Influenza vaccination for immunocompromised patients: systematic review and meta-analysis from a public health policy perspective.

Immunocompromised patients are vulnerable to severe or complicated influenza infection. Vaccination is widely recommended for this group. This systematic review and meta-analysis assesses influenza vaccination for immunocompromised patients in terms of preventing influenza-like illness and laboratory confirmed influenza, serological response and adverse events

Influenza vaccination for immunocompromised patients: summary of a systematic review and meta-analysis.

Vaccination of immunocompromised patients is recommended in many national guidelines to protect against severe or complicated influenza infection. However, due to uncertainties over the evidence base, implementation is frequently patchy and dependent on individual clinical discretion. We conducted a systematic review and meta-analysis to assess the evidence for influenza vaccination in this patient group. Healthcare databases and grey literature were searched and screened for eligibility. Data extraction and assessments of risk of bias were undertaken in duplicate, and results were synthesised narratively and using meta-analysis where possible. Our data show that whilst the serological response following vaccination of immunocompromised patients is less vigorous than in healthy controls, clinical protection is still meaningful, with only mild variation in adverse events between aetiological groups. Although we encountered significant clinical and statistical heterogeneity in many of our meta-analyses, we advocate that immunocompromised patients should be targeted for influenza vaccination

A Demographical Assessment of Different Insulin Regimens in Non-insulin Dependent Diabetics

Aims: The aim of present study was to evaluate the clinical efficacy of different insulin regimens in non-insulin dependent diabetic patients with respect to their age and gender. Methodology: A prospective cross-sectional study was conducted for comparing different insulin regimens in type II diabetic patients for better glycemic control with respect to age and gender. A total of 234 consenting, known type II diabetic patients, on any insulin regimens, with at least all relevant medical records of preceding three months participated in the study. Patients were segregated into six treatment arms depending upon type of insulin prescribed i.e., insulin pre-mix 70/30, insulin split mix (N & R), long acting insulin analogue, ultra-short acting insulin analogue, insulin pre-mix 70/30 analogue and insulin pre-mix 50/50 analogue, respectively. Moreover, patients from each group were evaluated regarding diabetes knowledge and medication adherence using Michigan Diabetes Research and Training Center’s Revised Diabetes Knowledge test Performa, 23 items and Morisky medication adherence scale, 4 items, respectively. Results: Data analysis showed highly significant association among different insulin regimens with respect to the patient education (p=0.000) level. Significant association was found among different insulin regimens and patient’s occupational status (P=0.013). However, Statistically non-significant associations were observed among different insulin regimens with gender (P=0.070), marital status (P=0.183) and age (P=0.084) respectively. Conclusion: In conclusion, data demonstrated that four treatment groups i.e., long acting insulin analogue, ultra-short acting insulin analogue, insulin premix 70/30 analogue insulin pre-mix 50/50 analogue were more effective than two conventional treatment groups i.e., Insulin pre-mix 70/30 and insulin split mix (N &R) in terms of clinical outcomes in population under study. Furthermore, it was also evident from the data female receiving more insulin than males

A Comparison of Artificial Intelligence and Human Doctors for the Purpose of Triage and Diagnosis

AI virtual assistants have significant potential to alleviate the pressure on overly burdened healthcare systems by enabling patients to self-assess their symptoms and to seek further care when appropriate. For these systems to make a meaningful contribution to healthcare globally, they must be trusted by patients and healthcare professionals alike, and service the needs of patients in diverse regions and segments of the population. We developed an AI virtual assistant which provides patients with triage and diagnostic information. Crucially, the system is based on a generative model, which allows for relatively straightforward re-parameterization to reflect local disease and risk factor burden in diverse regions and population segments. This is an appealing property, particularly when considering the potential of AI systems to improve the provision of healthcare on a global scale in many regions and for both developing and developed countries. We performed a prospective validation study of the accuracy and safety of the AI system and human doctors. Importantly, we assessed the accuracy and safety of both the AI and human doctors independently against identical clinical cases and, unlike previous studies, also accounted for the information gathering process of both agents. Overall, we found that the AI system is able to provide patients with triage and diagnostic information with a level of clinical accuracy and safety comparable to that of human doctors. Through this approach and study, we hope to start building trust in AI-powered systems by directly comparing their performance to human doctors, who do not always agree with each other on the cause of patients’ symptoms or the most appropriate triage recommendation.</jats:p