Background & Objective: Symptomatic sarcocystosis has been said to be rare until recent years, when there were reports of outbreaks of febrile myositis for travellers returning from the Malaysian island resorts. In 2012, an outbreak of Sarcocystis nesbitti infection involving 92 college students and staff occurred after returning from Pangkor Island, Malaysia. A few months after recovering from the febrile illness, some patients complained of hair loss. This study aimed to determine the prevalence, clinical features and outcome of this disorder. Methods: All patients who became sick in the outbreak were asked whether they had the hair loss. For those who had, they were interviewed with standard questionnaires, examined and investigated. Patients were followed-up via an online survey 2 years later. Results: Out of 89 patients who were ill, 19 patients (21.4%) complained of alopecia. The mean peak onset was 4 months after the initial illness. Eleven patients (57.9%) reported the hair fall of more than 100 per day. The other symptoms were itch 10 (52.6%), scaling 10 (52.6%), erythema 4 (21.1%), none had scarring. Eleven patients (57.8%) had positive antinuclear factor with high titre (speckled or nucleolar pattern). Two years after the event, 10 had complete or near complete spontaneous recovery, 1 had partial response and 1 had no improvement. Conclusions: A delayed transient diffuse alopecia is seen in close to half of patients with Sarcocystis nesbittiinfection. This high frequency of positive ANF suggested an immune-mediated mechanism.
Sarcocystosis

Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

Objectives: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced.Differences in QoL among demographic sub-groups were examined using the t-test and analysisof variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated usingPearson correlation coefficients. Results: Based on complete case analysis (n = 19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p < 0.001) to –0.42 (physical with mobility; p < 0.001). Conclusion Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice betweenthem should be guided by the specific research questions and the intended use of the data.

The World Health Organization estimates that annually 150 million people experience severe (catastrophic) financial difficulties as a result of healthcare payments. Therefore, a systematic review was carried out to identify the determinants of household catastrophic health expenditure (CHE) in low- to high-income countries around the world. Both electronic and manual searches were conducted. The main outcome of interest was the determinants of CHE due to healthcare payments. Thirty eight studies met the inclusion criteria for review. The analysis revealed that household economic status, incidence of hospitalisation, presence of an elderly or disabled household member in the family, and presence of a family member with a chronic illness were the common significant factors associated with household CHE. The crucial finding of the current study is that socioeconomic inequality plays an important role in the incidence of CHE all over the world, where low-income households are at high risk of financial hardship from healthcare payments. This suggests that healthcare financing policies should be revised in order to narrow the gap in socioeconomic inequality and social safety nets should be implemented and strengthened for people who have a high need for health care.

Background: The incidence of colorectal cancer (CRC) is rapidly rising in several Asian countries, including Malaysia, but there is little data on health care provider costs in this region. The aim of this study was to estimate the cost of CRC management from the perspective of the health care provider, based on standard operating procedures. Methods: A combination of top-down approach and activity-based costing was applied. The standard operating procedure (SOP) for CRC was developed for each stage according to national data and guidelines at the University of Malaya Medical Centre (UMMC). The unit cost was calculated and incorporated into the treatment pathway in order to obtain the total cost of managing a single CRC patient according to the stage of illness. The cost data were represented by means and standard deviation and the results were demonstrated by tabulation. All cost data are presented in Malaysian Ringgit (RM). The cost difference between early stage (Stage I) and late stage (Stage II–IV) was analysed using independent t-test. Results: The cost per patient increased with stage of CRC, from RM13,672 (USD4,410.30) for stage I, to RM27,972 (USD9,023.20) for Stage IV. The early stage had statistically significant lower cost compared to late stage t(2) = −4.729, P = 0.042. The highest fraction of the cost was related to surgery for Stage I, but was superseded by oncology day care treatment for Stages II–IV. CRC is a costly illness. From a provider perspective, the highest cost was found in Stages III and IV. The early stages conserved more resources than did the advanced stages of cancer. Conclusion: Early diagnosis and management of CRC, therefore, not only affects oncologic prognosis, but has implications for health care costs. This adds further justification to develop and implement CRC screening programmes in Malaysia.