Incorporating uncertainty in Indigenous sea Country monitoring with Bayesian statistics: Towards more informed decision-making

Partnerships in marine monitoring combining Traditional Ecological Knowledge and western science are developing globally to improve our understanding of temporal changes in ecological communities that better inform coastal management practices. A fuller communication between scientists and Indigenous partners about the limitations of monitoring results to identify change is essential to the impact of monitoring datasets on decision-making. Here we present a 5-year co-developed case study from a fish monitoring partnership in northwest Australia showing how uncertainty estimated by Bayesian models can be incorporated into monitoring management indicators. Our simulation approach revealed there was high uncertainty in detecting immediate change over the following monitoring year when translated to health performance indicators. Incorporating credibility estimates into health assessments added substantial information to monitoring trends, provided a deeper understanding of monitoring limitations and highlighted the importance of carefully selecting the way we evaluate management performance indicators.

Does the registry speak your language? A case study of the Global Angelman Syndrome Registry

Global disease registries are critical to capturing common patient related information on rare illnesses, allowing patients and their families to provide information about their condition in a safe, accessible, and engaging manner that enables researchers to undertake critical research aimed at improving outcomes. Typically, English is the default language of choice for these global digital health platforms. Unfortunately, language barriers can significantly inhibit participation from non-English speaking participants. In addition, there is potential for compromises in data quality and completeness. In contrast, multinational commercial entities provide access to their websites in the local language of the country they are operating in, and often provide multiple options reflecting ethnic diversity. This paper presents a case study of how the Global Angelman Syndrome Registry (GASR) has used a novel approach to enable multiple language translations for its website. Using a "semi-automated language translation" approach, the GASR, which was originally launched in English in September 2016, is now available in several other languages. In 2020, the GASR adopted a novel approach using crowd-sourcing and machine translation tools leading to the availability of the GASR in Spanish, Traditional Chinese, Italian, and Hindi. As a result, enrolments increased by 124% percent for Spain, 67% percent for Latin America, 46% percent for Asia, 24% for Italy, and 43% for India. We describe our approach here, which we believe presents an opportunity for cost-effective and timely translations responsive to changes to the registry and helps build and maintain engagement with global disease communities.

Pattern-based assessment of the influence of rainfall characteristics on urban stormwater quality

Urbanisation increases pollutant generation within catchments and their transport to receiving waters. Changes to rainfall patterns, particularly in the age of climate change, make pollution mitigation a challenging task. Understanding how rainfall characteristics could influence the changes to stormwater pollutant runoff is important for designing effective mitigation strategies. This study employed a pattern-based assessment of relationships between rainfall characteristics and stormwater quality in urban catchments to develop this understanding. The research outcomes showed that rainfall events could be distinctly clustered based on intensity and duration, and each cluster of events would produce different stormwater quality responses. The high-intensity bursts occurring in the latter part of long-duration events were found to produce uniform and low concentrations of suspended solids. One the contrary, high intensity bursts occurring in the initial part of short-duration events triggered the first-flush effect, thus producing high concentrations of suspended solids. Furthermore, the first-flush effect was likely to present when the high intensity bursts occurred in the mid portion of rainfall events and produced variable concentrations of suspended solids. It was also found that the average rainfall intensity plays a key role in mobilising and transporting pollutants accumulated on urban surfaces.

Angiotensin receptor blockers for the treatment of covid-19: pragmatic, adaptive, multicentre, phase 3, randomised controlled trial

OBJECTIVE

To determine whether disrupting the renin angiotensin system with angiotensin receptor blockers will improve clinical outcomes in people with covid-19.

DESIGN

CLARITY was a pragmatic, adaptive, multicentre, phase 3, randomised controlled trial.

SETTING

17 hospital sites in India and Australia.

PARTICIPANTS

Participants were at least 18 years old, previously untreated with angiotensin receptor blockers, with a laboratory confirmed diagnosis of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection who had been admitted to hospital for management of covid-19.

INTERVENTION

Oral angiotensin receptor blockers (telmisartan in India) or placebo (1:1) for 28 days.

MAIN OUTCOME MEASURES

The primary endpoint was covid-19 disease severity using a modified World Health Organization Clinical Progression Scale (WHO scale) at day 14. Secondary outcomes were WHO scale scores at day 28, mortality, intensive care unit admission, and respiratory failure. Analyses were evaluated on an ordinal scale in the intention-to-treat population.

RESULTS

Between 3 May 2020 and 13 November 2021, 2930 people were screened for eligibility, with 393 randomly assigned to angiotensin receptor blockers (of which 388 (98.7%) to telmisartan 40 mg/day) and 394 to the control group. 787 participants were randomised: 778 (98.9%) from India and nine (1.1%) from Australia. The median WHO scale score at day 14 was 1 (interquartile range 1-1) in 384 participants assigned angiotensin receptor blockers and 1 (1-1) in 382 participants assigned placebo (adjusted odds ratio 1.51 (95% credible interval 1.02 to 2.23), probability of an odds ratio of >1 (Pr(OR>1)=0.98). WHO scale scores at day 28 showed little evidence of difference between groups (1.02 (0.55 to 1.87), Pr(OR>1)=0.53). The trial was stopped when a prespecified futility rule was met.

CONCLUSIONS

In patients admitted to hospital for covid-19, mostly with mild disease, not requiring oxygen, no evidence of benefit, based on disease severity score, was found for treatment with angiotensin receptor blockers, using predominantly 40 mg/day of telmisartan.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04394117.

Comparing pregabalin and gabapentin for persistent neuropathic pain: A protocol for a pilot N-of-1 trial series

Background

Evidence-based management of neuropathic pain is commonly ineffective due to the large variability in response between cases. Patients often have to trial several drugs before finding one that provides adequate relief, leading to increased costs and worsened outcomes. There is thus a need for tools to guide and streamline prescribing decisions in neuropathic pain. N-of-1 trials provide a potentially precise and economical method of selecting between multiple interventions in an individual patient, and merit a feasibility assessment for use in clinical pain practice.

Aims

We aim to evaluate the feasibility of N-of-1 trials to compare pregabalin and gabapentin for individual presentations of neuropathic pain.

Methods

This is a double-blinded multiple crossover study, with recruitment from existing patients at an outpatient pain clinic in New South Wales, Australia. Participants will undergo three 4-week treatment pairs, comprising 2 weeks of pregabalin (150–600 mg/day) and 2 weeks of gabapentin (900–3600 mg/day), in an individually randomised order. Intervention doses will be derived from participants’ existing treatment dose. Medications will be taken orally three times daily. The primary outcome will be pain intensity; measures will be self-reported daily in patient diaries. After completing all three cycles, participants and their physicians will be presented with the results of the trial to form an informed decision about their treatment.

Discussion

As a stable yet debilitating condition, neuropathic pain is especially amenable to an N-of-1 study design. A successful trial would represent a significant quality of life improvement for the patient, possibly extending over the course of their lifetime.

The impact of functional combined anteversion on hip range of motion: a new optimal zone to reduce risk of impingement in total hip arthroplasty

AIMS

Pelvic tilt (PT) can significantly change the functional orientation of the acetabular component and may differ markedly between patients undergoing total hip arthroplasty (THA). Patients with stiff spines who have little change in PT are considered at high risk for instability following THA. Femoral component position also contributes to the limits of impingement-free range of motion (ROM), but has been less studied. Little is known about the impact of combined anteversion on risk of impingement with changing pelvic position.

METHODS

We used a virtual hip ROM (vROM) tool to investigate whether there is an ideal functional combined anteversion for reduced risk of hip impingement. We collected PT information from functional lateral radiographs (standing and sitting) and a supine CT scan, which was then input into the vROM tool. We developed a novel vROM scoring system, considering both seated flexion and standing extension manoeuvres, to quantify whether hips had limited ROM and then correlated the vROM score to component position.

RESULTS

The vast majority of THA planned with standing combined anteversion between 30° to 50° and sitting combined anteversion between 45° to 65° had a vROM score > 99%, while the majority of vROM scores less than 99% were outside of this zone. The range of PT in supine, standing, and sitting positions varied widely between patients. Patients who had little change in PT from standing to sitting positions had decreased hip vROM.

CONCLUSION

It has been shown previously that an individual's unique spinopelvic alignment influences functional cup anteversion. But functional combined anteversion, which also considers stem position, should be used to identify an ideal THA position for impingement-free ROM. We found a functional combined anteversion zone for THA that may be used moving forward to place total hip components. Cite this article: Bone Jt Open 2021;2(10):834-841.

Protocol for the Controlled evaLuation of Angiotensin Receptor blockers for COVID-19 respIraTorY disease (CLARITY): a randomised controlled trial

BACKGROUND

SARS-CoV-2 binds to membrane-bound angiotensin-converting enzyme 2 (ACE2) which may result in downregulation of membrane-bound ACE2. ACE2 is a key regulator of the renin-angiotensin system (RAS) and is responsible for degrading angiotensin II and thereby counteracting its pro-inflammatory, pro-fibrotic effects mediated through the angiotensin II type 1 receptor (AT1R). As AT1R is directly blocked by angiotensin receptor blockers (ARBs), these agents may offer a safe, low-cost solution for reducing COVID-19 respiratory outcomes.

METHODS AND DISCUSSION

CLARITY is a pragmatic, adaptive, two-arm, multi-centre, comparative effectiveness phase III randomised controlled trial that examines whether ARBs reduce COVID-19 severity among high-risk patients. Recruiting in India and Australia, the trial will compare treatment with a maximum tolerated daily dose of an ARB to standard of care. Treatment allocation is blinded in India but open-label in Australia due to interruptions to placebo supply in the latter. The primary endpoint is a 7-point ordinal scale of clinical states, ranging from no limitation of activities (category 1) to death (category 7), assessed on day 14. Secondary outcomes include the 7-point scale assessed at day 28 and 28- and 90-day mortality. The design adapts the sample size based on accumulating data via frequent interim analyses and the use of predictive probability to determine whether the current sample size is sufficient or continuing accrual would be futile. The trial commenced recruitment on 18 August 2020.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT04394117 . Registered on 19 May 2020. Clinical Trial Registry of India: CTRI/2020/07/026831).

A Bayesian approach to model the trends and variability in urban stormwater quality associated with catchment and hydrologic parameters

Stormwater runoff pollution has become a key environmental issue in urban areas. Reliable estimation of stormwater pollutant discharge is important for implementing robust water quality management strategies. Even though significant attempts have been undertaken to develop water quality models, deterministic approaches have proven inappropriate as they do not address the variability in stormwater quality. Due to the random nature of rainfall characteristics and the differences in catchment characteristics, it is difficult to generate the runoff pollutographs to a desired level of certainty. Bayesian hierarchical modelling is an effective tool for developing complex models with a large number of sources of variability. A Bayesian model does not look for a single value of the model parameters, but rather determines a distribution of the model parameters from which all inference is drawn. This study introduces a Bayesian hierarchical linear regression model to describe a catchment specific runoff pollutograph incorporating the associated uncertainties in the model parameters. The model incorporates catchment and rainfall characteristics including the effective impervious area, time of concentration, rain duration, average rainfall intensity and the antecedent dry period as the contributors to random effects.

Source quantification and risk assessment as a foundation for risk management of metals in urban road deposited solids

Urban road build-up solids contain toxic metals posing potential risks to human health. Management of human health risks arising from these metals is critical in urban areas. This study collected solids build-up data from 16 study sites with various land use and traffic characteristics. Source quantification was conducted using PCA/APCS receptor model. It was found that soil and asphalt wear are the largest contributors (69.43%) to risk and mainly contribute Al, Cr, Mn, Fe, Ni, Zn and Pb to build-up solids. Brake wear is the second largest contributor accounting for 17.20% and contributes Cd and Cu. Tyre wear is the third major contributor (11.38%) and it primarily contributes Ni, Zn and Cr. Mathematical equations were fitted to estimate the risk against daily traffic volume and land use fractions, and the uncertainty analysis highlighted that risk assessment should account for the variability in metal concentrations rather than a point value of concentrations at a given time and space. Based on source quantification and risk assessment, an integrated risk management model was developed to manage human health risks from toxic metals in build-up solids. This risk model provides guidance for urban planning and land use development to mitigate risk arising from urban road deposited solids.

Thermal Impacts of Apicultural Practice and Products on the Honey Bee Colony

Hive design and apicultural processes have not been fundamentally changed since the design and commercialization of the Langstroth moveable frame hive in 1854. Colonies of Apis mellifera Linnaeus (Hymentoptera: Apidae) (the honey bee) maintain a brood nest temperature within the narrow range of 34.5-35.5°C, critical for brood development. Apis mellifera invest considerable energy to maintain hive homeostasis through behavioral modification of the hive environment. Human honey-harvesting processes and removal of the honey-filled comb (a source of thermal mass) have a detrimental impact on hive temperature that requires an increased investment of energy to rectify. This additional energy demand on the bees is a form of stress to the colony and diverts workers away from other essential tasks to that of environmental management. We investigated the thermal energy loss resulting from the removal and extraction of honey, the rate of thermal loss of an Australian standard Langstroth 10 frame hive, and the effect of honey and wax as a thermal mass in unoccupied bee hive. The results demonstrate that considerable energy expenditure would be required to rectify the hive thermal environment after honey harvesting or honeycomb frame addition. Honey provides thermal mass in the beehive, acting as a thermal buffer to external temperature change, which may mediate part of the thermal losses from the simplistic design of the Langstroth hive. Identification of these impacts in current apicultural practice and hive design allows for the improvement in the design of beehives and associated practices. These improvements may reduce stress to the bee colony, increasing colony efficiency for pollination and nectar foraging.

New conceptualisation of first flush phenomena in urban catchments

First flush is an important phenomenon commonlyused in stormwater treatment system design where only the highly concentrated initial part of the runoff hydrograph is subject to treatment. Despite the existing methods for estimating the first flush, a robust quantitative definition is difficult to find. This paper discusses a novel approach, where a new parameter is introduced to analyse the variability in the discharge of pollutants at different times throughout a runoff event and thereby enable the identification of first flush. It was found that due to variability in rainfall, the first flush runoff volume varies from event to event. Therefore, a static estimate of the first flush is not applicable for a runoff event. The Monte Carlo simulation undertaken strengthened the analysis by providing credible limits to the outcomes. Accordingly, an interval estimation was obtained in which the first flush runoff can vary, and it was found that most commonly, the first flush can exist through the initial 30%-50% of the runoff. Therefore, in order to treat the stormwater runoff with minimum risk of discharging high loads of pollutants to the receiving water environment, at least the initial 30% of the runoff should be subject to treatment. This understanding provides a fundamental basis for the design of robust stormwater treatment systems.

Catchment based estimation of pollutant event mean concentration (EMC) and implications for first flush assessment

The Event Mean Concentration (EMC) is considered as a key analytical parameter for assessing the quality of stormwater. The conventional estimation methods to determine EMC do not necessarily address the variability associated with the hydrologic characteristics. Accordingly, this study was conducted to identify the potential hydrologic variables that can influence EMC and thereby to create a mathematical model to determine EMC using the hydrologic variables while incorporating the catchment as an influential factor. This paper introduces an innovative approach to estimate EMC of a runoff event using a stepwise multiple linear regression model. The model incorporates hydrologic variables together with their two-way interaction terms. The catchment was included in the model as a dummy variable. This allows identifying the variability of EMC between catchments. Model can reasonably predict the EMC with an overall prediction error of 0.811. The regression coefficients of the model specify that, maximum rainfall intensity is the most influential variable having a coefficient of 1.008, followed by the average intensity with a coefficient -0.586. The interaction term of rainfall depth and the antecedent dry period indicates that for a relatively small rainfall event (<5 mm), an optimum value of antecedent dry period exists that maximises the EMC. Subsequently, EMC was employed to define the first flush runoff as an alternative approach to the conventional approaches for determining the first flush. The dynamic mean concentration (DMC_t), was introduced as a parameter for estimating the first flush using EMC. The maximum accumulated runoff volume such that, DMC_t≥EMC was defined as the first flush runoff. It was found that residential catchments generate more intense first flush compared to catchments with totally impervious surface areas and thereby a significant pollutant load is transported within a small initial fraction of the runoff.f.

The application of a new laminitis scoring method to model the rate and pattern of improvement from equine endocrinopathic laminitis in a clinical setting

Background

Endocrinopathic, or hyperinsulinaemia-associated laminitis (HAL) is a common and debilitating equine foot disease, and although no pharmacological treatments are registered, several are under development. To evaluate the effect of such treatments, an accurate and consistent method is needed to track the clinical signs of laminitis over time, and the natural history of the disease, in terms of a ‘normal’ pattern of improvement, needs to be understood. This study examined the improvement pattern in clinical cases of naturally-occurring HAL subjected to a range of best-practice interventions, using two different scoring methods. Eighty horses and ponies with suspected HAL were enrolled in a study conducted at 16 veterinary practices across Germany. The severity of laminitis was assessed by independent veterinarians using both the traditional Obel method and a modified Obel method developed by Meier and colleagues. Assessments were made on the day of diagnosis (d 0), then on days 4, 9, 14, 25 and 42 during the intervention period. Pain medications were withheld for 24 h prior to clinical examination in all cases.

Results

Time to marked improvement from laminitis varied between individuals, but was difficult to monitor accurately using the Obel method, with the median grade being 2/4 on days 0 and 4, then 0/4 from d 9 onwards. More subtle changes could be identified using the Meier method, however, and the median scores were seen to follow the form of an exponential decay model in most horses, improving from 8/12 on d 0, to 0/12 on d 25. Within this composite scoring method, considerable variation was observed in the rate of improvement of individual clinical signs, with the average time taken for each sign to reach a median score of 0 ranging from 4 days (foot lift and weight shifting) to 25 days (gait when turned in a circle) across all 80 horses.

Conclusions

The Meier method provides a reliable and consistent method for monitoring the clinical status of horses with HAL, and despite the variability, the pattern of improvement described here should provide a useful benchmark against which individual cases and new treatments can be assessed.

Are the current gRNA ranking prediction algorithms useful for genome editing in plants?

Introducing a new trait into a crop through conventional breeding commonly takes decades, but recently developed genome sequence modification technology has the potential to accelerate this process. One of these new breeding technologies relies on an RNA-directed DNA nuclease (CRISPR/Cas9) to cut the genomic DNA, in vivo, to facilitate the deletion or insertion of sequences. This sequence specific targeting is determined by guide RNAs (gRNAs). However, choosing an optimum gRNA sequence has its challenges. Almost all current gRNA design tools for use in plants are based on data from experiments in animals, although many allow the use of plant genomes to identify potential off-target sites. Here, we examine the predictive uniformity and performance of eight different online gRNA-site tools. Unfortunately, there was little consensus among the rankings by the different algorithms, nor a statistically significant correlation between rankings and in vivo effectiveness. This suggests that important factors affecting gRNA performance and/or target site accessibility, in plants, are yet to be elucidated and incorporated into gRNA-site prediction tools.

Using virtual reality and thermal imagery to improve statistical modelling of vulnerable and protected species

Biodiversity loss and sparse observational data mean that critical conservation decisions may be based on little to no information. Emerging technologies, such as airborne thermal imaging and virtual reality, may facilitate species monitoring and improve predictions of species distribution. Here we combined these two technologies to predict the distribution of koalas, specialized arboreal foliovores facing population declines in many parts of eastern Australia. For a study area in southeast Australia, we complemented ground-survey records with presence and absence observations from thermal-imagery obtained using Remotely-Piloted Aircraft Systems. These field observations were further complemented with information elicited from koala experts, who were immersed in 360-degree images of the study area. The experts were asked to state the probability of habitat suitability and koala presence at the sites they viewed and to assign each probability a confidence rating. We fit logistic regression models to the ground survey data and the ground plus thermal-imagery survey data and a Beta regression model to the expert elicitation data. We then combined parameter estimates from the expert-elicitation model with those from each of the survey models to predict koala presence and absence in the study area. The model that combined the ground, thermal-imagery and expert-elicitation data substantially reduced the uncertainty around parameter estimates and increased the accuracy of classifications (koala presence vs absence), relative to the model based on ground-survey data alone. Our findings suggest that data elicited from experts using virtual reality technology can be combined with data from other emerging technologies, such as airborne thermal-imagery, using traditional statistical models, to increase the information available for species distribution modelling and the conservation of vulnerable and protected species.

Taxonomy of influential factors for predicting pollutant first flush in urban stormwater runoff

Pollutant first flush in urban stormwater runoff is an important phenomenon influenced by a range of rainfall and catchment related variables. Even though numerous studies have been undertaken to mathematically define the first flush and the influential variables of first flush, limited research have been carried out to rank such variables in terms of their level of importance in generating first flush. Identifying the degree of importance of the variables is critical for accurate predictions of first flush occurrence and understanding the main drivers of first flush. This research study undertook a comprehensive analysis of the variables influencing the predictions of first flush occurrence and their relative importance. The study results are expected to contribute to more accurate predictions of first flush by affording greater importance to the highly ranked factors and their impacts. The study outcomes confirmed that total rainfall depth was the most important variable influencing the prediction of first flush events while the maximum intensity was the second. Rain duration, runoff depth, runoff peak and average intensity were the next four most important variables. Antecedent dry period and effective impervious area fraction had relatively low ranking while the time of concentration and the event mean concentration were found to be the least important variables. Furthermore, the study outcomes highlight that the use of a combination of variables and due consideration of their interactions can yield better results than considering their individual roles.

Creating a hierarchy of hazard control for urban stormwater management

Urban stormwater reuse is becoming increasingly prevalent to overcome the serious urban water scarcity being experienced around the world. Therefore, the adoption of reliable approaches to minimise the human health risk posed by pollutants commonly present in urban stormwater such as heavy metals and polycyclic aromatic hydrocarbons (PAHs) is critical for safe stormwater reuse. This study collected a total of 40 pollutant build-up samples and analysed the concentrations of nine heavy metals and 15 PAH species. Based on pollutant build-up data, pollutant concentrations in stormwater were estimated through modelling. Risk assessment was conducted using an existing model developed by previous studies. The study outcomes confirmed that simply evaluating the individual pollutant concentrations based on guideline threshold values cannot comprehensively estimate the overall human health risk posed by these pollutants. Accordingly, it is recommended that the assessment of the overall human health risk should be based on the pollutant mix present as provided by the models discussed in this paper. The study also demonstrated the practical application of a robust risk assessment model to derive the hierarchy of hazard control to provide a reliable underpinning to urban stormwater risk management. The outcomes suggest that decentralised hazard control methods such as the provision of custom designed Water Sensitive Urban Design (WSUD) measures can be implemented in priority areas with high risk from stormwater pollution based on the risk assessment undertaken. Distributed hazard control methods can be applied to reduce the generation of primary toxic pollutants, especially chromium (Cr) and heavy PAHs, through elimination and substitution measures. The percentage reduction in traffic volume required to mitigate the human health risk can be quantified through the risk models presented. The study outcomes will contribute to the development of efficient, targeted and reliable stormwater management strategies and to identify viable opportunities for stormwater reuse.

A "modified Obel" method for the severity scoring of (endocrinopathic) equine laminitis

BACKGROUND

Laminitis is a common equine disease characterized by foot pain, and is commonly diagnosed using a five-grade Obel system developed in 1948 using sepsis-related cases. However, endocrinopathic laminitis is now the most common form of the disease and clinical signs may be mild, or spread across two Obel grades. This paper describes a modified method which assigns scores to discreet clinical signs, providing a wider scale suitable for use in a research setting.

METHODS

The "modified Obel" method was developed using an iterative process. First, a prototype method was developed during the detailed observation of 37 ponies undergoing a laminitis induction experiment. The final method was refined and validated using video footage taken during the induction study and from a clinical trial of naturally occurring endocrinopathic laminitis cases. The Obel method was deconstructed and key laminitis signs were evaluated to develop a three-stage, five criteria method that employs a severity scale of 0-12. Veterinarians (n = 28) were recruited to watch and assess 15 video recordings of cases of varying severity, using the Obel and "modified Obel" methods. The inter-observer agreement (reproducibility) was determined using Kendall's coefficient of concordance (Kendall W) and Krippendorf's alpha reliability coefficient. A total of 14 veterinarians repeated the exercise 2-4 weeks after their original assessment, to determine intra-observer agreement (repeatability), assessed using a weighted kappa statistic (kw). Agreement between methods was calculated by converting all "modified Obel" scores to Obel grades and calculating the mean and distribution of the differences.

RESULTS

The "modified Obel" and Obel methods showed excellent and similar inter-observer agreement based on the Kendall W value (0.87, P < 0.001 vs. 0.85, P < 0.001) and Krippendorf's alpha (95% CI) value (0.83 [0.53-0.90] vs. 0.77 [0.55-0.85]). Based on the kw value, the "modified Obel" method also had substantial repeatability, although slightly less than the Obel method, (0.80 vs. 0.91). Excellent agreement between the methods was found, with the mean difference (95% CI), comparing the Obel grade, with the "modified Obel" score converted to an Obel grade, being -0.12 (-0.19 to -0.06) grades. The Obel and converted "modified Obel" grades were identical 62% of the time (259/420) and a difference of one grade (higher or lower) occurred in 35% of cases (148/420).

CONCLUSION

Both methods show excellent agreement, reproducibility and repeatability when used to diagnose endocrinopathic laminitis. The "modified Obel" method is a three-step examination process for severity-scoring of endocrinopathic laminitis, initially proposed for use within a research setting. When using the modified method a diagnosis of laminitis also requires clinical acumen. The allocation of scores for specific clinical signs should be particularly useful in research trials monitoring laminitis recovery.

An efficient algorithm for estimating brain covariance networks

Often derived from partial correlations or many pairwise analyses, covariance networks represent the inter-relationships among regions and can reveal important topological structures in brain measures from healthy and pathological subjects. However both approaches are not consistent network estimators and are sensitive to the value of the tuning parameters. Here, we propose a consistent covariance network estimator by maximising the network likelihood (MNL) which is robust to the tuning parameter. We validate the consistency of our algorithm theoretically and via a simulation study, and contrast these results against two well-known approaches: the graphical LASSO (gLASSO) and Pearson pairwise correlations (PPC) over a range of tuning parameters. The MNL algorithm had a specificity equal to and greater than 0.94 for all sample sizes in the simulation study, and the sensitivity was shown to increase as the sample size increased. The gLASSO and PPC demonstrated a specificity-sensitivity trade-off over a range of values of tuning parameters highlighting the discrepancy in the results for misspecified values. Application of the MNL algorithm to the case study data showed a loss of connections between healthy and impaired groups, and improved ability to identify between lobe connectivity in contrast to gLASSO networks. In this work, we propose the MNL algorithm as an effective approach to find covariance brain networks, which can inform the organisational features in brain-wide analyses, particularly for large sample sizes.

Use of surrogate indicators for the evaluation of potential health risks due to poor urban water quality: A Bayesian Network approach

Urban water pollution poses risks of waterborne infectious diseases. Therefore, in order to improve urban liveability, effective pollution mitigation strategies are required underpinned by predictions generated using water quality models. However, the lack of reliability in current modelling practices detrimentally impacts planning and management decision making. This research study adopted a novel approach in the form of Bayesian Networks to model urban water quality to better investigate the factors that influence risks to human health. The application of Bayesian Networks was found to enhance the integration of quantitative and qualitative spatially distributed data for analysing the influence of environmental and anthropogenic factors using three surrogate indicators of human health risk, namely, turbidity, total nitrogen and fats/oils. Expert knowledge was found to be of critical importance in assessing the interdependent relationships between health risk indicators and influential factors. The spatial variability maps of health risk indicators developed enabled the initial identification of high risk areas in which flooding was found to be the most significant influential factor in relation to human health risk. Surprisingly, population density was found to be less significant in influencing health risk indicators. These high risk areas in turn can be subjected to more in-depth investigations instead of the entire region, saving time and resources. It was evident that decision making in relation to the design of pollution mitigation strategies needs to account for the impact of landscape characteristics on water quality, which can be related to risk to human health.

A series of N-of-1 trials to assess the therapeutic interchangeability of two enalapril formulations in the treatment of hypertension in Addis Ababa, Ethiopia: study protocol for a randomized controlled trial

BACKGROUND

Hypertension is one of the leading causes of morbidity and mortality in Ethiopia. Treatment usually involves lifelong medication use. Enalapril is a common drug for the treatment of hypertension in Ethiopia. However, the drug is expensive and, therefore, there is limited capacity for people to afford the treatment. Locally produced Enalapril is a cost-effective solution to treat the disease. However, as local medicines regulation does not include bioequivalence tests on locally produced drugs, physicians and patients need assurance about the effectiveness and safety of local generics. Evidence on therapeutic equivalence is needed on these untested local drugs.

METHODS

This is a hospital-based, randomized, partially blinded, three-cycle crossover trial in single patients, comparing a locally produced version of enalapril with enalapril imported from Europe. Patients involved in this trial are not blinded, as there is no local facility to produce relatively small numbers of placebos or encapsulated drugs. To ensure blinding of study investigators and data analysts, study medications are prepared by an independent pharmacy unit using opaque medication packaging. The importance of maintaining blinding is also part of patient pre-trial education. Each N-of-1 trial will consist of three successive 14-day treatment pairs, each pair comprising 7 days of 5-20 mg local and 7 days of 5-20 mg imported enalapril taken once daily in the morning. The primary outcome will be the average difference in systolic blood pressure as measured by home blood pressure measurements.

DISCUSSION

The number of locally produced products, such as enalapril, being approved without proof of bioequivalence is dramatically increasing. By bridging the information gap on bioequivalence, the trial will give rigorous evidence on therapeutic equivalence of locally produced enalapril in the treatment of hypertension. If there is no difference, the hypothesized result, then patients can take the local medicine with confidence. This trial will also will determine whether aggregated N-of-1 studies are feasible to evaluate untested generic drugs in resource-limited countries where bioequivalence testing centers are unavailable.

TRIAL REGISTRATION NUMBER

Australian and New Zealand Clinical Trial Registry, ID: ACTRN12616001088437p . Registered on 12 August 2016.

Catchment scale assessment of risk posed by traffic generated heavy metals and polycyclic aromatic hydrocarbons

Heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs) are among the most toxic chemical pollutants present in urban stormwater. Consequently, urban stormwater reuse is constrained due to the human health risk posed by these pollutants. This study developed a scientifically robust approach to assess the risk to human health posed by HMs and PAHs in urban stormwater in order to enhance its reuse. Accordingly, an innovative methodology was created consisting of four stages: quantification of traffic and land use parameters; estimation of pollutant concentrations for model development; risk assessment, and risk map presentation. This methodology will contribute to catchment scale assessment of the risk associated with urban stormwater and for risk mitigation. The risk map developed provides a simple and efficient approach to identify the critical areas within a large catchment. The study also found that heavy molecular weight PAHs (PAHs with 5-6 benzene rings) in urban stormwater pose higher risk to human health compared to light molecular PAHs (PAHs with 2-4 benzene rings). These outcomes will facilitate the development of practical approaches for applying appropriate mitigation measures for the safe management of urban stormwater pollution and for the identification of enhanced reuse opportunities.

Application of landscape epidemiology to assess potential public health risk due to poor sanitation

Clear identification of areas vulnerable to waterborne diseases is essential for protecting community health. This is particularly important in developing countries where unsafe disposal of domestic wastewater and limited potable water supply pose potential public health risks. However, data paucity can be a compounding issue. Under these circumstances, landscape epidemiology can be applied as a resource efficient approach for mapping potential disease risk areas associated with poor sanitation. However, in order to realise the full potential offered by this approach, an in-depth understanding of the impact of different classes of an explanatory variable on a target disease and the validity of hotspot analysis using limited datasets is needed. Accordingly, this research study focused on typhoid and diarrhoea incidence with respect to different classes of elevation, flood inundation, land use, soil permeability, population density and rainfall as explanatory variables. An integrated methodology consisting of hot spot analysis and Poisson regression was employed to map potential disease risk areas. The study findings confirmed the significant differences in the influence exerted by the various classes of an explanatory variable in relation to a target disease. The results also confirmed the feasibility of the hotspot analysis for identifying areas vulnerable to the target diseases using a limited dataset. The study outcomes are expected to contribute to creating an in-depth understanding of the relationship between disease prevalence and associated landscape factors for the delineation of disease risk zones in the context of data paucity.

Assessment and management of human health risk from toxic metals and polycyclic aromatic hydrocarbons in urban stormwater arising from anthropogenic activities and traffic congestion

Toxic metals (TMs) and polycyclic aromatic hydrocarbons (PAHs) in urban stormwater pose risk to human health, thereby constraining its reuse potential. Based on the hypothesis that stormwater quality is primarily influenced by anthropogenic activities and traffic congestion, the primary focus of the research study was to analyse the impacts on human health risk from TMs and PAHs in urban stormwater and thereby develop a quantitative risk assessment model. The study found that anthropogenic activities and traffic congestion exert influence on the risk posed by TMs and PAHs in stormwater from commercial and residential areas. Motor vehicle related businesses (FVS) and traffic congestion (TC) were identified as two parameters which need to be included as independent variables to improve the model. Based on the study outcomes, approaches for mitigating the risk associated with TMs and PAHs in urban stormwater are discussed. Additionally, a roadmap is presented for the assessment and management of the risk arising from these pollutants. The study outcomes are expected to contribute to reducing the human health risk associated urban stormwater pollution and thereby enhance its reuse potential.

Quantitative assessment of human health risk posed by polycyclic aromatic hydrocarbons in urban road dust

Among the numerous pollutants present in urban road dust, polycyclic aromatic hydrocarbons (PAHs) are among the most toxic chemical pollutants and can pose cancer risk to humans. The primary aim of the study was to develop a quantitative model to assess the cancer risk from PAHs in urban road dust based on traffic and land use factors and thereby to characterise the risk posed by PAHs in fine (<150μm) and coarse (>150μm) particles. The risk posed by PAHs was quantified as incremental lifetime cancer risk (ILCR), which was modelled as a function of traffic volume and percentages of different urban land uses. The study outcomes highlighted the fact that cancer risk from PAHs in urban road dust is primarily influenced by PAHs associated with fine solids. Heavy PAHs with 5 to 6 benzene rings, especially dibenzo[a,h]anthracene (D[a]A) and benzo[a]pyrene (B[a]P) in the mixture contribute most to the risk. The quantitative model developed based on traffic and land use factors will contribute to informed decision making in relation to the management of risk posed by PAHs in urban road dust.

Association between post-sternotomy tracheostomy and deep sternal wound infection: a retrospective analysis

BACKGROUND

Tracheostomy has traditionally been used as a means of facilitated mechanical ventilation in patients requiring respiratory management following cardiac surgery. However in the clinical setting, the advantages of tracheostomy has been questioned by concerns surrounding evidence of its association with increased risk of deep sternal wound infections (DSWI). The present study sought to evaluate retrospectively our experience with post-sternotomy tracheostomy among cardiac surgery patients and association with DSWI.

METHODS

Between July 2003 and June 2013, 11,795 patients underwent open cardiac surgery via sternotomy in our department. Among these, 225 underwent post-sternotomy tracheostomy. Data were obtained by reviewing and analyzing the Cardiac Surgical and Cardiac Intensive Care Unit (ICU) databases for adult cardiac patients.

RESULTS

Out of the 11,795 sternotomy patients analyzed, 225 (1.9%) underwent tracheostomy. The overall mortality rate for post-sternotomy tracheostomy patients was 21.3%. DSWI developed in 23 patients (10.2%) of the tracheostomy group. Seven of these 23 patients had DSWI after insertion of tracheostomy. DSWI was significantly higher in tracheostomy versus no-tracheostomy patients (10.2% vs. 0.48%; P<0.001). DSWI was also associated with higher mortality rates compared to non-DSWI patients (11.4% vs. 2.3%; P<0.001).

CONCLUSIONS

The present study demonstrated that tracheostomy was an independent risk factor for post-sternotomy DSWI, and that DSWI was a predictor of mortality. For tracheostomy patients, coronary artery bypass grafting (CABG) procedures and longer durations of tracheostomy were strong predictors of DSWI. Across all sternotomy patients, tracheostomy, diabetes, urgency status and blood transfusions were significant risk factors for DSWI. As such, the decision for tracheostomy post-sternotomy should be carefully considered on a case by case basis.

Resistive Index Variability in Anterior Cerebral Artery Measurements During Daily Transcranial Duplex Sonography: A Predictor of Cerebrovascular Complications in Infants Undergoing Extracorporeal Membrane Oxygenation?

OBJECTIVES

The purpose of this study was to determine the value of resistive index (RI) variability in predicting cerebrovascular complications during extracorporeal membrane oxygenation (ECMO).

METHODS

This retrospective study included 36 infants treated by ECMO. The RI was measured on daily transfontanellar duplex sonography, obtained first without fontanel compression and then after gentle compression with the transducer. The age at ECMO cannulation, sex, gestational age at birth, method of delivery, indication, and type and duration of ECMO were recorded.

RESULTS

There was a statistically significant difference in RI variability in infants who developed cerebrovascular complications as opposed to those who did not (P = .002). Resistive index variability of 10% or greater on any day was associated with an increased risk for cerebrovascular complications (P = .0482; χ² = 3.9). Variability in the first 5 days was significantly higher than on following days (P < .0001). The age at ECMO cannulation showed a significant difference, with mean ± SD values of 1.1 ± 0.9 days in the complications group and 2.7 ± 2.2 days in the no-complications group (P = .043).

CONCLUSIONS

Resistive index variability of 10% or greater on any day had a statistically significant risk of cerebrovascular complication development. Extracorporeal membrane oxygenation cannulation at younger than 3 days conferred an increased risk of cerebrovascular complications.

Assessing uncertainty in stormwater quality modelling

Designing effective stormwater pollution mitigation strategies is a challenge in urban stormwater management. This is primarily due to the limited reliability of catchment scale stormwater quality modelling tools. As such, assessing the uncertainty associated with the information generated by stormwater quality models is important for informed decision making. Quantitative assessment of build-up and wash-off process uncertainty, which arises from the variability associated with these processes, is a major concern as typical uncertainty assessment approaches do not adequately account for process uncertainty. The research study undertaken found that the variability of build-up and wash-off processes for different particle size ranges leads to processes uncertainty. After variability and resulting process uncertainties are accurately characterised, they can be incorporated into catchment stormwater quality predictions. Accounting of process uncertainty influences the uncertainty limits associated with predicted stormwater quality. The impact of build-up process uncertainty on stormwater quality predictions is greater than that of wash-off process uncertainty. Accordingly, decision making should facilitate the designing of mitigation strategies which specifically addresses variations in load and composition of pollutants accumulated during dry weather periods. Moreover, the study outcomes found that the influence of process uncertainty is different for stormwater quality predictions corresponding to storm events with different intensity, duration and runoff volume generated. These storm events were also found to be significantly different in terms of the Runoff-Catchment Area ratio. As such, the selection of storm events in the context of designing stormwater pollution mitigation strategies needs to take into consideration not only the storm event characteristics, but also the influence of process uncertainty on stormwater quality predictions.

Spatial response surface modelling in the presence of data paucity for the evaluation of potential human health risk due to the contamination of potable water resources

Potential human health risk from waterborne diseases arising from unsatisfactory performance of on-site wastewater treatment systems is driven by landscape factors such as topography, soil characteristics, depth to water table, drainage characteristics and the presence of surface water bodies. These factors are present as random variables which are spatially distributed across a region. A methodological framework is presented that can be applied to model and evaluate the influence of various factors on waterborne disease potential. This framework is informed by spatial data and expert knowledge. For prediction at unsampled sites, interpolation methods were used to derive a spatially smoothed surface of disease potential which takes into account the uncertainty due to spatial variation at any pre-determined level of significance. This surface was constructed by accounting for the influence of multiple variables which appear to contribute to disease potential. The framework developed in this work strengthens the understanding of the characteristics of disease potential and provides predictions of this potential across a region. The study outcomes presented constitutes an innovative approach to environmental monitoring and management in the face of data paucity.

Understanding the uncertainty associated with particle-bound pollutant build-up and wash-off: A critical review

Accurate prediction of stormwater quality is essential for developing effective pollution mitigation strategies. The use of models incorporating simplified mathematical replications of pollutant processes is the common practice for determining stormwater quality. However, an inherent process uncertainty arises due to the intrinsic variability associated with pollutant processes, which has neither been comprehensively understood, nor well accounted for in uncertainty assessment of stormwater quality modelling. This review provides the context for defining and quantifying the uncertainty associated with pollutant build-up and wash-off on urban impervious surfaces based on the hypothesis that particle size is predominant in influencing process variability. Critical analysis of published research literature brings scientific evidence together in order to establish the fact that particle size changes with time, and different sized particles exhibit distinct behaviour during build-up and wash-off, resulting in process variability. Analysis of the different adsorption behaviour of particles confirmed that the variations in pollutant load and composition are influenced by particle size. Particle behaviour and variations in pollutant load and composition are related due to the strong affinity of pollutants such as heavy metals and hydrocarbons for specific particle size ranges. As such, the temporal variation in particle size is identified as the key to establishing a basis for assessing build-up and wash-off process uncertainty. Therefore, accounting for pollutant build-up and wash-off process variability, which is influenced by particle size, would facilitate the assessment of the uncertainty associated with modelling outcomes. Furthermore, the review identified fundamental knowledge gaps where further research is needed in relation to: (1) the aggregation of particles suspended in the atmosphere during build-up; (2) particle re-suspension during wash-off; (3) pollutant re-adsorption by different particle size fractions; and (4) development of evidence-based techniques for assessing uncertainty; and (5) methods for translating the knowledge acquired from the investigation of process mechanisms at small scale into catchment scale for stormwater quality modelling.

Human health risk assessment of heavy metals in urban stormwater

Toxic chemical pollutants such as heavy metals (HMs) are commonly present in urban stormwater. These pollutants can pose a significant risk to human health and hence a significant barrier for urban stormwater reuse. The primary aim of this study was to develop an approach for quantitatively assessing the risk to human health due to the presence of HMs in stormwater. This approach will lead to informed decision making in relation to risk management of urban stormwater reuse, enabling efficient implementation of appropriate treatment strategies. In this study, risks to human health from heavy metals were assessed as hazard index (HI) and quantified as a function of traffic and land use related parameters. Traffic and land use are the primary factors influencing heavy metal loads in the urban environment. The risks posed by heavy metals associated with total solids and fine solids (<150μm) were considered to represent the maximum and minimum risk levels, respectively. The study outcomes confirmed that Cr, Mn and Pb pose the highest risks, although these elements are generally present in low concentrations. The study also found that even though the presence of a single heavy metal does not pose a significant risk, the presence of multiple heavy metals could be detrimental to human health. These findings suggest that stormwater guidelines should consider the combined risk from multiple heavy metals rather than the threshold concentration of an individual species. Furthermore, it was found that risk to human health from heavy metals in stormwater is significantly influenced by traffic volume and the risk associated with stormwater from industrial areas is generally higher than that from commercial and residential areas.

Assessing uncertainty in pollutant build-up and wash-off processes

Assessing build-up and wash-off process uncertainty is important for accurate interpretation of model outcomes to facilitate informed decision making for developing effective stormwater pollution mitigation strategies. Uncertainty inherent to pollutant build-up and wash-off processes influences the variations in pollutant loads entrained in stormwater runoff from urban catchments. However, build-up and wash-off predictions from stormwater quality models do not adequately represent such variations due to poor characterisation of the variability of these processes in mathematical models. The changes to the mathematical form of current models with the incorporation of process variability, facilitates accounting for process uncertainty without significantly affecting the model prediction performance. Moreover, the investigation of uncertainty propagation from build-up to wash-off confirmed that uncertainty in build-up process significantly influences wash-off process uncertainty. Specifically, the behaviour of particles <150 μm during build-up primarily influences uncertainty propagation, resulting in appreciable variations in the pollutant load and composition during a wash-off event.

Influence of uncertainty inherent to heavy metal build-up and wash-off on stormwater quality

Uncertainty inherent to heavy metal build-up and wash-off stems from process variability. This results in inaccurate interpretation of stormwater quality model predictions. The research study has characterised the variability in heavy metal build-up and wash-off processes based on the temporal variations in particle-bound heavy metals commonly found on urban roads. The study outcomes found that the distribution of Al, Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb were consistent over particle size fractions <150 μm and >150 μm, with most metals concentrated in the particle size fraction <150 μm. When build-up and wash-off are considered as independent processes, the temporal variations in these processes in relation to the heavy metals load are consistent with variations in the particulate load. However, the temporal variations in the load in build-up and wash-off of heavy metals and particulates are not consistent for consecutive build-up and wash-off events that occur on a continuous timeline. These inconsistencies are attributed to interactions between heavy metals and particulates <150 μm and >150 μm, which are influenced by particle characteristics such as organic matter content. The behavioural variability of particles determines the variations in the heavy metals load entrained in stormwater runoff. Accordingly, the variability in build-up and wash-off of particle-bound pollutants needs to be characterised in the description of pollutant attachment to particulates in stormwater quality modelling. This will ensure the accounting of process uncertainty, and thereby enhancing the interpretation of the outcomes derived from modelling studies.

Incorporating process variability into stormwater quality modelling

Process variability in pollutant build-up and wash-off generates inherent uncertainty that affects the outcomes of stormwater quality models. Poor characterisation of process variability constrains the accurate accounting of the uncertainty associated with pollutant processes. This acts as a significant limitation to effective decision making in relation to stormwater pollution mitigation. The study undertaken developed three theoretical scenarios based on research findings that variations in particle size fractions <150 μm and >150 μm during pollutant build-up and wash-off primarily determine the variability associated with these processes. These scenarios, which combine pollutant build-up and wash-off processes that takes place on a continuous timeline, are able to explain process variability under different field conditions. Given the variability characteristics of a specific build-up or wash-off event, the theoretical scenarios help to infer the variability characteristics of the associated pollutant process that follows. Mathematical formulation of the theoretical scenarios enables the incorporation of variability characteristics of pollutant build-up and wash-off processes in stormwater quality models. The research study outcomes will contribute to the quantitative assessment of uncertainty as an integral part of the interpretation of stormwater quality modelling outcomes.

Influence of pollutant build-up on variability in wash-off from urban road surfaces

Variability in the pollutant wash-off process is a concept which needs to be understood in-depth in order to better assess the outcomes of stormwater quality models, and thereby strengthen stormwater pollution mitigation strategies. Current knowledge about the wash-off process does not extend to a clear understanding of the influence of the initially available pollutant build-up on the variability of the pollutant wash-off load and composition. Consequently, pollutant wash-off process variability is poorly characterised in stormwater quality models, which can result in inaccurate stormwater quality predictions. Mathematical simulation of particulate wash-off from three urban road surfaces confirmed that the wash-off load of particle size fractions < 150 μm and > 150 μm after a storm event vary with the build-up of the respective particle size fractions available at the beginning of the storm event. Furthermore, pollutant load and composition associated with the initially available build-up of < 150 μm particles predominantly influence the variability in washed-off pollutant load and composition. The influence of the build-up of pollutants associated with > 150 μm particles on wash-off process variability is significant only for relatively shorter duration storm events.

Process variability of pollutant build-up on urban road surfaces

Knowledge of the pollutant build-up process is a key requirement for developing stormwater pollution mitigation strategies. In this context, process variability is a concept which needs to be understood in-depth. Analysis of particulate build-up on three road surfaces in an urban catchment confirmed that particles <150 μm and >150 μm have characteristically different build-up patterns, and these patterns are consistent over different field conditions. Three theoretical build-up patterns were developed based on the size-fractionated particulate build-up patterns, and these patterns explain the variability in particle behavior and the variation in particle-bound pollutant load and composition over the antecedent dry period. Behavioral variability of particles <150 μm was found to exert the most significant influence on the build-up process variability. As characterization of process variability is particularly important in stormwater quality modeling, it is recommended that the influence of behavioral variability of particles <150 μm on pollutant build-up should be specifically addressed. This would eliminate model deficiencies in the replication of the build-up process and facilitate the accounting of the inherent process uncertainty, and thereby enhance the water quality predictions.

Predictive models for water sources with high susceptibility for bromine-containing disinfection by-product formation: implications for water treatment

This study examines a matrix of synthetic water samples designed to include conditions that favour brominated disinfection by-product (Br-DBP) formation, in order to provide predictive models suitable for high Br-DBP forming waters such as salinity-impacted waters. Br-DBPs are known to be more toxic than their chlorinated analogues, in general, and their formation may be favoured by routine water treatment practices such as coagulation/flocculation under specific conditions; therefore, circumstances surrounding their formation must be understood. The chosen factors were bromide concentration, mineral alkalinity, bromide to dissolved organic carbon (Br/DOC) ratio and Suwannee River natural organic matter concentration. The relationships between these parameters and DBP formation were evaluated by response surface modelling of data generated using a face-centred central composite experimental design. Predictive models for ten brominated and/or chlorinated DBPs are presented, as well as models for total trihalomethanes (tTHMs) and total dihaloacetonitriles (tDHANs), and bromide substitution factors for the THMs and DHANs classes. The relationships described revealed that increasing alkalinity and increasing Br/DOC ratio were associated with increasing bromination of THMs and DHANs, suggesting that DOC lowering treatment methods that do not also remove bromide such as enhanced coagulation may create optimal conditions for Br-DBP formation in waters in which bromide is present.

The choice of spatial scales and spatial smoothness priors for various spatial patterns

Given the drawbacks for using geo-political areas in mapping outcomes unrelated to geo-politics, a compromise is to aggregate and analyse data at the grid level. This has the advantage of allowing spatial smoothing and modelling at a biologically or physically relevant scale. This article addresses two consequent issues: the choice of the spatial smoothness prior and the scale of the grid. Firstly, we describe several spatial smoothness priors applicable for grid data and discuss the contexts in which these priors can be employed based on different aims. Two such aims are considered, i.e., to identify regions with clustering and to model spatial dependence in the data. Secondly, the choice of the grid size is shown to depend largely on the spatial patterns. We present a guide on the selection of spatial scales and smoothness priors for various point patterns based on the two aims for spatial smoothing.

The impact of spatial scales and spatial smoothing on the outcome of bayesian spatial model

Discretization of a geographical region is quite common in spatial analysis. There have been few studies into the impact of different geographical scales on the outcome of spatial models for different spatial patterns. This study aims to investigate the impact of spatial scales and spatial smoothing on the outcomes of modelling spatial point-based data. Given a spatial point-based dataset (such as occurrence of a disease), we study the geographical variation of residual disease risk using regular grid cells. The individual disease risk is modelled using a logistic model with the inclusion of spatially unstructured and/or spatially structured random effects. Three spatial smoothness priors for the spatially structured component are employed in modelling, namely an intrinsic Gaussian Markov random field, a second-order random walk on a lattice, and a Gaussian field with Matérn correlation function. We investigate how changes in grid cell size affect model outcomes under different spatial structures and different smoothness priors for the spatial component. A realistic example (the Humberside data) is analyzed and a simulation study is described. Bayesian computation is carried out using an integrated nested Laplace approximation. The results suggest that the performance and predictive capacity of the spatial models improve as the grid cell size decreases for certain spatial structures. It also appears that different spatial smoothness priors should be applied for different patterns of point data.

A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies

Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models.