Methods: The medical records of 95 older patients (age ≥ 65) who attended the GMC from 16 December 2019 to 10 January 2020 were reviewed. Frailty was identified using the FRAIL scale and the CFS. Patient characteristics were investigated for their association with frailty and their difference in the prevalence of frailty by the FRAIL scale and CFS.
Results: The CFS identified nonsignificant higher prevalence of frailty compared to the FRAIL scale (21/95; 22.1% vs. 17/95; 17.9%, ratio of prevalence = 1.235, p=0.481). Minimal agreement was found between the FRAIL scale and the CFS (Kappa = 0.272, p < 0.001). Three out of 5 components of the FRAIL scale (resistance, ambulation, and loss of weight) were associated with frailty by the CFS. Higher prevalence of frailty was identified by the CFS in those above 70 years of age. The FRAIL scale identified more patients with frailty in ischaemic heart disease patients.
Conclusion: Patient characteristics influenced the choice of the frailty assessment tool. The FRAIL scale and the CFS may complement each other in providing optimized care to older patients who attended the GMC.
DESIGN: This longitudinal qualitative study was informed by the Normalisation Process Model and involved audiotaped semi-structured individual interviews with front-line clinicians before (Time 1) and after (Time 2) the PIPC intervention. The Framework Method was used in the thematic analysis of pre/post interview transcripts.
SETTING: Two government-operated primary care clinics in Penang, Malaysia.
PARTICIPANTS: 17 primary care medical, nursing and allied health staff recruited purposely to achieve a range of disciplines and a balanced representation from both clinics.
INTERVENTION: Psychiatrists, accompanied by medical students in small numbers, provided one half-day consultation visit per week, to front-line clinicians in each clinic over an 8-month period. The service involved psychiatric assessment of patients with suspected CMDs, with face-to-face discussion with the referring clinician before and after the patient assessment.
RESULTS: At Time 1 interviewees tended to equate CMDs with stress and embraced a holistic model of care while also reporting considerable autonomy in mental healthcare and positively appraising their current practices. At Time 2, post-intervention, participants demonstrated a shift towards greater understanding of CMDs as treatable conditions. They reported time pressures and the demands of key performance indicators in other areas as barriers to participation in PIPC. Yet they showed increased awareness of current service deficits and of their potential in delivering improved mental healthcare.
CONCLUSIONS: Despite resource-related and structural barriers to implementation of national mental health policy in Malaysian primary care settings, our findings suggest that front-line clinicians are receptive to future interventions designed to improve the mental healthcare capacity.
METHOD: Compartmental models were fitted. The final model was determined based on the objective function value and inspection of goodness-of-fit plots. The bias and precision of parameter estimates were compared between SAEM and FOCEi using stochastic simulations and estimations. For robustness, parameters were re-estimated as the initial estimates were perturbed 100 times and resultant changes evaluated.
RESULTS: The absorption kinetics of metformin depend significantly on food status. Under the fasted state, the first-order absorption into the central compartment was preceded by zero-order infusion into the depot compartment, whereas for the fed state, the absorption into the depot was instantaneous followed by first-order absorption from depot into the central compartment. The means of relative mean estimation error (rMEE) ( ME E SAEM ME E FOCEi ) and rRMSE ( RMS E SAEM RMS E FOCEi ) were 0.48 and 0.35, respectively. All parameter estimates given by SAEM appeared to be narrowly distributed and were close to the true value used for simulation. In contrast, the distribution of estimates from FOCEi were skewed and more biased. When initial estimates were perturbed, FOCEi estimates were more biased and imprecise.
DISCUSSION: nlmixr is reliable for NLMEM. SAEM was superior to FOCEi in terms of bias and precision, and more robust against initial estimate perturbations.