Bangkok Metropolitan Administration

Urban water supply systems require considerable electrical energy inputs across all operational processes: raw water abstraction, treatment, transmission, and distribution. Consequently, water loss within these processes represents not merely a loss of water volume, but also additional energy consumption and an increase in carbon emissions, given that electricity generation relies predominantly on fossil fuels. This study applied two methodological approaches to analyze the role of water loss within the Water–Energy–Carbon (WEC) Nexus of the Metropolitan Waterworks Authority (MWA), Thailand, over the period 2017–2024. The first method utilized a detailed WEC linkage analysis to balance water inputs and outputs in each process to quantify specific losses: raw water, in-plant, transmission, and distribution losses. The second method applied the International Water Association’s Leakage Emissions Initiative framework, focusing specifically on potable real water loss in distribution process, which constituted the largest volume (64.85% of total losses) and embodied the highest specific energy consumption. Based on the first method, the average annual potable real water loss was 534.71 MCM/yr (23.58% of water supplied to distribution), corresponding to embedded energy and carbon emissions of 103.76 GWh/yr (24.89% of total energy consumption) and 49,562 tCO2e/yr (24.89% of total carbon emission), respectively. Although the second method was considerably simplified, the estimated energy and carbon emission values were only slightly higher than those derived from the detailed method, demonstrating the second method’s effectiveness as a streamlined assessment tool. These findings underscored that water loss reduction initiatives are essential for minimizing energy consumption and carbon emissions, thereby supporting Thailand’s pathway toward Net Zero emissions by 2050.

To assess the impact of an integrated model of care in curing hepatitis C in people who use drugs in Thailand. The C-Free Study enrolled people with current or prior drug use and their partners in a prospective cohort at community drop-in centres providing harm reduction services. Participants were screened for human immunodeficiency virus (HIV), hepatitis C virus (HCV), hepatitis B virus (HBV) and sexually transmitted infections. Eligible participants with HCV infection received a 12-week course of sofosbuvir-velpatasvir. The main impact outcome was sustained virological response, measured 12 weeks after treatment completion. Between June 2019 and April 2023, we enrolled 2871 participants in 10 sites across Thailand: 1601 (55.8%) had HCV antibodies; 1275 (44.4%) had active HCV infection; 846 (29.5%) had HIV; and 221 (7.7%) had HBV. Of 1134 participants with active HCV who started treatment with sofosbuvir-velpatasvir, 939 (82.8%) achieved a sustained virological response. Among 987 participants completing treatment, 95.1% achieved a sustained virological response. In multivariable analysis, age > 40 years (adjusted odds ratio, aOR: 1.63; 95% confidence interval, CI: 1.04-2.54) and poor treatment adherence (aOR: 0.06; 95% CI: 0.02-0.20) were associated with sustained virological response. Of 34 serious adverse events during treatment, six led to treatment discontinuation including five non-treatment-related deaths. Community-based HCV treatment of people who use drugs in Thailand, within harm reduction settings, is safe and effective. Integration of this strategy into national programmes could enhance HCV elimination in people who use drugs.

IntroductionDependent older persons with limitations in activities of daily living (ADL) often experience reduced functioning and require ongoing support from family caregivers and community-based care systems.PurposeThis study aimed to explore the experiences, challenges, and care needs of family caregivers providing care for dependent older persons in urban communities.MethodsA qualitative descriptive design was employed. Twenty-five family caregivers residing in Bangkok, Thailand, were purposively recruited. Participants were family members who had provided primary care for a dependent older person for at least six months and were able to communicate in Thai. Data were collected through in-depth, semi-structured interviews guided by Donabedian’s model of healthcare quality. Each interview lasted approximately one hour, and the interview guide was validated by three experts (content validity index = 0.97). Data were analyzed using conventional content analysis.ResultsFamily caregivers reported six significant challenges: limited caregiving knowledge and skills, time constraints, caregiving isolation, declining personal health, financial burden, and difficulties managing the older person’s emotional and behavioral problems. Eight key needs emerged: access to reliable health information, online health services, transportation assistance, home healthcare, financial and psychosocial support, home environment modification, and recognition from healthcare authorities. Guided by Donabedian’s framework, findings were categorized into inputs (caregiver and system attributes), processes (caregiving activities), and outcomes (quality and continuity of care supported by digital technologies).ConclusionFamily caregivers of dependent older persons in urban settings face complex, interrelated challenges that affect their well-being. Strengthening integrated community and health support systems, together with evidence-based caregiver empowerment programs, is essential for promoting sustainable, high-quality long-term care.

<strong class="journal-contentHeaderColor">Abstract.</strong> We use measurements of near-surface aerosol backscatter, extinction, and depolarization acquired by four NASA Langley Research Center airborne High Spectral Resolution Lidars (HSRLs) in machine learning (ML) regression algorithms to derive concentrations of particulate matter (PM) with aerodynamic diameters less than 2.5&nbsp;mm (PM_2.5), 10 mm (PM₁₀), and the PM_2.5/PM₁₀ratio. The ML regression models are trained using airborne HSRL measurements acquired over major metropolitan regions in the United States and Asia that are coincident with hourly surface PM_2.5and PM₁₀ measurements from the EPA air quality system and similar networks in other countries. We examine several regression methods and find that exponential Gaussian Process regression (GPR) algorithms consistently give the best performance in terms of the lowest root-mean-square (RMS) errors and the highest correlations. When evaluated using surface measurements withheld from the training sets, ML models that use the HSRL near-surface measurements of aerosol backscatter and aerosol intensive properties such as depolarization, backscatter color ratio, and lidar ratio typically give the best performance with RMS differences in PM_2.5 retrievals around 5 mg m^-3 and correlation coefficients above 0.8, respectively. Corresponding RMS differences and correlation coefficients for PM₁₀ retrievals are 11 mg m^-3 and 0.7 and corresponding RMS differences and correlation coefficients for PM_2.5/PM₁₀ are 0.17 and 0.75. This retrieval performance is achieved using airborne HSRL measurements alone and so does not depend on external knowledge of or assumptions regarding aerosol type, aerosol mass extinction efficiency, aerosol hygroscopic growth, the ratio of PM_2.5to PM₁₀, particle density, or relative humidity. PM_2.5values in the training set range from about 5 to 80 mg m^-3; PM₁₀ values range from about 10 to 100&nbsp;mg m^-3. Accurate retrievals of PM outside these ranges would require commensurate training data. We present examples of PM retrievals in the United States as well as Asia when HSRL measurements were acquired when the aircraft flew systematic "raster-scan" patterns for several hours over major urban areas. We show that these PM_2.5 retrievals are in good agreement with PM_2.5 derived from coincident airborne <em>in situ</em> measurements near the surface as well as aloft. We describe also how the distribution of PM_2.5varies with aerosol type and altitude over these regions. We use the HSRL measurements of aerosol extinction and retrievals of surface PM_2.5along with HSRL retrievals of aerosol type to derive estimates of the fine mode aerosol mass extinction efficiency (MEE_f) for major aerosol types identified by an updated HSRL aerosol classification method. MEE_f ranges from about 2.6 &plusmn; 0.5&nbsp;m² g^-1 for maritime aerosol to 5.0 &plusmn; 0.7&nbsp;m² g^-1 for smoke. These estimates of MEE_f are also in good agreement with values derived from airborne <em>in situ</em> measurements. We also discuss how this methodology may be applied to measurements from the Atmospheric Lidar (ATLID) on the EarthCARE satellite.

<strong class="journal-contentHeaderColor">Abstract.</strong> We use measurements of near-surface aerosol backscatter, extinction, and depolarization acquired by four NASA Langley Research Center airborne High Spectral Resolution Lidars (HSRLs) in machine learning (ML) regression algorithms to derive concentrations of particulate matter (PM) with aerodynamic diameters less than 2.5&nbsp;mm (PM_2.5), 10 mm (PM₁₀), and the PM_2.5/PM₁₀ratio. The ML regression models are trained using airborne HSRL measurements acquired over major metropolitan regions in the United States and Asia that are coincident with hourly surface PM_2.5and PM₁₀ measurements from the EPA air quality system and similar networks in other countries. We examine several regression methods and find that exponential Gaussian Process regression (GPR) algorithms consistently give the best performance in terms of the lowest root-mean-square (RMS) errors and the highest correlations. When evaluated using surface measurements withheld from the training sets, ML models that use the HSRL near-surface measurements of aerosol backscatter and aerosol intensive properties such as depolarization, backscatter color ratio, and lidar ratio typically give the best performance with RMS differences in PM_2.5 retrievals around 5 mg m^-3 and correlation coefficients above 0.8, respectively. Corresponding RMS differences and correlation coefficients for PM₁₀ retrievals are 11 mg m^-3 and 0.7 and corresponding RMS differences and correlation coefficients for PM_2.5/PM₁₀ are 0.17 and 0.75. This retrieval performance is achieved using airborne HSRL measurements alone and so does not depend on external knowledge of or assumptions regarding aerosol type, aerosol mass extinction efficiency, aerosol hygroscopic growth, the ratio of PM_2.5to PM₁₀, particle density, or relative humidity. PM_2.5values in the training set range from about 5 to 80 mg m^-3; PM₁₀ values range from about 10 to 100&nbsp;mg m^-3. Accurate retrievals of PM outside these ranges would require commensurate training data. We present examples of PM retrievals in the United States as well as Asia when HSRL measurements were acquired when the aircraft flew systematic "raster-scan" patterns for several hours over major urban areas. We show that these PM_2.5 retrievals are in good agreement with PM_2.5 derived from coincident airborne <em>in situ</em> measurements near the surface as well as aloft. We describe also how the distribution of PM_2.5varies with aerosol type and altitude over these regions. We use the HSRL measurements of aerosol extinction and retrievals of surface PM_2.5along with HSRL retrievals of aerosol type to derive estimates of the fine mode aerosol mass extinction efficiency (MEE_f) for major aerosol types identified by an updated HSRL aerosol classification method. MEE_f ranges from about 2.6 &plusmn; 0.5&nbsp;m² g^-1 for maritime aerosol to 5.0 &plusmn; 0.7&nbsp;m² g^-1 for smoke. These estimates of MEE_f are also in good agreement with values derived from airborne <em>in situ</em> measurements. We also discuss how this methodology may be applied to measurements from the Atmospheric Lidar (ATLID) on the EarthCARE satellite.

Acquired partial lipodystrophy (APL) is an ultra-rare disorder characterized by unique loss of subcutaneous fat affecting mostly the face, neck, trunk, and upper extremities. The precise prevalence of metabolic derangements and other comorbidities among patients with APL is not clear. We report clinical features and metabolic and autoimmune derangements in a large cohort. A total of 86 participants (77 female, 9 male; median age 40 years [range 8-78 years]) with APL from 2 US tertiary referral centers, UT Southwestern and National Institute of Diabetes and Digestive and Kidney Diseases, were recruited into prospective observational studies. Demographic, health history, and laboratory data at the initial evaluation and follow-up were systematically collected and analyzed. The median age of onset of lipodystrophy was 7 years (range, 2-51 years). About 15% had autoimmune diseases, 38% had either diabetes mellitus or glucose intolerance, 43% had hypertriglyceridemia, and 61% had fatty liver or metabolic dysfunction-associated steatohepatitis (MASH). A total of 71% of patients had low serum complement 3 (C3) levels, 8% had membranoproliferative glomerulonephritis, while drusen occurred in 62% of those with fundus examination (n = 21). Patients with C3 hypocomplementemia, compared to those with normal serum C3 level, reported earlier onset of diabetes or glucose intolerance (median age 36 vs 56.5 years, P = .007), hypertriglyceridemia (30 vs 48 years, P = .03), and drusen (33 vs 60 years, P = .14). Our data reveal high risk of metabolic comorbidities and drusen in patients with APL, with earlier onset of these complications in those with C3 hypocomplementemia.

Malnutrition is a prevalent yet under-recognized condition in patients with advanced chronic kidney disease (CKD), contributing to increased morbidity, mortality, and healthcare burden. The aim of this study is to determine the prevalence and trends of malnutrition and investigate the impact of malnutrition on in-hospital outcomes, treatments, and resource utilization in hospitalized patients with advanced CKD. This study utilized the National Inpatient Sample (NIS) database to identify hospitalized patients with advanced CKD from 2016 to 2021. This study investigated temporal trends in the prevalence and in-hospital mortality across different degrees of malnutrition in advanced CKD patients. Multivariable regression models were used to assess the association between malnutrition and in-hospital outcomes. Out of 1,244,415 advanced CKD patients, 67,587 (5.4%) had mild to moderate malnutrition, and 63,785 (5.1%) had severe malnutrition. Malnourished patients exhibited significantly higher in-hospital mortality, with adjusted odds ratios of 1.70 (95% confidence interval (CI), 1.64-1.75) for mild to moderate cases and 2.67 (95% CI, 2.60-2.75) for severe cases. Severely malnourished patients were associated with longer mean hospital stay by 7.0 days and higher hospitalization costs by $97,767 compared with non-malnourished patients. The prevalence of severe malnutrition showed a significant uptrend from 4.2% in 2016 to 5.5% in 2021 (p for trend < 0.001). Malnutrition in advanced CKD is an increasingly prevalent condition linked to worsened in-hospital outcomes and heightened healthcare resource utilization. The rising trend of severe malnutrition underscores the need for early nutritional screening and the need for future interventional studies to mitigate adverse clinical outcomes in this high-risk population.

Performance discretization maps numerical performance values to ordinal categories or performance ranking labels. Norm-referenced performance discretization is extensively applied in human performance evaluation such as grading academic achievements and determining salary increases for employees. These tasks stipulate a common condition that certain performance ranking labels might have no associated performance values and are referred to as conditional discretization. Currently, the only statistical method available for norm-referenced performance discretization is Z score, which merely addresses partial conditions. To achieve a fully conditionally norm-referenced performance discretization, this article proposes four novel approaches enlisting a multi-modal technique that incorporates unsupervised machine-learning algorithms and a heuristic method as well as a novel decision function ensuring conditional unbiasedness. The machine-learning-based methods demonstrate superiority over the heuristic one across most testing data sets, achieving a conditional unbiasedness degree ranging from 0.11 to 0.82. On the other hand, the heuristic method notably outperforms for a specific data set, exhibiting a conditional unbiasedness degree up to 0.76. Leveraging the strengths of these constituent methods enable the effectiveness of the proposed multi-modal approach for conditionally norm-referenced performance discretization.

Home dialysis (peritoneal dialysis and home hemodialysis) is an important renal replacement therapy modality option for patients with end-stage kidney disease. As the Internet has become a primary source for healthcare information, this study aimed to analyze the global and regional interests in home dialysis using Google Trends™ data from January 2004 to March 2024. A comprehensive analysis was conducted using Google Trends™ with the search terms "Peritoneal Dialysis" and "Home Hemodialysis." This study extracted worldwide trends and detailed regional interests within the United States. Interest levels were quantitatively assessed based on Google Trends™ indices, providing insights into temporal patterns and geographical distributions of public interest. The study found a fluctuating pattern of global interest in Peritoneal Dialysis, with peak interest in March 2022 and lowest interest in December 2008. The most recent data from March 2024 showed significant interest level of 94, indicating a new upward trend. Mexico exhibited the highest relative interest in Peritoneal Dialysis. Within the United States, Tennessee demonstrated the highest interest. For Home Hemodialysis, the peak interest was in July 2004. The most recent data from March 2024 showed a modest increase in interest. The United States led in highest relative interest for Home Hemodialysis, followed by Australia, Canada, and the United Arab Emirates. Within the United States, Mississippi demonstrated the highest interest. This study offers crucial insights into the global and regional landscape of interest in home dialysis modalities over time, highlighting the significance of leveraging online platforms to increase public awareness, education, and engagement home dialysis modalities. By understanding the temporal and geographical patterns of interest, healthcare providers, policymakers, and patient advocacy groups can develop targeted strategies to better promote the benefits of home dialysis, address knowledge gaps, and improve access to these life-sustaining treatments.