Probability Of Disease Occurrence Research Articles

Short Communication on an Economic Burden of Infectious Diseases to Indian Shrimp Farming

Infectious diseases especially, Enterocytozoon Hepatopenaei (EHP), White Spot Syndrome Virus (WSSV) infections and are the major constraints in the development of shrimp farming in India. A questionnaire-based survey covering an area of 7259 ha in 23 coastal districts based on multistage stratified sampling (n=909) was conducted to estimate the Probability of Disease Occurrence (PDO) and the economic loss due to diseases was estimated. Loss of production (t ha-1 crop-1) due to WSSV and EHP was 2.58 ± 0.32 and 1.80 ± 0.24 respectively. Additionally, the total employment loss due to diseases was estimated at 1.65 M man-days worth US$ 7.07 M. The results of the study revealed 49% as the overall probability of infectious disease occurrence in the country leading to an annual loss of 0.14 M ton of harvest worth US$ 1.02 B. Prioritizing the research and implementation of specific policies would help in reducing the losses due to infectious diseases in Indian shrimp farming.

Economic loss due to diseases in Indian shrimp farming with special reference to Enterocytozoon hepatopenaei (EHP) and white spot syndrome virus (WSSV)

Economic loss due to Enterocytozoon hepatopenaei (EHP), white spot syndrome virus (WSSV) infections and other diseases to Penaeus vannamei farming in India was estimated through a questionnaire-based survey of shrimp farms in major shrimp farming states of India during 2018–2019. The survey covered an area of 7259 ha of shrimp farming in 23 coastal districts based on multistage stratified sampling (n = 909). Probability of disease occurrence (PDO) was calculated to estimate the economic loss due to diseases. Double log regression was employed to examine the relationship between economic loss with production, stocking density, culture period, average body weight, survival, FCR and incidence of disease. Loss of production (t ha−1crop−1) was highest in infections due to WSSV (2.58 ± 0.32), followed by EHP (1.80 ± 0.24), mixed infections of EHP and WSSV (1.89 ± 0.53), vibriosis (0.97 ± 0.42), running mortality syndrome (1.1 ± 0.39) and other diseases (1.72 ± 0.36). Although WSSV caused the highest loss of production (t ha−1 crop−1), EHP with a 17% probability of occurrence, accounted for a production loss of 0.77 M tons, with a corresponding revenue loss of Rs. 3977 crores (US$ 567.62 M). Although the probability of occurrence of WSSV was estimated (25%) to be higher than that of EHP, the estimated production loss due to WSSV was lower, 0.33 M tons, with a corresponding revenue loss of Rs. 1670 crores (US$ 238.33 M). National loss of revenue due to EHP was higher primarily because, in Andhra Pradesh, primary shrimp farming state had the PDO of 22% for EHP against 8% for WSSV. The total employment loss due to diseases was estimated to be 1.65 M man-days worth US$ 7.07 M. The study revealed that the overall probability of infectious disease occurrence in the country was at 49% leading to an annual loss of 0.21 M ton shrimp worth US$ 1.02 B. Economic loss due to shrimp diseases in Indian shrimp farming warrants prioritized implementation of better management practices (BMP) and biosecurity protocols along with policy interventions to reduce the direct and indirect losses.

The Role of Optimism and Pessimism in the Substitution Between Primary and Secondary Health Prevention Efforts

Abstract We study how apparently healthy individuals arbitrate between primary and secondary health prevention under ambiguity. In our model, each individual simultaneously chooses the level of effort for the two types of prevention. Ambiguity relates to either the probability of disease occurrence or the effectiveness of secondary prevention. We find that pessimistic individuals invest more in primary prevention and less in secondary prevention when the effectiveness of secondary prevention is ambiguous. Conversely, pessimistic individuals invest more in secondary prevention and less in primary prevention when the probability of disease occurrence is ambiguous. When the effectiveness of secondary prevention is ambiguous, optimistic individuals invest more in secondary than primary prevention. We find also a substitution effect between the two types of prevention. The latter generates indirect effects of pessimism and optimism on each type of prevention which may reverse our results when they outweigh the direct effects.

Which attributes of whole genome sequencing tests are most important to the general population? Results from a German preference study.

ObjectiveThe aim of this study was to identify the preferences for whole genome sequencing (WGS) tests without genetic counseling.MethodsA discrete choice experiment was conducted where participants chose between two hypothetical alternatives consisting of the following attributes: test accuracy, test costs, identified diseases, probability of disease occurrence, and data access. People from the general German population aged ≥18 years were eligible to participate in the survey. We estimated generalized linear mixed effects models, latent class mixed-logit models, and the marginal willingness to pay.ResultsThree hundred and one participants were included in the final analysis. Overall, the most favored WGS testing attributes were 95% test accuracy, report of severe hereditary diseases and 40% probability of disease development, test costs of €1,000, and access to test results for researchers. Subgroup analysis, however, showed differences in these preferences between males and females. For example, males preferred reporting of results at a 10% probability of disease development and females preferred reporting of results at a 40% probability. The test cost, participant’s educational level, and access to data influenced the willingness to participate in WGS testing in reality.ConclusionThe German general population was aware of the importance of genetic research and preferred to provide their own genetic data for researchers. However, among others, the reporting of results with a comparatively relatively low probability of disease development at a level of 40%, and the test accuracy of 95% had a high preference. This shows that the results and consequences of WGS testing without genetic counseling are hard to assess for individuals. Therefore, WGS testing should be supported by qualified genetic counseling, where the attributes and consequences are explained.

Factor Influencing the Willingness for Adoption of Livestock Insurance among Dairy Owners

India is an agrian country and livestock which plays a multifactoral role are raised as part of mixed farming systems especially in rural economy. Usually, the livestock economy is a source of self-insurance for farmers, but in the event of any disease outbreak/ disaster, livestock owners suffer. Therefore to mitigate these situation livestock insurance has emerged as a tools to transfer financial risk of dairy owners to a third party by paying a meger amount of premium. But the scheme has not shown encouraging result in term of its adoption by dairy farmers. Thus the study was conducted to identify the factors influencing the adoption of Livestock Insurance among dairy owners. The study was carried out purposively in the state of Uttar Pradesh. A total of 100 dairy farmers were selected randomly from 10 villages. The study reflects that adoption of livestock insurance among livestock owners is mainly effected by motivation by friends & community members followed by high probability of disease occurrence in particular area, effective risk assessment and previous experience of livestock owners. It was also concluded that the insurance institution should take measure like developing infrastructure, to reduce the premium and appoint proper individual to guide the farmers for the insurance.

A Supervised Learning Process to Validate Online Disease Reports for Use in Predictive Models.

Pathogen distribution models that predict spatial variation in disease occurrence require data from a large number of geographic locations to generate disease risk maps. Traditionally, this process has used data from public health reporting systems; however, using online reports of new infections could speed up the process dramatically. Data from both public health systems and online sources must be validated before they can be used, but no mechanisms exist to validate data from online media reports. We have developed a supervised learning process to validate geolocated disease outbreak data in a timely manner. The process uses three input features, the data source and two metrics derived from the location of each disease occurrence. The location of disease occurrence provides information on the probability of disease occurrence at that location based on environmental and socioeconomic factors and the distance within or outside the current known disease extent. The process also uses validation scores, generated by disease experts who review a subset of the data, to build a training data set. The aim of the supervised learning process is to generate validation scores that can be used as weights going into the pathogen distribution model. After analyzing the three input features and testing the performance of alternative processes, we selected a cascade of ensembles comprising logistic regressors. Parameter values for the training data subset size, number of predictors, and number of layers in the cascade were tested before the process was deployed. The final configuration was tested using data for two contrasting diseases (dengue and cholera), and 66%–79% of data points were assigned a validation score. The remaining data points are scored by the experts, and the results inform the training data set for the next set of predictors, as well as going to the pathogen distribution model. The new supervised learning process has been implemented within our live site and is being used to validate the data that our system uses to produce updated predictive disease maps on a weekly basis.

The relationship between malignant mesothelioma and an asbestos cement plant environmental risk: a spatial case–control study in the city of Bari (Italy)

To estimate the mesothelioma risk and environmental asbestos exposure (EAE) due to an asbestos-cement plant. A spatial case-control study including 48 malignant mesothelioma (MM) cases occurred in the period 1993-2003 selected from the regional mesothelioma register (RMR) and 273 controls. The disease risk was estimated by means of a logistic-regression model, in which the probability of disease-occurrence is expressed as a function of the classes of distances. A non-parametric method was applied to estimate the full relative risk surface. Significant MM odds ratio of 5.29 (95 CI: 1.18-23.74) was found for people living within a range up to 500 m centered on the plant. The non-parametric estimation of relative risk surface unveiled a marked peak near the plant not paralleled by the spatial distribution of controls. Evidence of an association between mesothelioma risk and EAE is highlighted. The role played by the RMR in increasing the public health local authorities awareness is stressed.

Multi‐scale patterns of human activity and the incidence of an exotic forest pathogen

Summary Plant pathogens can have major impacts on diverse taxa and ecological systems world‐wide, and some of the most conspicuous of these are invasive non‐native species. Although many factors are known to influence the distribution and abundance of plant pathogens, the extent to which humans play a role is less well‐known but still critical for understanding the dynamics of invasive pathogens in natural systems. One invasive pathogen of great concern world‐wide is Phytophthora ramorum, which causes Sudden Oak Death, an emerging forest disease. Here, we examined the influence of humans and a range of environmental factors on the distribution of P. ramorum at three distinct spatial scales in northern and central California. At the local scale, P. ramorum more commonly occurred in soil on hiking trails used heavily by humans than in soil from adjacent areas off trails. These results support the hypothesis that humans dispersed the pathogen within already infected areas and into areas lacking local sources of inoculum. At the landscape scale, using a network of 202 randomly located plots across a 275 km2 area, we found that forests on public land open to recreation exhibited higher prevalence of disease in a critical infectious host tree (bay laurel, Umbellularia californica), than forests on private lands. Infection levels were also higher in plots surrounded by large amounts of forest with warm climatic conditions and greater potential soil moisture. Although prevalence of diseased canker hosts (Quercus agrifolia, Q. kelloggii and Lithocarpus densiflora) was positively associated with dominance of bay laurel and rainy season temperatures, it was not significantly related to public access, and we hypothesize that this occurred because our study area was in the early stages of infection. At the regional scale, the probability of disease occurrence at 165 sites distributed across the geographic range of P. ramorum in California increased significantly as human population density increased in the surrounding area. Chances of infection also increased significantly with precipitation and presence of bay laurel. Synthesis. Our results suggest that human activity – along with temperature, moisture and host composition – is associated with increased prevalence of an influential exotic forest pathogen. These results indicate that there may be conflicts between humans and disease, and that efforts to address this issue may require aggressive management of human activity.

Use of Bayesian statistical approach in diagnosing secondary hypertension

Bayes's theorem is predominantly used in diagnosing based on the results of various diagnostic tests. This statistical approach is intuitive in differential diagnosis as it explicitly takes into consideration data from medical history, physical examination, laboratory findings and imaging. Bayes's theorem states that the probability of disease occurrence (or occurrence of other outcome) after new information is obtained, called a posteriori probability, depends directly on an a priori probability and the value of likelihood ratio associated with a given test result. This paper describes basic Bayesian analysis in relation to the diagnosis of two types of secondary hypertension; primary aldosteronism and pheochromocytoma. This choice is based on two facts; primary aldosteronism is believed to be the most common and the most commonly detected cause of symptomatic hypertension and pheochromocytoma is thought to have rapid progress and stormy clinical course. This article aims to draw physicians' attention to and increase the knowledge of Bayesian analysis, and to describe its use in everyday clinical decision making. On the basis of this theorem's foundations, the discussion in relation to the issue of differential diagnosis between physicians, their patients, and medical students should also improve. When used in practice, one should be aware, however, of Bayesian analysis limitations concerning the diagnostic test application and limited knowledge of diagnostic test accuracy, and insecure or faulty a priori probability estimates.

Human‐toxicological effect and damage factors of carcinogenic and noncarcinogenic chemicals for life cycle impact assessment

Chemical fate, effect, and damage should be accounted for in the analysis of human health impacts by toxic chemicals in life-cycle assessment (LCA). The goal of this article is to present a new method to derive human damage and effect factors of toxic pollutants, starting from a lognormal dose-response function. Human damage factors are expressed as disability-adjusted life years (DALYs). Human effect factors contain a disease-specific and a substance-specific component. The disease-specific component depends on the probability of disease occurrence and the distribution of sensitivities in the human population. The substance-specific component, equal to the inverse of the ED50, represents the toxic potency of a substance. The new method has been applied to calculate combined human damage and effect factors for 1,192 substances. The total range of 7 to 9 orders of magnitude between the substances is dominated by the range in toxic potencies. For the combined factors, the typical uncertainty, represented by the square root of the ratio of the 97.5th and 2.5th percentile, is a factor of 25 for carcinogenic effects and a factor of 125 for noncarcinogenic effects. The interspecies conversion factor, the (non)cancer effect conversion factor, and the average noncancer damage factor dominate the overall uncertainty.

Estimating relative risk of disease from outputs of logistic regression when the disease is not rare

Many epidemiologic studies in the veterinary field aim to quantify the relationships between risk factors and the occurrence of diseases. The strength of the association between a factor and a disease can be measured by (i) a relative risk (RR), or (ii) an odds ratio (OR) which is widely used because it is directly derived from the estimates of logistic regression. RR directly provides the relative increase in the probability of disease occurrence in case of exposure. OR is often interpretated as a multiplicative factor of the risk of disease occurrence when exposed, although it is not a good approximation of RR when the disease is not rare. The objective of this paper is to propose a method to estimate RR of disease from adjusted odds ratios derived from logistic regression when the disease is not rare. The method of estimation is developed for three different cases: (i) the factor and the outcome are dichotomous; (ii) the factor has more than two classes, and the outcome is dichotomous; and (iii) the factor and the outcome both have more than two classes. In all cases, the principles of estimation are the same: in a subpopulation including individuals diseased at level j ( D j ) and not diseased ( D 0), when exposed to level i ( F i ) or not exposed to the factor ( F 0), R ̂ R ij can be calculated with adjusted O ̂ R ij , and the frequencies of individuals exposed to level i ( n i& j ), of those not exposed ( n 0& j ) and of those diseased ( n i& j ) among the individuals exposed to level i and not exposed. R ̂ R ij is the positive solution of the formula: n i&j R ̂ R 2 ij+[n 0&j−(n &ij(1− O ̂ R ij))−(n i&j O ̂ R ij)] R ̂ R ij−(n 0&j O ̂ R ij)=0 Simulations were done to assess the relative weight of the exposure rate, disease risk and value of O ̂ R ij in the difference between R ̂ R ij and O ̂ R ij . The difference between O ̂ R ij and R ̂ R ij depends upon (i), first of all, the disease risk in the population, but also (ii) the value of O ̂ R ij , and to a less extent (iii) the exposure rate. Simulations also showed that ranking of the risk-factor levels according to their effect cannot always rely on OR.

An insight on the use of multiple logistic regression analysis to estimate association between risk factor and disease occurrence.

Multiple logistic regression is an accepted statistical method for assessing association between an anticedant characteristic (risk factor) and a quantal outcome (probability of disease occurrence), statistically adjusting for potential confounding effects of other covariates. Yet the method has potential drawbacks which are not generally recognized. This article considers one important drawback of logistic regression. Specifically the so-called main effect logistic model assumes that the probability of developing disease is linearly and additively related to the risk factors on the logistic scale. This assumption stipulates that for each risk factor, the odds ratio is constant over all reference exposure levels, and that the odds ratio exposed to two or more factors is equal to the product of individual risk factor odds ratios. If the observed odds ratios in the data follow this pattern, the model-predicted odds ratios will be accurate, and the meaning of the odds ratio for each risk factor will be straightforward. But if the observed odds ratios deviate from the model assumption, the model will not fit the data accurately, and the model-predicted odds ratios will not reflect those in the data. Although satisfactory fit can always be achieved by adding to the model polynomial and product terms derived from the original risk factors, the odds ratios estimated by such an interaction logistic model are difficult to interpret, viz., the odds ratio for each risk factor depends not only on the reference exposure levels of that factor, but also on the exposure level in other factors.(ABSTRACT TRUNCATED AT 250 WORDS)

Predicting Areas Endemic for Schistosomiasis Using Weather Variables and a Landsat Data Base

A system to predict the occurrence of schistosomiasis in the Philippines based on the integration of weather variables and Landsat data is described. An interpolation algorithm was developed to provide mean monthly temperature and precipitation data for any given site. Using the discriminant analysis model, the probability of disease occurrence can then be calculated for any given site. Means and variances from Landsat data were used in a regression model, to inject geographic variables into the system and so improve its predictive capabilities. A disease distribution map was produced, based on the statistical correlation between the probabilities of occurrence and the geographic variables.