West Nile virus transfusion-transmission risk in Australia associated with a seasonal outbreak in the United States.

BACKGROUND: West Nile virus (WNV) is a potentially transfusion-transmissible virus endemic in the US. The aim of this study was to estimate the monthly WNV transfusion transmission (TT) risk in Australia associated with donors returning from the US in 2018 and consider the implications for mitigation strategies. STUDY DESIGN AND METHODS: We used a probabilistic risk model to estimate the monthly WNV TT risks for each outbreak state/district in the US for the 2018 transmission season and the cumulative monthly risk for all US states/districts. RESULTS: The highest monthly cumulative transfusion risk in Australia occurred in August 2018 when 746 West Nile neuroinvasive disease cases were reported in the US and the estimated mean WNV TT risk in Australia was 1 in 1.0 × 108 donations (95% confidence interval [CI]: 1.6 × 108 -7.0 × 107 ). The highest risk during August was associated with California, with a mean risk of 1 in 4.1 × 108 donations (95% CI: 2.9 × 108 -6.6 × 108 ), representing 24% of the total risk in Australia. The cumulative TT risk in Australia for the other 11 months varied from 1 in 1.5 × 108 donations (95% CI: 2.3 × 108 -1.0 × 108 ) in September to 1 in 3.9 × 1010 donations (95% CI: 6.1 × 1010 -2.7 × 1010 ) in February. DISCUSSION: Our modeling indicates that the WNV TT risk in Australia associated with seasonal outbreaks in the US is extremely small and may not warrant donation restrictions for donors returning from the US.

Predictive models for cochlear implant outcomes: Performance, generalizability, and the impact of cohort size.

While cochlear implants have helped hundreds of thousands of individuals, it remains difficult to predict the extent to which an individual's hearing will benefit from implantation. Several publications indicate that machine learning may improve predictive accuracy of cochlear implant outcomes compared to classical statistical methods. However, existing studies are limited in terms of model validation and evaluating factors like sample size on predictive performance. We conduct a thorough examination of machine learning approaches to predict word recognition scores (WRS) measured approximately 12 months after implantation in adults with post-lingual hearing loss. This is the largest retrospective study of cochlear implant outcomes to date, evaluating 2,489 cochlear implant recipients from three clinics. We demonstrate that while machine learning models significantly outperform linear models in prediction of WRS, their overall accuracy remains limited (mean absolute error: 17.9-21.8). The models are robust across clinical cohorts, with predictive error increasing by at most 16% when evaluated on a clinic excluded from the training set. We show that predictive improvement is unlikely to be improved by increasing sample size alone, with doubling of sample size estimated to only increasing performance by 3% on the combined dataset. Finally, we demonstrate how the current models could support clinical decision making, highlighting that subsets of individuals can be identified that have a 94% chance of improving WRS by at least 10% points after implantation, which is likely to be clinically meaningful. We discuss several implications of this analysis, focusing on the need to improve and standardize data collection.

A MultiCenter Analysis of Factors Associated with Hearing Outcome for 2,735 Adults with Cochlear Implants.

While the majority of cochlear implant recipients benefit from the device, it remains difficult to estimate the degree of benefit for a specific patient prior to implantation. Using data from 2,735 cochlear-implant recipients from across three clinics, the largest retrospective study of cochlear-implant outcomes to date, we investigate the association between 21 preoperative factors and speech recognition approximately one year after implantation and explore the consistency of their effects across the three constituent datasets. We provide evidence of 17 statistically significant associations, in either univariate or multivariate analysis, including confirmation of associations for several predictive factors, which have only been examined in prior smaller studies. Despite the large sample size, a multivariate analysis shows that the variance explained by our models remains modest across the datasets (R2=0.12-0.21). Finally, we report a novel statistical interaction indicating that the duration of deafness in the implanted ear has a stronger impact on hearing outcome when considered relative to a candidate's age. Our multicenter study highlights several real-world complexities that impact the clinical translation of predictive factors for cochlear implantation outcome. We suggest several directions to overcome these challenges and further improve our ability to model patient outcomes with increased accuracy.

Modeling the West Nile virus transfusion transmission risk in a nonoutbreak country associated with traveling donors.

BACKGROUND: West Nile virus (WNV) is a mosquito-borne virus and transfusion transmission (TT) has been demonstrated. The European Union and neighboring countries experience an annual transmission season. STUDY DESIGN AND METHODS: We developed a novel probabilistic model to estimate the WNV TT risk in Australia attributable to returned donors who had travelled to the European Union and neighboring countries during the 2018. We estimated weekly WNV TT risks in Australia for each outbreak country and the cumulative risk for all countries. RESULTS: Highest mean weekly TT risk in Australia attributable to donors returning from a specific outbreak country was 1 in 23.3 million (plausible range, 16.8-41.9 million) donations during Week 39 in Croatia. Highest mean weekly cumulative TT risk was 1 in 8.5 million donations (plausible range, 5.1-17.8 million) during Week 35. CONCLUSIONS: The estimated TT risk in Australia attributable to returning donors from the European Union and neighboring countries in 2018 was very small, and additional risk mitigation strategies were not indicated. In the context of such low TT risks, a simpler but effective approach would be to monitor the number of weekly reported West Nile fever cases and implement risk modeling only when the reported cases reached a predefined number or trigger point.

Effects of agrochemical pollution on schistosomiasis transmission: a systematic review and modelling analysis.

BACKGROUND: Agrochemical pollution of surface waters is a growing global environmental challenge, especially in areas where agriculture is rapidly expanding and intensifying. Agrochemicals might affect schistosomiasis transmission through direct and indirect effects on Schistosoma parasites, their intermediate snail hosts, snail predators, and snail algal resources. We aimed to review and summarise the effects of these agrochemicals on schistosomiasis transmission dynamics. METHODS: We did a systematic review of agrochemical effects on the lifecycle of Schistosoma spp and fitted dose-response models to data regarding the association between components of the lifecycle and agrochemical concentrations. We incorporated these dose-response functions and environmentally relevant concentrations of agrochemicals into a mathematical model to estimate agrochemical effects on schistosomiasis transmission. Dose-response functions were used to estimate individual agrochemical effects on estimates of the agrochemically influenced basic reproduction number, R0, for Schistosoma haematobium. We incorporated time series of environmentally relevant agrochemical concentrations into the model and simulated mass drug administration control efforts in the presence of agrochemicals. FINDINGS: We derived 120 dose-response functions describing the effects of agrochemicals on schistosome lifecycle components. The median estimate of the basic reproduction number under agrochemical-free conditions, was 1·65 (IQR 1·47-1·79). Agrochemical effects on estimates of R0 for S haematobium ranged from a median three-times increase (R0 5·05, IQR 4·06-5·97) to transmission elimination (R0 0). Simulations of transmission dynamics subject to interacting annual mass drug administration and agrochemical pollution yielded a median estimate of 64·82 disability-adjusted life-years (DALYs) lost per 100 000 people per year (IQR 62·52-67·68) attributable to atrazine use. In areas where aquatic arthropod predators of intermediate host snails suppress transmission, the insecticides chlorpyrifos (6·82 DALYs lost per 100 000 people per year, IQR 4·13-8·69) and profenofos (103·06 DALYs lost per 100 000 people per year, IQR 89·63-104·90) might also increase the disability burden through their toxic effects on arthropods. INTERPRETATION: Expected environmental concentrations of agrochemicals alter schistosomiasis transmission through direct and indirect effects on intermediate host and parasite densities. As industrial agricultural practices expand in areas where schistosomiasis is endemic, strategies to prevent increases in transmission due to agrochemical pollution should be developed and pursued. FUNDING: National Science Foundation, National Institutes of Health.

Review of Mathematical Models of Vaccination for Preventing Congenital Cytomegalovirus Infection.

BACKGROUND: Several cytomegalovirus (CMV) vaccine candidates are under development. To reduce the burden of congenital CMV infection, potential strategies under consideration include vaccination of adult women, adolescent girls, and/or young children (both sexes). METHODS: We reviewed 5 studies that used infectious disease modeling to assess the potential impact of vaccination for preventing congenital CMV infection. All models assumed CMV vaccination would prevent primary infection and 2 models also assumed prevention of reinfections and reactivations. RESULTS: Despite differences in structure, assumptions, and population data, infant vaccination (both sexes) was the optimal strategy in all models, but in 1 model vaccinating seronegative women at 19-21 years of age was also optimal (for duration of vaccine protection ≥8 years). In 3 models, infant vaccination increased average age at primary infection as a result of decreased secondary transmission (herd immunity) combined with waning vaccine-induced immunity. This effect could increase the risk of congenital CMV infections in populations where primary CMV infection occurs early in childhood but could be minimized by administering a second dose of vaccine during adolescence. CONCLUSIONS: Understanding vaccine efficacy and duration of immunity, and how these might vary depending on CMV serostatus and age at vaccination, will be key to defining CMV vaccination strategies.

Factors Associated With Measles Transmission in the United States During the Postelimination Era.

Importance: Measles cases and outbreaks continue to occur in the United States after the introduction of measles from endemic settings. Objective: To discern the factors associated with measles transmission in the United States after measles had been eliminated. Design, Setting, and Participants: This cross-sectional study was conducted from January 1, 2001, to December 31, 2017, in the United States among US residents and international visitors with confirmed measles. A maximum likelihood algorithm that uses the observed dates of rash onset and the known distribution of the serial interval (time between symptom onset in related consecutive cases) was applied to outbreak notification data to estimate the effective reproduction number (R), or the mean number of new infections generated per case. Transmissibility was assessed by comparing R based on the characteristics of primary and secondary cases of measles. Exposures: Measles virus. Main Outcomes and Measures: Effective reproduction number (R), or the mean number of successful transmission events per case of measles (ie, the mean number of persons to whom each patient with measles spreads measles). Results: A total of 2218 individuals with confirmed measles cases (1025 female, 1176 male, and 17 sex not reported; median age, 15 years [range, 0-89 years]) reported from 2001 to 2017 were evaluated. Among patients who received no doses of measles vaccine, R was 0.76 (95% CI, 0.71-0.81); among patients who received 1 dose of measles vaccine, R was 0.17 (95% CI, 0.11-0.26); among patients who received 2 doses or more of measles vaccine, R was 0.27 (95% CI, 0.17-0.39); and among patients with unknown vaccination status, R was 0.52 (95% CI, 0.44-0.60). Among patients born before 1957, R was 0.35 (95% CI, 0.20-0.58), and among those born on or after 1957, R was 0.64 (95% CI, 0.61-0.68). R was higher when primary and secondary cases of measles were patients aged 5 to 17 years (0.36 [95% CI, 0.31-0.42]) compared with assortative transmission in other age groups (<1 year, 0.14 [95% CI, 0.10-0.20]; 1-4 years, 0.25 [95% CI, 0.20-0.30]; 18-29 years, 0.19 [95% CI, 0.15-0.24]; 30-49 years, 0.15 [95% CI, 0.11-0.20]; ≥50 years, 0.04 [95% CI, 0.01-0.10]). Conclusions and Relevance: The findings of this study support having high targets for 2-dose measles vaccine coverage, particularly among school-aged children in the United States.

Forecasting the 2014 West African Ebola Outbreak.

In 2014-2015, a large Ebola outbreak afflicted Liberia, Guinea, and Sierra Leone. We performed a systematic review of 26 manuscripts, published between 2014 and April 2015, that forecasted the West African Ebola outbreak while it was occurring, and we derived implications for how results could be interpreted by policymakers. Forecasted case counts varied widely. An important determinant of forecast accuracy for case counts was how far into the future predictions were made. Generally, forecasts for less than 2 months into the future tended to be more accurate than those made for more than 10 weeks into the future. The exceptions were parsimonious statistical models in which the decay of the rate of spread of the pathogen among susceptible individuals was dealt with explicitly. The most important lessons for policymakers regarding future outbreaks, when using similar modeling results, are: 1) uncertainty of forecasts will be greater in the beginning of the outbreak; 2) when data are limited, forecasts produced by models designed to inform specific decisions should be used complementarily for robust decision-making (e.g., 2 statistical models produced the most reliable case-counts forecasts for the studied Ebola outbreak but did not enable understanding of interventions' impact, whereas several compartmental models could estimate interventions' impact but required unavailable data); and 3) timely collection of essential data is necessary for optimal model use.

School dismissal as a pandemic influenza response: When, where and for how long?

We used individual-based computer simulation models at community, regional and national levels to evaluate the likely impact of coordinated pre-emptive school dismissal policies during an influenza pandemic. Such policies involve three key decisions: when, over what geographical scale, and how long to keep schools closed. Our evaluation includes uncertainty and sensitivity analyses, as well as model output uncertainties arising from variability in serial intervals and presumed modifications of social contacts during school dismissal periods. During the period before vaccines become widely available, school dismissals are particularly effective in delaying the epidemic peak, typically by 4-6 days for each additional week of dismissal. Assuming the surveillance is able to correctly and promptly diagnose at least 5-10% of symptomatic individuals within the jurisdiction, dismissals at the city or county level yield the greatest reduction in disease incidence for a given dismissal duration for all but the most severe pandemic scenarios considered here. Broader (multi-county) dismissals should be considered for the most severe and fast-spreading (1918-like) pandemics, in which multi-month closures may be necessary to delay the epidemic peak sufficiently to allow for vaccines to be implemented.

The role of super-spreading events in Mycobacterium tuberculosis transmission: evidence from contact tracing.

BACKGROUND: In current epidemiology of tuberculosis (TB), heterogeneity in infectiousness among TB patients is a challenge, which is not well studied. We aimed to quantify this heterogeneity and the presence of "super-spreading" events that can assist in designing optimal public health interventions. METHODS: TB epidemiologic investigation data notified between 1 January 2005 and 31 December 2015 from Victoria, Australia were used to quantify TB patients' heterogeneity in infectiousness and super-spreading events. We fitted a negative binomial offspring distribution (NBD) for the number of secondary infections and secondary active TB disease each TB patient produced. The dispersion parameter, k, of the NBD measures the level of heterogeneity, where low values of k (e.g. k < 1) indicate over-dispersion. Super-spreading was defined as patients causing as many or more secondary infections as the 99th centile of an equivalent homogeneous distribution. Contact infection was determined based on a tuberculin skin test (TST) result of ≥10 mm. A NBD model was fitted to identify index characteristics that were associated with the number of contacts infected and risk ratios (RRs) were used to quantify the strength of this association. RESULTS: There were 4190 (2312 pulmonary and 1878 extrapulmonary) index TB patients and 18,030 contacts. A total of 15,522 contacts were tested with TST, of whom 3213 had a result of ≥10 mm. The dispersion parameter, k for secondary infections was estimated at 0.16 (95%CI 0.14-0.17) and there were 414 (9.9%) super-spreading events. From the 3213 secondary infections, 2415 (75.2%) were due to super-spreading events. There were 226 contacts who developed active TB disease and a higher level of heterogeneity was found for this outcome than for secondary infection, with k estimated at 0.036 (95%CI 0.025-0.046). In regression analyses, we found that infectiousness was greater among index patients found by clinical presentation and those with bacteriological confirmation. CONCLUSION: TB transmission is highly over dispersed and super-spreading events are responsible for a substantial majority of secondary infections. Heterogeneity of transmission and super-spreading are critical issues to consider in the design of interventions and models of TB transmission dynamics.

Risk prediction system for dengue transmission based on high resolution weather data.

BACKGROUND: Dengue is the fastest spreading vector-borne viral disease, resulting in an estimated 390 million infections annually. Precise prediction of many attributes related to dengue is still a challenge due to the complex dynamics of the disease. Important attributes to predict include: the risk of and risk factors for an infection; infection severity; and the timing and magnitude of outbreaks. In this work, we build a model for predicting the risk of dengue transmission using high-resolution weather data. The level of dengue transmission risk depends on the vector density, hence we predict risk via vector prediction. METHODS AND FINDINGS: We make use of surveillance data on Aedes aegypti larvae collected by the Taiwan Centers for Disease Control as part of the national routine entomological surveillance of dengue, and weather data simulated using the IBM's Containerized Forecasting Workflow, a high spatial- and temporal-resolution forecasting system. We propose a two stage risk prediction system for assessing dengue transmission via Aedes aegypti mosquitoes. In stage one, we perform a logistic regression to determine whether larvae are present or absent at the locations of interest using weather attributes as the explanatory variables. The results are then aggregated to an administrative division, with presence in the division determined by a threshold percentage of larvae positive locations resulting from a bootstrap approach. In stage two, larvae counts are estimated for the predicted larvae positive divisions from stage one, using a zero-inflated negative binomial model. This model identifies the larvae positive locations with 71% accuracy and predicts the larvae numbers producing a coverage probability of 98% over 95% nominal prediction intervals. This two-stage model improves the overall accuracy of identifying larvae positive locations by 29%, and the mean squared error of predicted larvae numbers by 9.6%, against a single-stage approach which uses a zero-inflated binomial regression approach. CONCLUSIONS: We demonstrate a risk prediction system using high resolution weather data can provide valuable insight to the distribution of risk over a geographical region. The work also shows that a two-stage approach is beneficial in predicting risk in non-homogeneous regions, where the risk is localised.

Estimating the elimination feasibility in the 'end game' of control efforts for parasites subjected to regular mass drug administration: Methods and their application to schistosomiasis.

Progress towards controlling and eliminating parasitic worms, including schistosomiasis, onchocerciasis, and lymphatic filariasis, is advancing rapidly as national governments, multinational NGOs, and pharmaceutical companies launch collaborative chemotherapeutic control campaigns. Critical questions remain regarding the potential for achieving elimination of these infections, and analytical methods can help to quickly estimate progress towards-and the probability of achieving-elimination over specific timeframes. Here, we propose the effective reproduction number, Reff, as a proxy of elimination potential for sexually reproducing worms that are subject to poor mating success at very low abundance (positive density dependence, or Allee effects). Reff is the number of parasites produced by a single reproductive parasite at a given stage in the transmission cycle, over the parasite's lifetime-it is the generalized form of the more familiar basic reproduction number, R0, which only applies at the beginning of an epidemic-and it can be estimated in a 'model-free' manner by an estimator ('ε'). We introduce ε, demonstrate its estimation using simulated data, and discuss how it may be used in planning and evaluation of ongoing elimination efforts for a range of parasitic diseases.

Agent-based models of malaria transmission: a systematic review.

BACKGROUND: Much of the extensive research regarding transmission of malaria is underpinned by mathematical modelling. Compartmental models, which focus on interactions and transitions between population strata, have been a mainstay of such modelling for more than a century. However, modellers are increasingly adopting agent-based approaches, which model hosts, vectors and/or their interactions on an individual level. One reason for the increasing popularity of such models is their potential to provide enhanced realism by allowing system-level behaviours to emerge as a consequence of accumulated individual-level interactions, as occurs in real populations. METHODS: A systematic review of 90 articles published between 1998 and May 2018 was performed, characterizing agent-based models (ABMs) relevant to malaria transmission. The review provides an overview of approaches used to date, determines the advantages of these approaches, and proposes ideas for progressing the field. RESULTS: The rationale for ABM use over other modelling approaches centres around three points: the need to accurately represent increased stochasticity in low-transmission settings; the benefits of high-resolution spatial simulations; and heterogeneities in drug and vaccine efficacies due to individual patient characteristics. The success of these approaches provides avenues for further exploration of agent-based techniques for modelling malaria transmission. Potential extensions include varying elimination strategies across spatial landscapes, extending the size of spatial models, incorporating human movement dynamics, and developing increasingly comprehensive parameter estimation and optimization techniques. CONCLUSION: Collectively, the literature covers an extensive array of topics, including the full spectrum of transmission and intervention regimes. Bringing these elements together under a common framework may enhance knowledge of, and guide policies towards, malaria elimination. However, because of the diversity of available models, endorsing a standardized approach to ABM implementation may not be possible. Instead it is recommended that model frameworks be contextually appropriate and sufficiently described. One key recommendation is to develop enhanced parameter estimation and optimization techniques. Extensions of current techniques will provide the robust results required to enhance current elimination efforts.

The impact of shared sanitation facilities on diarrheal diseases with and without an environmental reservoir: a modeling study.

Epidemiological studies have identified an increased risk of diarrheal diseases associated with using shared sanitation facilities. We hypothesized that this might be related to differences in transmission routes of pathogens. We proposed a mathematical model of two fictitious pathogens, one transmitted with an environmental reservoir and one without. We assumed that individuals susceptible to one pathogen are not susceptible to the other, and therefore, decoupled the two models. We initialized the model with 99% individuals being susceptible. We sampled the parameter space using Latin Hypercube Sampling. We simulated 10,000 parameter sets. We varied the effective shared sanitation coverage (the product of latrine coverage and users' compliance). Our results show that, in our hypothetical scenario, across all levels of effective coverage of shared sanitation, the median final cumulative incidence of diarrheal disease was higher than that of zero coverage. Our simulation findings suggest that increasing effective coverage of shared sanitation may have limited benefits against diarrhea-causing pathogens with an environmental reservoir and may lack benefit against diarrhea-causing pathogens without an environmental reservoir given increased human contacts if latrines are poorly maintained.

Cost-effectiveness of dog rabies vaccination programs in East Africa.

BACKGROUND: Dog rabies annually causes 24,000-70,000 deaths globally. We built a spreadsheet tool, RabiesEcon, to aid public health officials to estimate the cost-effectiveness of dog rabies vaccination programs in East Africa. METHODS: RabiesEcon uses a mathematical model of dog-dog and dog-human rabies transmission to estimate dog rabies cases averted, the cost per human rabies death averted and cost per year of life gained (YLG) due to dog vaccination programs (US 2015 dollars). We used an East African human population of 1 million (approximately 2/3 living in urban setting, 1/3 rural). We considered, using data from the literature, three vaccination options; no vaccination, annual vaccination of 50% of dogs and 20% of dogs vaccinated semi-annually. We assessed 2 transmission scenarios: low (1.2 dogs infected per infectious dog) and high (1.7 dogs infected). We also examined the impact of annually vaccinating 70% of all dogs (World Health Organization recommendation for dog rabies elimination). RESULTS: Without dog vaccination, over 10 years there would a total of be approximately 44,000-65,000 rabid dogs and 2,100-2,900 human deaths. Annually vaccinating 50% of dogs results in 10-year reductions of 97% and 75% in rabid dogs (low and high transmissions scenarios, respectively), approximately 2,000-1,600 human deaths averted, and an undiscounted cost-effectiveness of $451-$385 per life saved. Semi-annual vaccination of 20% of dogs results in in 10-year reductions of 94% and 78% in rabid dogs, and approximately 2,000-1,900 human deaths averted, and cost $404-$305 per life saved. In the low transmission scenario, vaccinating either 50% or 70% of dogs eliminated dog rabies. Results were most sensitive to dog birth rate and the initial rate of dog-to-dog transmission (Ro). CONCLUSIONS: Dog rabies vaccination programs can control, and potentially eliminate, dog rabies. The frequency and coverage of vaccination programs, along with the level of dog rabies transmission, can affect the cost-effectiveness of such programs. RabiesEcon can aid both the planning and assessment of dog rabies vaccination programs.

Agrochemicals increase risk of human schistosomiasis by supporting higher densities of intermediate hosts.

Schistosomiasis is a snail-borne parasitic disease that ranks among the most important water-based diseases of humans in developing countries. Increased prevalence and spread of human schistosomiasis to non-endemic areas has been consistently linked with water resource management related to agricultural expansion. However, the role of agrochemical pollution in human schistosome transmission remains unexplored, despite strong evidence of agrochemicals increasing snail-borne diseases of wildlife and a projected 2- to 5-fold increase in global agrochemical use by 2050. Using a field mesocosm experiment, we show that environmentally relevant concentrations of fertilizer, a herbicide, and an insecticide, individually and as mixtures, increase densities of schistosome-infected snails by increasing the algae snails eat and decreasing densities of snail predators. Epidemiological models indicate that these agrochemical effects can increase transmission of schistosomes. Identifying agricultural practices or agrochemicals that minimize disease risk will be critical to meeting growing food demands while improving human wellbeing.

A Systematic Review and Meta-analysis of the Factors Associated With Nonadherence and Discontinuation of Statins Among People Aged ≥65 Years.

Background: Older individuals (aged ≥65 years) are commonly prescribed statins but may experience a range of barriers in adhering to therapy. The factors associated with poor statin adherence and/or discontinuation among this population have not been comprehensively reviewed. Methods: We conducted a systematic review to identify English articles published through December 12, 2016 that reported factors associated with nonadherence and/or discontinuation of statins among older persons. Data were pooled via random-effects meta-analysis techniques. Results: Forty-five articles reporting data from more than 1.8 million older statin users from 13 countries were included. The factors associated with increased statin nonadherence were black/non-white race (odds ratio [OR] 1.66, 95% confidence interval [CI] 1.39-1.98), female gender (OR 1.08, 95% CI 1.03-1.13), current smoker (OR 1.12, 95% CI 1.03-1.21), higher copayments (OR 1.38, 95% CI 1.25-1.52), new user (OR 1.58, 95% CI 1.21-2.07), lower number of concurrent cardiovascular medications (OR 1.08, 95% CI 1.06-1.09), primary prevention (OR 1.49, 95% CI 1.40-1.59), having respiratory disorders (OR 1.17, 95% CI 1.12-1.23) or depression (OR 1.11, 95% CI 1.06-1.16), and not having renal disease (OR 1.09, 95% CI 1.04-1.14). The factors associated with increased statin discontinuation were lower income status (OR 1.20, 95% CI 1.06-1.36), current smoker (OR 1.14, 95% CI 1.06-1.23), higher copayment (OR 1.61, 95% CI 1.53-1.70), higher number of medications (OR 1.04, 95% CI 1.01-1.06), presence of dementia (OR 1.18, 95% CI 1.02-1.36), cancer (OR 1.22, 95% CI 1.11-1.33) or respiratory disorders (OR 1.19, 95% CI 1.05-1.34), primary prevention (OR 1.66, 95% CI 1.24-2.22), and not having hypertension (OR 1.13, 95% CI 1.07-1.20) or diabetes (OR 1.09, 95% CI 1.04-1.15). Conclusion: Interventions that target potentially modifiable factors including financial and social barriers, patients' perceptions about disease risk as well as polypharmacy may improve statin use in the older population.

Adherence and Persistence Among Statin Users Aged 65 Years and Over: A Systematic Review and Meta-analysis.

Background: Older people (aged ≥ 65 years) have distinctive challenges with medication adherence. However, adherence and persistence patterns among older statin users have not been comprehensively reviewed. Methods: As part of a broader systematic review, we searched Medline, Embase, PsycINFO, CINAHL, Database of Abstracts of Reviews of Effects, CENTRAL, and the National Health Service Economic Evaluation Database through December 2016 for English articles reporting adherence and/or persistence among older statin users. Data were analyzed via descriptive methods and meta-analysis using random-effect modeling. Results: Data from more than 3 million older statin users in 82 studies conducted in over 40 countries were analyzed. At 1-year follow-up, 59.7% (primary prevention 47.9%; secondary prevention 62.3%) of users were adherent (medication possession ratio [MPR] or proportion of days covered [PDC] ≥ 80%). For both primary and secondary prevention subjects, 1-year adherence was worse among individuals aged more than 75 years than those aged 65-75 years. At 3 and ≥10 years, 55.3% and 28.4% of users were adherent, respectively. The proportion of users persistent at 1-year was 76.7% (primary prevention 76.0%; secondary prevention 82.6%). Additionally, 68.1% and 61.2% of users were persistent at 2 and 4 years, respectively. Among new statin users, 48.2% were nonadherent and 23.9% discontinued within the first year. The proportion of statin users who were adherent based on self-report was 85.5%. Conclusions: There is poor short and long term adherence and persistence among older statin users. Strategies to improve adherence and reduce discontinuation are needed if the intended cardiovascular benefits of statin treatment are to be realized.