Prospective cohort study of unmet supportive care needs, post-traumatic growth, coping strategy and social supports among patients with breast cancer: The PenBCNeeds study.

PURPOSE: While the unmet healthcare needs are still being improved upon, the wellbeing of cancer patients has increasingly become a prime concern in Malaysia. The objective of this study is to ascertain the trend of unmet supportive care needs, post-traumatic growth (P T G), coping strategies, and social supports among patients with breast cancer over the three time points of treatment: T1 at early diagnosis, T2 for three months after diagnosis, and T3 for six months after diagnosis. METHODS: A total of 240 cancer patients participated in this prospective cohort study, with follow-up visits from October 2019 until July 2021. Data were collected using several instruments: Brief COP E, the Source of Social Support Scale (SSSS), the Post-Traumatic Growth Inventory - Short Form (P T GI-SF), and a Malay version of the 34-Item Shortform Supportive Care Need Survey (SCNS-SF34). RESULTS: The results indicated a significant change from T1 to T3 for all domains of the unmet needs (p-value < 0.001), except for the sexual domain. A lower SCNS-SF34 score resulted from more unfavorable social support. The P T GI-SF results indicated a trend toward meeting the unmet needs, and a higher SCNS-SF-34 score predicted a considerably higher P T GI-SF score. CONCLUSIONS: Our study findings suggest that majority of the factors evaluated in terms of unmet needs among cancer patients have undergone considerable changes.

Stroke Survivors' Dependency Level and Informal Caregivers' Quality of Life in Kelantan, Malaysia: Examining the Mediating Role of Psychological Distress.

BACKGROUND: Caring for stroke survivors can affect the caregiver's life, especially with the increment of patients' dependency level. This led to depression, anxiety, and stress in the caregiver, impacting their quality of life (QoL). This study aims to model relationships between caregivers' factors and stroke survivors' factors with caregivers' QoL and to estimate the mediation effects of caregivers' depression, anxiety, and stress in the relationships between stroke survivors' dependency level and caregivers' QoL.  Methods: Data from a cross-sectional study with a sample size of 250 subjects was analyzed. Linear regression was done to model the relationship between stroke survivors' factors and caregivers' factors with caregivers' QoL. Hayes's PROCESS macro model 4 for bootstrapping indirect effects was used to estimate the mediation effects of depression, anxiety, and stress in the relationship between stroke survivors' dependency level and caregivers' QoL. RESULTS: Stroke survivors' dependency levels that were measured by the Modified Barthel Index score (b=0.14; 95% CI: 0.07, 0.20), caregivers' depression score (b=-0.81; 95% CI: -1.44, -0.20), and caregivers' anxiety score (b=-0.73; 95% CI: -1.34, -0.12) were found to be associated with caregivers' QoL. Caregivers' depression score (effect=0.02; 95% CI for bootstrapping: 0.01, 0.04) and caregivers' anxiety score (effect=0.01; 95% CI for bootstrapping: 0.01, 0.04) were found as partial mediators in the relationship between stroke survivors' dependency level and caregivers' QoL. CONCLUSION:  Stroke survivors' dependency level and caregivers' depression and anxiety significantly impact caregivers' QoL, with the latter factors partially mediating this relationship. Interventions such as providing caregivers with psychological support, stress management programs, and training in caregiving skills could help mitigate these impacts and improve caregivers' QoL.

The continuance intention to vaccinate against COVID-19: An empirical study from Malaysia.

BACKGROUND: Vaccination has been one of the most effective preventive strategies to contain the COVID-19 pandemic. However, as the COVID-19 vaccines' effect wanes off after some time and given their reduced level of protection against mutation strains of the virus, the calls for boosters and second boosters signal the need for continuous vaccination for the foreseeable future. As Malaysia transitions into the endemic phase, the nation's ability to co-exist with the virus in the endemic phase will hinge on people's continuance intention to be vaccinated against the virus. Adapting the expectations confirmation model (ECM) to the public health context and in a developing country, this study integrates the ECM with the health belief model (HBM) and the theory of reasoned action (TRA) to examine the inter-relationships of the predictors of people's continuance intention to vaccinate against COVID-19. METHODOLOGY: Data were collected using self-administered questionnaires from 1,914 respondents aged 18 and above by a marketing consulting firm via its online panel. The partial least squares structural equation modeling (PLS-SEM) technique was used to analyze the data. RESULTS: Out of the 1,914 respondents, 55.9% reported having a continuance intention to vaccinate against COVID-19, similar to other developing countries. The multivariate analysis revealed that perceived usefulness and satisfaction significantly influenced individuals' continuance intention to vaccinate against COVID-19. Additionally, attitude was found to play a key role in influencing behavioral change among individuals towards their perceptions of continuously getting vaccinated against COVID-19. CONCLUSIONS: By integrating three theoretical frameworks (i.e., HBM, TRA and ECM), this study showed that behavioral characteristics could provide insights towards continuance vaccination intention. Hence, policymakers and key stakeholders can develop effective public health strategies or interventions to encourage vaccine booster uptake by targeting behavioral factors such as perceived usefulness, attitude, satisfaction, and subjective norms.

Prevalence of undiagnosed metabolic syndrome using three different definitions and identifying associated risk factors among apparently healthy adults in Karachi, Pakistan: a cross-sectional survey in the year 2022.

OBJECTIVE: To determine the prevalence and associated risk factors of undiagnosed metabolic syndrome (MetS) using three different definitions among apparently healthy adults of Karachi, Pakistan. METHODS: This community-based cross-sectional survey was conducted in Karachi, Pakistan, from January 2022 to August 2022. A total of 1065 healthy individuals aged 25-80 years of any gender were consecutively included. MetS was assessed using the National Cholesterol Education Program for Adult Treatment Panel (NCEP-ATP) III guidelines, International Diabetes Federation (IDF), and modified NCEP-ATP III. RESULTS: The prevalence of MetS was highest with the modified NCEP-ATP III definition at 33.9% (95% CI: 31-36), followed by the IDF definition at 32.2% (95% CI: 29-35). In contrast, the prevalence was lower at 22.4% (95% CI: 19-25) when using the NCEP ATP III definition. The risk of MetS significantly increases with higher BMI, as defined by the IDF criteria (adjusted OR [ORadj] 1.13, 95% CI 1.09-2.43), NCEP-ATP III criteria (ORadj 1.15, 95% CI 1.11-1.19), and modified NCEP-ATP III criteria (ORadj 1.16, 95% CI 1.12-1.20). Current smokers had significantly higher odds of MetS according to the IDF (ORadj 2.72, 95% CI 1.84-4.03), NCEP-ATP III (ORadj 3.93, 95% CI 2.55-6.06), and modified NCEP-ATP III (ORadj 0.62, 95% CI 0.43-0.88). Areca nut use was associated with higher odds of MetS according to both IDF (ORadj 1.71, 95% CI 1.19-2.47) and modified NCEP-ATP III criteria (ORadj 1.58, 95% CI 1.10-2.72). Furthermore, low physical activity had significantly higher odds of MetS according to the NCEP-ATP III (ORadj 1.36, 95% CI 1.01-1.84) and modified NCEP-ATP III criteria (ORadj 1.56, 95% CI 1.08-2.26). CONCLUSION: One-third of the healthy individuals were diagnosed with MetS based on IDF, NCEP-ATP III, and modified NCEP-ATP III criteria. A higher BMI, current smoking, areca nut use, and low physical activity were significant factors.

Spontaneous Preterm Births in Malaysia: Are There Modifiable Antenatal Risk Factors?

Background Spontaneous preterm birth (SPB) is a global public health concern with devastating health effects on SPB survivors. This study aimed to determine modifiable antenatal risk factors associated with SPB among women attending government healthcare facilities in Malaysia. Methodology A retrospective record review of 49,416 national obstetrics registry data from 2015 was conducted and analyzed using binary logistic regression based on six antenatal factor divisions. Results Mothers with pre-existing diabetes had higher odds (adjusted odds ratio (aOR) = 3.09) of delivering prematurely than mothers without diabetes. Mothers with chronic hypertension with superimposed pre-eclampsia (aOR = 2.51) and gestational hypertension (aOR = 1.44) had higher odds of experiencing preterm birth than mothers with no hypertension. Underweight mothers had higher odds (aOR = 1.27) of delivering prematurely than mothers with an ideal body mass index (18.5 to <25.0 kg/m2). Mothers with moderate anemia (hemoglobin level: 7 to <9 g/dL) had higher odds (aOR = 1.18) of preterm birth than mothers with normal hemoglobin levels (≥11 g/dL). Conclusions Maternal biomarkers, such as glucose level, blood pressure, BMI, and hemoglobin level, play an important role in reducing the rate of SPB in Malaysia. This study recommends strengthening pre-pregnancy, antenatal, and postpartum care through multidisciplinary and multi-agency team collaboration, addressing both modifiable and non-modifiable risk factors and adopting a dual approach that combines preventive and curative care.

Machine Learning Models for Predicting Stroke Mortality in Malaysia: An Application and Comparative Analysis.

Background Stroke is a significant public health concern characterized by increasing mortality and morbidity. Accurate long-term outcome prediction for acute stroke patients, particularly stroke mortality, is vital for clinical decision-making and prognostic management. This study aimed to develop and compare various prognostic models for stroke mortality prediction. Methods In a retrospective cohort study from January 2016 to December 2021, we collected data from patients diagnosed with acute stroke from five selected hospitals. Data contained variables on demographics, comorbidities, and interventions retrieved from medical records. The cohort comprised 950 patients with 20 features. Outcomes (censored vs. death) were determined by linking data with the Malaysian National Mortality Registry. We employed three common survival modeling approaches, the Cox proportional hazard regression (Cox), support vector machine (SVM), and random survival forest (RSF), while enhancing the Cox model with Elastic Net (Cox-EN) for feature selection. Models were compared using the concordance index (C-index), time-dependent area under the curve (AUC), and discrimination index (D-index), with calibration assessed by the Brier score. Results The support vector machine (SVM) model excelled among the four, with three-month, one-year, and three-year time-dependent AUC values of 0.842, 0.846, and 0.791; a D-index of 5.31 (95% CI: 3.86, 7.30); and a C-index of 0.803 (95% CI: 0.758, 0.847). All models exhibited robust calibration, with three-month, one-year, and three-year Brier scores ranging from 0.103 to 0.220, all below 0.25. Conclusion The support vector machine (SVM) model demonstrated superior discriminative performance, suggesting its efficacy in developing prognostic models for stroke mortality. This study enhances stroke mortality prediction and supports clinical decision-making, emphasizing the utility of the support vector machine method.