DESIGN: Cross-sectional study, analysing baseline findings of a cohort of older adults.
SETTING: Kuala Pilah district, Negeri Sembilan state, Malaysia.
OBJECTIVES: To determine the prevalence of elder abuse among community dwelling older adults and its associated factors.
PARTICIPANTS: A total of 2112 community dwelling older adults aged 60 years and above were recruited employing a multistage sampling using the national census.
PRIMARY AND SECONDARY OUTCOME MEASURES: Elder abuse, measured using a validated instrument derived from previous literature and the modified Conflict Tactic Scales, similar to the Irish national prevalence survey on elder abuse with modification to local context. Factors associated with abuse and profiles of respondents were also examined.
RESULTS: The prevalence of overall abuse was reported to be 4.5% in the past 12 months. Psychological abuse was most common, followed by financial, physical, neglect and sexual abuse. Two or more occurrences of abusive acts were common, while clustering of various types of abuse was experienced by one-third of abused elders. Being male (adjusted OR (aOR) 2.15, 95% CI 1.23 to 3.78), being at risk of social isolation (aOR 1.96, 95% CI 1.07 to 3.58), a prior history of abuse (aOR 3.28, 95% CI 1.40 to 7.68) and depressive symptomatology (aOR 7.83, 95% CI 2.88 to 21.27) were independently associated with overall abuse.
CONCLUSION: Elder abuse occurred among one in every 20 elders. The findings on elder abuse indicate the need to enhance elder protection in Malaysia, with both screening of and interventions for elder abuse.
Methods: We administered relevant translations of the BOLD-1 questionnaire with additional questions from ECRHS-II, performed spirometry and arranged specialist clinical review for a sub-group to confirm the diagnosis. Using random sampling, we piloted a community-based survey at five sites in four LMICs and noted any practical barriers to conducting the survey. Three clinicians independently used information from questionnaires, spirometry and specialist reviews, and reached consensus on a clinical diagnosis. We used lasso regression to identify variables that predicted the clinical diagnoses and attempted to develop an algorithm for detecting asthma and COPD.
Results: Of 508 participants, 55.9% reported one or more chronic respiratory symptoms. The prevalence of asthma was 16.3%; COPD 4.5%; and 'other chronic respiratory disease' 3.0%. Based on consensus categorisation (n = 483 complete records), "Wheezing in last 12 months" and "Waking up with a feeling of tightness" were the strongest predictors for asthma. For COPD, age and spirometry results were the strongest predictors. Practical challenges included logistics (participant recruitment; researcher safety); misinterpretation of questions due to local dialects; and assuring quality spirometry in the field.
Conclusion: Detecting asthma in population surveys relies on symptoms and history. In contrast, spirometry and age were the best predictors of COPD. Logistical, language and spirometry-related challenges need to be addressed.