Local Geographic Areas Research Articles

The heart of the matter: neighborhood violent crime and deprivation, cardiometabolic disease, and the racial invariance thesis

ABSTRACT Studies have observed a tight spatial coupling of elevated violent crime rates, structural deprivation, and cardiometabolic disease prevalence across geographic areas. Conceptual perspectives in the health sciences provide a framework to explain this geographic concentration of poor health and crime, which differs from perspectives derived from macro-level criminology. Drawing on these different conceptual approaches, the current study uses administrative and survey data aggregated to Census tracts to examine whether violent crime and structural deprivation are factors for cardiometabolic disease prevalence. Drawing on the racial invariance thesis, the study also tests whether these patterns are invariant across neighborhoods of different racial and ethnic compositions. Multivariate results reveal that cardiometabolic disease varies directly with levels of violent crime but not structural deprivation. Furthermore, the results do not provide consistent support for the racial and ethnic invariance hypothesis. We discuss the implications of the findings for prevention models focused on modifiable risk factors of poor health in local geographic areas.

Birding specialization and satisfaction in Australian birders – a Big Year is not a big issue

ABSTRACT Birding as a non-consumptive, nature-related outdoor activity is becoming increasingly popular. Here, using an online survey of Australian birders, we focussed on birding as a construct of recreation specialization and considered three aspects: perceived level of skill/knowledge, behaviour, and involvement. We analysed demographic effects (age, degree and payment for birding services) in relation to level of birding specialization, as well as leisure satisfaction for the first time in Australian birders. We further studied the “Big Year”. Birders can be classified into three groups: casual/novice, intermediate and advanced. Their satisfaction with birding as a leisure activity was generally high. More highly skilled and committed birders reported higher satisfaction, as did birders who participated more frequently. We also found that paid birders reported higher skill/knowledge compared to unpaid birders. Most respondents never participated in a Big Year, but if they did, it was most often in their local geographic area. From a theoretical viewpoint, leisure activity satisfaction should be studied on the general (trait) level as well as on the situational place level in situ. As a managerial implication, institutionalizing a local Big Year in a nearby birding hotspot (with minimal travelling time and costs) may be an enhancing and satisfying task.

Recovery Support and Capacity Assessment Using the Calculating an Adequate System Tool: Two Case Studies.

There are multiple, reliable, and authoritative federally managed data sources for understanding the incidence and prevalence of substance use disorder (SUD) and its sequela. However, there remains a gap in metrics representing the need and capacity for treatment and related supports within local communities. To address this challenge, Calculating an Adequate System Tool (CAST) was developed in 2016 by an interdisciplinary group of researchers at the Substance Abuse and Mental Health Services Administration Center for Behavioral Health Statistics and Quality to assess the capacity of the SUD care system within a defined geographic area. It allows for risk assessment of local social and community determinants of substance abuse, as well as an assessment of local service needs across the continuum of SUD care. This article describes the application of the CAST to 2 counties in Ohio and 1 county in Montana. The purpose of using CAST for each area, results of the application, and experiences in utilizing the tool are described. Application of the CAST demonstrated unique findings within each of the geographic areas. In Ohio, recovery support services were lacking in both counties assessed, while differences in crime rate and alcohol outlet density were attributed to varying rates of drug-related hospitalization. Notable findings in Montana included an oversaturation of coalitions focused on substance use prevention and gaps in the areas of detoxification services, partial day treatment, recovery residences, and peer support specialists. CAST is a useful tool for guiding decision-making relative to substance use care needs and capacities for local geographic areas. Findings should be interpreted thoughtfully and in the context of data availability. CAST continues to be enhanced and further expanded for assessing capacity of local and statewide substance use care systems.

Diversity in the United Kingdom: Quantification for higher education in comparison to the general population

AbstractDiversity in the United Kingdom is regularly quantified through Census data. The latest figures (2021) for England and Wales indicate that 82% of the population identifies as white, 51% are females, 17.7%–22.3% are disabled, 18.2% hold no qualifications and 51.7% of households are deprived in at least one dimension. Furthermore, the median age in England and Wales is 40. All of these figures vary significantly across local geographical areas. Diversity in Higher Education (HE) is also monitored yearly by the Office for Students. The latest figures (2020/2021) indicate that 68.4% of entrants are under 21 years old, 56.5% are females, 14.8% report a disability and 21.8% are categorized as severely deprived. Some differences were observed between these figures and those from previous years. The current study aims to highlight how diversity in HE has evolved since 2010 and how the current landscape can illustrate significant differences between courses. Furthermore, comparisons with the general population are also measured in an attempt to describe potential bias in HE, together with new avenues that should be explored to level the HE field in regard to diversity. Our results indicate that access to HE needs to be improved for males, while strong discrepancies were observed between disciplines. Ethnic diversity remains high throughout the HE sector, although subject‐specific biases were noted. An increase in students from the most deprived areas has been found, although it was not the case for all subjects within the sector. Finally, reported disabilities are on the rise, especially regarding mental health, warranting additional support for affected students. These findings are discussed and put into context. To conclude, HE providers might need to collegially address subject‐specific discrepancies.

(454) Variations in Practice Patterns of Xiaflex Providers Throughout the United States

Abstract Introduction Intralesional Collagenase Clostridium Histolyticum (CCH; Xiaflex; Endo Pharmaceuticals, Dublin, Ireland) is the only FDA-approved intralesional therapy to treat bothersome penile curvature in men with Peyronie’s Disease (PD). Clinicians who administer CCH must be certified through a risk evaluation and management strategy (REMS) program. However, provider availability is highly dependent on geography and many patients must travel great distances to undergo evaluation and treatment with CCH by an experienced injector. Objective The goal of the current study is to describe the landscape of certified CCH injectors with respect to location and clinical background/training. Methods Patients are currently able to use an online “locate a specialist” tool through Endo Pharmaceuticals to identify clinicians in their local geographic area who are certified to administer CCH. Using this tool, a list of all providers who offer CCH injections was compiled throughout the continental United States. Pertinent information that was gathered included geographic location, type of provider or level of training [Medical Doctor (MD), Doctor of Osteopathic Medicine (DO), Physician Assistant (PA), Nurse Practitioner (NP), and Registered Nurse (RN)], and gender of providers. This information was compared to state population data (United State Census Bureau) to determine per capita coverage. Results A total of 1947 Xiaflex providers were identified: 1749 Doctors of Medicine (MD; 90%), 115 Doctor of Osteopathic Medicine (DO; 6%), 60 Physician Assistants (PA; 3%), 21 Nurse Practitioners (NP; 1%), and 1 Registered Nurse (RN; 0.05%). Out of the 1947 providers, only 95 (4.9%) were women. Most women administering Xiaflex were MDs (73%), followed by PAs (12%), NP and DOs (7%), and RN (1%). Xiaflex providers were available in all states except for Hawaii and Alaska. Providers were distributed throughout the United States with the average provider per capita ratio of 230,240. Wyoming, Washington D.C., Rhode Island, and Georgia had the best provider per capita at <10,000 while New Mexico had the worst provider per capita ratio at 2,117,522. Conclusions Our results show significant variation in certified CCH injector availability for patients across the United States. < 5% of all certified clinicians are female and 4% were advanced practice providers. Further work is needed to characterize and remedy gender disparities amongst providers, and ensure greater access for patients who desire an FDA-approved non-surgical treatment for their PD-associated curvature. Disclosure No

Analyzing COVID-19 data in the Canadian province of Manitoba: A new approach

The basic homogeneous SEIR (susceptible–exposed–infected–removed) model is a commonly used compartmental model for analysing infectious diseases such as influenza and COVID-19. However, in the homogeneous SEIR model, it is assumed that the population of study is homogeneous and, one cannot incorporate individual-level information (e.g., location of infected people, distance between susceptible and infected individuals, vaccination status) which may be important in predicting new disease cases. Recently, a geographically-dependent individual-level model (GD-ILM) within an SEIR framework was developed for when both regional and individual-level spatial data are available. In this paper, we propose to use an SEIR GD-ILM for each health region of Manitoba (central Canadian province) population to analyse the COVID-19 data. As different health regions of the population under study may act differently, we assume that each health region has its own corresponding parameters determined by a homogeneous SEIR model (such as contact rate, latent period, infectious period). A Monte Carlo Expectation Conditional Maximization (MCECM) algorithm is used for inference. Using estimated parameters we predict the infection rate at each health region of Manitoba over time to identify highly risk local geographical areas. Performance of the proposed approach is also evaluated through simulation studies.

Recruiting and engaging adolescents in creating overweight and obesity prevention policies: The CO-CREATE project.

The CO-CREATE project aims to collaborate with adolescents across Europe in developing policy ideas that contribute to overweight and obesity prevention. In this paper, we present the theoretical basis and methodological approach to recruitment and engagement in the project. The principles of youth-led participatory action research were employed to design Youth Alliances in which adolescents and adults could collaborate. These Alliances should serve to promote and support adolescent participation and to develop policy ideas that would contribute to obesity prevention. Alliance members were recruited in two local geographical areas per country with a focus on reaching out to underrepresented youth. We started with fieldwork to assess locally relevant forms of inclusion and exclusion. The methodology entailed a handbook combining existing tools which could be used flexibly, a collaborative organization, and budgets for the alliances. Engagement started in local organizations, that is, schools and scouts, and with peers. Health- and overweight-related challenges were addressed in their immediate surroundings and supported the inclusion of experiential knowledge. Adolescents were then supported to address the wider obesogenic system when designing policy ideas. The CO-CREATE Alliances provide a concrete example of how to engage youth in public health, in a manner that strives to be participatory, transformative, and inquiry based.

Model-based community health surveillance via multilevel small area estimation using state behavioral risk factor surveillance system (BRFSS): a case study in Connecticut

PurposeThe main objective of state behavioral risk factor surveillance system (BRFSS) is to produce reliable state-level estimates of various population health outcomes. A multilevel Regression and Post-stratification (MRP) methodology for small area estimation has been applied to the 500 Cities Project to provide population estimates at both city-level and census tract-level using national BRFSS data. To date, MRP has not been applied to any state BRFSS to produce health data at local geographic areas. In addition, the use of single year BRFSS might produce temporary inconsistency in small area estimates (SAEs). The predicted standard errors (SEs) and confidence intervals (CIs) of SAEs using Monte Carlo simulation could be substantially underestimated or overestimated. MethodsBy extending the current MRP approach and applying a parametric bootstrapping approach to Connecticut BRFSS (CT BRFSS), we were able to produce SAEs as well as SEs and CIs of SAEs for Connecticut counties and towns. We also applied this model to 5-year CT BRFSS (2011–2015) with an aim to improve the temporary consistency of SAEs. ResultsBoth single-year and 5-year estimates with SEs and CIs were generated for six selected population health indicators at town, county and state levels. Model-based SAEs were internally evaluated by comparing to single-year and 5-year direct BRFSS survey (2011–2015). SAEs were also externally validated when external data were available. ConclusionsModel-based SAEs are valid and could be used to characterize local geographic variations using single state BRFSS data.

Property valuation using machine learning algorithms on statistical areas in Greater Sydney, Australia

Property valuation plays a significant role in urban economics and is of great importance to various stakeholders who interact and shape the city, including property owners, buyers, banks, land developers, real estate agents, local councils and government planning authorities. In the literature, various predictive models have been proposed to automate the calculation of property value, most of which endeavour to factor in the combination of property characteristics, market factors and location-based attributes associated with individual properties use large citywide databases. At the same time, it has been widely acknowledged that regional sub-areas have impacts on property price prediction. Therefore, this paper aims to investigate the performance of various techniques on sub-areas using the Greater Sydney Region as the study area. The sub-area in this paper is defined as the statistical areas (SAs) as defined by the Australian Bureau of Statistics. In particular, two different SA geographies (SA4, SA3) along with the City Level are adopted to understand the spatial dependence which occurs at different levels. With real-world transaction records and data collected from a diverse range of sources, various methods including the traditional hedonic price model (HPM) and popular machine learning (ML) approaches are implemented and evaluated for property price prediction. Two different property markets for residential property are modelled, being for housing stock and apartment (unit) stock. Experimental results show that Random Forest and Gradient Boosting-based methods outperform other approaches in most scenarios and that the high spatial resolution property sub-area (SA3) improved the performance in terms of overall model accuracy. This research provides insights into how sub-area machine learning models can be employed in real estate to characterize property price, and helps understand the influential factors in different local geographical areas for policy-making.

Spatiotemporal Variation and Predictors of Unsuppressed Viral Load among HIV-Positive Men and Women in Rural and Peri-Urban KwaZulu-Natal, South Africa.

Unsuppressed HIV viral load is an important marker of sustained HIV transmission. We investigated the prevalence, predictors, and high-risk areas of unsuppressed HIV viral load among HIV-positive men and women. Unsuppressed HIV viral load was defined as viral load of ≥400 copies/mL. Data from the HIV Incidence District Surveillance System (HIPSS), a longitudinal study undertaken between June 2014 to June 2016 among men and women aged 15–49 years in rural and peri-urban KwaZulu-Natal, South Africa, were analysed. A Bayesian geoadditive regression model which includes a spatial effect for a small enumeration area was applied using an integrated nested Laplace approximation (INLA) function while accounting for unobserved factors, non-linear effects of selected continuous variables, and spatial autocorrelation. The prevalence of unsuppressed HIV viral load was 46.1% [95% CI: 44.3–47.8]. Predictors of unsuppressed HIV viral load were incomplete high school education, being away from home for more than a month, alcohol consumption, no prior knowledge of HIV status, not ever tested for HIV, not on antiretroviral therapy (ART), on tuberculosis (TB) medication, having two or more sexual partners in the last 12 months, and having a CD4 cell count of <350 cells/μL. A positive non-linear effect of age, household size, and the number of lifetime HIV tests was identified. The higher-risk pattern of unsuppressed HIV viral load occurred in the northwest and northeast of the study area. Identifying predictors of unsuppressed viral load in a localized geographic area and information from spatial risk maps are important for targeted prevention and treatment programs to reduce the transmission of HIV.

Who Cares More about Chemical Defenses — the Macroalgal Producer or Its Main Grazer?

The consequences of defensive secondary metabolite concentrations and interspecific metabolite diversity on grazers have been extensively investigated. Grazers which prefer certain food sources are often found in high abundance on their host and as a result, understanding the interaction between the two is important to understand community structure. The effects of intraspecific diversity, however, on the grazer are not well understood. Within a single, localized geographic area, the Antarctic red seaweed Plocamium sp. produces 15 quantitatively and qualitatively distinct mixtures of halogenated monoterpenes ("chemogroups"). Plocamium sp. is strongly chemically defended which makes it unpalatable to most grazers, except for the amphipod Paradexamine fissicauda. We investigated differences in the feeding and growth rates of both Plocamium sp. and P. fissicauda, in addition to grazer reproductive output, in relation to different chemogroups. Some chemogroups significantly reduced the grazer's feeding rate compared to other chemogroups and a non-chemically defended control. The growth rate of Plocamium sp. did not differ between chemogroups and the growth rates of P. fissicauda also did not show clear patterns between the feeding treatments. Reproductive output, however, was significantly reduced for amphipods on a diet of algae possessing one of the chemogroups when compared to a non-chemically defended control. Hence, intraspecific chemodiversity benefits the producer since certain chemogroups are consumed at a slower rate and the grazer's reproductive output is reduced. Nevertheless, the benefits outweigh the costs to the grazer as it can still feed on its host and closely associates with the alga for protection from predation.

Estimating the Effects of Public Health Measures by SEIR(MH) Model of COVID-19 Epidemic in Local Geographic Areas.

The COVID-19 pandemic of 2020–21 has been a major challenge to public health systems worldwide. Mathematical models of epidemic are useful tools for assessment of the situation and for providing decision-making support for relevant authorities. We developed and implemented SEIR(MH) model that extends the conventional SEIR model with parameters that define public lockdown (the level and start of lockdown) and the medical system capacity to contain patients. Comparative modeling of four regions in Europe that have similar population sizes and age structures, but different public health systems, was performed: Baden-Württemberg, Lombardy, Belgium, and Switzerland. Modeling suggests that the most effective measure for controlling epidemic is early lockdown (exponential effect), followed by the number of available hospital beds (linear effect if the capacity is insufficient, with diminishing returns when the capacity is sufficient). Dynamic management of lockdown levels is likely to produce better outcomes than strict lockdown.

Clustering of Socioeconomic Data in Hong Kong for Planning Better Community Health Protection.

The concept of socioeconomic vulnerability has made a substantial contribution to the understanding and conceptualization of health risk. To assess the spatial distribution of multi-dimensional socioeconomic vulnerability in an urban context, a vulnerability assessment scheme was proposed to guide decision-making in disaster resilience and sustainable urban development to reduce health risk. A two-stage approach was applied in Hong Kong to identify subgroups among Tertiary Planning Units (TPU) (i.e., the local geographic areas) with similar characteristics. In stage 1, principal components analysis was used for dimension reduction and to de-noise the socioeconomic data for each TPU based on the variables selected, while in stage 2, Gaussian mixture modeling was used to partition all the TPUs into different subgroups based on the results of stage 1. This study summarized socioeconomic-vulnerability-related data into five principal components, including indigenous degree, family resilience, individual productivity, populous grassroots, and young-age. According to these five principal components, all TPUs were clustered into five subgroups/clusters. Socioeconomic vulnerability is a concept that could be used to help identify areas susceptible to health risk, and even identify susceptible groups in affluent areas. More attention should be paid to areas with high populous grassroots scores and low young-age score since they were associated with a higher mortality rate.

Necrotising otitis externa: a serious condition becoming more frequently encountered.

Necrotising otitis externa is an aggressive infection of the external ear, which extends to the surrounding bone and soft tissue. In recent years, there has been an apparent increase in the number of patients admitted to our hospital with this condition. A retrospective review was conducted of all patients admitted to our hospital with necrotising otitis externa between July 2012 and June 2020. Among 39 patients included, only 9 were diagnosed in the first four years, and 30 were diagnosed in the last four years. There were 27 males and 12 females, and the mean age was 78.7 years. There were six non-diabetic immunocompetent patients. Cranial nerve palsies developed in 50 per cent of the patients. Disease-related mortality was 7.7 per cent. A favourable outcome was recorded in 66.7 per cent of the patients. Necrotising otitis externa is associated with high morbidity and mortality. The incidence of the disease is rising in our local geographical area.

Associations between the composition of functional tooth units and nutrient intake in older men: the Concord Health and Ageing in Men Project.

Inadequate nutrient intakes have been linked with poor dentition in older adults. The aim of this study was to investigate the associations between the composition of functional tooth units (FTU) and nutrient intakes in older men. A cross-sectional study with a standardised validated diet history assessment and comprehensive oral health assessments. FTU were categorised by dentition type: (i) Group A (Natural FTU Only), (ii) Group B (Natural and Replaced FTU) and (iii) Group C (No Natural FTU). Attainment of nutrient reference values (NRV) for sixteen micronutrients was incorporated into a micronutrient risk variable, dichotomised 'good' (≥ 12) or 'poor' (≤ 11), and for seven macronutrients into a macronutrient risk variable, dichotomised 'good' (≥ 5) or 'poor' (≤ 4). Subjects selected from the local Sydney geographical areas. Community-dwelling older men (n 608). 32 % (n 197) of participants were categorised as Group A, 27 % (n 167) as Group B and 40 % (n 244) as Group C. In adjusted logistic regression analysis, being in Group C, compared with Group A, was associated with intakes below NRV recommendations for fibre (OR: 2·30, 95 % CI 1·30, 4·05). Adjusted analysis also showed that men in Group C, compared with Group A, were more likely to have poor intake of macronutrients (OR: 2·00, 95 % CI 1·01, 3·94). Our study shows statistically significant associations between the composition of FTU and poor macronutrient intakes. Maintaining natural pairs of occluding FTU may be important for attaining adequate nutrient intakes in older men.

Habitat prediction modelling for vulture conservation in Gangetic-Thar-Deccan region of India.

Ecologically and economically important obligate scavengers like vultures are under threat of extinction in the old world. Several resident and migratory vulture sites and individuals are hosted by the Gangetic-Thar-Deccan region of India with varied landscapes. The landscape is under threat from anthropogenic activities and climate change impacting the habitat. Therefore, habitat suitability of vultures was analysed using species distribution model, MaxEnt, ensemble of global circulation models (CCSM4, HadGEM2AO and MIROC5), citizen science and expert collected data. Altogether, 51 models were developed and their robustness was assessed to be good for conservation purpose (AUC range 0.719-0.906). Predicted unsuitable and suitable area categories of all vultures, resident vultures and migratory vultures were identified for the present and future years (2050 and 2070) under moderate and extreme emission scenarios (RCP 4.5 and RCP 8.5). The short-term and long-term area suitability change varied between 1 and 3%. Area suitability differences were also noticed among larger (global) and smaller (local) geographical areas. The bioenvironmental parameters (land use, land cover and human footprint) played a major role in habitat determination in the current scenario. Bioclimatic factors, like precipitation parameters (precipitation seasonality bio 15 and annual precipitation bio12) and temperature parameters (isothermality bio 3 and temperature seasonality bio04), were the main model determining covariates for future prediction. An earlier hypothesis of higher suitability of forest and lower suitability of agriculture area tested in this study stood modified. Implications of the results are discussed, and conservation strategies are suggested with an advice of global strategy and local execution.

Geographic Variation in the Prevalence of Rheumatoid Arthritis in Alberta, Canada.

ObjectivesTimely access to rheumatologists remains a challenge in Alberta, a Canadian province with vast rural areas, whereas rheumatologists are primarily clustered in metro areas. To address the goal of timely and equitable access to rheumatoid arthritis (RA) care, health planners require information at the regional and local level to determine the RA prevalence and the associated health care needs.MethodsUsing Alberta Health administrative databases, we identified RA‐prevalent cases (April 1, 2015‐March 31, 2016) on the basis of a validated case definition. Age‐ and sex‐standardized prevalence rates per 1000 population members and the standardized rates ratio (SRR) were calculated. We applied Global Moran’s I and Gi* hotspot analysis using three different weight matrices to explore the geospatial pattern of RA prevalence in Alberta.ResultsAmong 38 350 RA cases (68% female; n = 26 236), the prevalence rate was 11.81 cases per 1000 population members (95% confidence interval [CI] 11.80‐11.81) after age and sex standardization. Approximately 60% of RA cases resided in metro (Calgary and Edmonton) and moderate metro areas. The highest rate was observed in rural areas (14.46; 95% CI 14.45‐14.47; SRR 1.28), compared with the lowest in metro areas (10.69; 95% CI 10.68‐10.69; SRR 0.82). The RA prevalence across local geographic areas ranged from 4.7 to 30.6 cases. The Global Moran’s I index was 0.15 using three different matrices (z‐score 3.96‐4.24). We identified 10 hotspots in the south and north rural areas and 18 cold spots in metro and moderate metro Calgary.ConclusionThe findings highlight notable rural–urban variation in RA prevalence in Alberta. Our findings can inform strategies aimed at reducing geographic disparities by targeting areas with high health care needs.

Geospatial patterns of comorbidity prevalence among people with osteoarthritis in Alberta Canada

BackgroundKnowledge of geospatial pattern in comorbidities prevalence is critical to an understanding of the local health needs among people with osteoarthritis (OA). It provides valuable information for targeting optimal OA treatment and management at the local level. However, there is, at present, limited evidence about the geospatial pattern of comorbidity prevalence in Alberta, Canada.MethodsFive administrative health datasets were linked to identify OA cases and comorbidities using validated case definitions. We explored the geospatial pattern in comorbidity prevalence at two standard geographic areas levels defined by the Alberta Health Services: descriptive analysis at rural-urban continuum level; spatial analysis (global Moran’s I, hot spot analysis, cluster and outlier analysis) at the local geographic area (LGA) level. We compared area-level indicators in comorbidities hotspots to those in the rest of Alberta (non-hotspots).ResultsAmong 359,638 OA cases in 2013, approximately 60% of people resided in Metro and Urban areas, compared to 2% in Rural Remote areas. All comorbidity groups exhibited statistically significant spatial autocorrelation (hypertension: Moran’s I index 0.24, z score 4.61). Comorbidity hotspots, except depression, were located primarily in Rural and Rural Remote areas. Depression was more prevalent in Metro (Edmonton-Abbottsfield: 194 cases per 1000 population, 95%CI 192–195) and Urban LGAs (Lethbridge-North: 169, 95%CI 168–171) compared to Rural areas (Fox Creek: 65, 95%CI 63–68). Comorbidities hotspots included a higher percentage of First Nations or Inuit people. People with OA living in hotspots had lower socioeconomic status and less access to care compared to non-hotspots.ConclusionsThe findings highlight notable rural-urban disparities in comorbidities prevalence among people with OA in Alberta, Canada. Our study provides valuable evidence for policy and decision makers to design programs that ensure patients with OA receive optimal health management tailored to their local needs and a reduction in current OA health disparities.

Oil and Fat Business of Ukraine: Marketing Orientation for Achieving Competitive Advantages

The article examines theoretical approaches to achieving the competitive advantages of Ukrainian oil and fat enterprises in modern market conditions. Special attention is paid to the peculiarities of the use in business management of marketing concepts of business organization, which are oriented at the market, needs of consumers, society and other stakeholders. The most effective results for the enterprises are provided by the implementation of marketing management concepts that are oriented at identifying and meeting consumer demand as well as consideration of the interests of all other stakeholders in the supply chain of oil and fat products, the individual, community and society, i.e. concepts of marketing interaction and holistic marketing.&#x0D; The supply chain of oil and fat products includes the economic interests of business entities that develop varieties of oil and fat seeds, machinery and technology, supply of resources, production of raw materials, processing, storage and transportation, etc. A key position in the supply chain is occupied by the consumer. The success of the marketing strategy of the enterprise depends on the degree of consideration of the interests of the individual, communities, society and the state.&#x0D; It is concluded that oil and fat companies in the food industry can gain sustainable competitive advantages due to integration efforts aimed to form agricultural and food clusters in local geographical areas from all stakeholders and relationships in the supply chain of oil and fat products including suppliers, manufacturers, intermediaries, consumers, government and other organizations, etc.&#x0D; Keywords: oil and fat business, marketing orientation, competitive advantages, supply chain, stakeholders, interests, oil and fat cluster.

Genomic Analysis Reveals the Genetic Determinants Associated With Antibiotic Resistance in the Zoonotic Pathogen Campylobacter spp. Distributed Globally.

The genus Campylobacter groups 32 Gram-negative bacteria species, several being zoonotic pathogens and a major cause of human gastroenteritis worldwide. Antibiotic resistant Campylobacter is considered by the World Health Organization as a high priority pathogen for research and development of new antibiotics. Genetic elements related to antibiotic resistance in the classical C. coli and C. jejuni species, which infect humans and livestock, have been analyzed in numerous studies, mainly focused on local geographical areas. However, the presence of these resistance determinants in other Campylobacter species, as well as in C. jejuni and C. coli strains distributed globally, remains poorly studied. In this work, we analyzed the occurrence and distribution of antibiotic resistance factors in 237 Campylobacter closed genomes available in NCBI, obtained from isolates collected worldwide, in different dates, from distinct hosts and comprising 22 Campylobacter species. Our data revealed 18 distinct genetic determinants, genes or point mutations in housekeeping genes, associated with resistance to antibiotics from aminoglycosides, β-lactams, fluoroquinolones, lincosamides, macrolides, phenicols or tetracyclines classes, which are differentially distributed among the Campylobacter species tested, on chromosomes or plasmids. Three resistance determinants, the blaOXA–493 and blaOXA–576 genes, putatively related to β-lactams resistance, as well as the lnu(AN2) gene, putatively related to lincosamides resistance, had not been reported in Campylobacter; thus, they represent novel determinants for antibiotic resistance in Campylobacter spp., which expands the insight on the Campylobacter resistome. Interestingly, we found that some of the genetic determinants associated with antibiotic resistance are Campylobacter species-specific; e.g., the blaOXA–493 gene and the T86V mutation in gyrA were found only in the C. lari group, whereas genes associated with aminoglycosides resistance were found only in C. jejuni and C. coli. Additional analyses revealed how are distributed the resistance and multidrug resistance Campylobacter genotypes assessed, with respect to hosts, geographical locations, and collection dates. Thus, our findings further expand the knowledge on the factors that can determine or favor the antibiotic resistance in Campylobacter species distributed globally, which can be useful to choose a suitable antibiotic treatment to control the zoonotic infections by these bacteria.