Effects of mobile health interventions on health-related outcomes in older adults with type 2 diabetes: A systematic review and meta-analysis.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a chronic metabolic condition that is associated with multiple comorbidities. Apart from pharmacological approaches, patient self-management remains the gold standard of care for diabetes. Improving patients' self-management among the elderly with mobile health (mHealth) interventions is critical, especially in times of the COVID-19 pandemic. However, the extent of mHealth efficacy in managing T2DM in the older population remains unknown. Hence, the present review examined the effectiveness of mHealth interventions on cardiometabolic outcomes in older adults with T2DM. METHODS: A systematic search from the inception till May 31, 2021, in the MEDLINE, Embase, and PubMed databases was conducted, and 16 randomized controlled trials were included in the analysis. RESULTS: The results showed significant benefits on glycosylated hemoglobin (HbA1c) (mean difference -0.24%; 95% confidence interval [CI]: -0.44, -0.05; p = 0.01), postprandial blood glucose (-2.91 mmol/L; 95% CI: -4.78, -1.03; p = 0.002), and triglycerides (-0.09 mmol/L; 95% CI: -0.17, -0.02; p = 0.010), but not on low-density lipoprotein cholesterol (-0.06 mmol/L; 95% CI: -0.14, 0.02; p = 0.170), high-density lipoprotein cholesterol (0.05 mmol/L; 95% CI: -0.03, 0.13; p = 0.220), and blood pressure (systolic blood pressure -0.82 mm Hg; 95% CI: -4.65, 3.00; p = 0.670; diastolic blood pressure -1.71 mmHg; 95% CI: -3.71, 0.29; p = 0.090). CONCLUSIONS: Among older adults with T2DM, mHealth interventions were associated with improved cardiometabolic outcomes versus usual care. Its efficacy can be improved in the future as the current stage of mHealth development is at its infancy. Addressing barriers such as technological frustrations may help strategize approaches to further increase the uptake and efficacy of mHealth interventions among older adults with T2DM.

Analysing the impact of migration on HIV/AIDS cases using epidemiological modelling to guide policy makers.

In this paper, we present the impact of migration on the spread of HIV and AIDS cases. A simple model for HIV and AIDS that incorporates migration and addresses its contributions to the spread of HIV and AIDS cases was constructed. The model was calibrated to HIV and AIDS incidence data from Malaysia. We explore the use of Markov chain Monte Carlo (MCMC) simulation method to estimate uncertainty in all the unknown parameters incorporated in our proposed model. Among the migrant population, 1.5572e-01 were susceptible to HIV transmission, which constituted 67,801 migrants. A proportion of migrants, 6.3773e-04, were estimated to be HIV infected, constituting 278 migrants. There were 72 (per 10,000) migrants estimated to have had AIDS, representing a proportion of 1.6611e-08. The result suggests that the disease-free steady state was unstable since the estimated basic reproduction number R 0 was 2.0906 and 2.3322 for the models without and with migration, respectively. This is not a good indicator from the public health point of view, as the aim is to stabilize the epidemic at the disease-free equilibrium. The advantage of introduction of migration to the simple model validated the true R 0 and the transmission rate ß associated with HIV and AIDS epidemic disease in Malaysia. It also indicates an approximately 12 percentage points increase in the rate of HIV infection with migration.

The impact of implementing HIV prevention policies therapy and control strategy among HIV and AIDS incidence cases in Malaysia.

Malaysia is faced with a high HIV/AIDS burden that poses a public health threat. We constructed and applied a compartmental model to understand the spread and control of HIV/AIDS in Malaysia. A simple model for HIV and AIDS disease that incorporates condom and uncontaminated needle-syringes interventions and addresses the relative impact of given treatment therapy for infected HIV newborns on reducing HIV and AIDS incidence is presented. We demonstrated how treatment therapy for new-born babies and the use of condoms or uncontaminated needle-syringes impact the dynamics of HIV in Malaysia. The model was calibrated to HIV and AIDS incidence data from Malaysia from 1986 to 2011. The epidemiological parameters are estimated using Bayesian inference via Markov chain Monte Carlo simulation method. The reproduction number optimal for control of the HIV/AIDS disease obtained suggests that the disease-free equilibrium was unstable during the 25 years. However, the results indicated that the use of condoms and uncontaminated needle-syringes are pivotal intervention control strategies; a comprehensive adoption of the intervention may help stop the spread of HIV disease. Treatment therapy for newborn babies is also of high value; it reduces the epidemic peak. The combined effect of condom use or uncontaminated needle-syringe is more pronounced in controlling the spread of HIV/AIDS.

Modelling the Impact of Different Tuberculosis Control Interventions on the Prevalence of Tuberculosis in an Overcrowded Prison.

The aim of this study was to simulate the effects of tuberculosis (TB) treatment strategies interventions in an overcrowded and poorly ventilated prison with both high (5 months) and low (3 years) turnover of inmates against improved environmental conditions. We used a deterministic transmission model to simulate the effects of treatment of latent TB infection and active TB, or the combination of both treatment strategies. Without any intervention, the TB prevalence is estimated to increase to 8.8% for a prison with low turnover of inmates but modestly stabilize at 5.8% for high-turnover prisons in a 10-year period. Reducing overcrowding from 6 to 4 inmates per housing cell and increasing the ventilation rate from 2 to 12 air changes per hour combined with any treatment strategy would further reduce the TB prevalence to as low as 0.98% for a prison with low inmate turnover.

An Application of Bayesian Approach in Modeling Risk of Death in an Intensive Care Unit.

BACKGROUND AND OBJECTIVES: There are not many studies that attempt to model intensive care unit (ICU) risk of death in developing countries, especially in South East Asia. The aim of this study was to propose and describe application of a Bayesian approach in modeling in-ICU deaths in a Malaysian ICU. METHODS: This was a prospective study in a mixed medical-surgery ICU in a multidisciplinary tertiary referral hospital in Malaysia. Data collection included variables that were defined in Acute Physiology and Chronic Health Evaluation IV (APACHE IV) model. Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied in the development of four multivariate logistic regression predictive models for the ICU, where the main outcome measure was in-ICU mortality risk. The performance of the models were assessed through overall model fit, discrimination and calibration measures. Results from the Bayesian models were also compared against results obtained using frequentist maximum likelihood method. RESULTS: The study involved 1,286 consecutive ICU admissions between January 1, 2009 and June 30, 2010, of which 1,111 met the inclusion criteria. Patients who were admitted to the ICU were generally younger, predominantly male, with low co-morbidity load and mostly under mechanical ventilation. The overall in-ICU mortality rate was 18.5% and the overall mean Acute Physiology Score (APS) was 68.5. All four models exhibited good discrimination, with area under receiver operating characteristic curve (AUC) values approximately 0.8. Calibration was acceptable (Hosmer-Lemeshow p-values > 0.05) for all models, except for model M3. Model M1 was identified as the model with the best overall performance in this study. CONCLUSION: Four prediction models were proposed, where the best model was chosen based on its overall performance in this study. This study has also demonstrated the promising potential of the Bayesian MCMC approach as an alternative in the analysis and modeling of in-ICU mortality outcomes.

A Markov Chain Monte Carlo Approach to Estimate AIDS after HIV Infection.

The spread of human immunodeficiency virus (HIV) infection and the resulting acquired immune deficiency syndrome (AIDS) is a major health concern in many parts of the world, and mathematical models are commonly applied to understand the spread of the HIV epidemic. To understand the spread of HIV and AIDS cases and their parameters in a given population, it is necessary to develop a theoretical framework that takes into account realistic factors. The current study used this framework to assess the interaction between individuals who developed AIDS after HIV infection and individuals who did not develop AIDS after HIV infection (pre-AIDS). We first investigated how probabilistic parameters affect the model in terms of the HIV and AIDS population over a period of time. We observed that there is a critical threshold parameter, R0, which determines the behavior of the model. If R0 ≤ 1, there is a unique disease-free equilibrium; if R0 < 1, the disease dies out; and if R0 > 1, the disease-free equilibrium is unstable. We also show how a Markov chain Monte Carlo (MCMC) approach could be used as a supplement to forecast the numbers of reported HIV and AIDS cases. An approach using a Monte Carlo analysis is illustrated to understand the impact of model-based predictions in light of uncertain parameters on the spread of HIV. Finally, to examine this framework and demonstrate how it works, a case study was performed of reported HIV and AIDS cases from an annual data set in Malaysia, and then we compared how these approaches complement each other. We conclude that HIV disease in Malaysia shows epidemic behavior, especially in the context of understanding and predicting emerging cases of HIV and AIDS.

An External Independent Validation of APACHE IV in a Malaysian Intensive Care Unit.

INTRODUCTION: Intensive care unit (ICU) prognostic models are predominantly used in more developed nations such as the United States, Europe and Australia. These are not that popular in Southeast Asian countries due to costs and technology considerations. The purpose of this study is to evaluate the suitability of the acute physiology and chronic health evaluation (APACHE) IV model in a single centre Malaysian ICU. MATERIALS AND METHODS: A prospective study was conducted at the single centre ICU in Hospital Sultanah Aminah (HSA) Malaysia. External validation of APACHE IV involved a cohort of 916 patients who were admitted in 2009. Model performance was assessed through its calibration and discrimination abilities. A first-level customisation using logistic regression approach was also applied to improve model calibration. RESULTS: APACHE IV exhibited good discrimination, with an area under receiver operating characteristic (ROC) curve of 0.78. However, the model's overall fit was observed to be poor, as indicated by the Hosmer-Lemeshow goodness-of-fit test (C = 113, P <0.001). Predicted in-ICU mortality rate (28.1%) was significantly higher than the actual in-ICU mortality rate (18.8%). Model calibration was improved after applying first-level customisation (C = 6.39, P = 0.78) although discrimination was not affected. CONCLUSION: APACHE IV is not suitable for application in HSA ICU, without further customisation. The model's lack of fit in the Malaysian study is attributed to differences in the baseline characteristics between HSA ICU and APACHE IV datasets. Other possible factors could be due to differences in clinical practice, quality and services of health care systems between Malaysia and the United States.

The impact of the wavelet propagation distribution on SEIRS modeling with delay.

Previous models of disease spread involving delay have used basic SIR (susceptible--infectious--recovery) formulae and approaches. This paper demonstrates how time-varying SEIRS (S--exposed--I - R - S) models can be extended with delay to produce wave propagations that simulate periodic wave fronts of disease spread in the context of population movements. The model also takes into account the natural mortality associated with the disease spread. Understanding the delay of an infectious disease is critical when attempting to predict where and how fast the disease will propagate. We use cellular automata to model the delay and its effect on the spread of infectious diseases where population movement occurs. We illustrate an approach using wavelet transform analysis to understand the impact of the delay on the spread of infectious diseases. The results indicate that including delay provides novel ways to understand the effects of migration and population movement on disease spread.

Effect of Muslim prayer (Salat) on α electroencephalography and its relationship with autonomic nervous system activity.

OBJECTIVES: This study investigated the effect of Muslim prayer (salat) on the α relative power (RPα) of electroencephalography (EEG) and autonomic nervous activity and the relationship between them by using spectral analysis of EEG and heart rate variability (HRV). METHODS: Thirty healthy Muslim men participated in the study. Their electrocardiograms and EEGs were continuously recorded before, during, and after salat practice with a computer-based data acquisition system (MP150, BIOPAC Systems Inc., Camino Goleta, California). Power spectral analysis was conducted to extract the RPα and HRV components. RESULTS: During salat, a significant increase (p<.05) was observed in the mean RPα in the occipital and parietal regions and in the normalized unit of high-frequency (nuHF) power of HRV (as a parasympathetic index). Meanwhile, the normalized unit of low-frequency (nuLF) power and LF/HF of HRV (as sympathetic indices) decreased according to HRV analyses. RPα showed a significant positive correlation in the occipital and parietal electrodes with nuHF and significant negative correlations with nuLF and LF/HF. CONCLUSIONS: During salat, parasympathetic activity increased and sympathetic activity decreased. Therefore, regular salat practices may help promote relaxation, minimize anxiety, and reduce cardiovascular risk.

The influence of firm age on the relationships of airline performance, economic situation and internal operation.

The ways in which airline performance depends on the economic situation and internal operation are well established in the literature. One of the contextual factors that may change the nature of these relationships is firm age. As such, the aim of this study is to investigate the moderating influence of firm age on airline performance outcomes. Thirty airline companies from the Asia Pacific region were selected, and relevant data from 2006 to 2011 were collected. It can be deduced that company experience or firm age can help in taking control of the relationship between the constructs; thus, this measurement acts as a moderator in the research model.

Testing the agreement of medical instruments: overestimation of bias in the Bland-Altman analysis.

OBJECTIVES: The Bland-Altman method is the most popular method used to assess the agreement of medical instruments. The main concern about this method is the presence of proportional bias. The slope of the regression line fitted to the Bland-Altman plot should be tested to exclude proportional bias. The aim of this study was to determine whether the overestimation of bias in the Bland-Altman analysis is still present even when the proportional bias has been excluded. METHODS: Data were collected from participants attending a workplace health screening program in a public university in Malaysia between 2009 and 2010. Variables collected were blood glucose level, body weight and systolic blood pressure (n=300 per variable). Readings from the original clinical dataset were compared with twenty randomly generated datasets for each variable. The Bland-Altman limits of agreement was used to determine the agreement. The presence of proportional bias was excluded for all datasets using the recommended method. RESULTS: The range of predicted bias was higher than the simulated bias for all datasets. The overestimation of bias increased as the range of actual bias increased. CONCLUSION: Testing the slope of regression line of the Bland-Altman plot does not remove the artifactual bias in the prediction.

Statistical methods used to test for agreement of medical instruments measuring continuous variables in method comparison studies: a systematic review.

BACKGROUND: Accurate values are a must in medicine. An important parameter in determining the quality of a medical instrument is agreement with a gold standard. Various statistical methods have been used to test for agreement. Some of these methods have been shown to be inappropriate. This can result in misleading conclusions about the validity of an instrument. The Bland-Altman method is the most popular method judging by the many citations of the article proposing this method. However, the number of citations does not necessarily mean that this method has been applied in agreement research. No previous study has been conducted to look into this. This is the first systematic review to identify statistical methods used to test for agreement of medical instruments. The proportion of various statistical methods found in this review will also reflect the proportion of medical instruments that have been validated using those particular methods in current clinical practice. METHODOLOGY/FINDINGS: Five electronic databases were searched between 2007 and 2009 to look for agreement studies. A total of 3,260 titles were initially identified. Only 412 titles were potentially related, and finally 210 fitted the inclusion criteria. The Bland-Altman method is the most popular method with 178 (85%) studies having used this method, followed by the correlation coefficient (27%) and means comparison (18%). Some of the inappropriate methods highlighted by Altman and Bland since the 1980s are still in use. CONCLUSIONS: This study finds that the Bland-Altman method is the most popular method used in agreement research. There are still inappropriate applications of statistical methods in some studies. It is important for a clinician or medical researcher to be aware of this issue because misleading conclusions from inappropriate analyses will jeopardize the quality of the evidence, which in turn will influence quality of care given to patients in the future.

Clinicians' diagnostic practice of dengue infections.

BACKGROUND: Difficulties in the classification of dengue infection have been documented. Such difficulties could be due to the low awareness of the World Health Organization diagnostic guidelines among clinicians. OBJECTIVE: To study the diagnostic practices of clinicians in classifying patients as dengue fever (DF) or dengue haemorrhagic fever (DHF)/dengue shock syndrome (DSS) at the time of discharge during an outbreak. METHODS: A prospective descriptive study of clinical features and disease classification in adult and pediatric dengue patients in the University of Malaya Medical Centre. RESULTS: Five hundred and twenty adult and 191 pediatric patients were enrolled. Thrombocytopenia and evidence of plasma leakage were present in 8% of adult and 19% of pediatric patients. Of these, 93% and 49%, respectively, were given the discharge diagnoses of DF instead of DHF/DSS. Hemoconcentration, serous effusion and thrombocytopenia were not recognized in clinicians' discharge diagnosis of DHF/DSS for adult patients. The receiver operating characteristic (ROC) curve suggested a lack of consistency in the use of WHO guidelines in establishing DHF/DSS in adult patients, while implying otherwise for pediatric patients. CONCLUSION: DHF/DSS is an under-recognized condition by clinicians managing these patients. This can affect the case fatality rate of DHF/DSS and the economic burden of the disease. The lack of awareness in disease manifestations especially plasma leakage, can lead to delayed recognition of DHF/DSS.

Smoothing a discrete hazard function for the number of patients colonized with Methicillin-resistant Staphylococcus Aureus in an intensive care unit.

A new method for estimating the time to colonization of Methicillin-resistant Staphylococcus Aureus (MRSA) patients is developed in this paper. The time to colonization of MRSA is modelled using a Bayesian smoothing approach for the hazard function. There are two prior models discussed in this paper: the first difference prior and the second difference prior. The second difference prior model gives smoother estimates of the hazard functions and, when applied to data from an intensive care unit (ICU), clearly shows increasing hazard up to day 13, then a decreasing hazard. The results clearly demonstrate that the hazard is not constant and provide a useful quantification of the effect of length of stay on the risk of MRSA colonization which provides useful insight.

'Online' monitoring and retrospective analysis of hospital outcomes based on a scan statistic.

Statistical tests based on the scan statistic are introduced for detecting possible increases in the occurrence of hospital events. The tests use a moving window and the theoretical aspects of the tests are investigated using Markov chain theory. The main objective of this study is to provide a statistical technique to assist hospital staff in deciding whether the variation they observe is greater than usually expected under random variation. In this paper we develop the test for Poisson data and apply the theory to monitor the occurrence of orthopaedic wound infection and Methicillin-resistant Staphylo- coccus aureus colonization. We find that this method is sensitive in detecting the change in the process parameter which may not be detected by standard control chart methods. Both online and retrospective analyses are considered.