Enhancement of systematic sampling for clinical survey: systematic sampling with consecutive approach / Mohamad Adam Haji Bujang

Survey is a one of the common primary data collection approaches in research in various fields including the clinical field. Findings from clinical surveys are important because recommendations from the findings will have a direct impact towards public’s health. Data collection process in clinical survey usually involves an ordered sampling frame and has become very challenging for clinical researchers, who need to handle multiple tasks in their clinical service whereby the clinical service is their top priority. Therefore, due to time constraints, the general practice of data collection in clinical survey is to adopt non-probability sampling such as consecutive sampling. The consequence of this kind of practice would produce results that can be invalid since the results could be influenced by sampling bias. In order to reduce sampling bias and to obtain more precise results is to promote the use of probability sampling technique in a clinical survey

Sample Size Guideline for Correlation Analysis

Correlation analysis is a common statistical analysis in various fields. The aim is usually to determine to what extent two numerical variables are correlated with each other. One of the issues that are important to be considered before conducting any correlation analysis is to plan for the sufficient sample size. This is to ensure, the results that to be derived from the analysis be able to reach a desired minimum correlation coefficient value with sufficient power and desired type I error or p-value. Sample size estimation for correlation analysis should be in line with the study objective. Researchers who are not statistician need simpler guideline to determine the sufficient sample size for correlation analysis. Therefore, this study aims to tabulate tables that show sample size calculation based on desired correlation coefficient, power and type 1 error (p-value) values. Moving towards that, simpler guidelines are proposed to estimate sufficient sample size requirements in different scenarios

Determination of Minimum Sample Size Requirement for Multiple Linear Regression and Analysis of Covariance Based on Experimental and Non-experimental Studies

  Background: MLR and ANCOVA are common statistical techniques and are used for both experimental and non-experimental studies. However, both types of study designs may require different basis of sample size requirement. Therefore, this study aims to proposed sample size guidelines for MLR and ANCOVA for both experimental and non-experimental studies. Methods: We estimated the minimum sample sizes required for MLR and ANCOVA by using Power and Sample Size software (PASS) based on the pre-specified values of alpha, power and effect size (R2). In addition, we also performed validation of the estimates using a real clinical data to evaluate how close the approximations of selected statistics which were derived from the samples were to the actual parameters in the targeted populations. All the coefficients, effect sizes and r-squared obtained from the sample were then compared with their respective parameters in the population. Results: Small minimum sample sizes required for performing both MLR and ANCOVA when r-squared is used as the effect size. However, the validation results based on an evaluation from a real-life dataset suggest that a minimum sample size of 300 or more is necessary to generate a close approximation of estimates with the parameters in the population. Conclusions: We proposed sample size calculation when r-squared is used as an effect size is more suitable for experimental studies. However, taking a larger sample size such as 300 or more is necessary for clinical survey that is conducted in a non-experimental manner

DASS21: a useful tool in the psychological profile evaluation of dialysis patients

Background: The effect of dialysis treatment is complex, with both clinical and socio-psychological effects. In this study, we aimed to assess the psychological status of this growing population of end-stage renal disease. Methods: Using the Short Form of Depression, Anxiety and Stress Scale (DASS21) questionnaire, we aimed (1) to measure the psychological states of hemodialysis (HD) or peritoneal dialysis (PD) subjects from 15 sites, (2) to compare DASS21 scores between HD and PD, and (3) to identify the associated demographic and medical factors of better psychological states. Results: A total of 1,332 were eligible for analysis. Stress (48%) recorded the highest negative emotional states, followed by depression (37%) and anxiety (20%). By multivariate analysis, normal body mass index weight status, religion and absence of coronary artery disease were associated with lower score for depression, anxiety and stress, respectively. Tertiary education was associated with the lowest score in depression and anxiety, whereas HD had a lower score in stress than PD. A younger age was associated with worse DASS21 score of anxiety and stress. Conclusions: Obesity, religion and coronary artery disease were significantly associated with all 3 symptoms of depression, anxiety and stress. Older age has a protective effect on anxiety and stress. Further study is needed to evaluate the relationship between these significant factors and each psychological state

Determinants of uncontrolled dyslipidaemia among adult type 2 diabetes in Malaysia: the Malaysian Diabetes Registry 2009

Numerous studies with compelling evidence had shown a clear relationship between dyslipidaemia and cardiovascular (CV) events in patients with diabetes mellitus. This was an observational study based on secondary data from the online registry database Adult Diabetes Control and Management (ADCM) looking into the determinants of uncontrolled dyslipidaemia in type 2 diabetes mellitus patients. Independent predictors were identified using multivariate logistic regression. A total of 303 centres (289 health clinics, 14 hospitals) contributed a total of 70,889 patients (1972 or 2.8% patients were from hospital). About thirty eight percent were reported to have dyslipidaemia. There were 40.7% patients on lipid-lowering agents and of those above age 40 years old, only 38.1% of them were on a statin. Malay ethnicity and younger age groups (<50 years old) were two major determinants of uncontrolled LDL-C, TG and HDL-C. Female gender and uncontrolled blood pressure were determinants of uncontrolled LDL-C, and poor glycaemic control was related independently to high TG. This study has highlighted the suboptimal management of diabetic dyslipidaemia in Malaysia. Pharmacological treatment of dyslipidaemia could be more effective. Healthcare stakeholders in this country, especially in the primary care, have to recognize these shortfalls and take immediate remedial measures

Predictors of poor glycaemic control in older persons with type 2 diabetes mellitus

INTRODUCTION: We assessed the predictors of poor glycaemic control among older patients with type 2 diabetes mellitus (T2DM) in Malaysia. METHODS: This cross-sectional study used the data of 21,336 patients aged ≥ 60 years with T2DM from the Adult Diabetes Control and Management Registry 2008-2009. RESULTS: Predictors of poor glycaemic control were: age groups 60-69 years (odds ratio [OR] 1.96, 95% confidence interval [CI] 1.66-2.33) and 70-79 years (OR 1.43, 95% CI 1.20-1.71); Malay (OR 1.53, 95% CI 1.41-1.66) and Indian (OR 1.32, 95% CI 1.19-1.46) ethnicities; T2DM durations of 5-10 years (OR 1.46, 95% CI 1.35-1.58) and > 10 years (OR 1.75, 95% CI 1.59-1.91); the use of oral antidiabetic agents only (OR 5.86, 95% CI 3.32-10.34), insulin only (OR 17.93, 95% CI 9.91-32.43), and oral antidiabetic agents and insulin (OR 29.42, 95% CI 16.47-52.53); and elevated blood pressure (OR 1.10, 95% CI 1.01-1.20), low-density lipoprotein cholesterol (OR 1.48, 95% CI 1.38-1.59) and triglycerides (OR 1.61, 95% CI 1.51-1.73). Hypertension (OR 0.71, 95% CI 0.64-0.80), hypertension and dyslipidaemia (OR 0.68, 95% CI 0.61-0.75), pre-obesity (OR 0.89, 95% CI 0.82-0.98) and obesity (OR 0.76, 95% CI 0.70-0.84) were less likely to be associated with poor glycaemic control. CONCLUSION: Young-old and middle-old age groups (i.e. < 80 years), Malay and Indian ethnicities, longer T2DM duration, the use of pharmacological agents, and elevated blood pressure and lipid levels were associated with poor glycaemic control. The presence of comorbidities, pre-obesity and obesity were less likely to be associated with poor glycaemic control

Type 2 diabetes mellitus patient profiles, diseases control and complications at four public health facilities: a cross-sectional study based on the Adult Diabetes Control and Management (ADCM) Registry 2009

INTRODUCTION: Diabetes care at different healthcare facilities varied from significantly better at one setting to no difference amongst them. We examined type 2 diabetes patient profiles, disease control and complication rates at four public health facilities in Malaysia. MATERIALS AND METHODS: This study analyzed data from diabetes registry database, the Adult Diabetes Control and Management (ADCM). The four public health facilities were hospital with specialist (HS), hospital without specialist (HNS), health clinics with family physicians (CS) and health clinic without doctor (CND). Independent risk factors were identified using multivariate regression analyses. RESULTS: The means age and duration of diabetes in years were significantly older and longer in HS (ANOVA, p< 0.0001). There were significantly more patients on insulin (31.2%), anti-hypertensives (80.1%), statins (68.1%) and antiplatelets (51.2%) in HS. Patients at HS had significantly lower means BMI, HbA1c, LDL-C and higher mean HDL-C. A significant larger proportion of type 2 diabetes patients at HS had diabetes-related complications (2-5 times). Compared to the HS, the CS was more likely to achieve HbA1c ≤ 6.5% (adjusted OR 1.2) and BP target < 130/80 mmHg (adjusted OR 1.4), the HNS was 3.4 times more likely not achieving LDL-C target < 2.6 mmol/L. CONCLUSION: Public hospitals with specialists in Malaysia were treating older male Chinese type 2 diabetes patients with more complications, and prescribed more medications. Patients attending these hospitals achieved better LDL-C target but poorer in attaining BP and lower HbA1c targets as compared to public health clinics with doctors and family physicians

Control of glycemia and other cardiovascular disease risk factors in older adults with type 2 diabetes mellitus: data from the adult diabetes control and management

Aim: The aims of the present study were to assess the control of glycemia and other cardiovascular disease risk factors, and the association between age and these controls among older adults with type 2 diabetes in Malaysia. Methods: A cross‐sectional study was carried out using cases notified to the Adult Diabetes Control and Management database between 1 January and 31 December 2009. A total of 10 363 people aged over 60 years with type 2 diabetes mellitus were included in the analyses. A standard online case report form was used to record demographic data, clinical factors (diabetes duration, comorbid condition and treatment modalities), cardiovascular disease risk factors, diabetes complications and laboratory assessments. The cardiovascular disease risk factors controls assessed included glycosylated hemoglobin (HbA1c) <7.0%, blood pressure, body mass index, waist circumference and lipid profiles. Results: The proportion of older adults who achieved target HbA1c (<7.0%) was 41.7%. A greater proportion of older adults aged ≥80 years significantly achieved the targets of HbA1c <7% (P  < 0.001), waist circumference (P  < 0.001), low‐density lipoprotein cholesterol <2.6 mmol/L (P  = 0.007) and triglycerides <1.7 mmol/L (P  = 0.001) when compared with the younger elderly groups. They were also associated with achieving target HbA1c <7.0% (OR  = 1.90, 95% CI 1.68–2.26) and triglycerides <1.7 mmol/L (OR  = 1.20, 95%CI 1.04–1.46) than those aged 60–69 years. Conclusion: The control of cardiovascular disease risk factors was suboptimal in older adults with type 2 diabetes. The oldest elderly were more likely to achieve target HbA1c (<7.0%) and triglycerides (<1.7 mmol/L) than older adults aged 60–69 years

Poor glycemic control in younger women attending Malaysian public primary care clinics: findings from adults diabetes control and management registry

Background: Women of reproductive age are a group of particular concern as diabetes may affect their pregnancy outcome as well as long-term morbidity and mortality. This study aimed to compare the clinical profiles and glycemic control of reproductive and non-reproductive age women with type 2 diabetes (T2D) in primary care settings, and to determine the associated factors of poor glycemic control in the reproductive age group women. Methods: This was a cross-sectional study using cases reported by public primary care clinics to the Adult Diabetes Control and Management registry from 1st January to 31st December 2009. All Malaysian women aged 18 years old and above and diagnosed with T2D for at least 1 year were included in the analysis. The target for glycemic control (HbA1c < 6.5%) is in accordance to the recommended national guidelines. Both univariate and multivariate approaches of logistic regression were applied to determine whether reproductive age women have an association with poor glycemic control. Results: Data from a total of 30,427 women were analyzed and 21.8% (6,622) were of reproductive age. There were 12.5% of reproductive age women and 18.0% of non-reproductive age women that achieved glycemic control. Reproductive age group women were associated with poorer glycemic control (OR = 1.5, 95% CI = 1.2-1.8). The risk factors associated with poor glycemic control in the reproductive age women were being of Malay and Indian race, longer duration of diabetes, patients on anti-diabetic agents, and those who had not achieved the target total cholesterol and triglycerides. Conclusion: Women with T2D have poor glycemic control, but being of reproductive age was associated with even poorer control. Health care providers need to pay more attention to this group of patients especially for those with risk factors. More aggressive therapeutic strategies to improve their cardiometabolic control and pregnancy outcome are warranted