ABSTRACT
Aims:To studysocietal determinants of anemia among women of reproductive age (WRA) and elaborate the role of community pharmacist for its management. Study Design:To investigatethe societal determinants of anemia, a cross-sectional study was conducted in the Muzaffarabad district of AJK, Pakistan.Methodology:We collected the data from 384 women of reproductive age (15-49 years) having at least one child using a self-constructed interview schedule.The population sample size was calculated using Krejcie formula and purposive sampling was used for data collection. Two hospitals, Abbas Institute of Medical Sciences and combined military hospital Muzaffarabad were selected for blood samples to screen the hemoglobin (Hb) level of the respondents and data collection. Univariate analysis was performed to examine the frequency distributions and percentages of cases depending on a single variable at a time. The bivariate analysis was performed usingchi-square test to determine empirical relationship between the anemia severity and socio-cultural risk factors of this ailment. The odd ratios were computed to investigate the odds of occurrence of anemia among respondents. The results were considered statistically significant at significance level ≤ 0.05. Results:The findings revealed that prevalence of mild, moderate and severe anemia are 26.3%, 40.9% and 14.3% respectively among WRA, which showsthat anemia is a significant health problem from public health perspective in the region. Major contributing factors are respondent’s and her husband’s education, age at marriage, number of pregnancies, knowledge about balanced diet and anemia, male preference in food intake and violence.Conclusion:Anemia is a multifactorial problem among WRA in the study population, which can be dealt with using an integrated approach by combating malnutrition, provision of adequate healthcare, quality education and devising strategies for avoiding domestic violence. The community pharmacists can play an effective role to educate people about the selection of iron supplementation for adequate management of anemia among WRA
ABSTRACT
Background: The medical researchers are developing different non-invasive methods for early detection of Neurodegenerative Diseases (NDDs) when pharmacological interventions are still possible to further prevent the disease progression. The NDDs are associated with the degradation in the complex gait dynamicsand motor activity. The classification ofgait data using machine learning techniques can assist the physiciansfor early diagnosis of the neural disorder when clinical manifestation of the diseases is not yet apparent. Aims: The present study was undertaken to classify the control and NDD subjects using decision trees based classifiers (Random Forest (RF), J48 and REPTree).Methodology:The data used in the study comprises of 16 control, 20 Huntington’s Disease (HD), 15 Parkinson’s Disease (PD), and 13 Amyotrophic Lateral Sclerosis (ALS) subjects, which were taken from publicly available database from Physionet. The age range of control subjects was 20-74, HD subjects was 36-70, PD subjects was 44-80, and ALS subjects was 29-71. There were 13 attributes associated with the data. Important features/attributes of the data were selected using correlation feature selection -subset evaluation (cfs) method. Three tree based machine learning algorithms (RF, J48 and REPTree) were used to classify the control and NDD subjects. The performance of classifiers were evaluated using Precision, Recall, F-Measure, MAE and RMSE.Results:In order to evaluate the performance of tree based classifiers, two different settings of data i.e. complete features and selected featureswere used. In classifying control vs HD subjects, RF provides the robust separation with classification accuracy of 84.79% using complete features and 83.94% using selected features. While in classifying control vs PD subjects, and control vs ALS subjects, RF also provides the best separation with classification accuracy of 86.51% and 94.95% respectively using complete features and 85.19% and 93.64% respectively using selected features.Conclusion:The variability analysis of physiological signals provides a valuable non-invasive tool for quantifying the system of dynamics of healthy subjects and to examine the alternations in the controlling mechanism of these systems with aging and disease. It is concluded that selected features encode adequate information about neural control of the gait. Moreover,the selected featuresalong with tree based machine learning algorithms can play a vital for early detection of NDDs, when pharmacological interventions are still possible