Role of infrastructure and operation in disease prevalence in dairy farms: groundwork for disease prevention-based antibiotic stewardship

Attempts at regulating misuse of antibiotics in the dairy industry have been ineffective, especially in low- and middle-income countries, who also typically have high burden of preventable infectious disease, we propose a disease prevention-based approach to minimize the need and in turn consumption of antibiotics in dairy farms. Since the immediate environment of the animals is key to disease prevalence, we targeted the infrastructure- and operation-related factors in dairy farms and their link with prevalence of most common diseases and symptoms. We conducted four focused group discussions and a cross-sectional survey in 378 dairy farms to investigate disease prevalence and associated infrastructural (housing system, and manger shape), and operational (waste management, feed management, and type of cleaning agent) parameters. The most common diseases (Mastitis and secondary infections related to Foot-and-mouth disease) and symptoms (fever and diarrhoea) in the focus area were linked with the infrastructural and operational factors on the dairy farm with higher disease prevalence reported in dairy farms, where the animals were exposed to variations in diurnal temperatures or were hard to clean. We further used ML classifiers - Neural Network (NN), k-Nearest Neighbour (kNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) - to corroborate the relationship between infrastructure and operations of the dairy farms and disease prevalence- The DT classifier on randomly sampled data could predict the prevalence of the two most common diseases (accuracy = 92%, F1-score = 0.919) Our results open new avenues for cost-effective interventions such as use of curve-edged mangers, use of rubber mats on floors, not reusing leftover feed etc. in dairy farms to prevent the most common diseases and symptoms in dairy farms and reduce the need and consumption of antibiotics.

Overcoming denominator problems in refugee settings with fragmented electronic records for health and immigration data: a prediction-based approach

BACKGROUND

Epidemiological studies in refugee settings are often challenged by the denominator problem, i.e. lack of population at risk data. We develop an empirical approach to address this problem by assessing relationships between occupancy data in refugee centres, number of refugee patients in walk-in clinics, and diseases of the digestive system.

METHODS

Individual-level patient data from a primary care surveillance system (PriCarenet) was matched with occupancy data retrieved from immigration authorities. The three relationships were analysed using regression models, considering age, sex, and type of centre. Then predictions for the respective data category not available in each of the relationships were made. Twenty-one German on-site health care facilities in state-level registration and reception centres participated in the study, covering the time period from November 2017 to July 2021.

RESULTS

445 observations ("centre-months") for patient data from electronic health records (EHR, 230 mean walk-in clinics visiting refugee patients per month and centre; standard deviation sd: 202) of a total of 47.617 refugee patients were available, 215 for occupancy data (OCC, mean occupancy of 348 residents, sd: 287), 147 for both (matched), leaving 270 observations without occupancy (EHR-unmatched) and 40 without patient data (OCC-unmatched). The incidence of diseases of the digestive system, using patients as denominators in the different sub-data sets were 9.2% (sd: 5.9) in EHR, 8.8% (sd: 5.1) when matched, 9.6% (sd: 6.4) in EHR- and 12% (sd 2.9) in OCC-unmatched. Using the available or predicted occupancy as denominator yielded average incidence estimates (per centre and month) of 4.7% (sd: 3.2) in matched data, 4.8% (sd: 3.3) in EHR- and 7.4% (sd: 2.7) in OCC-unmatched.

CONCLUSIONS

By modelling the ratio between patient and occupancy numbers in refugee centres depending on sex and age, as well as on the total number of patients or occupancy, the denominator problem in health monitoring systems could be mitigated. The approach helped to estimate the missing component of the denominator, and to compare disease frequency across time and refugee centres more accurately using an empirically grounded prediction of disease frequency based on demographic and centre typology. This avoided over-estimation of disease frequency as opposed to the use of patients as denominators.

Can polygenic risk scores help explain disease prevalence differences around the world? A worldwide investigation

Complex disorders are caused by a combination of genetic, environmental and lifestyle factors, and their prevalence can vary greatly across different populations. The extent to which genetic risk, as identified by Genome Wide Association Study (GWAS), correlates to disease prevalence in different populations has not been investigated systematically. Here, we studied 14 different complex disorders and explored whether polygenic risk scores (PRS) based on current GWAS correlate to disease prevalence within Europe and around the world. A clear variation in GWAS-based genetic risk was observed based on ancestry and we identified populations that have a higher genetic liability for developing certain disorders. We found that for four out of the 14 studied disorders, PRS significantly correlates to disease prevalence within Europe. We also found significant correlations between worldwide disease prevalence and PRS for eight of the studied disorders with Multiple Sclerosis genetic risk having the highest correlation to disease prevalence. Based on current GWAS results, the across population differences in genetic risk for certain disorders can potentially be used to understand differences in disease prevalence and identify populations with the highest genetic liability. The study highlights both the limitations of PRS based on current GWAS but also the fact that in some cases, PRS may already have high predictive power. This could be due to the genetic architecture of specific disorders or increased GWAS power in some cases.

Gastric troubles in Iran: The role of social and economic factors in Helicobacter pylori infection

Background

Helicobacter pylori infection is a major risk factor for gastric cancer in Iran, but the impact of socioeconomic factors on its prevalence is poorly understood. This study aimed to assess the socioeconomic inequalities and risk factors associated with H. pylori infection in Iran.

Methods

This cross-sectional study was conducted based on the PERSIAN cohort study. A total of 20460 individuals aged 35 to 70 years in Ardabil, Iran were included in the study. H. pylori infection was determined based on stool tests and clinical records. Multilevel logistic regression models with random intercepts at household and community levels were used to identify risk factors associated with H. pylori prevalence. The concentration index (CIn) and concentration curve (CC) were employed to assess socioeconomic-related inequality.

Results

In this study, 70.4% (CI 69.6-71.0) of the participants were infected with H. pylori, with a higher prevalence in women (71.2%) than men (69.6%). Age (OR: 1.37, CI: 1.17-1.61), sex (OR: 1.20, CI: 1.12-1.28), level of education (OR: 1.33, CI: 1.17-1.49), cardiac disease (OR: 1.32, CI:1.18-1.46), and BMI groups (OR: 2.49, CI: 1.11-5.58) were significantly associated with H. pylori infection based on the multivariable logistic regression. The results of the CIn and CC indicated that H. pylori were more prevalent among economically disadvantaged groups (CIn: -0.1065; [-0.1374 to -0.0755]).

Conclusion

The prevalence of H. pylori in Iran is higher than in other developing countries, and significant socioeconomic inequality exists between the poor and the rich. To reduce the rate of gastric cancer, socio-economic and demographic factors, especially the poor and people with low levels of education, should be considered.

Human mobility and disease prevalence

We examine the effect of human mobility on disease prevalence by studying the dependence of the total infected population at endemic equilibria with respect to population diffusion rates of a diffusive epidemic model. For small diffusion rates, our results indicate that the total infected population size is strictly decreasing with respect to the ratio of the diffusion rate of the infected population over that of the susceptible population. Moreover, when the disease local reproductive function is spatially heterogeneous, we found that: (i) for large diffusion rate of the infected population, the total infected population size is strictly maximized at large diffusion rate of the susceptible population when the recovery rate is spatially homogeneous, while it is strictly maximized at intermediate diffusion rate of the susceptible population when the difference of the transmission and recovery rates are spatially homogeneous; (ii) for large diffusion rate of the susceptible population, the total infected population size is strictly maximized at intermediate diffusion rate of the infected population when the recovery rate is spatially homogeneous, while it is strictly minimized at large diffusion rate of the infected population when the difference of the transmission and recovery rates is spatially homogeneous. Numerical simulations are provided to complement the theoretical results. Our studies may provide some insight into the impact of human mobility on disease outbreaks and the severity of epidemics.

Prevalence of Ophidiomyces ophidiicola and epizootiology of snake fungal disease in free-ranging Northern Pine Snakes (Pituophis melanoleucus melanoleucus) in New Jersey

Snake fungal disease, caused by Ophidiomyces ophidiicola, is recognized as a potential concern for North American snakes. We tested skin swabs from Northern Pine Snakes (Pituophis melanoleucus melanoleucus) in the New Jersey pinelands for the presence of O. ophidiicola before emergence from hibernation. We used qPCR to test the collected swabs for the presence of O. ophidiicola, then determined pathogen prevalence as a function of sampling year, sampling location (skin lesion, healthy ventral skin, healthy head skin) sex, and age. There were no temporal trends in O. ophidiicola detection percentages on snakes, which varied from 58 to 83% in different years. Ophidiomyces ophidiicola detection on snakes was highest in swabs of skin lesions (71%) and lowest in head swabs (29%). Males had higher prevalence than females (82% versus 62%). The fungus was not detected in hatchling snakes (age 0) in the fall, but 75% of juveniles tested positive at the end of hibernation (age 1 year). We also screened hibernacula soil samples for the presence of O. ophidiicola. Where snakes hibernated, 69% of soil samples were positive for O. ophidiicola, and 85% of snakes lying on positive soil samples also tested positive for the pathogen. Although a high proportion of snakes (73%) tested positive for O. ophidiicola during our 4-year study, the snakes appeared healthy except for small skin lesions. We conclude that O. ophidiicola prevalence is high on hibernating Northern Pine Snakes and in the hibernacula soil, with a strong association between snakes and positive adjacent soil. This is the first demonstration that snakes likely become infected during hibernation.

Relative prevalence-based dispersal in an epidemic patch model

In this paper, we propose a two-patch SIRS model with a nonlinear incidence rate: [Formula: see text] and nonconstant dispersal rates, where the dispersal rates of susceptible and recovered individuals depend on the relative disease prevalence in two patches. In an isolated environment, the model admits Bogdanov-Takens bifurcation of codimension 3 (cusp case) and Hopf bifurcation of codimension up to 2 as the parameters vary, and exhibits rich dynamics such as multiple coexistent steady states and periodic orbits, homoclinic orbits and multitype bistability. The long-term dynamics can be classified in terms of the infection rates [Formula: see text] (due to single contact) and [Formula: see text] (due to double exposures). In a connected environment, we establish a threshold [Formula: see text] between disease extinction and uniform persistence under certain conditions. We numerically explore the effect of population dispersal on disease spread when [Formula: see text] and patch 1 has a lower infection rate, our results indicate: (i) [Formula: see text] can be nonmonotonic in dispersal rates and [Formula: see text] ([Formula: see text] is the basic reproduction number of patch i) may fail; (ii) the constant dispersal of susceptible individuals (or infective individuals) between two patches (or from patch 2 to patch 1) will increase (or reduce) the overall disease prevalence; (iii) the relative prevalence-based dispersal may reduce the overall disease prevalence. When [Formula: see text] and the disease outbreaks periodically in each isolated patch, we find that: (a) small unidirectional and constant dispersal can lead to complex periodic patterns like relaxation oscillations or mixed-mode oscillations, whereas large ones can make the disease go extinct in one patch and persist in the form of a positive steady state or a periodic solution in the other patch; (b) relative prevalence-based and unidirectional dispersal can make periodic outbreak earlier.

Expanding ACMG variant classification guidelines into a general framework

BACKGROUND

The American College of Medical Genetics and Genomics (ACMG)-recommended five variant classification categories (pathogenic, likely pathogenic, uncertain significance, likely benign, and benign) have been widely used in medical genetics. However, these guidelines are fundamentally constrained in practice owing to their focus upon Mendelian disease genes and their dichotomous classification of variants as being either causal or not. Herein, we attempt to expand the ACMG guidelines into a general variant classification framework that takes into account not only the continuum of clinical phenotypes, but also the continuum of the variants' genetic effects, and the different pathological roles of the implicated genes.

MAIN BODY

As a disease model, we employed chronic pancreatitis (CP), which manifests clinically as a spectrum from monogenic to multifactorial. Bearing in mind that any general conceptual proposal should be based upon sound data, we focused our analysis on the four most extensively studied CP genes, PRSS1, CFTR, SPINK1 and CTRC. Based upon several cross-gene and cross-variant comparisons, we first assigned the different genes to two distinct categories in terms of disease causation: CP-causing (PRSS1 and SPINK1) and CP-predisposing (CFTR and CTRC). We then employed two new classificatory categories, "predisposing" and "likely predisposing", to replace ACMG's "pathogenic" and "likely pathogenic" categories in the context of CP-predisposing genes, thereby classifying all pathologically relevant variants in these genes as "predisposing". In the case of CP-causing genes, the two new classificatory categories served to extend the five ACMG categories whilst two thresholds (allele frequency and functional) were introduced to discriminate "pathogenic" from "predisposing" variants.

CONCLUSION

Employing CP as a disease model, we expand ACMG guidelines into a five-category classification system (predisposing, likely predisposing, uncertain significance, likely benign, and benign) and a seven-category classification system (pathogenic, likely pathogenic, predisposing, likely predisposing, uncertain significance, likely benign, and benign) in the context of disease-predisposing and disease-causing genes, respectively. Taken together, the two systems constitute a general variant classification framework that, in principle, should span the entire spectrum of variants in any disease-related gene. The maximal compliance of our five-category and seven-category classification systems with the ACMG guidelines ought to facilitate their practical application.

Estimating prevalence of human traits among populations from polygenic risk scores

The genetic basis of phenotypic variation across populations has not been well explained for most traits. Several factors may cause disparities, from variation in environments to divergent population genetic structure. We hypothesized that a population-level polygenic risk score (PRS) can explain phenotypic variation among geographic populations based solely on risk allele frequencies. We applied a population-specific PRS (psPRS) to 26 populations from the 1000 Genomes to four phenotypes: lactase persistence (LP), melanoma, multiple sclerosis (MS) and height. Our models assumed additive genetic architecture among the polymorphisms in the psPRSs, as is convention. Linear psPRSs explained a significant proportion of trait variance ranging from 0.32 for height in men to 0.88 for melanoma. The best models for LP and height were linear, while those for melanoma and MS were nonlinear. As not all variants in a PRS may confer similar, or even any, risk among diverse populations, we also filtered out SNPs to assess whether variance explained was improved using psPRSs with fewer SNPs. Variance explained usually improved with fewer SNPs in the psPRS and was as high as 0.99 for height in men using only 548 of the initial 4208 SNPs. That reducing SNPs improves psPRSs performance may indicate that missing heritability is partially due to complex architecture that does not mandate additivity, undiscovered variants or spurious associations in the databases. We demonstrated that PRS-based analyses can be used across diverse populations and phenotypes for population prediction and that these comparisons can identify the universal risk variants.

Politicized disease surveillance: A theoretical lens for understanding sociopolitical influence on the monitoring of disease epidemics

Sociopolitical forces commonly influence the collection, analysis, dissemination, and general perceptions of epidemiological information. Yet few theoretical lenses provide insight into the mechanisms through which such influence occurs. In this article, I draw and expand upon empirical findings to propose a novel theoretical lens, politicized disease surveillance, which I define as extreme or undue sociopolitical influence on public health surveillance systems or processes in ways that impact disease incidences and prevalences, or estimates or perceptions thereof. This lens foregrounds disease incidence and prevalence as objects of contestation and policy influence and articulates how certain facets of sociopolitical forces shape disease incidences and prevalences-especially amid an epidemic-through one or more channels: (1) the diagnostic construct; (2) screening tools, procedures, or systems; or (3) the behaviors of individuals who are living with or at risk for a certain disease. I provide several contemporary illustrations of politicized disease surveillance and discuss its theoretical and practical implications.

Clustering of countries for COVID-19 cases based on disease prevalence, health systems and environmental indicators

The coronavirus has a high basic reproduction number ( R 0 ) and has caused the global COVID-19 pandemic. Governments are implementing lockdowns that are leading to economic fallout in many countries. Policy makers can take better decisions if provided with the indicators connected with the disease spread. This study is aimed to cluster the countries using social, economic, health and environmental related metrics affecting the disease spread so as to implement the policies to control the widespread of disease. Thus, countries with similar factors can take proactive steps to fight against the pandemic. The data is acquired for 79 countries and 18 different feature variables (the factors that are associated with COVID-19 spread) are selected. Pearson Product Moment Correlation Analysis is performed between all the feature variables with cumulative death cases and cumulative confirmed cases individually to get an insight of relation of these factors with the spread of COVID-19. Unsupervised k-means algorithm is used and the feature set includes economic, environmental indicators and disease prevalence along with COVID-19 variables. The learning model is able to group the countries into 4 clusters on the basis of relation with all 18 feature variables. We also present an analysis of correlation between the selected feature variables, and COVID-19 confirmed cases and deaths. Prevalence of underlying diseases shows strong correlation with COVID-19 whereas environmental health indicators are weakly correlated with COVID-19.

How do biases in sex ratio and disease characteristics affect the spread of sexually transmitted infections?

The epidemiology of sexually transmitted infections (STIs) is inherently linked to host mating dynamics. Studies across many taxa show that adult sex ratio, a major determinant of host mating dynamics, is often skewed - sometimes strongly - toward males or females. However, few predictions exist for the effects of skewed sex ratio on STI epidemiology, and none when coupled with sex biased disease characteristics. Here we use mathematical modelling to examine how interactions between sex ratio and disease characteristics affect STI prevalence in males and females. Notably, we find that while overall disease prevalence peaks at equal sex ratios, prevalence per sex peaks at skewed sex ratios. Furthermore, disease characteristics, sex-biased or not, drive predictable differences in male and female STI prevalence as sex ratio varies, with higher transmission and lower virulence generally increasing differences between the sexes for a given sex ratio. Our work reveals new insights into how STI prevalence in males and females depends on a complex interaction between host population sex ratio and disease characteristics.

Impact of State-Dependent Dispersal on Disease Prevalence

Based on a susceptible-infected-susceptible patch model, we study the influence of dispersal on the disease prevalence of an individual patch and all patches at the endemic equilibrium. Specifically, we estimate the disease prevalence of each patch and obtain a weak order-preserving result that correlated the patch reproduction number with the patch disease prevalence. Then we assume that dispersal rates of the susceptible and infected populations are proportional and derive the overall disease prevalence, or equivalently, the total infection size at no dispersal or infinite dispersal as well as the right derivative of the total infection size at no dispersal. Furthermore, for the two-patch submodel, two complete classifications of the model parameter space are given: one addressing when dispersal leads to higher or lower overall disease prevalence than no dispersal, and the other concerning how the overall disease prevalence varies with dispersal rate. Numerical simulations are performed to further investigate the effect of movement on disease prevalence.

Mapping chronic disease prevalence based on medication use and socio-demographic variables: an application of LASSO on administrative data sources in healthcare in the Netherlands

BACKGROUND

Policymakers generally lack sufficiently detailed health information to develop localized health policy plans. Chronic disease prevalence mapping is difficult as accurate direct sources are often lacking. Improvement is possible by adding extra information such as medication use and demographic information to identify disease. The aim of the current study was to obtain small geographic area prevalence estimates for four common chronic diseases by modelling based on medication use and socio-economic variables and next to investigate regional patterns of disease.

METHODS

Administrative hospital records and general practitioner registry data were linked to medication use and socio-economic characteristics. The training set (n = 707,021) contained GP diagnosis and/or hospital admission diagnosis as the standard for disease prevalence. For the entire Dutch population (n = 16,777,888), all information except GP diagnosis and hospital admission was available. LASSO regression models for binary outcomes were used to select variables strongly associated with disease. Dutch municipality (non-)standardized prevalence estimates for stroke, CHD, COPD and diabetes were then based on averages of predicted probabilities for each individual inhabitant.

RESULTS

Adding medication use data as a predictor substantially improved model performance. Estimates at the municipality level performed best for diabetes with a weighted percentage error (WPE) of 6.8%, and worst for COPD (WPE 14.5%)Disease prevalence showed clear regional patterns, also after standardization for age.

CONCLUSION

Adding medication use as an indicator of disease prevalence next to socio-economic variables substantially improved estimates at the municipality level. The resulting individual disease probabilities could be aggregated into any desired regional level and provide a useful tool to identify regional patterns and inform local policy.

Developmental hip dysplasia and hip ultrasound frequency in a large American payer database

Developmental dysplasia of the hip (DDH) is an important contributor to musculoskeletal morbidity, but effective strategies to screen for DDH remain controversial. The current utilization of hip ultrasound (US) screening for DDH in the United States is not defined. This study utilized Optum's de-identified Clinformatics® Data Mart, a large commercial and Medicare Advantage claims database. The frequency of DDH and hip US utilization was estimated using billing data on an average of 2.9 million relevant beneficiaries included annually from 2007 through 2017. A total of 6806 DDH cases were identified with an average annual prevalence of 1.7 per 1000 infants, which was stable during the study period. Girls were more likely to be screened and diagnosed with DDH, comprising 72% of DDH cases with an OR of 2.55 (95% CI 2.42-2.69), p < 0.001. Hip US screening was employed in 0.9% of the infant population on average but increased substantially from 2007 (0.4%) to 2017 (2.2%). Most common billing diagnoses included hip deformity (27.4%), breech delivery (20.4%), and physical exam abnormality (17.7%). The average imaging costs per patient for all screened children was $108.94. Insurance claims reflect the current American practice of selective hip US with relative adherence to American Academy of Pediatrics guidelines based on reported diagnoses. While hip US utilization increased during the study period, prevalence of DDH diagnoses did not increase. Our results suggest that expansion of hip US screening may not effectively increase DDH detection although further investigation is needed to ascertain optimal screening strategies to improve patient outcomes.

Estimating asymptomatic SARS-CoV-2 infections in a geographic area of low disease incidence

Background

The SARS-CoV-2 infection has emerged as a rapidly spreading infection. Today it is relatively easy to isolate Covid-19 symptomatic cases, while remains problematic to control the disease spread by infected but symptom-free individuals. The control of this possible path of contagion requires drastic measures of social distancing, which imply the suspension of most activities and generate economic and social issues. This study is aimed at estimating the percentage of asymptomatic SARS-CoV-2 infection in a geographic area with relatively low incidence of Covid-19.

Methods

Blood serum samples from 388 healthy volunteers were analyzed for the presence of anti-SARS-CoV-2 IgG by using an ELISA assay based on recombinant viral nucleocapsid protein.

Results

We found that 7 out of 388 healthy volunteers, who declared no symptoms of Covid-19, like fever, cough, fatigue etc., in the preceding 5 months, have bona fide serum anti-SARS-CoV-2 IgG, that is 1.8% of the asymptomatic population (95% confidence interval: 0.69–2.91%).

Conclusions

The estimated range of asymptomatic individuals with anti-SARS-CoV-2 IgG should be between 26,565 and 112, 350. In the same geographic area, there are 4665 symptomatic diagnosed cases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06054-2.

Estimation of Covid-19 prevalence from serology tests: A partial identification approach

We propose a partial identification method for estimating disease prevalence from serology studies. Our data are results from antibody tests in some population sample, where the test parameters, such as the true/false positive rates, are unknown. Our method scans the entire parameter space, and rejects parameter values using the joint data density as the test statistic. The proposed method is conservative for marginal inference, in general, but its key advantage over more standard approaches is that it is valid in finite samples even when the underlying model is not point identified. Moreover, our method requires only independence of serology test results, and does not rely on asymptotic arguments, normality assumptions, or other approximations. We use recent Covid-19 serology studies in the US, and show that the parameter confidence set is generally wide, and cannot support definite conclusions. Specifically, recent serology studies from California suggest a prevalence anywhere in the range 0%-2% (at the time of study), and are therefore inconclusive. However, this range could be narrowed down to 0.7%-1.5% if the actual false positive rate of the antibody test was indeed near its empirical estimate ( ∼ 0.5%). In another study from New York state, Covid-19 prevalence is confidently estimated in the range 13%-17% in mid-April of 2020, which also suggests significant geographic variation in Covid-19 exposure across the US. Combining all datasets yields a 5%-8% prevalence range. Our results overall suggest that serology testing on a massive scale can give crucial information for future policy design, even when such tests are imperfect and their parameters unknown.

The Global Consortium Study of Neurological Dysfunction in COVID-19 (GCS-NeuroCOVID): Development of Case Report Forms for Global Use

Since its original report in January 2020, the coronavirus disease 2019 (COVID-19) due to Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infection has rapidly become one of the deadliest global pandemics. Early reports indicate possible neurological manifestations associated with COVID-19, with symptoms ranging from mild to severe, highly variable prevalence rates, and uncertainty regarding causal or coincidental occurrence of symptoms. As neurological involvement of any systemic disease is frequently associated with adverse effects on morbidity and mortality, obtaining accurate and consistent global data on the extent to which COVID-19 may impact the nervous system is urgently needed. To address this need, investigators from the Neurocritical Care Society launched the Global Consortium Study of Neurological Dysfunction in COVID-19 (GCS-NeuroCOVID). The GCS-NeuroCOVID consortium rapidly implemented a Tier 1, pragmatic study to establish phenotypes and prevalence of neurological manifestations of COVID-19. A key component of this global collaboration is development and application of common data elements (CDEs) and definitions to facilitate rigorous and systematic data collection across resource settings. Integration of these elements is critical to reduce heterogeneity of data and allow for future high-quality meta-analyses. The GCS-NeuroCOVID consortium specifically designed these elements to be feasible for clinician investigators during a global pandemic when healthcare systems are likely overwhelmed and resources for research may be limited. Elements include pediatric components and translated versions to facilitate collaboration and data capture in Latin America, one of the epicenters of this global outbreak. In this manuscript, we share the specific data elements, definitions, and rationale for the adult and pediatric CDEs for Tier 1 of the GCS-NeuroCOVID consortium, as well as the translated versions adapted for use in Latin America. Global efforts are underway to further harmonize CDEs with other large consortia studying neurological and general aspects of COVID-19 infections. Ultimately, the GCS-NeuroCOVID consortium network provides a critical infrastructure to systematically capture data in current and future unanticipated disasters and disease outbreaks.

Prevalence of post-traumatic stress disorder during the COVID-19 pandemic in Saudi Arabia

Background

The coronavirus diseases of 2019 (COVID-19) pandemic was classified as one of the worst pandemics in the 21st century. Its rapid transmission, unpredicted mortality rate, and the uncertainty surrounding its transmission method have evoked additional fear and anxiety. Nonetheless, to the best of our knowledge, no prior study has explored PTSD prevalence three months after the start of the quarantine procedures in Saudi Arabia nor has examined PTSD prevalence by three different methods.

Objective

This observational cross-sectional study aimed to identify the prevalence, severity, and influencing factors of PTSD in different regions of Saudi Arabia three months after the onset of the quarantine procedures related to the COVID-19 pandemic.

Methods

Through the month of June 2020, 1374 people (49.05% men and 50.95% women) completed a 35-item, 10-minute online. The prevalence of PTSD was measured using PCL-S (specific for COVID-19) that assesses the 17 symptoms of PTSD. Resilience was measured using 2-items Arabic version of the Connor-Davidson Resilience Scale 2 (CD-RISC 2).

Results

We calculated the prevalence by three methods, namely, PTSD cut-off score, criteria, and combined, and the prevalence was 22.63%, 24.8%, and 19.6%, respectively. Female participants showed higher prevalence than male. As well, participants who were either tested positive or suspected of having been infected with COVID-19 showed higher PTSD prevalence. Higher resilience was associated with lower PTSD prevalence.

Conclusions

This was the first study to report PTSD prevalence by three differential methods three months after the onset of the quarantine procedures related to the COVID-19 pandemic in Saudi Arabia. We observed a significant impact of the COVID-19 pandemic in the Saudi population; therefore, great attention should be performed in implementing new procedures that deal with the highlighted risk factors, especially in vulnerable groups, to overcome the psychological impact of the COVID-19 pandemic.

The rise in the number of long-term survivors from different diseases can slow the increase in life expectancy of the total population

Background

Recent improvements in life expectancy in many countries stem from reduced mortality from cardiovascular disease and cancer above the age of 60. This is the combined result of decreased incidence and improved survival among those with disease. The latter has led to a higher proportion in the population of people with a past history of disease. This is a group with higher mortality than the general population. How growing shares of persons with past history of disease and improved survival with disease have affected changes in life expectancy of the total population is the objective of this paper.

Methods

Using register data for the total Swedish population, we stratified the population based on whether individuals have been diagnosed with myocardial infarction, stroke, hip fracture, colon cancer, or breast cancer. Using a novel decomposition approach, we decomposed the changes in life expectancy at age 60 between 1994 and 2016 into contributions from improved survival with disease and from changes in proportion of people with past history of disease.

Results

Improvements in survival from disease resulted in gains of life expectancy for the total population. However, while the contributions to life expectancy improvements from myocardial infarction, stroke and breast cancer were substantial, the contributions from the other diseases were minor. These gains were counteracted, to various degrees, by the increasing proportion of people with raised mortality due to a past history of disease. For instance, the impact on life expectancy by improved survival from breast cancer was almost halved by the increasing share of females with a past history of breast cancer.

Conclusion

Rising numbers of survivors of different diseases can slow the increase in life expectancy. This dynamic may represent the costs associated with successful treatment of diseases, and thus, a potential “failure of success.” This dynamic should be considered when assessing mortality and life expectancy trends. As populations are aging and disease survival continues to improve, this issue is likely to become even more important in the future.

A rapid spread of the stony coral tissue loss disease outbreak in the Mexican Caribbean

Caribbean reef corals have experienced unprecedented declines from climate change, anthropogenic stressors and infectious diseases in recent decades. Since 2014, a highly lethal, new disease, called stony coral tissue loss disease, has impacted many reef-coral species in Florida. During the summer of 2018, we noticed an anomalously high disease prevalence affecting different coral species in the northern portion of the Mexican Caribbean. We assessed the severity of this outbreak in 2018/2019 using the AGRRA coral protocol to survey 82 reef sites across the Mexican Caribbean. Then, using a subset of 14 sites, we detailed information from before the outbreak (2016/2017) to explore the consequences of the disease on the condition and composition of coral communities. Our findings show that the disease outbreak has already spread across the entire region by affecting similar species (with similar disease patterns) to those previously described for Florida. However, we observed a great variability in prevalence and tissue mortality that was not attributable to any geographical gradient. Using long-term data, we determined that there is no evidence of such high coral disease prevalence anywhere in the region before 2018, which suggests that the entire Mexican Caribbean was afflicted by the disease within a few months. The analysis of sites that contained pre-outbreak information showed that this event considerably increased coral mortality and severely changed the structure of coral communities in the region. Given the high prevalence and lethality of this disease, and the high number of susceptible species, we encourage reef researchers, managers and stakeholders across the Western Atlantic to accord it the highest priority for the near future.

Association between vegetarian diets and cardiovascular risk factors in non-Hispanic white participants of the Adventist Health Study-2

The association between dietary patterns and CVD risk factors among non-Hispanic whites has not been fully studied. Data from 650 non-Hispanic white adults who participated in one of two clinical sub-studies (about 2 years after the baseline) of the Adventist Health Study-2 (AHS-2) were analysed. Four dietary patters were identified using a validated 204-item semi-quantitative FFQ completed at enrolment into AHS-2: vegans (8·3 %), lacto-ovo-vegetarians (44·3 %), pesco-vegetarians (10·6 %) and non-vegetarians (NV) (37·3 %). Dietary pattern-specific prevalence ratios (PR) of CVD risk factors were assessed adjusting for confounders with or without BMI as an additional covariable. The adjusted PR for hypertension, high total cholesterol and high LDL-cholesterol were lower in all three vegetarian groups. Among the lacto-ovo-vegetarians the PR were 0·57 (95 % CI 0·45, 0·73), 0·72 (95 % CI 0·59, 0·88) and 0·72 (95 % CI 0·58, 0·89), respectively, which remained significant after additionally adjusting for BMI. The vegans and the pesco-vegetarians had similar PR for hypertension at 0·46 (95 % CI 0·25, 0·83) and 0·62 (95 % CI 0·42, 0·91), respectively, but estimates were attenuated and marginally significant after adjustment for BMI. Compared with NV, the PR of obesity and abdominal adiposity, as well as other CVD risk factors, were significantly lower among the vegetarian groups. Similar results were found when limiting analyses to participants not being treated for CVD risk factors, with the vegans having the lowest mean BMI and waist circumference. Thus, compared with the diet of NV, vegetarian diets were associated with significantly lower levels of CVD risk factors among the non-Hispanic whites.

Childhood onset type 1 diabetes at a tertiary hospital in south-western Iran during 2000-2015: Rapid increase in admissions and high prevalence of DKA at diagnosis

AIMS

Incidence of type 1 diabetes mellitus (T1DM) is increasing worldwide. We aimed to study trends in presentation and incidence of childhood diabetes in the last 15years in south-western Iran.

METHODS

During a detailed review of compiled records of the main tertiary children's hospital of southwest of Iran, from January 2000 to December 2009, the following clinical information relevant to diabetes were analysed: admissions, demographic data, clinical and laboratory findings, hospital course, and mortality. Study continued 5years more to find out the real rate of increase. Significant findings with respect to the incidence of DKA, gender and age of subjects were summarized from among these cases. A total of 297 (known and new) cases were enrolled in the 1st (10year) stage of study and 691 new subjects in the 2nd (5year) period to check the trend in the whole 15year study period.

RESULTS

In the 1st period; excluding 129 repeated admissions, 297 cases were enrolled for analysis: 223 new and 74 known cases. Among the new cases, 67.3% presented with DKA, without any gender bias. Mortality rate in DKA subjects was 4% with higher risk in the <2year group and in girls (boy: girl=1:7; p=0.039). Adding the 2nd study period (total 15years) disclosed a rapid rise of incidence (new cases/5year) as 89, 134 and 691 new diabetes cases for the 1st, 2nd and 3rd 5year period of study respectively. The final annual incidence for <15year age group in 2015 was 13.35/100000.

CONCLUSIONS

The most new cases of T1DM presented with DKA, which is similar to the other developing countries. Such an increasing incidence of DM proposes more attention for periodic retraining of families and health staff to earlier diagnosis and management of new subjects, and to reduce morbidity and mortality rates.

Coral disease prevalence estimation and sampling design

In the last decades diseases have changed coral communities’ structure and function in reefs worldwide. Studies conducted to evaluate the effect of diseases on corals frequently use modified adaptations of sampling designs that were developed to study ecological aspects of coral reefs. Here we evaluate how efficient these sampling protocols are by generating virtual data for a coral population parameterized with mean coral density and disease prevalence estimates from the Caribbean scleractinian Orbicella faveolata at the Mexican Caribbean. Six scenarios were tested consisting of three patterns of coral colony distribution (random, randomly clustered and randomly over-dispersed) and two disease transmission modes (random and contagious). The virtual populations were sampled with the commonly used method of belt-transects with variable sample-unit sizes (10 × 1, 10 × 2, 25 × 2, 50 × 2 m). Results showed that the probability of obtaining a mean coral disease prevalence estimate of ±5% of the true prevalence value was low (range: 11–48%) and that two-sample comparisons achieved rather low power, unless very large effect sizes existed. Such results imply low statistical confidence to assess differences or changes in coral disease prevalence. The main problem identified was insufficient sample size because local mean colony size, density and spatial distribution of targeted coral species was not taken into consideration to properly adjust the sampling protocols.

Epidemiology of Granulomatosis with Polyangiitis in Poland, 2011-2015

Granulomatosis with polyangiitis (GPA) is a form of vasculitis that affects small- and medium-sized vessels in many organs. The aim of the study was to describe the epidemiology of GPA in Poland in 2011-2015, including the incidence and prevalence rates. The authors conducted a retrospective, population-based study, using hospital discharge records with GPA diagnosis. GPA incidence was estimated on the basis of the data from the Polish hospital morbidity study carried out by the National Institute of Public Health. The final study group consisted of 1491 patients (749 females, 742 males) who were first time hospitalized with the diagnosis of GPA. The average annual incidence of GPA in Poland was 7.7/1,000,000 (95% CI, 4.1-11.4), and the point prevalence at the end of 2015 was 36/1,000,000. A statistically significant decrease in the GPA incidence was noticed in this study. A 1-year survival rate for GPA was 94%. In conclusion, the incidence and prevalence rates of GPA in Poland are similar to that reported in other European countries. The study provides recent epidemiological data on GPA in Poland, which may be useful for comparisons with other geographical regions.

Time trends in the prevalence of cancer and non-cancer diseases among older U.S. adults: Medicare-based analysis

Longer lifespan is accompanied by a larger number of chronic diseases among older adults. Because of a growing proportion of older adults in the U.S., this brings the problem of age-related morbidity to the forefront as a major contributor to rising medical expenditures. We evaluated 15-year time trends (from 1998 to 2013) in the prevalence of 48 acute and chronic non-cancer diseases and cancers in older U.S. adults aged 65+ and estimated the annual percentage changes of these prevalence trends using SEER-Medicare and HRS-Medicare data. We found that age-adjusted prevalence of cancers of kidney, pancreas, and melanoma, as well as diabetes, renal disease, limb fracture, depression, anemia, weight deficiency, dementia/Alzheimer's disease, drug/medications abuse and several other diseases/conditions increased over time. Conversely, prevalence of myocardial infarction, heart failure, cardiomyopathy, pneumonia/influenza, peptic ulcer, and gastrointestinal bleeding, among others, decreased over time. There are also diseases whose prevalence did not change substantially over time, e.g., a group of fast progressing cancers and rheumatoid arthritis. Analysis of trends of multiple diseases performed simultaneously within one study design with focus on the same time interval and the same population for all diseases allowed us to provide insight into the epidemiology of these conditions and identify the most alarming and/or unexpected trends and trade-offs. The obtained results can be used for health expenditures planning for growing sector of older adults in the U.S.

An asymptotic approximation to the N-mixture model for the estimation of disease prevalence

N-mixture models are probability models that estimate abundance using replicate observed counts while accounting for imperfect detection. In this article, we propose an asymptotic approximation to the N-mixture model which efficiently estimates large abundances without the computational limitations of the generalized N-mixture model introduced by Dail and Madsen in 2011. It has been suggested in the literature that N-mixture models do not perform well when counts from the same sites show weak patterns of population dynamics. Our proposed model addresses this issue by using the asymptotic information matrix to diagnose model parameter estimability and to derive parameter standard errors. A simulation study show that this model performs almost as well as the Dail-Madsen Generalized N-mixture model at low abundances and improves on it at higher abundances. We illustrate the procedure using two data sets: the American robin data from Dail and Madsen (2011), and counts of chlamydia cases in the state of Oregon from 2007-2016. The chlamydia data exhibit very large abundances and demonstrate the potential usefulness of the proposed model for disease surveillance data.

Hospital Morbidity Database for Epidemiological Studies on Churg-Strauss Syndrome

Churg-Strauss syndrome or more accurately eosinophilic granulomatosis with polyangiitis (EGPA) is a small-vessel necrotizing vasculitis with a characteristic late-onset allergic rhinitis and asthma. The use of hospital morbidity database is an important element of the epidemiological analysis of this rare disease. The present study was undertaken to assess the incidence of EGPA and factors related to its epidemiology in Poland; the first analysis of the kind in Poland, enabling a comparison in the European context. This is a retrospective, population-based study using hospital discharge records with EGPA diagnosis, collected for a National Institute of Public Health survey covering the period from 2008 to 2013. The group consisted of 344 patients (206 females and 138 males) with the first-time hospitalization for EGPA. The major findings are that the annual incidence of EGPA in Poland was 1.5 per million (95% confidence intervals: 1.2-1.8), with the point prevalence of 8.8 per million at the end of 2013. A greater incidence of EGPA was observed in the regions with urban predominance. We conclude that discharge records may be a useful element of epidemiological studies on EGPA.

Demographics, Management, Preventive Health Care and Disease in Aged Horses

Gerontology has become increasingly important in equine veterinary medicine, with aged animals representing a significant proportion of the equine population. Horses are defined as geriatric or aged from age 15 years onwards but can have a life span of more than 40 years. Despite a high level of owner concern for the well-being of their geriatric animal, provision of preventive health care may be suboptimal. Owners seem to under-recognize some of the most prevalent diseases identified in geriatric horses. This review focuses on the demographic characteristics of the equine geriatric population and management and preventive care practices of older horses.

Cats are not small dogs: is there an immunological explanation for why cats are less affected by arthropod-borne disease than dogs?

It is widely recognized that cats appear to be less frequently affected by arthropod-borne infectious diseases than dogs and share fewer zoonotic pathogens with man. This impression is supported by the relative lack of scientific publications related to feline vector-borne infections. This review explores the possible reasons for the difference between the two most common small companion animal species, including the hypothesis that cats might have a genetically-determined immunological resistance to arthropod vectors or the microparasites they transmit. A number of simple possibilities might account for the lower prevalence of these diseases in cats, including factors related to the lifestyle and behaviour of the cat, lesser spend on preventative healthcare for cats and reduced opportunities for research funding for these animals. The dog and cat have substantially similar immune system components, but differences in immune function might in part account for the markedly distinct prevalence and clinicopathological appearance of autoimmune, allergic, idiopathic inflammatory, immunodeficiency, neoplastic and infectious diseases in the two species. Cats have greater genetic diversity than dogs with much lower linkage disequilibrium in feline compared with canine breed groups. Immune function is intrinsically related to the nature of the intestinal microbiome and subtle differences between the canine and feline microbial populations might also impact on immune function and disease resistance. The reasons for the apparent lesser susceptibility of cats to arthropod-borne infectious diseases are likely to be complex, but warrant further investigation.

Prevalence and factors associated with nontuberculous mycobacteria in non-cystic fibrosis bronchiectasis: a multicenter observational study

Background

Data on the prevalence of and factors associated with nontuberculous mycobacteria (NTM) in patients with non–cystic fibrosis (CF) bronchiectasis are limited. Our aim was to determine the prevalence and factors associated with isolation of NTM in this population.

Methods

We performed a multicenter observational study of historical cohorts comprising consecutive patients with non-CF bronchiectasis and at least 2 sputum samples cultured for mycobacteria over a period of 5 years.

Results

The study population included 218 adult patients (61.9 % women) with a mean (SD) age of 55.7 (16) years and a mean (SD) of 5.1 (3.3) cultures/patient. NTM was isolated from sputum in 18 patients (8.3 %). Of these, 5 patients (28 %) met the American Thoracic Society criteria for NTM disease. Mycobacterium avium complex was the most frequently isolated microorganism (9 patients, 4.1 %). The variables independently associated with isolation of NTM were FVC ≥ 75 % predicted (OR, 4.84; 95 % CI 1.47 to 15.9; p < 0.05), age ≥ 50 years (OR, 4.74; 95 % CI 1.25 to 17.97; p < 0.05), and body mass index (BMI) ≤ 23 kg/m² (OR, 2.97; 95 % CI 1.03-8.58; p < 0.05). Patients with these three characteristics had a 40 % probability of having at least one isolation of NMT.

Conclusions

A significant number of patients with non-CF bronchiectasis are positive for the isolation of NTM. M. avium complex is the most frequently isolated mycobacteria. FVC ≥ 75 % predicted, age ≥ 50 years, and a BMI ≤ 23 kg/m² were independently associated with the presence of NTM in patients with non-CF bronchiectasis.

Comorbidity in chronic obstructive pulmonary disease

Patients with chronic obstructive pulmonary diseases (COPD) often experience comorbid conditions. The most common comorbidities that have been associated with COPD include cardiovascular diseases, lung cancer, metabolic disorder, osteoporosis, anxiety and depression, skeletal muscle dysfunction, cachexia, gastrointestinal diseases, and other respiratory conditions. Not only are comorbidities common but they also considerably influence disease prognosis and patients׳ health status, and are associated with poor clinical outcomes. However, perusal of literature indicates that little has been done so far to effectively assess, manage, and treat comorbidities in patients with COPD. The aim of this review is to comprehensively narrate the comorbid conditions that often coexist with COPD, along with their reported prevalence and their significant impacts in the disease management of COPD. A perspective on integrated disease management approaches for COPD is also discussed.

Preventive health care and owner-reported disease prevalence of horses and ponies in Great Britain

This study aimed to describe the provision of preventive health care and owner-reported disease prevalence in horses and ponies within Great Britain (GB), and to assess geographical variations in health care provision. A cross-sectional survey was conducted, using a postal questionnaire administered to a random sample of veterinary-registered owners of horses and ponies in GB (n=797). The majority of animals received regular preventive health care: 95.6% had regular hoof care; 71.3% were vaccinated for both influenza and tetanus and median time since last anthelmintic administration was 8.7 weeks. Thirty-one percent of owners indicated their animal was overweight/obese. A new health problem within the previous 7 days was reported for 7.4% of animals, 59.3% of which were veterinary-diagnosed. Thirty-two percent of animals were reported to have a long-term/recurrent condition, of which osteoarthritis (13.9%) was the most prevalent. Obesity, musculoskeletal disorders, and dermatological conditions were the most prevalent conditions affecting veterinary-registered horses/ponies.

Utilization of a mobile medical van for delivering pediatric care in the bateys of the Dominican Republic

Background

Bateys are impoverished areas of housing for migrant Haitian sugar cane workers in the Dominican Republic (DR). In these regions, preventative health care is almost non-existent, public service accessibility is limited, and geographic isolation prevents utilization of care even by those families with resources. Consequently, the development of a viable mobile system is vital to the delivery of acute and preventative health care in this region.

Aims

This study evaluated an existing mobile medical system. The primary goal was to describe the population served, diseases treated, and resources utilized. A secondary goal was to determine qualitatively an optimal infrastructure for sustainable health care delivery within the bateys.

Methods

Information on basic demographic data, diagnosis, chronicity of disease, and medications dispensed was collected on all pediatric patients seen in conjunction with an existing mobile medical system over a 3-month period in the DR. Health statistics for the region were collected and interviews were conducted with health care workers (HCWs) and community members on existing and optimal health care infrastructure.

Results

Five hundred eighty-four pediatric patients were evaluated and treated. Median age was 5 years (range 2 weeks to 20 years), and 53.7% of patients seen were 5 years of age or younger. The mean number of complaints per patient was 2.8 (range 0 to 6). Thirty-six percent (373) of all diagnoses were for acute complaints, and 64% (657) were chronic medical problems. The most common pediatric illnesses diagnosed clinically were gastrointestinal parasitic infection (56.6%), skin/fungal infection (46.2%), upper respiratory tract infections (URIs) (22.8%), previously undiagnosed asthma and allergies (8.2%), and symptomatic anemia (7.2%). Thirty HCWs and community members were interviewed, and all cited the need for similar resources: a community clinic and hospital referral site, health promoters within each community, and the initiation of pediatric training for community HCWs.

Conclusion

A mobile medical system is a sustainable, efficient mechanism for delivering acute and preventive care in the Haitian bateys of the Dominican Republic. The majority of patients served were 8 years of age or younger with multiple presenting symptoms. A pediatric protocol for identifying the most appropriate drugs and supplies for mobile units in the DR can be created based upon diseases evaluated. Qualitative data from HCWs and community members identified the need for an integrative health care delivery infrastructure and community health promoters versed in pediatric care who can aid in education of batey members and monitor chronic and acute illnesses. We are planning follow-up visits to implement these programs.