Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: an example from a vertigo phase III study with longitudinal count data as primary endpoint

Swimming behaviour of Atlantic salmon kelts migrating past a hydropower plant dam: Effects of hydraulics and dam operations

Hydropower plants commonly impede the downstream migration of Atlantic salmon (Salmo salar) kelts. Thus, understanding the effects of hydraulic conditions on kelt behaviour and passage performance at dams is crucial for developing effective mitigation measures. In this study, we investigated the influence of hydraulic conditions on kelt passage performance and swimming behaviour at a Norwegian hydropower plant. We combined biological data from 48 kelts collected via acoustic telemetry with hydraulic data modelled using computational fluid dynamics. We assessed kelt passage performance using metrics such as time-to-pass, total number of detections, and total number of detections per day. Additionally, we analysed swimming depths and speeds in relation to the hydraulic conditions created by different dam operating conditions. We found that the dam operation schedule impacted the kelts' ability to find a route past the dam. Though kelts could have passed the dam throughout the study period via a submerged pipe at the dam (which had seemingly sufficient discharge for the kelts to find), 98 % of the kelts instead waited for a spill gate to open partway through the study period. The swimming depth analysis indicated diel variation, with kelts swimming nearer to the water surface during the night. We found that swimming speed increased with increasing kelt body length, particularly under high turbulence kinetic energy and during the day. Furthermore, kelts swam faster as water velocity increased, but slowed down again as turbulence intensity increased. Our findings reveal the effects of hydraulic conditions and dam operations on the migration behaviour of Atlantic salmon kelts. This provides valuable insights for developing strategies to optimise dam operations and improve fish passage performance, including the need to spill enough water to increase passage success and will contribute to sustainable management of Atlantic salmon populations in regulated rivers.

Modelling spatiotemporal patterns of visceral leishmaniasis incidence in two endemic states in India using environment, bioclimatic and demographic data, 2013-2022

BACKGROUND

As of 2021, the National Kala-azar Elimination Programme (NKAEP) in India has achieved visceral leishmaniasis (VL) elimination (<1 case / 10,000 population/year per block) in 625 of the 633 endemic blocks (subdistricts) in four states. The programme needs to sustain this achievement and target interventions in the remaining blocks to achieve the WHO 2030 target of VL elimination as a public health problem. An effective tool to analyse programme data and predict/ forecast the spatial and temporal trends of VL incidence, elimination threshold, and risk of resurgence will be of use to the programme management at this juncture.

METHODOLOGY/PRINCIPAL FINDINGS

We employed spatiotemporal models incorporating environment, climatic and demographic factors as covariates to describe monthly VL cases for 8-years (2013-2020) in 491 and 27 endemic and non-endemic blocks of Bihar and Jharkhand states. We fitted 37 models of spatial, temporal, and spatiotemporal interaction random effects with covariates to monthly VL cases for 6-years (2013-2018, training data) using Bayesian inference via Integrated Nested Laplace Approximation (INLA) approach. The best-fitting model was selected based on deviance information criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) and was validated with monthly cases for 2019-2020 (test data). The model could describe observed spatial and temporal patterns of VL incidence in the two states having widely differing incidence trajectories, with >93% and 99% coverage probability (proportion of observations falling inside 95% Bayesian credible interval for the predicted number of VL cases per month) during the training and testing periods. PIT (probability integral transform) histograms confirmed consistency between prediction and observation for the test period. Forecasting for 2021-2023 showed that the annual VL incidence is likely to exceed elimination threshold in 16-18 blocks in 4 districts of Jharkhand and 33-38 blocks in 10 districts of Bihar. The risk of VL in non-endemic neighbouring blocks of both Bihar and Jharkhand are less than 0.5 during the training and test periods, and for 2021-2023, the probability that the risk greater than 1 is negligible (P<0.1). Fitted model showed that VL occurrence was positively associated with mean temperature, minimum temperature, enhanced vegetation index, precipitation, and isothermality, and negatively with maximum temperature, land surface temperature, soil moisture and population density.

CONCLUSIONS/SIGNIFICANCE

The spatiotemporal model incorporating environmental, bioclimatic, and demographic factors demonstrated that the KAMIS database of the national programmme can be used for block level predictions of long-term spatial and temporal trends in VL incidence and risk of outbreak / resurgence in endemic and non-endemic settings. The database integrated with the modelling framework and a dashboard facility can facilitate such analysis and predictions. This could aid the programme to monitor progress of VL elimination at least one-year ahead, assess risk of resurgence or outbreak in post-elimination settings, and implement timely and targeted interventions or preventive measures so that the NKAEP meet the target of achieving elimination by 2030.

Two-step spatiotemporal anomaly detection corrected for lag reporting time with application to real-time dengue surveillance in Thailand

BACKGROUND

Dengue infection ranges from asymptomatic to severe and life-threatening, with no specific treatment available. Vector control is crucial for interrupting its transmission cycle. Accurate estimation of outbreak timing and location is essential for efficient resource allocation. Timely and reliable notification systems are necessary to monitor dengue incidence, including spatial and temporal distributions, to detect outbreaks promptly and implement effective control measures.

METHODS

We proposed an integrated two-step methodology for real-time spatiotemporal cluster detection, accounting for reporting delays. In the first step, we employed space-time nowcasting modeling to compensate for lags in the reporting system. Subsequently, anomaly detection methods were applied to assess adverse risks. To illustrate the effectiveness of these detection methods, we conducted a case study using weekly dengue surveillance data from Thailand.

RESULTS

The developed methodology demonstrated robust surveillance effectiveness. By combining space-time nowcasting modeling and anomaly detection, we achieved enhanced detection capabilities, accounting for reporting delays and identifying clusters of elevated risk in real-time. The case study in Thailand showcased the practical application of our methodology, enabling timely initiation of disease control activities.

CONCLUSION

Our integrated two-step methodology provides a valuable approach for real-time spatiotemporal cluster detection in dengue surveillance. By addressing reporting delays and incorporating anomaly detection, it complements existing surveillance systems and forecasting efforts. Implementing this methodology can facilitate the timely initiation of disease control activities, contributing to more effective prevention and control strategies for dengue in Thailand and potentially other regions facing similar challenges.

Bayesian evaluation of the accuracy of a thoracic auscultation scoring system in dairy calves with bronchopneumonia using a standard lung sound nomenclature

BACKGROUND

Although thoracic auscultation (AUSC) in calves is quick and easy to perform, the definition of lung sounds is highly variable and leads to poor to moderate accuracy in diagnosing bronchopneumonia (BP).

HYPOTHESIS/OBJECTIVES

Evaluate the diagnostic accuracy of an AUSC scoring system based on a standard lung sound nomenclature at different cut-off values, accounting for the absence of a gold standard test for BP diagnosis.

ANIMALS

Three hundred thirty-one calves.

METHODS

We considered the following pathological lung sounds: increased breath sounds (score 1), wheezes and crackles (score 2), increased bronchial sounds (score 3), and pleural friction rubs (score 4). Thoracic auscultation was categorized as AUSC1 (positive calves for scores ≥1), AUSC2 (positive calves for scores ≥2), and AUSC3 (positive calves for scores ≥3). The accuracy of AUSC categorizations was determined using 3 imperfect diagnostic tests with a Bayesian latent class model and sensitivity analysis (informative vs weakly informative vs noninformative priors and with vs without covariance between ultrasound and clinical scoring).

RESULTS

Based on the priors used, the sensitivity (95% Bayesian confidence interval [BCI]) of AUSC1 ranged from 0.89 (0.80-0.97) to 0.95 (0.86-0.99), with a specificity (95% BCI) of 0.54 (0.45-0.71) to 0.60 (0.47-0.94). Removing increased breath sounds from the categorizations resulted in increased specificity (ranging between 0.97 [0.93-0.99] and 0.98 [0.94-0.99] for AUSC3) at the cost of decreased sensitivity (0.66 [0.54-0.78] to 0.81 [0.65-0.97]).

CONCLUSIONS AND CLINICAL IMPORTANCE

A standardized definition of lung sounds improved AUSC accuracy for BP diagnosis in calves.

Diagnostic accuracy of an immunomagnetic separation-PCR assay to detect pathogenic Leptospira spp. in urine from dairy cattle, using a Bayesian latent class model

Leptospirosis is a zoonotic disease that has spread worldwide and causes significant economic losses in the dairy industry. The causal agents of this infectious disease are members of the genus Leptospira, known as pathogenic Leptospira spp. Specific clinical signs of the infection are difficult to detect. Therefore, the disease is normally under-diagnosed, mostly due to the lack of a cost-effective technique for diagnosing animals with a low bacterial load in their urine. The aim of this study was to assess the diagnostic accuracy of a qPCR coupled with a previous Immunomagnetic separation (IMS) step (IMS-qPCR) against a qPCR without using IMS, using a Bayesian latent class model (2 tests, 3 populations) to determine the leptospirosis infectious status in naturally infected dairy cattle. The results revealed that IMS qPCR had a sensitivity (Se) of 95.7% (95% Probability Interval (PI) = 85.0; 99.4%) and a specificity (Sp) of 98% (95% PI = 96.1; 99.4%), indicating that it is more sensitive than conventional qPCR (Se = 69.7% (95% PI = 59.2; 79.0%); median difference = 25.2% (Monte Carlo Error = 10.2%); and the Sp = 98.8% (95% PI = 97.6; 99.5%), median difference = 0.8% (Monte Carlo Error = 2.1%). Therefore, results shows that IMS-qPCR is a more useful diagnostic tool in terms of accuracy for detecting infectious animals with pathogenic Leptospira in their urine.

Spatio-temporal variations in neonatal mortality rates in Ghana: An application of hierarchical Bayesian methods

Ghana might not meet the SDGs target 3.2 of reducing neonatal mortality to 12 deaths per 1000 live births by 2030. Identifying core determinants of neonatal deaths provide policy guidelines and a framework aimed at mitigating the effect of neonatal deaths. Most studies have identified household and individual-level factors that contribute to neonatal mortality. However, there are relatively few studies that have rigorously assessed geospatial covariates and spatiotemporal variations of neonatal deaths in Ghana. This study focuses on modeling and mapping of spatiotemporal variations in the risk of neonatal mortality in Ghana using Bayesian Hierarchical Spatiotemporal models. This study used data from the Ghana Demographic and Health Surveys (GDHS) conducted in 1993, 1998, 2003, 2008, and 2014. We employed Bayesian Hierarchical Spatiotemporal regression models to identify geospatial correlates and spatiotemporal variations in the risk of neonatal mortality. The estimated weighted crude neonatal mortality rate for the period under consideration was 33.2 neonatal deaths per 1000 live births. The results obtained from Moran's I statistics and CAR model showed the existence of spatial clustering of neonatal mortality. The map of smooth relative risk identified Ashanti region as the most consistent hot-spot region for the entire period under consideration. Small body size babies contributed significantly to an increased risk of neonatal mortality at the regional level [Posterior Mean: 0.003 (95% CrI: 0.00,0.01)]. Hot spot GDHS clusters exhibiting high risk of neonatal mortality were identified by LISA cluster map. Rural residents, small body size babies, parity, and aridity contributed significantly to a higher risk of neonatal mortality at the GDHS cluster level. The findings provide actionable and insightful information to prioritize and distribute the scarce health resources equitably to tackle the menace of neonatal mortality. The regions and GDHS clusters with excess risk of neonatal mortality should receive optimum attention and interventions to reduce the neonatal mortality rate.

Diagnostic accuracy of Wisconsin and California scoring systems to detect bovine respiratory disease in preweaning dairy calves under subtropical environmental conditions

Bovine respiratory disease (BRD) is a multifactorial disease which causes short- and long-term negative effects. Early detection is crucial for a prompt response to therapy, as well as to decrease mortality risk. Clinical scoring systems have been developed mostly in North America for screening calves at risk or suspected of having BRD, and these tools have also been applied in subtropical and tropical countries. However, it has been unknown whether these scoring systems had the same accuracy in tropical environmental conditions. Therefore, this study evaluated the accuracy of 4 different field techniques, as well as serum haptoglobin (HAP), to diagnose BRD in Holstein dairy calves in subtropical conditions. The tests used to diagnose BRD were thoracic ultrasound (TUS; positive if consolidation depth ≥1 cm), thoracic auscultation (AUSC; positive if crackles, wheezes, or silent areas were present), Wisconsin score (WISC; ≥2 categories with scores of ≥2), and California score (CALIF; positive if total score ≥5). Also, HAP was measured and classified as positive if ≥15 mg/dL. Heifers between 30 d of age and weaning (n = 482), residing on 17 commercial dairies in São Paulo state, were enrolled in this study. Bayesian latent class models were used with informative priors to evaluate the accuracy of TUS, AUSC, and HAP, and noninformative priors for the accuracy of WISC and CALIF. The percentage of calves positive for each test on each farm ranged from 0 to 56% for WISC, 11-51% for CALIF, 0-72% for TUS, 0-32% for AUSC, and 0-100% for HAP. The sensitivity (Se; 95% credible interval) and specificity (Sp) for WISC were 77.9% (64.8-90.2) and 81.9% (76.3-88.2). For CALIF, the Se was 67.1% (53.6-80.1) and Sp 79.1% (73.9-84.6). For TUS Se was 59.8% (46.5-73.1) and Sp was 84.8% (80.0-89.5), and for AUSC, Se was 58.8% (41.3-79.8) and Sp was 98.6% (95.7-99.9). The Se and Sp of HAP was 67.6% (55.3-78.8) and 46.7% (41.4-52.2), respectively. The performance of the scoring systems was similar to, or better than, the performance found in North American studies, despite the fact that calves were in a tropical environment.

A flexible Bayesian nonconfounding spatial model for analysis of dispersed count data

In employing spatial regression models for counts, we usually meet two issues. First, the possible inherent collinearity between covariates and the spatial effect could lead to misleading inferences. Second, real count data usually reveal over- or under-dispersion where the classical Poisson model is not appropriate to use. We propose a flexible Bayesian hierarchical modeling approach by joining nonconfounding spatial methodology and a newly reconsidered dispersed count modeling from the renewal theory to control the issues. Specifically, we extend the methodology for analyzing spatial count data based on the gamma distribution assumption for waiting times. The model can be formulated as a latent Gaussian model, and consequently, we can carry out the fast computation by using the integrated nested Laplace approximation method. We examine different popular approaches for handling spatial confounding and compare their performances in the presence of dispersion. Two real applications from a crime study against women in India as well as stomach cancer incidences in Slovenia motivate the suggested methods. We also perform a simulation study to understand the proposed approach's merits better. Supplementary Materials for this article are available.

Performance of bovine genital campylobacteriosis diagnostic tests in bulls from Uruguay: a Bayesian latent class model approach

The sensitivity (Se) and specificity (Sp) of three diagnostic tests for the detection of Campylobacter fetus venerealis (Cfv) using field samples were estimated using a Bayesian latent class model (BLCM), accounting for the absence of a gold standard. The tests included in this study were direct immunofluorescence antibody test (IFAT), polymerase chain reaction (PCR), and real-time PCR (RT-PCR). Twelve farms from two different populations were selected and bull prepuce samples were collected. The IFAT was performed according to the OIE Manual. The conventional PCR was performed as multiplex, targeting the gene nahE for C. fetus species identification and insertion element ISCfe1 for Cfv identification. The RT-PCR was performed as uniplex: one targeting the gene nahE for C. fetus and the other targeting the insertion ISCfe1 (ISC2) for Cfv. Results from the BLCM showed a median Se of 11.7% (Bayesian credibility interval (BCI): 1.93-29.79%), 53.7% (BCI: 23.1-95.0%), and 36.1% (BCI: 14.5-71.7%) for IFAT, PCR, and RT-PCR respectively. The Sp were 94.5% (BCI: 90.1-97.9%), 97.0% (BCI: 92.9-99.3%), and 98.4% (BCI: 95.3-99.7%) for IFAT, PCR, and RT-PCR respectively. The correlation between PCR and RT-PCR was positive and low in samples from both sampled population (0.63% vs 8.47%). These results suggest that diagnostic sensitivity of the studied tests is lower using field samples than using pure Cfv strains.

Comparison between thoracic ultrasonography and thoracic radiography for the detection of thoracic lesions in dairy calves using a two-stage Bayesian method

Infectious bronchopneumonia is a lower respiratory tract disease with major economic consequences in dairy calves. Thoracic radiography (TR) and thoracic ultrasonography (TUS) are two imaging diagnostic procedures available in bovine medicine for identifying thoracic lesions. However, no study has investigated whether one of these tests is superior to the other or if they provide comparable results for the detection of thoracic lesions in calves. The objective of this study was therefore to estimate and to compare the performances of TUS and TR for the detection of thoracic lesions in dairy calves. A prospective cross-sectional study was performed in a hospital setting. A total of 50 calves (≥7 days old; ≤100 kg; standing; pCO2 ≥ 53 mmHg; any reason of presentation) were enrolled. Every calf underwent TUS and TR. Only calves with thoracic lesions on TUS and/or TR were controlled by thoracic computed tomography (CT) (the gold standard). Calves without lesions were not controlled by CT. A two-stage Bayesian framework was used. The sensitivities (Se) and specificities (Sp) of both tests individually and used in series or parallel were estimated. The Se and Sp of TUS were 0.81 (95 % BCI (Bayesian Credible Interval): 0.65; 0.92) and 0.90 (95 % BCI: 0.81; 0.96), respectively. The Se and Sp of TR were 0.86 (95 % BCI: 0.62; 0.99) and 0.89 (95 % BCI: 0.67; 0.99), respectively. This study did not reveal any differences between both tests. Using TUS and TR in series was more specific than using both tests in parallel. The performances of TUS alone were not different from the performances of both tests in series or in parallel. In conclusion, TUS and TR were equivalent in detecting thoracic lesions in this study. Using TUS alone allowed an accurate detection of thoracic lesions in dairy calves. Further studies enrolling a larger sample (> 400 calves) and allowing adequate power to be achieved would be necessary to confirm these results.

Time series non-Gaussian Bayesian bivariate model applied to data on HMPV and RSV: a case of Dadaab in Kenya

Background

Human metapneumovirus (HMPV) have similar symptoms to those caused by the respiratory syncytial virus (RSV). The modes of transmission and dynamics of time series data still remain poorly understood. Climatic factors have long been suspected to be implicated in impacting on the number of cases for these epidemics. Currently, only a few models satisfactorily capture the dynamics of time series data of these two viruses. Our objective was to assess the presence of influence of high incidences between the viruses and to ascertain whether higher incidences of one virus are influenced by the other.

Methods

In this study, we used a negative binomial model to investigate the relationship between RSV and HMPV while adjusting for climatic factors. We specifically aimed at establishing the heterogeneity in the autoregressive effect to account for the influence between these viruses.

Results

In this study, our findings showed that RSV incidence contributed to the severity of HMPV incidence. This was achieved through comparison of 12 models with different structures, including those with and without interaction between climatic factors. The models with climatic factors out-performed those without.

Conclusions

The study has improved our understanding of the dynamics of RSV and HMPV in relation to climatic cofactors thereby setting a platform to devise better intervention measures to combat the epidemics. We conclude that preventing and controlling RSV infection subsequently reduces the incidence of HMPV.

Validation of a clinical scoring system for bovine respiratory disease complex diagnosis in preweaned dairy calves using a Bayesian framework

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A prediction rule using thoracic ultrasound as an imperfect test for BRD diagnosis was modeled.

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Selection of the optimal threshold for case definition was proposed accounting for misclassification cost term analysis.

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Probability of active infection of the lower respiratory tract was determined for all 64 clinical signs combinations.

Bovine respiratory disease complex is a major cause of illness in dairy calves. The diagnosis of active infection of the lower respiratory tract is challenging on daily basis in the absence of accurate clinical signs. Clinical scoring systems such as the Californian scoring system, are appealing but were developed without considering the imperfection of reference standard tests used for case definition. This study used a Bayesian latent class model to update Californian prediction rules. The results of clinical examination and ultrasound findings of 608 preweaned dairy calves were used. A model accounting for imperfect accuracy of thoracic ultrasound examination was used to obtain updated weights for the clinical signs included in the Californian scoring system. There were 20 points (95% Bayesian credible intervals: 11–29) for abnormal breathing pattern, 16 points (95% BCI: 4–29) for ear drop/head tilt, 16 points (95% BCI: 9–25) for cough, 10 points (95% BCI: 3–18) for the presence of nasal discharge, 7 points (95% BCI: −1 to 8) for rectal temperature ≥39.2 °C, and −1 points (95% BCI: −9 to 8) for the presence of ocular discharge. The optimal cut-offs were determined using the misclassification cost-term term (MCT) approach with different possible scenarios of expected prevalence and different plausible ratio of false negative costs/false positive costs. The predicted probabilities of active infection of the lower respiratory tract were also obtained using posterior densities of the main logistic regression model. Depending on the context, cut-off varying from 9 to 16 can minimized the MCT. The optimal cut-off decreased when expected prevalence of disease and false negative/false positive ratio increased.

Evaluation of spatio-temporal Bayesian models for the spread of infectious diseases in oil palm

In the field of epidemiology, studies are often focused on mapping diseases in relation to time and space. Hierarchical modeling is a common flexible and effective tool for modeling problems related to disease spread. In the context of oil palm plantations infected by the fungal pathogen Ganoderma boninense, we propose and compare two spatio-temporal hierarchical Bayesian models addressing the lack of information on propagation modes and transmission vectors. We investigate two alternative process models to study the unobserved mechanism driving the infection process. The models help gain insight into the spatio-temporal dynamic of the infection by identifying a genetic component in the disease spread and by highlighting a spatial component acting at the end of the experiment. In this challenging context, we propose models that provide assumptions on the unobserved mechanism driving the infection process while making short-term predictions using ready-to-use software.

Efficacy and safety of betahistine treatment in patients with Meniere's disease: primary results of a long term, multicentre, double blind, randomised, placebo controlled, dose defining trial (BEMED trial)

STUDY QUESTION

What is the long term efficacy of betahistine dihydrochloride on the incidence of vertigo attacks in patients with Meniere's disease, compared with placebo?

METHODS

The BEMED trial is a multicentre, double blind, randomised, placebo controlled, three arm, parallel group, phase III, dose defining superiority trial conducted in 14 German tertiary referral centres (for neurology or ear, nose, and throat). Adults aged 21-80 years (mean age 56 years) with definite unilateral or bilateral Meniere's disease were recruited from March 2008 to November 2012. Participants received placebo (n=74), low dose betahistine (2 × 24 mg daily, (n=73)), or high dose betahistine (3 × 48 mg daily, (n=74)) over nine months. The primary outcome was the number of attacks per 30 days, based on patients' diaries during a three month assessment period at months seven to nine. An internet based randomisation schedule performed a concealed 1:1:1 allocation, stratified by study site. Secondary outcomes included the duration and severity of attacks, change in quality of life scores, and several observer-reported parameters to assess changes in audiological and vestibular function.

STUDY ANSWER AND LIMITATIONS

Incidence of attacks related to Meniere's disease did not differ between the three treatment groups (P=0.759). Compared with placebo, attack rate ratios were 1.036 (95% confidence interval 0.942 to 1.140) and 1.012 (0.919 to 1.114) for low dose and high dose betahistine, respectively. The overall monthly attack rate fell significantly by the factor 0.758 (0.705 to 0.816; P<0.001). The population based, mean monthly incidence averaged over the assessment period was 2.722 (1.304 to 6.309), 3.204 (1.345 to 7.929), and 3.258 (1.685 to 7.266) for the placebo, low dose betahistine, and high dose betahistine groups, respectively. Results were consistent for all secondary outcomes. Treatment was well tolerated with no unexpected safety findings. Without a control group of patients who did not receive any intervention to follow the natural course of the disease, the placebo effect could not be accurately assessed and differentiated from spontaneous remission and fluctuation of symptoms.

WHAT THIS STUDY ADDS

Current evidence is limited as to whether betahistine prevents vertigo attacks caused by Meniere's disease, compared with placebo. The trial provides information on symptom relief on placebo intervention which is relevant for the design of future studies on potential disease modifying treatments in patients with Meniere's disease.

FUNDING, COMPETING INTERESTS, DATA SHARING

Support from the German Federal Ministry of Education and Research (BMBF support code 01KG0708). Potential competing interests have been reported in full at the end of the paper on thebmj.com. Data are available from the corresponding author (Michael.Strupp@med.uni-muenchen.de) or biostatistician (mansmann@ibe.med.uni-muenchen.de). Study registration EudraCT no 2005-000752-32; ISRCTN no ISRCTN44359668.

Effects and tolerability of betahistine in patients with vestibular vertigo: results from the Romanian contingent of the OSVaLD study

Background and methods

An efficacy population of 245 patients with vertigo of peripheral vestibular origin was recruited in Romania as part of a 3-month multinational, post-marketing surveillance study of open-label betahistine 48 mg/day (OSVaLD). Endpoints were changes in the Dizziness Handicap Index (primary endpoint), Medical Outcome Study Short-Form 36 (SF-36v2^®), and the Hospital Anxiety and Depression Scale.

Results

During treatment, the total Dizziness Handicap Index score improved by 41 points (on a 100-point scale). Statistically significant improvements of 12–14 points were recorded in all three domains of the Dizziness Handicap Index scale (P<0.0001). Betahistine therapy was also accompanied by progressive improvements in mean Hospital Anxiety and Depression anxiety and depression scores (P<0.0001) and significant improvements in both the physical and mental component summary of the SF-36v2 (P<0.0001). Betahistine was well tolerated, with only one suspected adverse drug reaction recorded in the Romanian safety population (n=259).

Conclusion

Betahistine 48 mg/day was associated with improvements in multiple measures of health-related quality of life and had a good tolerability profile in these Romanian patients with recurrent peripheral vestibular vertigo.