Association between Alzheimer\u27s disease and Rural Northeast Tennessee Region between 2013 and 2015

Background: Alzheimer’s disease (AD) is a type of Dementia and a neurodegenerative disease that is characterized by the gradual degrading of both memory and cognitive functions. According to the World Health Organization (WHO), the prevalence of AD is increasing globally. Currently, AD is the sixth leading cause of mortality in the United States. As the ageing population increases in the United States, it is possible that AD will move up the ladder in the top cause of mortality. Although the prevalence of AD in most urban parts of developed nations such as the United States is widely known, little is known about the prevalence and early diagnosis of the disease among the rural populations. According to a study by the Centers for Disease Control and Prevention (CDC), on deaths from AD between 1999 and 2014, most mortality are concentrated in the rural counties of the Appalachian region of the United States, where the mortality rate has increased by an alarming 75%. Our study focuses on the Northeast Tennessee region, which is a prominent part of the Appalachian region. We examine the prevalence of Alzheimer’s disease in the Northeast Tennessee region compared to other parts of the state of Tennessee. We sought to understand whether there is a likely association between the disease and the rural counties in the Northeast Tennessee region. Methods: We performed a cross-sectional study that computes and compares between the Prevalence Odds Ratio (POR) of the 2013 to 2015 Centers for Medicare and Medicaid Services Public Use Files data on rural versus urban counties in the Northeast Tennessee region followed by the Northeast Tennessee counties versus other counties in Tennessee. In addition, we collected primary data from 44 experts and professionals working in AD-related fields within the Northeast Tennessee region using an online survey that captures the perceived observation of the experts and professionals about the increasing prevalence of AD over the last five years. Results: Findings show that the rural counties within the Northeast Tennessee region had 18.3% (POR: 1.183, C.I: 1.113-1.258), 4.7% (POR: 1.047, C.I: 0.982-1.117), and 19% (POR: 1.190, C.I: 1.121-1.264) increased odds of prevalence of AD compared to the urban counties within the region in 2013, 2014, and 2015, respectively. Similarly, the Northeast Tennessee region as a whole, had increased odds of 22.7% (POR: 1.227, C.I: 1.203-1.250), 22.5% (POR: 1.225, C.I: 1.202-1.249), and 21.2% (POR: 1.212, C.I: 1.189-1.235) of AD compared to all other counties in Tennessee during the same periods. Conclusions: Statistical analysis and findings from experts and professionals working with patients with AD in the Northeast Tennessee region show that there are more cases of AD in the Northeast Tennessee region compared to the last five years. We suggest early screening strategies for possible decrease in the morbidity and mortality rates in Northeast Tennessee region

An Appraisal of Tourists’ Satisfaction with Community-Based Tourism for Sustainability in Ekiti State, Nigeria

This research work appraises the level of satisfaction of community-based tourism for sustainability in Ekiti State. The study used sustainability indicators as variables for assessing the tourists’ satisfaction. The study used the Demand and Supply Theory as the basis of its theoretical bedrock. A primary source of data was employed for the study. A total of 900 copies of well-structured questionnaires were administered to target respondents in nine communities in the study area. Eight hundred sixty-five copies of the administered questionnaires were retrieved and subjected to coding and analysis using MsExcel and SPSS, respectively. The data collected were represented using frequency and mean tables. It was found that the tourists and community dwellers were not satisfied with community-based tourism using sustainability indicators. It was recommended that amenities be made available at the host communities, including power supply, potable water, good roads, accommodation, parking facilities, relaxation centre, electricity, pipe-borne water, etc. This will undoubtedly increase the visibility of tourists sites in Ekiti State and beyond

An Appraisal of Tourists’ Satisfaction with Community-Based Tourism for Sustainability in Ekiti State, Nigeria

This research work appraises the level of satisfaction of community-based tourism for sustainability in Ekiti State. The study used sustainability indicators as variables for assessing the tourists’ satisfaction. The study used the Demand and Supply Theory as the basis of its theoretical bedrock. A primary source of data was employed for the study. A total of 900 copies of well-structured questionnaires were administered to target respondents in nine communities in the study area. Eight hundred sixty-five copies of the administered questionnaires were retrieved and subjected to coding and analysis using MsExcel and SPSS, respectively. The data collected were represented using frequency and mean tables. It was found that the tourists and community dwellers were not satisfied with community-based tourism using sustainability indicators. It was recommended that amenities be made available at the host communities, including power supply, potable water, good roads, accommodation, parking facilities, relaxation centre, electricity, pipe-borne water, etc. This will undoubtedly increase the visibility of tourists sites in Ekiti State and beyond

Deep Language Space Neural Network for Classifying Mild Cognitive Impairment and Alzheimer-Type Dementia

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. It has been quite a challenge to diagnose Mild Cognitive Impairment due to Alzheimer\u27s disease (MCI) and Alzheimer-type dementia (AD-type dementia) using the currently available clinical diagnostic criteria and neuropsychological examinations. As such we propose an automated diagnostic technique using a variant of deep neural networks language models (DNNLM) on the verbal utterances of affected individuals. Motivated by the success of DNNLM on natural language tasks, we propose a combination of deep neural network and deep language models (D2NNLM) for classifying the disease. Results on the DementiaBank language transcript clinical dataset show that D2NNLM sufficiently learned several linguistic biomarkers in the form of higher order n-grams to distinguish the affected group from the healthy group with reasonable accuracy on very sparse clinical datasets

Health Information Seeking and its Associated Factors among University Students: A Case in a Middle-Income Setting

This paper aims to describe health information seeking behaviour and identify its associated factors among undergraduate university students in developing countries. An online survey is used to collect data from 138 students. The data is analysed using the multivariate logistic regression analysis method. Results reveal that a substantial number of students have sought health information mostly from the Internet. Health literacy, perceived susceptibility to health problems and alcohol consumption are found to be the significant factors influencing health information seeking behaviour. Results provide an understanding of health information seeking behaviour in developing countries

Diet and Foraging Ecology of Fork Tailed Drongo (Dicrurusadsimilis) in Leventis Foundation Nigeria, Agricultural School South West Nigeria

This research study investigated the diet and foraging ecology of the Fork Tailed Drongo (Dicrurusadsimilis) inLeventis Foundation Nigeria Agricultural Training School, South Western Nigeria. Direct field observation method was used to collect data for 12 months on the diet and foraging ecology of these bird species. The study area was divided into three compartments according to land use types (secondary forest, Farmland and Developed Area). The result revealed that the Fork Tailed Drongo consumed variety of insects and pla nt species resources in the study area.Grasshoppers, butter flies and termites are the majorfood source and they also consumed the leaves and flowers of the Moringaoleifera and seeds, fruits of some tree species. Insect species provided the highest food source of 86% and plant species 14%. The result revealed that the Fork Tailed Drongo utilized the three Compartments within the study area and that secondary forest provided highest food materials of 65%, Farmland 23% and Developed area 12%

Behavioral Correlates for Quitting Opioids among Opioid-Dependent Pregnant and Non-Pregnant Women of Childbearing Age in Rural Appalachia

Background: The opioid epidemic is particularly worrisome in the pregnant population, wherein concerns are raised about the health of a mother and her child, resulting in an alarming incidence and prevalence of Neonatal Abstinence Syndrome (NAS). The 2016 National Survey on Drug Use and Health (NSDUH) show the rate of illicit psychoactive substance use among the females aged 12 or older was 15.5% in the past year. Among pregnant women aged 15 to 44, 6.3% were illicit psychoactive substance users. In Tennessee, the number of hospital discharged NAS cases from 2002 to 2013 increased from 1.50 to 16.6 cases per 1,000 live births. This number is triple the national incidence of NAS cases over the same time period. Between 2013 and 2016, at least 52.5% of children diagnosed with NAS in Tennessee have had exposure to one prescription drug, while 27.2% were exposed to a combination of prescribed medications and illicit substances. We examined the behavioral correlates that determine the wish to quit opioids or not to quit opioids among opioid-dependent pregnant and non-pregnant women in rural Appalachia. Methods: Ten women of childbearing age, whether pregnant or not, who were receiving prescribed opioids, were recruited to join the study. All the participating women were also receiving physician-managed Medication Assisted Treatment (MAT) therapy for the treatment of severe opioid use disorder, or are currently being prescribed an opioid medication. Study variables included age, Hamilton Depression Rating Scale (HAM-D), Visual Analogue Scale – Pain (VAS-P), the Modified Opiate Craving Scale (MOCS), the Visual Analog Commitment to Quit Opiates, the McGill Pain Index (MPI), prescriptions, tobacco and nicotine use, illicit substance use, the Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES), and the Adverse Childhood Experience (ACE) questionnaire. The HAM-D, MOCS, MPI, and SOCRATES scores were log-transformed to approximate a normal distribution. Descriptive statistics and the Spearman’s rank correlation (with a 95% Confidence Interval) were conducted to examine significant behavioral correlates for quitting opioids. Results: Descriptive statistics show that women with higher HAM-D and MOCS scores are not likely to express willingness to quit opioids. There is a statistically significant strong positive correlation of 0.679 (pppp Conclusion: Women who recognize the need to quit opioids or are “taking steps” to quit are more likely to quit opioids. Women with high depression and pain scores are not likely to quit opioids. Non-opioid medications may reduce the number of opioid-dependent pregnant and non-pregnant women of childbearing age, and, in turn, lower the currently high incidence and prevalence rates of NAS