Respiratory fit test panel representing population of Malaysia.

BACKGROUND: The existing respiratory fit test panels (RFTPs) are based on Bivariate and Principal Component Analysis (PCA) which utilise American and Chinese head and facial dimensions. As RFTPs based on local facial anthropometric data for Malaysia are not available, this study was conducted with the aim to develop new RFTPs using Malaysian data. METHODOLOGY: A cross-sectional study was conducted across Malaysia among 3,324 participants of the study of National Health and Morbidity Survey 2020 aged 18 and above. Ten head and facial dimensions were measured. Face length and face width were used to construct bivariate facial panel, whereas the scores from the first two PCA were used to develop the PCA panel. RESULTS: This study showed that Malaysians have the widest upper limit for facial width. It also found that three factors could be reduced from the PCA analysis. However only 2 factors were selected with PCA 1 representing head and facial size and PCA 2 representing facial shape. Our bivariate panel could accommodate 95.0% of population, while our PCA panel accommodated 95.6%. CONCLUSION: This was the first study to use Malaysian head and facial anthropometry data to create bivariate and PCA panels. Respirators constructed using these panels are likely to fit ≥ 95.0% of Malaysia's population.

The effect of N95 designs on respirator fit and its associations with gender and facial dimensions.

This study examined the association of various brands of NIOSH-certified N95 filtering face-piece respirators (FFR) fit with facial dimensions and gender. One hundred and thirty-five participants (77 females and 58 males) were recruited from the previous facial anthropometry study among Malaysians in 2020. Quantitative respirator fit testing of six FFR were performed using the TSI Portacount Pro+ 8038 which comprised of four exercises (bending over, talking, up-down head movement, and side to side head movement). An overall fit factor (FF) of ≥ 100 was considered a pass for each FFR. Analysis was done using T-test, Pearson's correlations, and generalised linear regression. The passing rates for the six FFR were 36.3% (Cup B), 50.4% (Trifold A), 54.1% (Duckbill A), 57.0% (Cup A), 74.1% (Trifold B), and 83.7% (Duckbill B). Both Duckbill B and Trifold B had the highest passing rates for both genders. However, certain FFR models (Cup B, Trifold A, Trifold B, and Duckbill A) fit better for participants with large facial size who were mostly males, while others (Cup A and Duckbill B) specifically fit better for those with small facial size, who were mostly females. This study showed significant positive effect of nose protrusion, nasal root and subnasale-sellion and the negative effect of menton-sellion, bigonial breadth and nose breadth on fit factors of various FFR. The results of this study emphasized the importance of choosing and designing FFR based on local anthropometry data, with careful consideration on the dimensions that affect the respirator fit. Since N95 are commonly used in the healthcare settings to prevent airborne transmission, the practice of respirator fit testing and selecting N95 with high passing rates for healthcare workers need to be emphasized.

Droplet digital PCR application for the detection of SARS-CoV-2 in air sample.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) may transmit through airborne route particularly when the aerosol particles remain in enclosed spaces with inadequate ventilation. There has been no standard recommended method of determining the virus in air due to limitations in pre-analytical and technical aspects. Furthermore, the presence of low virus loads in air samples could result in false negatives. Our study aims to explore the feasibility of detecting SARS-CoV-2 ribonucleic acid (RNA) in air samples using droplet digital polymerase chain reaction (ddPCR). Active and passive air sampling was conducted between December 2021 and February 2022 with the presence of COVID-19 confirmed cases in two hospitals and a quarantine center in Klang Valley, Malaysia. SARS-CoV-2 RNA in air was detected and quantified using ddPCR and real-time reverse transcriptase-polymerase chain reaction (RT-PCR). The comparability of two different digital PCR platforms (QX200 and QIAcuity) to RT-PCR were also investigated. Additionally negative staining transmission electron microscopy was performed to visualize virus ultrastructure. Detection rates of SARS-CoV-2 in air samples using ddPCR were higher compared to RT-PCR, which were 15.2% (22/145) and 3.4% (5/145), respectively. The sensitivity and specificity of ddPCR was 100 and 87%, respectively. After excluding 17 negative samples (50%) by both QX200 and QIAcuity, 15% samples (5/34) were found to be positive both ddPCR and dPCR. There were 23.5% (8/34) samples that were detected positive by ddPCR but negative by dPCR. In contrast, there were 11.7% (4/34) samples that were detected positive by dPCR but negative by ddPCR. The SARS-CoV-2 detection method by ddPCR is precise and has a high sensitivity for viral RNA detection. It could provide advances in determining low viral titter in air samples to reduce false negative reports, which could complement detection by RT-PCR.

Multidrug-Resistant Salmonella Species and Their Mobile Genetic Elements from Poultry Farm Environments in Malaysia.

The prevalence and persistent outbreaks of multidrug-resistant (MDR) Salmonella in low-income countries have received growing attention among the public and scientific community. Notably, the excessive use of antibiotics in chicken feed for the purpose of treatment or as prophylaxis in the poultry industry have led to a rising rate of antimicrobial resistance. Therefore, this study aimed to determine the presence of antimicrobial-resistant Salmonella species and its mobile genetic elements from soil and effluent samples of 33 randomly selected poultry farms in Selangor, Malaysia. Salmonella species were isolated on selective media (CHROMagar™ Salmonella). VITEK® 2 system was used to identify the isolates and their antimicrobial susceptibility. Subsequently, eight isolates were subjected to the whole genome sequencing (WGS). Based on the results, Salmonella spp. was detected in 38.1% (24/63) of samples, with the highest resistance to ampicillin (62.5%), followed by ampicillin/sulbactam (50.0%) and ciprofloxacin (45.8%). Meanwhile, the identified serovars were Salmonella enterica subspecies enterica serovar Weltevreden (S. Weltevreden), S. Jedburgh, and S. Brancaster. The most prevalent resistance genes detected include qnrS1, blaTEM-176, dfrA14, and tet(A). The IncX1 plasmid, with encoded resistance genes, was also detected in four isolates. Furthermore, mutations in the quinolone resistant-determining regions (QRDR) were discovered, specifically in the gyrA, gyrB, and parC genes. In short, surveillance such as continuous monitoring of antimicrobial resistance and emerging trends in resistance patterns through farm environmental samples could provide information to formulate public health interventions for effective infection prevention and disease control.

Prevalence of Antibiotic-Resistant Pathogenic Bacteria and Level of Antibiotic Residues in Hospital Effluents in Selangor, Malaysia: Protocol for a Cross-sectional Study.

BACKGROUND: Antimicrobial resistance (AMR) has emerged as a major global public health challenge due to the overuse and misuse of antibiotics for humans and animals. Hospitals are among the major users of antibiotics, thereby having a large contribution to AMR. OBJECTIVE: The aim of this study is to determine the prevalence of antibiotic-resistant pathogenic bacteria and the level of antibiotic residues in the hospital effluents in Selangor, Malaysia. METHODS: A cross-sectional study will be performed in the state of Selangor, Malaysia. Tertiary hospitals will be identified based on the inclusion and exclusion criteria. The methods are divided into three phases: sample collection, microbiological analysis, and chemical analysis. Microbiological analyses will include the isolation of bacteria from hospital effluents by culturing on selective media. Antibiotic sensitivity testing will be performed on the isolated bacteria against ceftriaxone, ciprofloxacin, meropenem, vancomycin, colistin, and piperacillin/tazobactam. The identification of bacteria will be confirmed using 16S RNA polymerase chain reaction (PCR) and multiplex PCR will be performed to detect resistance genes (ermB, mecA, blaNDM-L, blaCTX-M, blaOXA-48, blaSHV, VanA, VanB, VanC1, mcr-1, mcr-2, mcr-3, Intl1, Intl2, and qnrA). Finally, the level of antibiotic residues will be measured using ultrahigh-performance liquid chromatography. RESULTS: The expected outcomes will be the prevalence of antibiotic-resistant Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter (ESKAPE) bacterial species from the hospital effluents, the occurrence of antibiotic resistance genes (ARGs) from the isolated ESKAPE bacteria, and the level of antibiotic residues that may be detected from the effluent. Sampling has been conducted in three hospitals. Data analysis from one hospital showed that as of July 2022, 80% (8/10) of E. faecium isolates were resistant to vancomycin and 10% (1/10) were resistant to ciprofloxacin. Further analysis will be conducted to determine if the isolates harbor any ARGs and effluent samples are being analyzed to detect antibiotic residues. Sampling activities will be resumed after being suspended due to the COVID-19 pandemic and are scheduled to end by December 2022. CONCLUSIONS: This study will provide the first baseline information to elucidate the current status of AMR of highly pathogenic bacteria present in hospital effluents in Malaysia. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39022.

Arsenic in drinking water, hair, and prevalence of arsenicosis in Perak, Malaysia.

Arsenic is a carcinogen element that occurs naturally in our environment. Humans can be exposed to arsenic through ingestion, inhalation, and dermal absorption. However, the most significant exposure pathway is via oral ingestion. Therefore, a comparative cross-sectional study was conducted to determine the local arsenic concentration in drinking water and hair. Then, the prevalence of arsenicosis was evaluated to assess the presence of the disease in the community. The study was conducted in two villages, namely Village AG and Village P, in Perak, Malaysia. Socio-demographic data, water consumption patterns, medical history, and signs and symptoms of arsenic poisoning were obtained using questionnaires. In addition, physical examinations by medical doctors were performed to confirm the signs reported by the respondents. A total of 395 drinking water samples and 639 hair samples were collected from both villages. The samples were analyzed using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) to determine arsenic concentration. The results showed that 41% of water samples from Village AG contained arsenic concentrations of more than 0.01 mg/L. In contrast, none of the water samples from Village P exceeded this level. Whilst, for hair samples, 85 (13.5%) of total respondents had arsenic levels above 1 µg/g. A total of 18 respondents in Village AG had at least one sign of arsenicosis and hair arsenic levels of more than 1 µg/g. Factors significantly associated with increased arsenic levels in hair were female, increasing age, living in Village AG and smoking. The prevalence of arsenicosis in the exposed village indicates chronic arsenic exposure, and immediate mitigation action needs to be taken to ensure the wellbeing of the residents in the exposed village.

Concerns, perceived impact, practices, preventive measures, and stress among healthcare workers during COVID-19 pandemic in Malaysia.

Introduction: Healthcare workers (HCWs) have been continually exposed to patients with COVID-19 and are at higher risk of contracting the disease. Their psychological health is important for overall wellbeing and productivity, which could lead to a reduction in human errors during the pandemic crisis. This study aimed to measure the level of concerns, work practices, adequacy of preventive measures among HCWs, and the impacts on their life and work, including mental health status during the second wave of the COVID-19 pandemic in Malaysia. Methods: An online questionnaire was distributed randomly to 1,050 HCWs from the Ministry of Health facilities in the Klang Valley who were involved directly in managing or screening COVID-19 cases from May to August 2020. The questionnaire was divided into five domains, which were concerns, impact on life and work, practice, perceived adequacy of preventive measures, and Revised Impact of Event Scale (IES-R). Logistic regression was used to identify sociodemographic predictors of the five domains. Results: A total of 907 respondents (86.4%) participated in this survey. Approximately half of the respondents had a low concern (50.5%), most of them had a good practice (85.1%), with 67.5% perceiving there were adequate preventive measures, and they perceived the outbreak had a low impact (92%) on their life and work. From the IES-R domain, 18.6% of respondents potentially suffered from post-traumatic stress disorder (PTSD). Conclusion: During the second wave of the COVID-19 outbreak in Malaysia, HCWs practiced high levels of precautions and preventive measures because they were aware of the risk of infection as an occupational hazard. With the adequate implementation of policy and control measures, the psychological wellbeing of the majority HCWs remained well and adequately supported.

Head and face anthropometric study for respirators in the multi-ethnic Asian population of Malaysia.

Background: Existing anthropometric studies for respirator designs are based on the head and facial dimensions of Americans and Chinese nationals, with no studies for multi-ethnic countries like Malaysia. This study aimed to create head and facial morphological database for Malaysia, specifically to identify morphological differences between genders, ethnicities, and birthplaces, as well as predictors of the dimensions. Design: A cross-sectional study. Setting: Malaysia. Participants: A nation-wide cross-sectional study using a complex survey design with two stage-stratified random sampling was conducted among 3,324 participants, aged 18 years and above who were also participants of the National Health and Morbidity Survey 2020. Primary and secondary outcomes: The study collected data on sociodemographic, measurement of Body Mass Index (BMI) and 10 head and facial dimensions (3 dimensions were measured using direct measurement, and 7 others using Digimizer software for 2-dimension images). Linear regression was performed to determine the association between gender, ethnicity, birthplace, age and BMI and the dimensions. Results: There were significant differences in all the dimensions between sex, birthplace and ethnicity (p < 0.005). Further analysis using linear regression showed sex, ethnicity, birthplace, age and BMI were significant predictors of the dimensions. In comparison to studies from the United States and China, our study population had a wider interpupillary distance and nose breadth for both male and female participants, but smaller bigonial breadth and smaller minimal frontal breadth. Conclusion: These findings could assist in the design and sizing of respirators that will fit Malaysians and possibly other Southeast Asian population.

Resistance towards Critically Important Antimicrobials among Enterococcus faecalis and E. faecium in Poultry Farm Environments in Selangor, Malaysia.

Multidrug resistant (MDR) enterococci pose significant public health challenges. However, the extent of resistance in the environment is less explored. This study aimed to determine the antibiotic resistance in a poultry farm environment. Eighty enterococcal isolates recovered from the soil and effluent water of 28 poultry farms in Selangor state were included in the study for further bacterial identification and antibiotic susceptibility testing using a VITEK 2 system. Data were analyzed using Statistical Package for Social Science (SPSS) version 27. The resistance rate and MDR of enterococcal isolates were reported. Out of 80 isolates recovered, 72 (90%) exhibited resistance to at least one antibiotic, with 50 isolates (62.5%) being found to be MDR. All linezolid-resistant enterococci (LRE) exhibit MDR, which constituted 40% of resistance among all the isolates recovered from poultry environment. Since linezolid is listed as critically important antibiotics for clinical use by the World Health Organization (WHO), the higher resistance towards it and other critically important antibiotic for human use is a serious concern. Hence, relevant agencies need to investigate the use of clinically important antimicrobials in poultry farms paying special attention towards linezolid or any other antibiotics that can facilitate the development of LRE.

Compliance to Infection Prevention and Control Practices Among Healthcare Workers During COVID-19 Pandemic in Malaysia.

Healthcare workers (HCWs) are at risk of contracting coronavirus disease-2019 (COVID-19) in their workplace. Infection prevention guidelines and standard operating procedures were introduced to reduce risk of exposure and prevent transmission. Safe practices during interaction with patients with COVID-19 are crucial for infection prevention and control (IPC). This study aimed to assess HCWs' compliance to IPC and to determine its association with sociodemographic and organizational factors. A cross-sectional study was conducted between March and April 2021 at public healthcare facilities in the east coast of Peninsular Malaysia. HCWs who were involved with COVID-19-related works were invited to participate in the online survey. The questionnaire was adapted from the World Health Organization (WHO) Interim Guidance: WHO Risk Assessment and Management of Exposure of Healthcare Workers in the Context of COVID-19. Respondents were categorized as compliant or non-compliant to IPC. A total of 600 HCWs involved in COVID-19-related works participated in the survey. Most of them (63.7%) were compliant to IPC as they responded to all items as "always, as recommended" during interaction with patients with COVID-19. The multivariate analysis showed that non-compliance was significantly associated with working in the emergency department (AOR = 3.16; 95% CI = 1.07-9.31), working as laboratory personnel (AOR = 15.13; 95% CI = 1.36-168.44), health attendant (AOR = 4.42; 95% CI = 1.74-11.24), and others (AOR = 3.63; 95% CI = 1.1-12.01), as well as work experience of more than 10 years (AOR = 4.71; 95% CI = 1.28-17.27). The odds of non-compliance among respondents without adequate new norms and personal protective equipment training were 2.02 (95% CI = 1.08-3.81) more than those with adequate training. Although most of the respondents complied to IPC protocols, compliance status differed according to department, work category, and years of service. Ensuring adequate training that will hopefully lead to behavioral change is crucial to prevent breach in IPC and thus minimize the risk of exposure to and transmission of COVID-19 in healthcare facilities.

Multidrug-Resistant Gram-Negative Bacteria and Extended-Spectrum ß-Lactamase-Producing Klebsiella pneumoniae from the Poultry Farm Environment.

The indiscriminate use and overuse of various antibiotics have caused the rapid emergence of antibiotic-resistant bacteria (ARB) in poultry products and the surrounding environment, giving rise to global public health issues. This study aimed to determine the prevalence of multidrug-resistant (MDR) Gram-negative bacteria (GNB) found in the environment of poultry farms and to evaluate the risk of contamination in these farms based on multiple antibiotic resistance (MAR) index values. Soil and effluent samples were collected from 13 poultry farms. The VITEK 2 system was used for bacterial identification and susceptibility testing of the isolates. The identified Gram-negative isolates were Acinetobacter spp., Aeromonas spp., Enterobacter spp., Klebsiella pneumoniae, Proteus spp., Providencia spp., Pseudomonas spp., and Sphingomonas paucimobilis. The results showed that Enterobacter spp., Aeromonas spp., and Providencia spp. exhibited the highest MDR rates and MAR indices; 14% of K. pneumoniae isolates (3/21 isolates) were resistant to 13 antibiotics and found to be extended-spectrum ß-lactamase (ESBL)-producing bacteria. As for the tested antibiotics, 96.6% of the isolates (28/29 isolates) demonstrated resistance to ampicillin, followed by ampicillin-sulbactam (55.9% [33/59 isolates]) and cefazolin (54.8% [57/104 isolates]). The high percentage of MDR bacteria and the presence of ESBL-producing K. pneumoniae strains suggested the presence of MDR genes from the poultry farm environment, which poses an alarming threat to the effectiveness of the available antibiotic medicines to treat infectious diseases. Therefore, the use of antibiotics should be regulated and controlled, while studies addressing One Health issues are vital for combating and preventing the development and spread of ARB. IMPORTANCE The occurrence and spread of ARB due to high demand in poultry industries are of great public health concern. The widespread emergence of antibiotic resistance, particularly MDR among bacterial pathogens, poses challenges in clinical treatment. Some pathogens are now virtually untreatable with current antibiotics. However, those pathogens were rarely explored in the environment. In alignment with the concept of One Health, it is imperative to study the rate of resistance in the environment, because this domain plays an important role in the dissemination of bacteria to humans, animals, and other environmental areas. Reliable data on the prevalence of MDR bacteria are crucial to curb the spread of bacterial pathogens that can cause antimicrobial-resistant infections.

GIS based interpolation method to urinary metal concentrations in Malaysia.

Recently, there has been an increasing ecological and global public health concern associated with environmental contamination by arsenic (As), cadmium (Cd), nickel (Ni) and lead (Pb). This study aims to visualize distribution of these heavy metals concentration in urine of 817 Malaysian adults in Malaysia. This cross-sectional study compiled urine samples from October 2017 to March 2018. Spatial inverse distance weighted (IDW) interpolation method was used to derive a map which illustrate the distribution of these metals from urine samples. Moran's I Index was used to assess the spatial autocorrelation of these metals between the study area. The mean concentration for Ni and Cd was highest in Perak while for As and Pb, they were predominantly in East Coast region (Terengganu and Kelantan). Moran's I index showed significant spatial autocorrelation exist in the study area for all metals except for Pb (p-value<0.05). According to IDW, people in North East and Western regions had the highest concentrations for As and Cd. Individuals in Central region were less exposed than those in North region. These techniques can assist health authorities a better prediction in assessing exposure to heavy metals in population of an area to prioritize their control and preventive measure.

Exposure Assessment of methyl mercury from consumption of fish and seafood in Peninsular Malaysia.

The concentration of meHg in freshwater fish and seafood was investigated, as well as the consumption patterns of fish and seafood by different demographic groups (age, ethnicity, gender). A potential alarm for human health hazards was also assessed, and the results were compared to the provisional tolerable weekly intakes (PTWIs) and the hazard quotient parameter (HQ). The results showed that meHg levels of 67 species ranged from 0.013 to 0.252 mg/kg of wet weight (WW) with significant differences between different fish and seafood groups (χ2KW = 49.09; p < 0.001). Median concentrations of meHg in fish and seafood groups in descending orders are as follows: demersal fish (0.1006 mg/kg WW) > pelagic fish (0.0686 mg/kg WW) > freshwater fish 0.045 mg/kg WW) > cephalopods (0.0405 mg/kg WW) crustaceans (0.0356 mg/kg WW). The results revealed that older population (> 40 years old) consumed significantly (p = 0.000) more fish compared to younger generations and the elderly consumed the highest amounts of fish (104.0 ± 113.0 g/day). The adolescents (10-17 years old) consumed more than double of amount for both cephalopod and crustacean compared to the older populations (p < 0.05). Malay ethnic (96.1 ± 99.6 g/day) consumed significantly (p = 0.000) higher amounts of fish and seafood compared to other ethnicities, similar to male subjects (95.2 ± 102 g/day; p = 0.026) when compared to the female (86 ± 96.3 g/day). The estimated weekly intake (EWI) values showed results below 1.6 µg/kg BW/week, the tolerable levels recommended by the Joint FAO/WHO Expert Committee on Food Additives (JECFA) for all different demographic factors except for higher consumers at 75th percentile and above. Consumption of marine fish contributed to a higher value of PTWI to all different demographic groups (the estimated weekly intake (EWI) range: 0.2988-0.6893 µg/kg BW/week) but for the adolescents, where from the consumption of crustaceans (0.3488 µg/kg BW/week or 21.8% of PTWI) and cephalopods (0.504 µg/kg BW/week or 31.5% of PTWI). The results from this study also revealed the HQ value for overall consumption of fish and seafood by the adolescents and elderly exceeded one. This was contributed from the consumption of demersal fish and cephalopods, thus indicating the nonacceptable level of noncarcinogenic adverse health effects.

Prediction of tuberculosis cases based on sociodemographic and environmental factors in gombak, Selangor, Malaysia: A comparative assessment of multiple linear regression and artificial neural network models.

Background: Early prediction of tuberculosis (TB) cases is very crucial for its prevention and control. This study aims to predict the number of TB cases in Gombak based on sociodemographic and environmental factors. Methods: The sociodemographic data of 3325 TB cases from January 2013 to December 2017 in Gombak district were collected from the MyTB web and TB Information System database. Environmental data were obtained from the Department of Environment, Malaysia; Department of Irrigation and Drainage, Malaysia; and Malaysian Metrological Department from July 2012 to December 2017. Multiple linear regression (MLR) and artificial neural network (ANN) were used to develop the prediction model of TB cases. The models that used sociodemographic variables as the input datasets were referred as MLR1 and ANN1, whereas environmental variables were represented as MLR2 and ANN2 and both sociodemographic and environmental variables together were indicated as MLR3 and ANN3. Results: The ANN was found to be superior to MLR with higher adjusted coefficient of determination (R2) values in predicting TB cases; the ranges were from 0.35 to 0.47 compared to 0.07 to 0.14, respectively. The best TB prediction model, that is, ANN3 was derived from nationality, residency, income status, CO, NO2, SO2, PM10, rainfall, temperature, and atmospheric pressure, with the highest adjusted R2 value of 0.47, errors below 6, and accuracies above 96%. Conclusions: It is envisaged that the application of the ANN algorithm based on both sociodemographic and environmental factors may enable a more accurate modeling for predicting TB cases.

Environment as the risk factor for tuberculosis in Malaysia: a systematic review of the literature.

OBJECTIVE: To investigate the prevalence and incidence of TB by focusing on its environmental risk factor in Malaysia. CONTENT: Databases search of Scopus, ScienceDirect, PubMed, Directory of Open Access Journals (DOAJ), Cumulative Index to Nursing and Allied Health Literature (CINAHL) Plus, MyJournal, Biblioteca Regional de Medicina (BIREME), BioMed Central (BMC) Public Health, Medline, Commonwealth Agricultural Bureaux (CAB), EMBASE (Excerpta Medica dataBASE) OVID, and Web of Science (WoS) was performed, which include the article from 1st January 2008 until 31st August 2018 using medical subject heading (MeSH). Articles initially identified were screened for relevance. SUMMARY: Out of 744 papers screened, nine eligible studies did meet our inclusion criteria. Prison and housing environments were evaluated for TB transmission in living environment, while the other factor was urbanization. However, not all association for these factors were statistically significant, thus assumed to be conflicting or weak to end up with a strong conclusion. OUTLOOK: Unsustainable indoor environment in high congregate setting and overcrowding remained as a challenge for TB infection in Malaysia. Risk factors for transmission of TB, specifically in high risk areas, should focus on the implementation of specialized program. Further research on health care environment, weather variability, and air pollution are urgently needed to improve the management of TB transmission.

Urinary Concentrations of Metals and Metalloids in Malaysian Adults.

Exposure to environmental pollutants in humans can be conducted through direct measurement of biological media such as blood, urine or hair. Assessment studies of metals and metalloids in Malaysia is very scarce although cross-sectional nationwide human biomonitoring surveys have been established by the USA, Canada, Germany, Spain, France, and Korea. This study aims to assess urinary metal levels namely cadmium (Cd), nickel (Ni), lead (Pb) and arsenic (As) among Malaysian adults. This was a cross-sectional study involving 1440 adults between the age of 18 and 88 years old. After excluding those with 24 h urine samples of less than 500 ml, urine creatinine levels < 0.3 or > 3.0 g/L and those who refuse to participate in the study, a total of 817 respondents were included for analysis. A questionnaire with socio-demographic information such as age, gender, occupation, ethnic, academic qualification and medical history was administered to the respondents. Twenty-four-hour urine samples were collected in a container before being transported at 4 °C to the laboratory. Samples were then aliquoted into 15 ml tubes and kept at - 80 °C until further analysis. Urine was diluted ten-fold with ultrapure water, filtered and analysed for metals and metalloids using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). The geometric mean of urinary As, Ni, Cd and Pb concentrations among adults in Malaysia was 48.21, 4.37, 0.32, and 0.80 µg/L, respectively. Males showed significantly higher urinary metal concentrations compared to females for As, Cd and Pb except for Ni. Those who resided in rural areas exhibited significantly higher As, Cd and Pb urinary concentrations than those who resided in urban areas. As there are no nationwide data on urinary metals, findings from this study could be used to identify high exposure groups, thus enabling policy makers to improve public health strategically.

Development of a web-geographical information system application for plotting tuberculosis cases.

In the last few decades, public health surveillance has increasingly applied statistical methods to analyze the spatial disease distributions. Nevertheless, contact tracing and follow up control measures for tuberculosis (TB) patients remain challenging because public health officers often lack the programming skills needed to utilize the software appropriately. This study aimed to develop a more user-friendly application by applying the CodeIgniter framework for server development, ArcGIS JavaScript for data display and a web application based on JavaScript and Hypertext Preprocessor to build the server's interface, while a webGIS technology was used for mapping. The performance of this approach was tested based on 3325 TB cases and their sociodemographic data, such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency status, and smoking status between 1st January 2013 and 31st December 2017 in Gombak, Selangor, Malaysia. These data were collected from the Gombak District Health Office and Rawang Health Clinic. Latitude and longitude of the location for each case was geocoded by uploading spatial data using Google Earth and the main output was an interactive map displaying location of each case. Filters are available for the selection of the various sociodemographic factors of interest. The application developed should assist public health experts to utilize spatial data for the surveillance purposes comprehensively as well as for the drafting of regulations aimed at to reducing mortality and morbidity and thus minimizing the public health impact of the disease.

Air pollution and cardiorespiratory hospitalization, predictive modeling, and analysis using artificial intelligence techniques.

Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. One of the major effects of air pollution on health is hospitalizations associated with air pollution. Recently, the estimation and prediction of air pollution-based hospitalization is carried out using artificial intelligence (AI) and machine learning (ML) techniques, i.e., deep learning and long short-term memory (LSTM). However, there is ample room for improvement in the available applied methodologies to estimate and predict air pollution-based hospital admissions. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the artificial intelligence (AI) techniques. We propose the enhanced long short-term memory (ELSTM) model and provide a comparison with other AI techniques, i.e., LSTM, DL, and vector autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The utilized dataset contains the data from January 2006 to December 2016 for five study locations, i.e., Klang (KLN), Shah Alam (SA), Putrajaya (PUJ), Petaling Jaya (PJ), and Cheras, Kuala Lumpur (CKL). The dataset for Banting contains data from April 2010 to December 2016, and the data for Batu Muda, Kuala Lumpur, contains data from January 2009 to December 2016. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia.

Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia.

Tuberculosis (TB) cases have increased drastically over the last two decades and it remains as one of the deadliest infectious diseases in Malaysia. This cross-sectional study aimed to establish the spatial distribution of TB cases and its association with the sociodemographic and environmental factors in the Gombak district. The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency, and smoking status from 1st January 2013 to 31st December 2017 in Gombak district were collected from the MyTB web and Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic. Environmental data consisting of air pollution such as air quality index (AQI), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter 10 (PM10,) were obtained from the Department of Environment Malaysia from 1st July 2012 to 31st December 2017; whereas weather data such as rainfall were obtained from the Department of Irrigation and Drainage Malaysia and relative humidity, temperature, wind speed, and atmospheric pressure were obtained from the Malaysia Meteorological Department in the same period. Global Moran's I, kernel density estimation, Getis-Ord Gi* statistics, and heat maps were applied to identify the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to determine the spatial association of sociodemographic and environmental factors with the TB cases. Spatial autocorrelation analysis indicated that the cases was clustered (p<0.05) over the five-year period and year 2016 and 2017 while random pattern (p>0.05) was observed from year 2013 to 2015. Kernel density estimation identified the high-density regions while Getis-Ord Gi* statistics observed hotspot locations, whereby consistently located in the southwestern part of the study area. This could be attributed to the overcrowding of inmates in the Sungai Buloh prison located there. Sociodemographic factors such as gender, nationality, employment status, health care worker status, income status, residency, and smoking status as well as; environmental factors such as AQI (lag 1), CO (lag 2), NO2 (lag 2), SO2 (lag 1), PM10 (lag 5), rainfall (lag 2), relative humidity (lag 4), temperature (lag 2), wind speed (lag 4), and atmospheric pressure (lag 6) were associated with TB cases (p<0.05). The GWR model based on the environmental factors i.e. GWR2 was the best model to determine the spatial distribution of TB cases based on the highest R2 value i.e. 0.98. The maps of estimated local coefficients in GWR models confirmed that the effects of sociodemographic and environmental factors on TB cases spatially varied. This study highlighted the importance of spatial analysis to identify areas with a high TB burden based on its associated factors, which further helps in improving targeted surveillance.

Reliability and Accuracy of 2D Photogrammetry: A Comparison With Direct Measurement.

OBJECTIVE: Facial anthropometric data is important for the design of respirators. Two-dimensional (2D) photogrammetry has replaced direct anthropometric method, but the reliability and accuracy of 2D photogrammetry has not been quantified. This study aimed to assess inter-rater reliability of 2D photogrammetry and to examine the reliability and accuracy of 2D photogrammetry with direct measurement. DESIGN: A cross-sectional study. SETTING: Malaysia. PARTICIPANTS: A subset of 96 participants aged 18 and above. PRIMARY AND SECONDARY OUTCOMES: Ten facial dimensions were measured using direct measurement and 2D photogrammetry. An assessment of inter-rater reliability was performed using intra-class correlation (ICC) of the 2D images. In addition, ICC and Bland-Altman analyses were used to assess the reliability and agreement of 2D photogrammetry with direct measurement. RESULTS: Except for head breadth and bigonial breadth, which were also found to have low inter-rater reliability, there was no significant difference in the inter-rater mean value of the 2D photogrammetry. The mean measurements derived from direct measurement and 2D photogrammetry were mostly similar. However, statistical differences were noted for two facial dimensions, i.e., bizygomatic breadth and bigonial breadth, and clinically the magnitude of difference was also significant. There were no statistical differences in respect to the remaining eight facial dimensions, where the smallest mean difference was 0.3 mm and biggest mean difference was 1.0 mm. The ICC showed head breadth had poor reliability, whilst Bland-Altman analyses showed seven out of 10 facial dimensions using 2D photogrammetry were accurate, as compared to direct measurement. CONCLUSION: Only certain facial measurements can be reliably and accurately measured using 2D photogrammetry, thus it is important to conduct a reliability and validation study before the use of any measurement methods in anthropometric studies. The results of this study also suggest that 2D photogrammetry can be used to supplement direct measurement for certain facial dimensions.