Portal TB Gombak: Web-Based Application for Plotting of Tuberculosis Cases in Gombak, Selangor, Malaysia

Background: In the last few decades, public health surveillance has been conducted using various programming languages implementing statistical methods to analyze the spatial distribution of a disease. Nevertheless, contact tracing and follow up control measures for tuberculosis (TB) patients remain challenging because many public health officers lack the appropriate programming skills to use the related software. Therefore, this study aimed to develop a TB mapping application associated with sociodemographic factor in Gombak. Methods: The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of birth, educational level, employment status, healthcare worker, income status, residency, and smoking status between January 2013 and December 2017 in Gombak district were collected from the Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic and myTB website. Apart from that, the sociodemographic data of TB cases were extracted from the Aeronautical Reconnaissance Coverage Geographic Information System (ArcGIS) version 10.7 and subsequently uploaded into the Portal TB Gombak. The application was set up in the Python Shapefile (PHP) CodeIgniter framework with ArcGIS JavaScript API 3.7 and HyperText Markup Language (HTML), Cascading Style Sheet (CSS), JavaScript, and PHP as programming languages to build the system. Additionally, the ESRI map was used as the base map and combined with the web GIS technology via ArcGIS Application Programming Interface (API). Results: The application displays the location of TB cases on an interactive map based on sociodemographic factor. Conclusion: Portal TB Gombak allows public health officers to visualize the potential risk areas of TB cases without a trained programmer and geospatial statistician. This application will help healthcare personnel better understand TB transmission, thus improving case detection and minimize the public health impact of the disease.

Persuasive Da’wah Agenda based on Sociodemographic Factors: A Study in Cimahi

This paper is a result of primary research titled “Mapping the Profile of Persuasive Da’wah Agenda in Cimahi City.” Cimahi is well known as the industrial centre of ‘creative animation’. The method of research is quantitative by conducting surveys through questionnaires to 399 respondents in Cimahi using multistage random sampling technique. This paper presents important data on the socio-demographic factors of the citizens which can be basic in planning the effective da’wah activities. The sociodemographic factor aforementioned are age, education, and gender of respondents. Those factors must be accurately identified in order to plan the persuasive da’wah. The views of the citizens toward da’wah activity can be parameters toward persuasive da’wah, including what religious theme they preferred to, their preference on the actual da’wah themes, their liking on entrepreneurial da’wah, etc. Citizens give responses to da’wah agenda of both bil-lisan (da’wah through speech) and bil-hal (da’wah through one’s character). The more accepted a da’wah, it means the more persuasive the da’wah is. When citizens do not like the da’wah, it can be said an unpersuasive one. Da’wah persuasion is closely related to its acceptability by the citizens.

Social Class, Meritocracy, and the Geography of the “American Dream”

This paper uses original survey data to explore the landscape of support for a number of aspects of what might be called “the American Dream:” the notion that hard work is rewarded, and individuals succeed and fail due primarily to their own efforts. In general, I find that Americans generally endorse the idea that hard work leads to success and that economic mobility is possible for those willing to put in the effort, but that there is significant individual and contextual variation in support for those beliefs. I find that most variation in support for meritocratic belief is a function of simple partisan politics: differences between liberals and conservatives, and between Democrats and Republicans, are far more important to explaining meritocratic belief than any other sociodemographic factor. But attributes of the context in which one lives matter as well. Among other things, this paper highlights the importance of local economic decline as an important topic for future research: those who live in communities that have seen their relative economic fortunes drop over the past decade are less likely to believe in meritocracy than those who have seen their communities thrive.

Abstract TP142: Factors Influencing Post-stroke Discharge to Institutional Rehabilitation Settings versus Home

Introduction: We assessed relative impacts of sociodemographic, clinical, and geographic factors on discharge to rehabilitation or home in a population-based stroke study. Methods: Stroke survivors were identified from 2011-2013 in the Brain Attack Surveillance in Corpus Christi (BASIC) Project (death<30 days or nursing home residence excluded). Sociodemographic factors (age, sex, ethnicity, insurance type), clinical factors (risk factors, comorbidities, NIH Stroke Scale), and discharge location (inpatient rehabilitation facility (IRF), skilled nursing facility (SNF), home) were collected from medical records and interviews. Geographic factors were distance to nearest IRF/SNF and number of IRF/SNFs within 5 miles of the survivor’s home. Multinomial logistic regression models were used to calculate probabilities of discharge to IRF, SNF, and home for each factor set, fixing the other two factors at their means. High vs low probabilities (25 vs 75 percentile) of discharge to IRF, SNF, and home were compared to show the impact of each factor set. Results: Discharge location was available for 796/942 (85%) survivors; 15% were discharged to IRF, 26% SNF, and 59% home. Median age was 68 (IQR 58-79); 64% were Mexican American and 36% non-Hispanic white. High/low probabilities of discharge to IRF differed by 2% for sociodemographic factor variation, 7% for clinical factors, and 9% for geographic factors; probabilities of discharge to SNF differed by 22% for sociodemographic factor variation, 14% for clinical factors, and 6% for geographic factors; and probabilities of discharge home differed by 25% for sociodemographic factor variation, 24% for clinical factors, and 6% for geographic factors. Conclusion: The probability of discharge to IRF is most affected by variation in clinical factors; discharge to SNF by sociodemographic factors; and discharge home by sociodemographic and clinical factors. Geographic factors do not substantially affect discharge location.