Trends of completed suicide rates among Malaysian elderly between 1995 and 2020.

BACKGROUND: Suicide among the elderly has become a global public health concern. This study was carried out to determine the trend of completed suicide rates according to age, sex, and ethnicity and the suicidal methods among the elderly in Malaysia. METHODS: All suicide-related deaths in elderly aged 60 years and above from the Year 1995 to 2020 reported to the National Registration Department (NRD) were analyzed. Causes of death for suicide were coded based on the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10). The completed suicide rate was calculated by dividing the completed suicide number by the total elderly population for the respective year. RESULTS: Overall, the analysis of 1,600 suicide-related deaths was investigated over 26 years. Male was seen to be correlated with higher suicidal risk, with a male-female relative risk (RR) = 1.89 (95%CI:1.46,2.45). The risk of suicide was also found to be significantly higher for those aged 60 to 74 years old and Chinese, with RR = 4.26 (95%CI:2.94, 6.18) and RR = 5.81 (95%CI: 3.70, 9.12), respectively. Hanging was found to be a statistically significant suicide method (IRR:4.76, 95%CI:2.34,9.65) as compared to pesticide poisoning. The completed suicide rate was fluctuating over the years. CONCLUSIONS: In conclusion, it is believed that Malaysia's elderly suicide rate has reached an alarmingly high incidence. By identifying the crucial criteria of sociodemographic factors, the government and responsible agencies have the essential and additional information to put together a more effective strategy and approach to overcome the issue in the future.

Cancer impact on lower-income patients in Malaysian public healthcare: An exploration of out-of-pocket expenses, productivity loss, and financial coping strategies.

Cancer patients often grapple with substantial out-of-pocket (OOP) expenses and productivity loss, with the ramifications being particularly crucial for lower-income households. This study aims to estimate OOP costs incurred by cancer patients, assess their productivity loss, and analyse the financial coping mechanisms employed by individuals within the lower-income bracket. The study employed face-to-face interviews among cancer patients aged 40 years and above, currently undergoing treatment, and belonging to the lower-income group. Participants were recruited from six public cancer referral hospitals. OOP expenses, encompassing medical and non-medical costs, along with productivity loss, were measured. A generalized linear model was applied to identify potential OOP determinants. Additionally, the coping mechanisms employed by individuals to finance their cancer OOP expenses were also determined. Among the 430 participants recruited, predominantly female (63.5%), and aged 60 or older (53.9%). The annual mean total cancer costs per patient were US$ 2,398.28 (±2,168.74), including 15% for medical costs US$ 350.95 (±560.24), 34% for non-medical costs US$820.24 (±818.24), and 51% for productivity loss costs US$1,227.09 (±1,809.09). Transportation, nutritional supplements, outpatient treatment, and medical supplies were notable cost contributors to total OOP expenditures. Ethnicity (ß = 1.44; 95%CI = 1.15-1.79), household income (ß = 1.40; 95%CI = 1.10-1.78), annual outpatient visits (ß = 1.00; 95%CI = 1.00-1.01), age (ß = 0.74; 95%CI = 0.56-0.98), and employment status (ß = 0.54; 95%CI = 0.72-1.34) were identified as significant predictors of OOP costs among cancer patients. Notably, 91% of participants relied on household salaries and savings, while 15% resorted to interest-free borrowing, 11% sold possessions, and 0.5% borrowed with interest to finance their expenses. This study offers crucial insights into the economic impact of cancer on individuals and their families, providing policymakers with valuable information to tackle challenges faced in their journey. Despite substantial public healthcare subsidies, the study revealed that cancer costs can remain a potential barrier to accessing essential treatment. Therefore, there is a need for reinforced system-level infrastructure to facilitate targeted financial navigation services.

The Direct and Indirect Costs of Cancer among the Lower-Income Group: Estimates from a Pilot and Feasibility Study.

BACKGROUND: Healthcare in Malaysia is largely publicly funded, however, cancer could still result in out-of-pocket (OOP) expenses, which may burden the affected patients. This is especially relevant to those in the lower-income group. This pilot study was conducted to estimate the direct and indirect costs of cancer and evaluate the feasibility of obtaining these costs information from the lower-income cancer patients undergoing treatment. METHODS: A cross-sectional study of patients with cancer was conducted in Hospital Kuala Lumpur between September and October 2020. Self-reported data from the patients were collected using face-to-face interviews. Detailed information about cancer-related OOP expenses including direct medical, direct non-medical, and productivity loss in addition to financial coping strategies were collected. Costs data were estimated and reported as average annual total costs per patient. RESULTS: The mean total cost of cancer was estimated at MYR 7955.39 (US$ 1893.46) per patient per year. The direct non-medical cost was the largest contributor to the annual cost, accounting for 46.1% of the total cost. This was followed by indirect costs and direct medical costs at 36.0% and 17.9% of the total annual costs, respectively. Supplemental food and transportation costs were the major contributors to the total non-medical costs. The most frequently used financial coping strategies were savings and financial support received from relatives and friends. CONCLUSION: This study showed that estimation of the total cost of cancer from the patient's perspective is feasible. Considering the significant impact of direct non-medical and indirect costs on the total costs, it is vital to conduct further exploration of its cost drivers and variations using a larger sample size.