Chronic Conditions
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2021 ◽  
S.N. Yadav ◽  
N. Ahmed ◽  
A.J. Nath ◽  
P.K. Boro

The haematological analysis is one of the essential diagnostic and prognostic tools for the health practitioner. Routine hematology consists of erythrocyte, leucocyte and platelet parameters estimation. Erythrocyte parameters (RBC, RDW, haemoglobin, haematocrit, MCV, MCH, MCHC) estimation plays a crucial role in identifying anemia and several other acute and chronic conditions. Accurate and precise haematology results depend on correct blood collection procedures, suitable anticoagulants, proper storage and effective blood transport. The individual reference value variance can be due to age, sex, stress, diet, body condition, hydration status and reproductive status. Automatic haeamtology analyzer can yield quick and accurate results provided the sample is free from any artifacts. In conclusion, the accuracy of the result of automatic haematology analyzer in canine medicine is impeded by the lack of precise and rapid comparison procedure, instability and complexity of blood cells. Therefore the findings of the automatic haemotolyzer should always be corroborated with the clinical findings and another laboratory test.

2021 ◽  
Chinmay Belthangady ◽  
Stefanos Giampanis ◽  
Will Stedden ◽  
Paula Alves ◽  
Stephanie Chong ◽  

Abstract Type 2 Diabetes is associated with severe health outcomes, the effects of which are responsible for approximately 1/4 of total U.S. healthcare spending. Current treatment guidelines endorse a massive number of potential anti-hyperglycemic treatment options in various permutations and combinations. Personalized strategies for optimizing treatment selection are lacking. Real-world data from a nationwide population of over one million diabetics was analyzed to evaluate the comparative effectiveness of more than 80 different treatment strategies ranging from monotherapy up to combinations of five concomitant classes of drugs across each of 10 clinical subgroups defined by age, insulin dependence, and number of other chronic conditions. A causal deep learning approach developed on such data allows for more personalized recommendations of treatment selection. Significant differences were observed in blood sugar reduction between patients receiving high vs low ranked treatment options and that less than 2% of the population is on a highly ranked treatment. This method can be extended to explore treatment optimization of other chronic conditions.

Kathrin Zürcher ◽  
Carole Dupont ◽  
Peter Weber ◽  
Sebastian Grunt ◽  
Ilca Wilhelm ◽  

AbstractEvidence on the use and efficacy of medical cannabis for children is limited. We examined clinical and epidemiological characteristics of medical cannabis treatment and caregiver-reported effects in children and adolescents in Switzerland. We collected clinical data from children and adolescents (< 18 years) who received Δ9-tetrahydrocannabinol (THC), cannabidiol (CBD), or a combination of the two between 2008 and 2019 in Switzerland. Out of 205 contacted families, 90 agreed to participate. The median age at the first prescription was 11.5 years (interquartile range (IQR) 6–16), and 32 patients were female (36%). Fifty-one (57%) patients received CBD only and 39 (43%) THC. Patients were more likely to receive THC therapy if one of the following symptoms or signs were present: spasticity, pain, lack of weight gain, vomiting, or nausea, whereas seizures were the dominant indication for CBD therapy. Improvements were reported in 59 (66%) study participants. The largest treatment effects were reported for pain, spasticity, and frequency of seizures in participants treated with THC, and for those treated with pure CBD, the frequency of seizures. However, 43% of caregivers reported treatment interruptions, mainly because of lack of improvement (56%), side effects (46%), the need for a gastric tube (44%), and cost considerations (23%).Conclusions: The effects of medical cannabis in children and adolescents with chronic conditions are unknown except for rare seizure disorders, but the caregiver-reported data analysed here may justify trials of medical cannabis with standardized concentrations of THC or CBD to assess its efficacy in the young. What is Known:• The use of medical cannabis (THC and CBD) to treat a variety of diseases among children and adolescents is increasing.• In contrast to adults, there is no evidence to support the use of medical cannabis to treat chronic pain and spasticity in children, but substantial evidence to support the use of CBD in children with rare seizure disorders. What is New:• This study provides important insights into prescription practices, dosages, and treatment outcomes in children and adolescents using medical cannabis data from a real-life setting.• The effects of medical cannabis in children and adolescents with chronic conditions shown in our study support trials of medical cannabis for chronic conditions.

2021 ◽  
pp. 019394592110319
Sophia Liu ◽  
Christian Nwabueze ◽  
Yue Pan ◽  
Suzy Mascaro Walter ◽  
Brenda Su ◽  

This study examined the associations of polysubstance use, mood disorders, and chronic conditions with the history of anxiety disorder among patients with opioid use disorder (OUD). We performed a secondary analysis of the baseline data from a clinical trial including 1,645 individuals with OUD, of which 513 had anxiety disorder. Substance use disorders (SUDs) included alcohol, amphetamines, cannabis, cocaine, and sedative use disorders. Mood disorders included major depressive disorder (MDD) and bipolar disorder (BD). Chronic conditions were allergies, gastrointestinal problem(s), skin problem(s), and hypertension. Sedative use disorder, MDD, BD, skin problems, and hypertension were significantly associated with anxiety disorder ( p < 0.05). Additionally, more than two SUDs, two mood disorders, and more than two chronic conditions were significantly associated with anxiety disorder ( p < 0.05). These findings highlight the comorbid mental health and physical health problems in individuals with OUD, as well as the need for integrated multidisciplinary treatment plans.

Geriatrics ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 71
Supa Pengpid ◽  
Karl Peltzer

This study aimed to determine the prevalence of geriatric conditions and their association with disability in older community-dwelling adults in India. The cross-sectional sample consisted of 31,477 individuals (≥60 years) from the Longitudinal Ageing Study in India (LASI) Wave 1 in 2017–2018. Geriatric conditions assessed included injurious falls, impaired cognition, underweight, dizziness, incontinence, impaired vision and impaired hearing. More than two in five participants (44.3%) had no geriatric condition, 32.7% had one, 15.9% two and 7.1% had three or more geriatric conditions; 26.9% were underweight, 14.5% dizziness, 13.7% had impaired vision, 9.6% impaired hearing, 9.3% impaired cognition, 8.2% major depressive disorder, 5.7% injurious falls, 4.0% incontinence, and 7.4% had Activity of Daily Living (ADL) dependencies. In logistic regression analysis, adjusted by sociodemographic factors and the number of chronic conditions, we found a higher number of geriatric conditions, and a higher number of chronic conditions were associated with ADL dependencies. In a model adjusted for sociodemographic factors and the type of chronic conditions, we found that a higher number of geriatric conditions and heart disease, stroke, and bone or joint disorder were positively associated with ADL dependencies. The odds of ADL dependencies increased with impaired cognition, impaired vision, impaired hearing, and major depressive disorder. Impaired cognition, incontinence, impaired vision and major depressive disorder were positively associated with dressing, bathing, eating, transferring, and toileting dependency. In addition, impaired hearing was associated with transferring and toileting dependency. More than half of older adults in India had at least one geriatric condition. The prevalence of geriatric conditions was as high as the prevalence of chronic conditions, which in some cases were associated with disability. Geriatric conditions should be included in health care management.

2021 ◽  
Vol 8 ◽  
Francisco T. T. Lai ◽  
Patrick E. Beeler ◽  
Benjamin H. K. Yip ◽  
Marcus Cheetham ◽  
Patsy Y. K. Chau ◽  

Background: Multimorbidity, defined as the co-occurrence of ≥2 chronic conditions, is clinically diverse. Such complexity hinders the development of integrated/collaborative care for multimorbid patients. In addition, the universality of multimorbidity patterns is unclear given scarce research comparing multimorbidity profiles across populations. This study aims to derive and compare multimorbidity profiles in Hong Kong (HK, PRC) and Zurich (ZH, Switzerland).Methods: Stratified by sites, hierarchical agglomerative clustering analysis (dissimilarity measured by Jaccard index) was conducted with the objective of grouping inpatients into clinically meaningful clusters based on age, sex, and 30 chronic conditions among 20,000 randomly selected discharged multimorbid inpatients (10,000 from each site) aged ≥ 45 years. The elbow point method based on average within-cluster dissimilarity, complemented with a qualitative clinical examination of disease prevalence, was used to determine the number of clusters.Results: Nine clusters were derived for each site. Both similarities and dissimilarities of multimorbidity patterns were observed. There was one stroke-oriented cluster (3.9% in HK; 6.5% in ZH) and one chronic kidney disease-oriented cluster (13.1% in HK; 11.5% ZH) in each site. Examples of site-specific multimorbidity patterns, on the other hand, included a myocardial infarction-oriented cluster in ZH (2.3%) and several clusters in HK with high prevalence of heart failure (&gt;65%) and chronic pain (&gt;20%).Conclusion: This is the first study using hierarchical agglomerative clustering analysis to profile multimorbid inpatients from two different populations to identify universalities and differences of multimorbidity patterns. Our findings may inform the coordination of integrated/collaborative healthcare services.

2021 ◽  
Vol 5 (4) ◽  
pp. 381
Shaorin Tanira

Background: From health monitoring to health education and from behaviour change to falls sensing and health alerts to the simple pleasure of communication and connectedness, the mobile technologies (smartphone applications) are changing the lives of older adults.Objective: To examine current evidence of use of smartphones by older adults for health purposes (including communication, education, and health monitoring), and understand gaps and challenges in order to inform the design of future systems given the ubiquity of mobile phone technology.Methods: MEDLINE, CINAHL and Google scholar databases were searched from October 2016 to January 2017. Keywords used include ‘smartphone apps’, ‘mobile phone’, ‘chronic disease’, ‘chronic condition’, ‘older adults’ and ‘elderly’. A total of 12 articles were selected for quality assessment and grading of evidence.Results: Twelve different articles were found and categorized into nine different clinical domains with specific health related interventions. Articles were focused on diabetes care (2 articles), followed by COPD (2 articles), heart disease (1 article), Alzheimer’s/dementia Care (2 articles), osteoarthritis and pain management (1 article), fall prevention (1 article), colon cancer (1 article), palliative care (1 article), chronic kidney disease (1 article). Areas of interest studied included feasibility, acceptability, functionality and thereby determining their effectiveness. There were many different clinical domains; however, most of the studies were pilot studies. Current work in using mobile phones for older adult use are spread across a variety of clinical domains. Findings from different studies indicate that the use of mobile phone interventions has the potential to support successful management of chronic conditions and health behaviour change in older adults.Conclusion: Perceived benefits and willingness to use the smartphone apps are high; however, technical training and cost are main concerns. A common problem with elderly users was their reluctance to press buttons due to the fear of breaking something which has been resolved by touch screen technology of the smartphones. However, the advanced user clicked around the screen until he found what he was looking for, while the others spent a lot of time observing the screen and trying to determine the correct step. Promotion of user-friendly apps are expected especially for older adults having a diminished physical and cognitive abilities.International Journal of Human and Health Sciences Vol. 05 No. 04 October’21 Page: 381-387

2021 ◽  
Vol 21 (1) ◽  
Eng Sing Lee ◽  
Poay Sian Sabrina Lee ◽  
Ying Xie ◽  
Bridget L. Ryan ◽  
Martin Fortin ◽  

Abstract Background The prevalence of multimorbidity varies widely due to the lack of consensus in defining multimorbidity. This study aimed to measure the prevalence of multimorbidity in a primary care setting using two definitions of multimorbidity with two different lists of chronic conditions. Methods We conducted a cross-sectional study of 787,446 patients, aged 0 to 99 years, who consulted a family physician between July 2015 to June 2016. Multimorbidity was defined as ‘two or more’ (MM2+) or ‘three or more’ (MM3+) chronic conditions using the Fortin list and Chronic Disease Management Program (CDMP) list of chronic conditions. Crude and standardised prevalence rates were reported, and the corresponding age, sex or ethnic-stratified standardised prevalence rates were adjusted to the local population census. Results The number of patients with multimorbidity increased with age. Age-sex-ethnicity standardised prevalence rates of multimorbidity using MM2+ and MM3+ for Fortin list (25.9, 17.2%) were higher than those for CDMP list (22.0%; 12.4%). Sex-stratified, age-ethnicity standardised prevalence rates for MM2+ and MM3+ were consistently higher in males compared to females for both lists. Chinese and Indians have the highest standardised prevalence rates among the four ethnicities using MM2+ and MM3+ respectively. Conclusions MM3+ was better at identifying a smaller number of patients with multimorbidity requiring higher needs compared to MM2+. Using the Fortin list seemed more appropriate than the CDMP list because the chronic conditions in Fortin’s list were more commonly seen in primary care. A consistent definition of multimorbidity will help researchers and clinicians to understand the epidemiology of multimorbidity better.

Anna Viljanen ◽  
Marika Salminen ◽  
Kerttu Irjala ◽  
Elisa Heikkilä ◽  
Raimo Isoaho ◽  

Abstract Purpose The ageing population is increasingly multimorbid. This challenges health care and elderly services as multimorbidity is associated with institutionalization. Especially dementia increases with age and is the main risk factor for institutionalization. The aim of this study was to assess the association of chronic conditions and multimorbidity with institutionalization in home-dwelling older people, with and without dementia. Methods In this prospective study with 18-year follow-up, the data on participants’ chronic conditions were gathered at the baseline examination, and of conditions acquired during the follow-up period from the municipality’s electronic patient record system and national registers. Only participants institutionalized or deceased by the end of the follow-up period were included in this study. Different cut-off-points for multimorbidity were analyzed. Cox regression model was used in the analyses. Death was used as a competing factor. Results The mean age of the participants (n = 820) was 74.7 years (64.0‒97.0). During the follow-up, 328 (40%) were institutionalized. Dementia, mood disorders, neurological disorders, and multimorbidity defined as five or more chronic conditions were associated with a higher risk of institutionalization in all the participants. In people without dementia, mood disorders and neurological disorders increased the risk of institutionalization. Conclusion Having dementia, mood or neurological disorder and/or five or more chronic conditions were associated with a higher risk of institutionalization. These risk factors should be recognized when providing and targeting care and support for older people still living at home.

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