NASHmap: clinical utility of a machine learning model to identify patients at risk of NASH in real-world settings.

The NASHmap model is a non-invasive tool using 14 variables (features) collected in standard clinical practice to classify patients as probable nonalcoholic steatohepatitis (NASH) or non-NASH, and here we have explored its performance and prediction accuracy. The National Institute of Diabetes and Digestive Kidney Diseases (NIDDK) NAFLD Adult Database and the Optum Electronic Health Record (EHR) were used for patient data. Model performance metrics were calculated from correct and incorrect classifications for 281 NIDDK (biopsy-confirmed NASH and non-NASH, with and without stratification by type 2 diabetes status) and 1,016 Optum (biopsy-confirmed NASH) patients. NASHmap sensitivity in NIDDK is 81%, with a slightly higher sensitivity in T2DM patients (86%) than non-T2DM patients (77%). NIDDK patients misclassified by NASHmap had mean feature values distinct from correctly predicted patients, particularly for aspartate transaminase (AST; 75.88 U/L true positive vs 34.94 U/L false negative), and alanine transaminase (ALT; 104.09 U/L vs 47.99 U/L). Sensitivity was slightly lower in Optum at 72%. In an undiagnosed Optum cohort at risk for NASH (n = 2.9 M), NASHmap predicted 31% of patients as NASH. This predicted NASH group had AST and ALT mean levels above normal range of 0-35 U/L, and 87% had HbA1C levels > 5.7%. Overall, NASHmap demonstrates good sensitivity in predicting NASH status in both datasets, and NASH patients misclassified as non-NASH by NASHmap have clinical profiles closer to non-NASH patients.

Can we use existing guidance to support the development of robust real-world evidence for health technology assessment/payer decision-making?

Advances in the digitization of health systems and expedited regulatory approvals of innovative treatments have led to increased potential for the use of real-world data (RWD) to generate real-world evidence (RWE) to complement evidence from clinical trials. However, health technology assessment (HTA) bodies and payers have concerns about the ability to generate RWE of sufficient quality to be pivotal evidence of relative treatment effectiveness. Consequently, there is a growing need for HTA bodies and payers to develop guidance for the industry and other stakeholders about the use of RWD/RWE to support access, reimbursement, and pricing. We therefore sought to (i) understand barriers to the use of RWD/RWE by HTA bodies and payers; (ii) review potential solutions in the form of published guidance; and (iii) review findings with selected HTA/payer bodies. Four themes considered key to shaping the generation of robust RWE for HTA bodies and payers were identified as: (i) data (availability, governance, and quality); (ii) methodology (design and analytics); (iii) trust (transparency and reproducibility); and (iv) policy and partnerships. A range of guidance documents were found from trusted sources that could address these themes. These were discussed with HTA experts. This commentary summarizes the potential guidance solutions available to help resolve issues faced by HTA decision-makers in the adoption of RWD/RWE. It shows that there is alignment among stakeholders about the areas that need improvement in the development of RWE and that the key priority to move forward is better collaboration to make data usable for multiple purposes.

Impact of the COVID-19 pandemic on healthcare resource utilization across selected disease areas in the USA.

Aim: To analyze the impact of the COVID-19 pandemic on US healthcare resource utilization. Methods: Optum claims data were used to compare all-cause healthcare visits and healthcare spending for selected diseases between the prepandemic and pandemic periods. Telemedicine use was only assessed for the pandemic period owing to data availability. Results: During the first wave of the pandemic, all-cause healthcare visits across all selected disease areas displayed a rapid decline compared with the prepandemic period, followed by a period of recovery. A reduction in outpatient and home healthcare spending was observed, whereas inpatient and prescription spending increased. Conclusion: Changes in healthcare resource utilization trends were observed during the pandemic. The magnitude of these changes can inform subsequent studies that utilize COVID-19-era data.

Development of a novel machine learning model to predict presence of nonalcoholic steatohepatitis.

OBJECTIVE: To develop a computer model to predict patients with nonalcoholic steatohepatitis (NASH) using machine learning (ML). MATERIALS AND METHODS: This retrospective study utilized two databases: a) the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) nonalcoholic fatty liver disease (NAFLD) adult database (2004-2009), and b) the Optum® de-identified Electronic Health Record dataset (2007-2018), a real-world dataset representative of common electronic health records in the United States. We developed an ML model to predict NASH, using confirmed NASH and non-NASH based on liver histology results in the NIDDK dataset to train the model. RESULTS: Models were trained and tested on NIDDK NAFLD data (704 patients) and the best-performing models evaluated on Optum data (~3,000,000 patients). An eXtreme Gradient Boosting model (XGBoost) consisting of 14 features exhibited high performance as measured by area under the curve (0.82), sensitivity (81%), and precision (81%) in predicting NASH. Slightly reduced performance was observed with an abbreviated feature set of 5 variables (0.79, 80%, 80%, respectively). The full model demonstrated good performance (AUC 0.76) to predict NASH in Optum data. DISCUSSION: The proposed model, named NASHmap, is the first ML model developed with confirmed NASH and non-NASH cases as determined through liver biopsy and validated on a large, real-world patient dataset. Both the 14 and 5-feature versions exhibit high performance. CONCLUSION: The NASHmap model is a convenient and high performing tool that could be used to identify patients likely to have NASH in clinical settings, allowing better patient management and optimal allocation of clinical resources.

Modelling Decline in Cognition to Decline in Function in Alzheimer's Disease.

OBJECTIVES: The study aimed to evaluate and quantify the temporal link between cognitive and functional decline, and assess the impact of the apolipoprotein E4 (APOE-e4) genotype on Alzheimer's disease (AD) progression. METHODS: A nonlinear mixed-effects Emax model was developed using longitudinal data from 659 patients with dementia due to AD sourced from the Alzheimer's disease neuroimaging initiative (ADNI) database. A cognitive decline model was first built using a cognitive subscale of the AD assessment scale (delayed word recall) as the endpoint, followed by a functional decline model, using the functional assessment questionnaire (FAQ) as the endpoint. Individual and population cognitive decline from the first model drove a functional decline in the second model. The impact of the APOE-e4 genotype status on the dynamics of AD progression was evaluated using the model. RESULTS: Mixed-effects Emax models adequately quantified population average and individual disease trajectories. The model captured a higher initial cognitive impairment and final functional impairment in APOE-e4 carriers than non-carriers. The age at cognitive decline and diagnosis of dementia due to AD was significantly lower in APOE-e4 carriers than that of non-carriers. The average [standard deviation] time shift between cognitive and functional decline, i.e. the time span between half of the maximum cognitive decline and half of the maximum functional decline, was estimated as 1.5 [1.6] years. CONCLUSION: The present analysis quantifies the temporal link between a cognitive and functional decline in AD progression at the population and individual level, and provides information about the potential benefits of pre-clinical AD treatments on both cognition and function.

Development and validation of a Bayesian survival model for inclusion body myositis.

BACKGROUND: Associations between disease characteristics and payer-relevant outcomes can be difficult to establish for rare and progressive chronic diseases with sparse available data. We developed an exploratory bridging model to predict premature mortality from disease characteristics, and using inclusion body myositis (IBM) as a representative case study. METHODS: Candidate variables that may be potentially associated with premature mortality were identified by disease experts and from the IBM literature. Interdependency between candidate variables in IBM patients were assessed using existing patient-level data. A Bayesian survival model for the IBM population was developed with identified variables as predictors for premature mortality in the model. For model selection and external validation, model predictions were compared to published mortality data in IBM patient cohorts. After validation, the final model was used to simulate the increased risk of premature death in IBM patients. Baseline survival was based on age- and gender-specific survival curves for the general population in Western countries as reported by the World Health Organisation. RESULTS: Presence of dysphagia, aspiration pneumonia, falls, being wheelchair-bound and 6-min walking distance (6MWD in meters) were identified as candidate variables to be used as predictors for premature mortality based on inputs received from disease experts and literature. There was limited correlation between these functional performance measures, which were therefore treated as independent variables in the model. Based on the Bayesian survival model, among all candidate variables, presence of dysphagia and decrease in 6MWD [m] were associated with poorer survival with contributing hazard ratios (HR) 1.61 (95% credible interval [CrI]: 0.84-3.50) and 2.48 (95% CrI: 1.27-5.00) respectively. Excess mortality simulated in an IBM cohort vs. an age- and gender matched general-population cohort was 4.03 (95% prediction interval 1.37-10.61). CONCLUSIONS: For IBM patients, results suggest an increased risk of premature death compared with the general population of the same age and gender. In the absence of hard data, bridging modelling generated survival predictions by combining relevant information. The methodological principle would be applicable to the analysis of associations between disease characteristics and payer-relevant outcomes in progressive chronic and rare diseases. Studies with lifetime follow-up would be needed to confirm the modelling results.

Burden of illness and healthcare resource use in United States patients with sporadic inclusion body myositis.

INTRODUCTION: We analyzed the burden of illness of sporadic inclusion body myositis (sIBM) patients and the costs to the healthcare system. METHODS: A retrospective cohort analysis of 333 sIBM patients aged ≥ 50 years was performed using United States (U.S.) claims data. sIBM patients were matched in a 1:5 ratio to randomly selected individuals with ≥1 healthcare encounter within the year of index date. RESULTS: sIBM patients presented with higher rates of disease- and muscle-related conditions, such as myalgia, myositis, muscle weakness, dysphagia, pneumonia, and falls. Use of healthcare resources, including physical therapy, office visits, emergency room (ER) visits, and hospitalizations, was greater in sIBM patients. This was also reflected in significantly higher overall healthcare costs in the sIBM population driven mainly by more all-cause office visits, all-cause ER visits and hospitalizations. CONCLUSIONS: sIBM imposes a substantial burden on U.S. patients in terms of additional healthcare usage and associated costs. Muscle Nerve 56: 861-867, 2017.

A Systematic Review and Meta-Analysis of Prevalence Studies of Sporadic Inclusion Body Myositis.

BACKGROUND: Sporadic Inclusion Body Myositis (sIBM) is a rare and slowly progressive debilitating muscle disease with symptoms generally developing≥50 years of age. OBJECTIVE: To conduct a systematic review and meta-analysis of the prevalence of sIBM literature, including a methodological quality assessment of the selected papers. METHODS: A systematic search of Medline, Embase, Cochrane Database of Systematic Reviews and major Myositis and Neurological conferences was conducted. Articles reporting prevalence and published in English up to March 2017 were assessed for methodology quality using the Loney quality assessment, Downs & Black score, and the Methodological Evaluation of Observational Research checklists. Meta-analyses using random effects were completed on both general population and≥50 years prevalence estimates. RESULTS: 315 articles were retrieved and data were extracted from 10 relevant studies. One study was subsequently excluded due to methodological issues. The meta-prevalence estimate from 9 papers was 24.8/1,000,000 (95% CI: 20.0-29.6). The methodological quality results were consistent across assessment tools with four articles scoring 4 or 5 out of 8 in the Loney assessment. The meta-prevalence of these four articles was 45.6/ 1,000,000 (95% CI: 35.9-55.2). CONCLUSION: There was high variability in reported sIBM prevalence estimates and the quality of the studies conducted. Existing evidence suggests an increase of prevalence estimates over time, which may be explained by growing disease awareness, improvements in diagnostic criteria and study methodologies. Further high quality studies are needed to understand if prevalence varies across geographies or ethnicities.

Mortality and comorbidities in patients with multiple sclerosis compared with a population without multiple sclerosis: An observational study using the US Department of Defense administrative claims database.

BACKGROUND: Data are limited for mortality and comorbidities in patients with multiple sclerosis (MS). OBJECTIVES: Compare mortality rates and event rates for comorbidities in MS (n=15,684) and non-MS (n=78,420) cohorts from the US Department of Defense (DoD) database. METHODS: Comorbidities and all-cause mortality were assessed using the database. Causes of death (CoDs) were assessed through linkage with the National Death Index. Cohorts were compared using mortality (MRR) and event (ERR) rate ratios. RESULTS: All-cause mortality was 2.9-fold higher in the MS versus non-MS cohort (MRR, 95% confidence interval [CI]: 2.9, 2.7-3.2). Frequent CoDs in the MS versus non-MS cohort were infectious diseases (6.2, 4.2-9.4), diseases of the nervous (5.8, 3.7-9.0), respiratory (5.0, 3.9-6.4) and circulatory (2.1, 1.7-2.7) systems and suicide (2.6, 1.3-5.2). Comorbidities including sepsis (ERR, 95% CI: 5.7, 5.1-6.3), ischemic stroke (3.8, 3.5-4.2), attempted suicide (2.4, 1.3-4.5) and ulcerative colitis (2.0, 1.7-2.3), were higher in the MS versus non-MS cohort. The rate of cancers was also higher in the MS versus the non-MS cohort, including lymphoproliferative disorders (2.2, 1.9-2.6) and melanoma (1.7, 1.4-2.0). CONCLUSIONS: Rates of mortality and several comorbidities are higher in the MS versus non-MS cohort. Early recognition and management of comorbidities may reduce premature mortality and improve quality of life in patients with MS.

Network meta-analysis combining individual patient and aggregate data from a mixture of study designs with an application to pulmonary arterial hypertension.

BACKGROUND: Network meta-analysis (NMA) is a methodology for indirectly comparing, and strengthening direct comparisons of two or more treatments for the management of disease by combining evidence from multiple studies. It is sometimes not possible to perform treatment comparisons as evidence networks restricted to randomized controlled trials (RCTs) may be disconnected. We propose a Bayesian NMA model that allows to include single-arm, before-and-after, observational studies to complete these disconnected networks. We illustrate the method with an indirect comparison of treatments for pulmonary arterial hypertension (PAH). METHODS: Our method uses a random effects model for placebo improvements to include single-arm observational studies into a general NMA. Building on recent research for binary outcomes, we develop a covariate-adjusted continuous-outcome NMA model that combines individual patient data (IPD) and aggregate data from two-arm RCTs with the single-arm observational studies. We apply this model to a complex comparison of therapies for PAH combining IPD from a phase-III RCT of imatinib as add-on therapy for PAH and aggregate data from RCTs and single-arm observational studies, both identified by a systematic review. RESULTS: Through the inclusion of observational studies, our method allowed the comparison of imatinib as add-on therapy for PAH with other treatments. This comparison had not been previously possible due to the limited RCT evidence available. However, the credible intervals of our posterior estimates were wide so the overall results were inconclusive. The comparison should be treated as exploratory and should not be used to guide clinical practice. CONCLUSIONS: Our method for the inclusion of single-arm observational studies allows the performance of indirect comparisons that had previously not been possible due to incomplete networks composed solely of available RCTs. We also built on many recent innovations to enable researchers to use both aggregate data and IPD. This method could be used in similar situations where treatment comparisons have not been possible due to restrictions to RCT evidence and where a mixture of aggregate data and IPD are available.

New opportunities of real-world data from clinical routine settings in life-cycle management of drugs: example of an integrative approach in multiple sclerosis.

The assessment and demonstration of a positive benefit-risk balance of a drug is a life-long process and includes specific data from preclinical, clinical development and post-launch experience. However, new integrative approaches are needed to enrich evidence from clinical trials and sponsor-initiated observational studies with information from multiple additional sources, including registry information and other existing observational data and, more recently, health-related administrative claims and medical records databases. To illustrate the value of this approach, this paper exemplifies such a cross-package approach to the area of multiple sclerosis, exploring also possible analytic strategies when using these multiple sources of information.

Expanding the use of administrative claims databases in conducting clinical real-world evidence studies in multiple sclerosis.

OBJECTIVE: Administrative claims databases provide a wealth of data for assessing the effect of treatments in clinical practice. Our aim was to propose methodology for real-world studies in multiple sclerosis (MS) using these databases. RESEARCH DESIGN AND METHODS: In three large US administrative claims databases: MarketScan, PharMetrics Plus and Department of Defense (DoD), patients with MS were selected using an algorithm identified in the published literature and refined for accuracy. Algorithms for detecting newly diagnosed ('incident') MS cases were also refined and tested. Methodology based on resource and treatment use was developed to differentiate between relapses with and without hospitalization. RESULTS: When various patient selection criteria were applied to the MarketScan database, an algorithm requiring two MS diagnoses at least 30 days apart was identified as the preferred method of selecting patient cohorts. Attempts to detect incident MS cases were confounded by the limited continuous enrollment of patients in these databases. Relapse detection algorithms identified similar proportions of patients in the MarketScan and PharMetrics Plus databases experiencing relapses with (2% in both databases) and without (15-20%) hospitalization in the 1 year follow-up period, providing findings in the range of those in the published literature. LIMITATION: Additional validation of the algorithms proposed here would increase their credibility. CONCLUSIONS: The methods suggested in this study offer a good foundation for performing real-world research in MS using administrative claims databases, potentially allowing evidence from different studies to be compared and combined more systematically than in current research practice.

Indirect comparisons of ranibizumab and dexamethasone in macular oedema secondary to retinal vein occlusion.

BACKGROUND: Two treatments, ranibizumab and dexamethasone implant, for visual impairment due to macular oedema (ME) secondary to retinal vein occlusion (RVO) have recently been studied in clinical trials. There have been no head to head comparisons of the two treatments, and improvement measured as gain in Best Corrected Visual Acuity (BCVA) was reported using different outcomes thresholds between trials. To overcome these limitations, and inform an economic model, we developed a combination of a multinomial model and an indirect Bayesian comparison model for multinomial outcomes. METHODS: Outcomes of change from baseline in BCVA for dexamethasone compatible with those available for ranibizumab, reported by 4 randomised controlled trials, were estimated by fitting a multinomial distribution model to the probability of a patient achieving outcomes in a range of changes from baseline in BCVA (numbers of letters) at month 1. A Bayesian indirect comparison multinomial model was then developed to compare treatments in the Branch RVO (BRVO) and Central RVO (CRVO) populations. RESULTS: The multinomial model had excellent fit to the observed results. With the Bayesian indirect comparison, the probabilities of achieving ≥20 letters, with 95% credible intervals, at month 1 in patients with BRVO were 0.191 (0.130, 0.261) with ranibizumab and 0.093 (0.027, 0.213) with dexamethasone. In patients with CRVO, probabilities were 0.133 (0.082, 0.195) (ranibizumab) and 0.063 (0.016, 0.153) (dexamethasone). Probabilities of a gain in ≥10 letters in BRVO patients were 0.500 (0.365, 0.650) v 0.459 (0.248, 0.724) and in CRVO patients 0.459 (0.332, 0.602) v 0.498 (0.263, 0.791) for ranibizumab and dexamethasone treatments respectively. The comparisons also favoured ranibizumab at month 6 although changes to therapies after month 3 may have introduced bias. CONCLUSION: The newly developed combination of multinomial and indirect Bayesian comparison models indicated a trend for ranibizumab association with a greater percentage of ME patients achieving visual gains than dexamethasone at months 1 and 6 in a common clinical context, although results were not classically significant. The method was a useful tool for comparisons of probability distributions between clinical trials that reported events on different categorical scales and estimates can be used to inform economic models.

Persistence with and adherence to fingolimod compared with other disease-modifying therapies for the treatment of multiple sclerosis: a retrospective US claims database analysis.

OBJECTIVE: Achieving therapeutic goals in multiple sclerosis (MS) requires strict adherence to treatment schedules. This retrospective study analyzed persistence with, and adherence to, fingolimod compared with injectable/infusible disease-modifying therapies (DMTs) in patients with MS. METHODS: Patients in the PharMetrics Plus™ US administrative claims database with at least one prescription for, or administration of, fingolimod, glatiramer acetate (GA), interferon (IFN), or natalizumab (index DMT) between October 1, 2010 and September 30, 2011 were included. Patients were naïve to index DMT (no claim in the previous 360 days) and had an MS diagnosis code within 360 days of the first index DMT prescription. Outcomes were persistence, risk of discontinuing index DMT (evaluated by a Cox proportional hazards model), adherence (measured using the medication possession ratio [MPR] and proportion of days covered [PDC] in patients with at least two index DMT prescriptions), and the risk of being non-adherent (MPR <80% and PDC <80%, assessed using a logistic regression model). RESULTS: The study included 3750 patients (fingolimod, n = 889; GA, n = 1233; any IFN, n = 1341; natalizumab, n = 287). Discontinuation rates (fingolimod, 27.9%; GA, 39.5%; IFN, 43.7%; natalizumab, 39.5%; all p < 0.001) and risk of discontinuation were significantly higher (hazard ratios vs fingolimod [95% confidence interval]: GA, 1.75 [1.49-2.07]; IFN, 2.01 [1.71-2.37]; natalizumab, 1.53 [1.22-1.91]) for patients receiving other DMTs compared with fingolimod. The risk of being non-adherent was also lower for patients in the fingolimod cohort than the other treatment cohorts, irrespective of whether non-adherence was defined as MPR <80% (p < 0.05 for all) or PDC <80% (p < 0.05 for GA and IFN). LIMITATIONS: As with all studies assessing real-world treatment patterns it is unclear if medications were used as prescribed. CONCLUSIONS: In a real-world setting, persistence with, and adherence to, oral fingolimod was higher than for injectable and infusible DMTs.

Relapse rates in patients with multiple sclerosis switching from interferon to fingolimod or glatiramer acetate: a US claims database study.

BACKGROUND: Approximately one-third of patients with multiple sclerosis (MS) are unresponsive to, or intolerant of, interferon (IFN) therapy, prompting a switch to other disease-modifying therapies. Clinical outcomes of switching therapy are unknown. This retrospective study assessed differences in relapse rates among patients with MS switching from IFN to fingolimod or glatiramer acetate (GA) in a real-world setting. METHODS: US administrative claims data from the PharMetrics Plus™ database were used to identify patients with MS who switched from IFN to fingolimod or GA between October 1, 2010 and March 31, 2012. Patients were matched 1∶1 using propensity scores within strata (number of pre-index relapses) on demographic (e.g. age and gender) and disease (e.g. timing of pre-index relapse, comorbidities and symptoms) characteristics. A claims-based algorithm was used to identify relapses while patients were persistent with therapy over 360 days post-switch. Differences in both the probability of experiencing a relapse and the annualized relapse rate (ARR) while persistent with therapy were assessed. RESULTS: The matched sample population contained 264 patients (n = 132 in each cohort). Before switching, 33.3% of patients in both cohorts had experienced at least one relapse. During the post-index persistence period, the proportion of patients with at least one relapse was lower in the fingolimod cohort (12.9%) than in the GA cohort (25.0%), and ARRs were lower with fingolimod (0.19) than with GA (0.51). Patients treated with fingolimod had a 59% lower probability of relapse (odds ratio, 0.41; 95% confidence interval [CI], 0.21-0.80; p = 0.0091) and 62% fewer relapses per year (rate ratio, 0.38; 95% CI, 0.21-0.68; p = 0.0013) compared with those treated with GA. CONCLUSIONS: In a real-world setting, patients with MS who switched from IFNs to fingolimod were significantly less likely to experience relapses than those who switched to GA.

Comparative effectiveness of fingolimod versus interferons or glatiramer acetate for relapse rates in multiple sclerosis: a retrospective US claims database analysis.

OBJECTIVE: Disease-modifying therapies, such as fingolimod, interferon (IFN) and glatiramer acetate (GA), have differing effects on relapse rates in patients with multiple sclerosis (MS), but little is known about the real-world differences in relapse rates with these treatments. This retrospective study assessed relapse rates in patients with active MS initiating fingolimod, IFN or GA therapy in a real-world setting. METHODS: Using administrative claims data from the US PharMetrics Plus database, we identified previously treated and untreated patients with MS who initiated fingolimod, IFN or GA treatment between 1 October 2010 and 31 March 2011 and had experienced a relapse in the previous year. A claims-based algorithm was used to identify relapses over the persistence period in patients with 540 days of post-index continuous enrolment. A logistic regression model assessed the probability of having at least one relapse and a generalized linear model estimated differences in annualized relapse rates (ARRs). RESULTS: The study enrolled 525 patients (fingolimod, n = 128; combined IFN/GA cohort, n = 397) of the 31,041 initially identified. Similar findings for fingolimod and IFN/GA were observed for the unadjusted proportion of patients experiencing relapses (31.3% vs. 34.0%, respectively; p = 0.5653) and ARRs (0.50 vs. 0.55, respectively) while persistent to treatment. After adjusting for baseline differences, fingolimod was associated with a 52% reduction in the probability of having a relapse (odds ratio, 0.48; 95% confidence interval [CI], 0.28-0.84; p = 0.0097) and a 50% reduction in ARR (rate ratio, 0.50; 95% CI, 0.34-0.75; p = 0.0006) compared with IFN/GA. LIMITATIONS: Identification of relapses is based on the claims in the database rather than on a clinical assessment. CONCLUSIONS: In a real-world setting, fingolimod was shown to be associated with significantly lower relapse rates than IFN/GA in patients with MS who had a history of relapses.

Trends in primary care consultations for asthma in Switzerland, 1989-2002.

BACKGROUND: There is widespread debate about trends in the occurrence of asthma in industrialized countries. This study was conducted to investigate time trends in consultations for asthma in primary care in Switzerland. METHODS: Prospective observational study from 1989 to 2002 within the Swiss Sentinel Surveillance Network; a primary care surveillance system. We used time series analysis and non-parametric smoothing methods to investigate long-term and short-term trends in rates of asthma episodes per 1000 consultations. From 1994 to 2002 we compared rates of first episodes with all subsequent consultations for asthma. RESULTS: Overall consultation rates for asthma per 1000 primary care consultations increased from 1989 to 1994 then stabilized and have declined since 2000. Long-term trends showed a small decline in first consultations for asthma from an average of 0.78 (95% credibility intervals (CI) 0.74-0.81) in 1999 to 0.62 (95% CI 0.55-0.69) per 1000 consultations in 2002. Subsequent consultations for asthma have been declining since at least 1994, from an average of 1.5 (95% CI 1.40-1.61) per 1000 consultations in 1994 to 0.93 (95% CI 0.82-1.04) in 2002. In addition, the ratio of subsequent to first episodes of asthma fell in all age groups. CONCLUSIONS: In Switzerland, primary care consultations for asthma, subsequent to the initial diagnosis, have been declining since 1994. This is more likely to be owing to an increase in the use of home medication than to a shift in care to hospital settings. The incidence of diagnosed asthma might also be decreasing.

Natural variation in baseline data: when do we call a new sample 'resistant'?

Mortality of pear psylla to amitraz was studied by means of bioassays. Variation between samples, temporal variation within the season in one orchard and spatial variation between Swiss regions were considered. Variation between samples was large enough to produce different Probit functions and LC50 values. Temporal and spatial variations were too small to indicate resistance. Prediction intervals of the pooled functions using bootstrapping were calculated to determine if future samples come from a population with decreased sensitivity. Probabilistic criteria on the population level were proposed for resistance.