic prescriptions per 1000 population.

RESULTS

Of the 184,032 sampled visits, 12.6% of visits (95% CI, 12.0%-13.3%) resulted in antibiotic prescriptions. Sinusitis was the single diagnosis associated with the most antibiotic prescriptions per 1000 population (56 antibiotic prescriptions [95% CI, 48-64]), followed by suppurative otitis media (47 antibiotic prescriptions [95% CI, 41-54]), and pharyngitis (43 antibiotic prescriptions [95% CI, 38-49]). Collectively, acute respiratory conditions per 1000 population led to 221 antibiotic prescriptions (95% CI, 198-245) annually, but only 111 antibiotic prescriptions were estimated to be appropriate for these conditions. Per 1000 population, among all conditions and ages combined in 2010-2011, an estimated 506 antibiotic prescriptions (95% CI, 458-554) were written annually, and, of these, 353 antibiotic prescriptions were estimated to be appropriate antibiotic prescriptions.

CONCLUSIONS AND RELEVANCE

In the United States in 2010-2011, there was an estimated annual antibiotic prescription rate per 1000 population of 506, but only an estimated 353 antibiotic prescriptions were likely appropriate, supporting the need for establishing a goal for outpatient antibiotic stewardship.

Potential cost-effectiveness of supervised injection facilities in Toronto and Ottawa, Canada

BACKGROUND AND AIMS

Supervised injection facilities (legally sanctioned spaces for supervised consumption of illicitly obtained drugs) are controversial public health interventions. We determined the optimal number of facilities in two Canadian cities using health economic methods.

DESIGN

Dynamic compartmental model of HIV and hepatitis C transmission through sexual contact and sharing of drug use equipment.

SETTING

Toronto and Ottawa, Canada.

PARTICIPANTS

Simulated population of each city.

INTERVENTIONS

Zero to five supervised injection facilities.

MEASUREMENTS

Direct health-care costs and quality-adjusted life-years (QALYs) over 20 years, discounted at 5% per year; incremental cost-effectiveness ratios.

FINDINGS

In Toronto, one facility cost $4.1 million and resulted in a gain of 385 QALYs over 20 years, for an incremental cost-effectiveness ratio (ICER) of $10,763 per QALY [95% credible interval (95CrI): cost-saving to $278,311]. Establishing one facility in Ottawa had an ICER of $6127 per QALY (95CrI: cost-saving to $179,272). At a $50,000 per QALY threshold, three facilities would be cost-effective in Toronto and two in Ottawa. The probability that establishing three, four, or five facilities in Toronto was cost-effective was 17, 21, and 41%, respectively. Establishing one, two, or three facilities in Ottawa was cost-effective with 13, 35, and 41% probability, respectively. Establishing no facility was unlikely to be the most cost-effective option (14% in Toronto and 10% in Ottawa). In both cities, results were robust if the reduction in needle-sharing among clients of the facilities was at least 50% and fixed operating costs were less than $2.0 million.

CONCLUSIONS

Using a $50,000 per quality-adjusted life-years threshold for cost-effectiveness, it is likely to be cost-effective to establish at least three legally sanctioned spaces for supervised injection of illicitly obtained drugs in Toronto, Canada and two in Ottawa, Canada.

Network analysis of cattle movements in Uruguay: Quantifying heterogeneity for risk-based disease surveillance and control

Movement of livestock between premises is one of the foremost factors contributing to the spread of infectious diseases of livestock. In part to address this issue, the origin and destination for all cattle movements in Uruguay are registered by law. This information has great potential to be used in assessing the risk of disease spread in the Uruguayan cattle population. Here, we analyze cattle movements from 2008 to 2013 using network analysis in order to understand the flows of animals in the Uruguayan cattle industry and to identify targets for surveillance and control measures. Cattle movements were represented as seasonal and annual networks in which farms represented nodes and nodes were linked based on the frequency and quantity of cattle moved. At the farm level, the distribution of the number of unique farms each farm is connected to through outgoing and incoming movements, as well as the number of animals moved, was highly right-skewed; the majority of farms had few to no contacts, whereas the 10% most highly connected farms accounted for 72-83% of animals moved annually. This extreme level of heterogeneity in movement patterns indicates that some farms may be disproportionately important for pathogen spread. Different production types exhibited characteristic patterns of farm-level connectivity, with some types, such a dairies, showing consistently higher levels of centrality. In addition, the observed networks were characterized by lower levels of connectivity and higher levels of heterogeneity than random networks of the same size and density, both of which have major implications for disease dynamics and control strategies. This represents the first in-depth analysis of farm-level livestock movements within South America, and highlights the importance of collecting livestock movement data in order to understand the vulnerability of livestock trade networks to invasion by infectious diseases.

Link removal for the control of stochastically evolving epidemics over networks: a comparison of approaches

For many communicable diseases, knowledge of the underlying contact network through which the disease spreads is essential to determining appropriate control measures. When behavior change is the primary intervention for disease prevention, it is important to understand how to best modify network connectivity using the limited resources available to control disease spread. We describe and compare four algorithms for selecting a limited number of links to remove from a network: two "preventive" approaches (edge centrality, R0 minimization), where the decision of which links to remove is made prior to any disease outbreak and depends only on the network structure; and two "reactive" approaches (S-I edge centrality, optimal quarantining), where information about the initial disease states of the nodes is incorporated into the decision of which links to remove. We evaluate the performance of these algorithms in minimizing the total number of infections that occur over the course of an acute outbreak of disease. We consider different network structures, including both static and dynamic Erdös-Rényi random networks with varying levels of connectivity, a real-world network of residential hotels connected through injection drug use, and a network exhibiting community structure. We show that reactive approaches outperform preventive approaches in averting infections. Among reactive approaches, removing links in order of S-I edge centrality is favored when the link removal budget is small, while optimal quarantining performs best when the link removal budget is sufficiently large. The budget threshold above which optimal quarantining outperforms the S-I edge centrality algorithm is a function of both network structure (higher for unstructured Erdös-Rényi random networks compared to networks with community structure or the real-world network) and disease infectiousness (lower for highly infectious diseases). We conduct a value-of-information analysis of knowing which nodes are initially infected by comparing the performance improvement achieved by reactive over preventive strategies. We find that such information is most valuable for moderate budget levels, with increasing value as disease spread becomes more likely (due to either increased connectedness of the network or increased infectiousness of the disease).

From network analysis to risk analysis--An approach to risk-based surveillance for bovine tuberculosis in Minnesota, US

Bovine tuberculosis (bTB) was first detected in 2005 in cattle in northwestern Minnesota (MN) through slaughter surveillance. By the end of 2008, 12 cattle herds were infected with bTB, and the main cause for infection was determined to be the movement of infected animals between herds. Bovine tuberculosis was contained in a smaller area in northwestern Minnesota classified as modified accredited (MA), corresponding to a prevalence inferior to 0.1% in cattle. From January 2008 to 2011, all cattle movements within the bTB MA were recorded electronically. The primary objectives of this study were to characterize cattle movements within this region and identify cattle herds with higher risk of bTB introduction based on network parameters and known risk factors from the published literature. During the period that data was collected, 57,460 cattle were moved in 3762 movements corresponding to permits issued to 682 premises, mostly representing private farms, sale yards, slaughter facilities and county or state fairs. Although sale yards represented less than 2% of the premises (nodes), 60% of the movements were to or from a sale yard. The network showed an overall density of 0.4%, a clustering coefficient of 14.6% and a betweenness centralization index of 12.7%, reflecting the low connectivity of this cattle network. The degree distribution showed that 20% of nodes performed 90% of the movements. Farms were ranked based on the total risk score and divided into high, medium, and low risk groups based on the score and its variability. The higher risk group included 14% (n=50) of the farms, corresponding to 80% of the cumulative risk for the farms in the bTB area. This analysis provides a baseline description about the contact structure of cattle movements in an area previously infected with bTB and develops a framework for a targeted surveillance approach for bTB to support future surveillance decisions.

Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach

BACKGROUND

To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single goodness-of-fit (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better.

METHODS

We demonstrate the Pareto frontier approach in the calibration of 2 models: a simple, illustrative Markov model and a previously published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to 2 possible weighted-sum GOF scoring systems, and we compare the health economic conclusions arising from these different definitions of best-fitting.

RESULTS

For the simple model, outcomes evaluated over the best-fitting input sets according to the 2 weighted-sum GOF schemes were virtually nonoverlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95% CI 72,500-87,600] v. $139,700 [95% CI 79,900-182,800] per quality-adjusted life-year [QALY] gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95% CI 64,900-156,200] per QALY gained). The TAVR model yielded similar results.

CONCLUSIONS

Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets.

Cost-effectiveness of treatment of diabetic macular edema

BACKGROUND

Macular edema is the most common cause of vision loss among patients with diabetes.

OBJECTIVE

To determine the cost-effectiveness of different treatments of diabetic macular edema (DME).

DESIGN

Markov model.

DATA SOURCES

Published literature and expert opinion.

TARGET POPULATION

Patients with clinically significant DME.

TIME HORIZON

Lifetime.

PERSPECTIVE

Societal.

INTERVENTION

Laser treatment, intraocular injections of triamcinolone or a vascular endothelial growth factor (VEGF) inhibitor, or a combination of both.

OUTCOME MEASURES

Discounted costs, gains in quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs).

RESULTS OF BASE-CASE ANALYSIS

All treatments except laser monotherapy substantially reduced costs, and all treatments except triamcinolone monotherapy increased QALYs. Laser treatment plus a VEGF inhibitor achieved the greatest benefit, gaining 0.56 QALYs at a cost of $6975 for an ICER of $12 410 per QALY compared with laser treatment plus triamcinolone. Monotherapy with a VEGF inhibitor achieved similar outcomes to combination therapy with laser treatment plus a VEGF inhibitor. Laser monotherapy and triamcinolone monotherapy were less effective and more costly than combination therapy.

RESULTS OF SENSITIVITY ANALYSIS

VEGF inhibitor monotherapy was sometimes preferred over laser treatment plus a VEGF inhibitor, depending on the reduction in quality of life with loss of visual acuity. When the VEGF inhibitor bevacizumab was as effective as ranibizumab, it was preferable because of its lower cost.

LIMITATION

Long-term outcome data for treated and untreated diseases are limited.

CONCLUSION

The most effective treatment of DME is VEGF inhibitor injections with or without laser treatment. This therapy compares favorably with cost-effective interventions for other conditions.

PRIMARY FUNDING SOURCE

Agency for Healthcare Research and Quality.

Optimal link removal for epidemic mitigation: a two-way partitioning approach

The structure of the contact network through which a disease spreads may influence the optimal use of resources for epidemic control. In this work, we explore how to minimize the spread of infection via quarantining with limited resources. In particular, we examine which links should be removed from the contact network, given a constraint on the number of removable links, such that the number of nodes which are no longer at risk for infection is maximized. We show how this problem can be posed as a non-convex quadratically constrained quadratic program (QCQP), and we use this formulation to derive a link removal algorithm. The performance of our QCQP-based algorithm is validated on small Erdős-Renyi and small-world random graphs, and then tested on larger, more realistic networks, including a real-world network of injection drug use. We show that our approach achieves near optimal performance and out-performs other intuitive link removal algorithms, such as removing links in order of edge centrality.

Assessing effectiveness and cost-effectiveness of concurrency reduction for HIV prevention

We estimated the effectiveness and cost-effectiveness of changes in concurrent sexual partnerships in reducing the spread of HIV in sub-Saharan Africa. Using data from Swaziland, Tanzania, Uganda and Zambia, we estimated country-specific concurrency behaviour from sexual behaviour survey data on the number of partners in the past 12 months, and we developed a network model to compare the impact of three behaviour changes on the HIV epidemic: (1) changes in concurrent partnership patterns to strict monogamy; (2) partnership reduction among those with the greatest number of partners; and (3) partnership reduction among all individuals. We estimated the number of new HIV infections over 10 years and the cost per infection averted. Given our assumptions and model structure, we find that reducing concurrency among high-risk individuals averts the most infections and increasing monogamy the least (11.7% versus 8.7% reduction in new infections, on average, for a 10% reduction in concurrent partnerships). A campaign that costs US$1 per person annually is likely cost-saving if it reduces concurrency by 9% on average, given our baseline estimates of concurrency. In sensitivity analysis, the rank ordering of behaviour change scenarios was unaffected by potential over-estimation of concurrency, though the number of infections averted decreased and the cost per HIV infection averted increased. Concurrency reduction programmes may be effective and cost-effective in reducing HIV incidence in sub-Saharan Africa if they can achieve even modest impacts at similar costs to past mass media campaigns in the region. Reduced concurrency among high-risk individuals appears to be most effective in reducing HIV incidence, but concurrency reduction in other risk groups may yield nearly as much benefit.

Population strategies to decrease sodium intake and the burden of cardiovascular disease: a cost-effectiveness analysis

BACKGROUND

Sodium consumption raises blood pressure, increasing the risk for heart attack and stroke. Several countries, including the United States, are considering strategies to decrease population sodium intake.

OBJECTIVE

To assess the cost-effectiveness of 2 population strategies to reduce sodium intake: government collaboration with food manufacturers to voluntarily cut sodium in processed foods, modeled on the United Kingdom experience, and a sodium tax.

DESIGN

A Markov model was constructed with 4 health states: well, acute myocardial infarction (MI), acute stroke, and history of MI or stroke.

DATA SOURCES

Medical Panel Expenditure Survey (2006), Framingham Heart Study (1980 to 2003), Dietary Approaches to Stop Hypertension trial, and other published data.

TARGET POPULATION

U.S. adults aged 40 to 85 years.

TIME HORIZON

Lifetime.

PERSPECTIVE

Societal.

OUTCOME MEASURES

Incremental costs (2008 U.S. dollars), quality-adjusted life-years (QALYs), and MIs and strokes averted.

RESULTS OF BASE-CASE ANALYSIS

Collaboration with industry that decreases mean population sodium intake by 9.5% averts 513 885 strokes and 480 358 MIs over the lifetime of adults aged 40 to 85 years who are alive today compared with the status quo, increasing QALYs by 2.1 million and saving $32.1 billion in medical costs. A tax on sodium that decreases population sodium intake by 6% increases QALYs by 1.3 million and saves $22.4 billion over the same period.

RESULTS OF SENSITIVITY ANALYSIS

Results are sensitive to the assumption that consumers have no disutility with modest reductions in sodium intake.

LIMITATION

Efforts to reduce population sodium intake could result in other dietary changes that are difficult to predict.

CONCLUSION

Strategies to reduce sodium intake on a population level in the United States are likely to substantially reduce stroke and MI incidence, which would save billions of dollars in medical expenses.

PRIMARY FUNDING SOURCE

Department of Veterans Affairs, Stanford University, and National Science Foundation.