DESIGN: Two-year prospective cohort study.
SETTING: Kuala Pilah, a district in Negeri Sembilan approximately 100 km from the capital city, Kuala Lumpur.
PARTICIPANTS: Community-dwelling older adults aged 60 and older. Using a multistage cluster sampling strategy, 1,927 respondents were recruited and assessed at baseline, of whom 1,189 were re-assessed 2 years later.
MEASURES: EAN was determined using the modified Conflict Tactic Scale, and chronic pain was assessed through self-report using validated questions.
RESULTS: The prevalence of chronic pain was 20.4%. Cross-sectional results revealed 8 variables significantly associated with chronic pain-age, education, income, comorbidities, self-rated health, depression, gait speed, and EAN. Abused elderly adults were 1.52 times as likely to have chronic pain (odds ratio=1.52, 95% confidence interval (CI)=1.03-2.27), although longitudinal analyses showed no relationship between EAN and risk of chronic pain (risk ratio=1.14, 95% CI=0.81-1.60). This lack of causal link was consistent when comparing analysis with complete cases with that of imputed data.
CONCLUSION: Our findings indicate no temporal relationship between EAN and chronic pain but indicated cross-sectional associations between the two. This might indicate that, although EAN does not lead to chronic pain, individuals with greater physical limitations are more vulnerable to abuse. Our study also shows the importance of cohort design in determining causal relationships between EAN and potentially linked health outcomes.
METHODS: We report on the development of the EWARS tool, based on users' recommendations into a convenient, user-friendly and reliable software aided by a user's workbook and its field testing in 30 health districts in Brazil, Malaysia and Mexico.
FINDINGS: 34 Health officers from the 30 study districts who had used the original EWARS for 7 to 10 months responded to a questionnaire with mainly open-ended questions. Qualitative content analysis showed that participants were generally satisfied with the tool but preferred open-access vs. commercial software. EWARS users also stated that the geographical unit should be the district, while access to meteorological information should be improved. These recommendations were incorporated into the second-generation EWARS-R, using the free R software, combined with recent surveillance data and resulted in higher sensitivities and positive predictive values of alarm signals compared to the first-generation EWARS. Currently the use of satellite data for meteorological information is being tested and a dashboard is being developed to increase user-friendliness of the tool. The inclusion of other Aedes borne viral diseases is under discussion.
CONCLUSION: EWARS is a pragmatic and useful tool for detecting imminent dengue outbreaks to trigger early response activities.
RESULTS: Using Open Data Kit GeoODK, we designed and piloted an electronic questionnaire for rolling cross sectional surveys of health facility attendees as part of a malaria elimination campaign in two predominantly rural sites in the Rizal, Palawan, the Philippines and Kulon Progo Regency, Yogyakarta, Indonesia. The majority of health workers were able to use the tablets effectively, including locating participant households on electronic maps. For all households sampled (n = 603), health facility workers were able to retrospectively find the participant household using the Global Positioning System (GPS) coordinates and data collected by tablet computers. Median distance between actual house locations and points collected on the tablet was 116 m (IQR 42-368) in Rizal and 493 m (IQR 258-886) in Kulon Progo Regency. Accuracy varied between health facilities and decreased in less populated areas with fewer prominent landmarks.
CONCLUSIONS: Results demonstrate the utility of this approach to develop real-time high-resolution maps of disease in resource-poor environments. This method provides an attractive approach for quickly obtaining spatial information on individuals presenting at health facilities in resource poor areas where formal addresses are unavailable and internet connectivity is limited. Further research is needed on how to integrate these with other health data management systems and implement in a wider operational context.