Stakeholder collaboration in long-term care of older people in Lithuania

Demographic situation, changes in the role of women in society and growing demand for long-term care (LTC) of older people have challenged the ability to meet the growing LTC needs in most developed countries. In countries where responsibility for LTC is still largely laid on families, it is, however, even more critical and calls for improvements in formal LTC systems. More intensive stakeholder collaboration in LTC policy development, organising and delivery are of primary importance in improving LTC systems. Such collaboration, however, is not always successful; thus, it is critical to understand what makes it effective and efficient. In this paper, we specifically look into multistakeholder collaboration in LTC in Lithuania, one of the fastest ageing countries in the EU, with the demand for LTC services growing fast and exceeding the supply despite rising business and NGO engagement. To determine facilitators of such collaboration, we build on the data obtained through eight focus group discussions with all key stakeholder representatives (LTC policymakers, organisers and service providers [public, private and NGOs], 54 participants in total). Our findings indicate that in addition to national and organisational level facilitators studied in prior research, there are important individual level factors, such as meaningfulness at work, concern and care for others, possibility for personal growth and development, satisfaction with supervision, a sense of belonging and role clarity. On the other hand, our results show that collaboration is constrained by a shortage of human resources, increased workload caused by growing LTC demand, bureaucratic requirements, legal restrictions, lack of awareness of LTC service availability among elder persons, and prevailing social norms and attitudes to institutionalised care. Interestingly, a lack of financial resources is not perceived as a major constraint.     

Trends in HIV sexual prevention: Attitudinal beliefs and behavioral intention in Spanish young people over the past two decades (1999–2020)

Despite preventive efforts, HIV exposure is still a concern for public health. The current prevalence is related to unsafe sex, which is based on socio-cognitive variables. Therefore, information about these variables is required to verify whether the past preventive strategies have been effective and improve the future ones. However, there is not updated information to adjust future preventive interventions. For this reason, this study analyzes trends by gender in Spanish young people over the past two decades in perceived susceptibility to HIV, severity perceived to HIV, condom confidence and the intention of condom use, from 1999 to 2020. For this purpose, 11,665 Spanish young people (from 17 to 40 years old) completed the AIDS Prevention Questionnaire in each year. Following our results, in general, the socio-cognitive variables have revealed an unsatisfactory trend: a low perceived susceptibility to HIV and a behavioral intention that have remained stable or even declined slightly over the past years. Particularly, men are more likely to report a riskier level in socio-cognitive variables although, in the latest evaluations, women would decrease condom use intention largely than men. In general, regarding age, young adult would report better levels of condom confidence, but early youth would get higher results in perceived severity, as well as better scores in the latest evaluations of condom use intention. Considering these results, policies should make an effort in HIV prevention programs, and emphasize the attention to attitudinal beliefs and behavioral intention to improve their effectiveness.     

南充市社区中老年人群血脂血糖现状水平分析

【摘要】 目的 了解社区中老年人群的血脂、血糖现状水平，探讨其与性别和年龄的关系，为本地区心脑血管疾病、糖尿病等疾病的防治提供诊疗依据。方法 对我市38078名社区居民采集清晨空腹血清标本，检测血糖（GLU）、甘油三酯（TG）、总胆固醇（TC）、高密度脂蛋白胆固醇（HDL C）和低密度脂蛋白胆固醇（LDL C）的结果，并对其进行年龄、性别分组统计分析。结果 不同性别和年龄组间的血脂、血糖水平差异有统计学意义（P＜005），74~89岁年龄组血脂、血糖总体水平高于其他各年龄组（P＜005）；女性血脂水平普遍高于男性（P＜005）。结论 中老年人群血脂、血糖异常率高，应定期对中老年人进行血脂、血糖监测，开展宣传教育活动，提高自我保健意识，预防高血脂、高血糖的发生和发展。     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.