Validation of self reported measures of adherence to ART and factors associated with adherence in Jinja, Uganda

A research report submitted to the Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirements for the Degree of Master of Science in Epidemiology and Biostatistics Johannesburg, 14th December 2016Background: Good adherence to ART prolongs survival and improves quality of life in people living with HIV/AIDS. Adherence is commonly assessed using self-reported measures, but these tend to over-estimate adherence. Viral load testing is the gold standard for measuring ART adherence but it is unaffordable in resource limited settings. Therefore, the aims of this small sub-study were to validate self-reported measures of adherence and to find factors associated with adherence to ART in Jinja, Uganda. Methods: This study was a secondary analysis of data collected from a cluster randomized equivalence trial which was carried out to compare facility based ART care versus home based care. In the main study, 1453 participants aged 18 and above were enrolled. A total of 1276 men and women qualified for this sub-study. Receiver operating characteristic (ROC) was computed to see how well two self-reported measures of adherence predicted virological failure. The two self-reported measures were firstly a visual analogue score (VAS) where participants rated the number of doses that they had taken in the past month on a scale from 0 (meaning no ART taken) to 100 (meaning that all required doses had been taken) and secondly an adherence score based on the number of pills missed in the three days before the visit. Logistic regression models were fitted with survey estimator to find factors associated with virological failure. Tobit models were fitted to find factors associated with self-reported adherence measures, since these were restricted to the range of 0-100% and censored. We then compared associated factors among the three different outcome measures. Results: There were 914 women and 362 men in this study. Home based care had larger number of patients (754) than facility based care (522). The median age of the patients was 38 years (IQR 32.0-44.0). Most of the participants were either married (518) or single (456). The majority of the trial participants had primary school education (n=713) and very few achieved tertiary education. A large number of participants had CD4 cell counts of less than 50 cells/mm3 (n=351), and very few of the patients in the trial had CD4 counts greater than 200 cells/mm3. The median CD4 count of the study participants was 116 cells / mm3 (IQR 43.0-167.0). A very large number of the patients were either in WHO clinical stage II or III (Stage II: n= 595; Stage III: n=577). A total of n=1079 (84.56%) and n=197 (13.44%) participants had no virological failure and failure respectively. The ROC methods showed that the iv self-reported adherence measures estimated virological failure with a sensitivity that ranged between 35-65%. Female patients had lower odds of experiencing virological failure (odds ratio: 0.7; 95% CI: 0.485, 0.968; p=0.033). The odds of virological failure decreased with each one year increase in age (OR: 0.95; 95% CI: 0.928, 0.979; p=0.001). Participants who found adherence reminders very useful were less likely to experience virological failure (P=0.001). Conclusion: This study show that self-reported measures are not good predictors of ART adherence since approximately only a half of the Jinja participants with virological failure were predicted by such measures. None of the factors associated with virological failure was also associated with both of the self-reported adherence measures. Viral load testing should be encouraged in place of self-reported adherence measures to ART. In addition, alternative methods of measuring adherence such as electronic medication monitoring, pharmacy refills and drug level detection should be investigated.MT201

Studi Awal Pembuatan Membran Chitosan-Silica Based dari Berbagai Limbah

Jember memiliki potensi perikanan terutama udang sebagai sumber kitin yang merupakan dasar pembuatan kitosan. Kitosan berasal dari limbah kulit udang yang dicampur dengan limbah lain yaitu padatan silika dari Pembangkit Listrik Panas Bumi dan fly ash dari Pembangkit Listrik Tenaga Uap untuk menghasilkan membran yang dapat digunakan dalam filtrasi logam berat dalam air. Membran tanpa silika dibuat sebagai negative control dan dengan silika murni sebagai positive control. Membran kitosan dibuat dengan cara mencampurkan 1,0 gram kitosan dalam 100 mL larutan asam asetat 2% v/v dan 0,8 gram senyawa silika serta 0,5 gram polyethylene glycol. Membran yang telah dibuat diuji untuk menyaring larutan yang mengandung logam berat Cu dan Pb. Konsentratsi optimum dari Cu dan Pb dalam badan cairan dapat dikurangi sebesar 87% dan 80% menggunakan membran kitosan-silika

Bayesian hierarchical modelling of historical data of the South African coal mining industry for compliance testing

Bayesian hierarchical framework for exposure data compliance testing is highly recommended in occupational hygiene. However, it has not been used for coal dust exposure compliance testing in South Africa (SA). The Bayesian analysis incorporates prior information, which increases solid decision making regarding risk management. This study compared the posterior 95th percentile (P95) of the Bayesian non-informative and informative prior from historical data relative to the occupational exposure limit (OEL) and exposure categories, and the South African Mining Industry Code of Practice (SAMI CoP) approach. A total of nine homogenous exposure groups (HEGs) with a combined 243 coal mine workers’ coal dust exposure data were included in this study. Bayesian framework with Markov chain Monte Carlo (MCMC) simulation to draw a full P95 posterior distribution relative to the OEL was used to investigate compliance. We obtained prior information from historical data and employed non-informative prior distribution to generate the posterior findings. The findings were compared to the SAMI CoP. The SAMI CoP 90th percentile (P90) indicated that one HEG was compliant (below the OEL), while none of the HEGs in the Bayesian methods were compliant. The analysis using non-informative prior indicated a higher variability of exposure than the informative prior according to the posterior GSD. The median P95 from the non-informative prior were slightly lower with wider 95% credible intervals (CrI) than the informative prior. All the HEGs in both Bayesian approaches were in exposure category four (poorly controlled), with the posterior probabilities slightly lower in the non-informative uniform prior distribution. All the methods mainly indicated non-compliance from the HEGs. The non-informative prior, however, showed a possible potential of allocating HEGs to a lower exposure category, but with high uncertainty compared to the informative prior distribution from historical data. Bayesian statistics with informative prior derived from historical data should be highly encouraged in coal dust overexposure assessments in South Africa for correct decision making

Face Images Classification using VGG-CNN

Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning with a pre-trained model architecture. Specifically, the features extracted from an image can be numeric vectors. The model will use this vector to describe specific features in an image.  The face image is divided into two, 17% of data test and 83% of data train. The result shows that the value of accuracy validation (val_accuracy), loss, and loss validation (val_loss) are excellent. However, the best training results are images produced from digital cameras with modified classifications. Val_accuracy's result of val_accuracy is very high (99.84%), not too far from the accuracy value (94.69%). Those slight differences indicate an excellent model, since if the difference is too much will causes underfit. Other than that, if the accuracy value is higher than the accuracy validation value, then it will cause an overfit. Likewise, in the loss and val_loss, the two values are val_loss (0.69%) and loss value (10.41%)

COVID-19 hospital admissions and mortality among healthcare workers in South Africa, 2020–2021

AVAILABILITY OF DATA AND MATERIALS : The datasets generated and/or analyzed during this current study are available in the repository of the National Institute of Communicable Diseases. The data can be made available on request, which may be directed to [email protected]. Those requesting data will need to sign a data access agreement. The request will require approval by the National Department of Health.OBJECTIVES : This study describes the characteristics of admitted HCWs reported to the DATCOV surveillance system, and the factors associated with in-hospital mortality in South African HCWs. METHODS : Data from March 5, 2020 to April 30, 2021 were obtained from DATCOV, a national hospital surveillance system monitoring COVID-19 admissions in South Africa. Characteristics of HCWs were compared with those of non-HCWs. Furthermore, a logistic regression model was used to assess factors associated with in-hospital mortality among HCWs. RESULTS : In total, there were 169 678 confirmed COVID-19 admissions, of which 6364 (3.8%) were HCWs. More of these HCW admissions were accounted for in wave 1 (48.6%; n = 3095) than in wave 2 (32.0%; n = 2036). Admitted HCWs were less likely to be male (28.2%; n = 1791) (aOR 0.3; 95% CI 0.3–0.4), in the 50–59 age group (33.1%; n = 2103) (aOR 1.4; 95% CI 1.1–1.8), or accessing the private health sector (63.3%; n = 4030) (aOR 1.3; 95% CI 1.1–1.5). Age, comorbidities, race, wave, province, and sector were significant risk factors for COVID-19-related mortality. CONCLUSION : The trends in cases showed a decline in HCW admissions in wave 2 compared with wave 1. Acquired SARS-COV-2 immunity from prior infection may have been a reason for reduced admissions and mortality of HCWs despite the more transmissible and more severe beta variant in wave 2.DATCOV is funded by the National Institute for Communicable Diseases (NICD) and the South African National Government.http://www.elsevier.com/locate/ijregihj2023School of Health Systems and Public Health (SHSPH

Prevalence of respiratory health symptoms among landfill waste recyclers in the City of Johannesburg, South Africa

Abstract: Please refer to full text to view abstract

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

PERBANDINGAN BIAYA PEMELIHARAAN BANGUNAN HOTEL PADA LOKASI TENGAH KOTA DAN TEPI PANTAI

Biaya pemeliharaan gedung merupakan komponen penting pada biaya yang perlu dialokasikan selama gedung tersebut dimanfaatkan. Pemeliharaan gedung dapat dipengaruhi oleh lingkungan dimana gedung tersebut didirikan. Penelitian ini bertujuan untuk membandingkan biaya pemeliharaan bangunan hotel pada lokasi tengah kota dan tepi pantai serta mencari tahu apakah efek lingkungan berpengaruh pada perencanaan pemeliharaan pada bangunan masing-masing Penelitian ini dilakukan melalui wawancara yang dilakukan kepada chief engineer, bagian pemeliharaan, dan HRD yang mengetahui tentang pemeliharaan bangunan hotel tersebut. Data yang telah dianalisis disajikan dalam bentuk tabel perbandingan sedangkan analisis data dilakukan dengan cara perhitungan sederhana. Hasil penelitian menunjukkan bahwa biaya pemeliharaan kedua hotel ini memang berbeda. Jika dibandingkan biaya pemeliharaan dari kedua bangunan hotel ini, hotel tepi pantai memiliki frekuensi pemeliharaan dan biaya lebih besar dari hotel yang berada di tengah kota. Perbandingan biaya pemeliharaan Discovery Kartika Plaza Hotel dengan Hotel Borobudur mencakup 9 komponen, yaitu dinding keramik, dinding marmer, lantai marmer, lantai keramik, lantai karpet, kuda-kuda atap baja, atap genteng, dan atap sirap, dan atap beton. Pada Discovery Kartika Plaza Hotel dinding keramik sebesar Rp 296.831,42 per m2 ; dinding marmer sebesar Rp 696.810,00 per m2; lantai marmer sebesar Rp 696.810,36 per m2; lantai keramik sebesar Rp 332.710,36 per m2 ; lantai karpet sebesar Rp 630.969,95 per m2 ; kuda-kuda atap baja sebesar Rp 260.360,36 per m2 ; atap genteng sebesar Rp 256.869,95 per m2; atap sirap sebesar Rp 506.269,95 per m2; atap beton sebesar Rp 821.510,36 per m2, sedangkan pada Hotel Borobudur, biaya pemeliharaan dinding keramik sebesar Rp 279.052,30 per m2; dinding marmer sebesar Rp 456.014,30 per m2; lantai marmer sebesar Rp 456.014,30 per m2; lantai keramik sebesar Rp 337.972,30 per m2; lantai karpet sebesar Rp 561.150,- per m2; kuda-kuda atap baja Rp 252.578,96 per m2; atap genteng sebesar Rp 270.602,51 per m2; atap sirap sebesar 204.923,51 per m2; dan atap beton sebesar Rp 643.064,30 per m2. Dari hasil ini membuktikan pengaruh lokasi bangunan berpengaruh pada rencana dan biaya pemeliharaan bangunan tersebut

Implementation of C4.5 algorithm to determine hospital readmission rate of diabetes patient

Diabetes is a disease in which the body's ability to produce or respond to the hormone insulin is impaired, resulting in abnormal metabolism of carbohydrates and elevated levels of glucose in the blood and urine. It can be suffered by everyone and until now there is no cure for it. A hospital readmission is an episode when a patient who had been discharged from a hospital is admitted again within a specified time interval. Readmission rates have increasingly been used as a quality benchmark for health systems. In this research, C.45 Algorithm is used to determine hospital readmission rate of diabetes patient. Dataset used in this study is taken from UCI Machine Learning Repository, which contain diabetic patient data from 130 hospitals in United States for 10 years (1999-2008). Several experiments are done to get the best result, and the best result is 74.5% for accuracy. This result is obtained by doing several pre-process data i.e. filling all missing value, using numeric and nominal attribute type, and by not including several attributes

Manajemen Anestesi Perioperatif pada Kehamilan dengan Trombositopenia

Trombositopenia dalam kehamilan adalah suatu kondisi dimana jumlah hitung trombosit kurang dari 150.000 /µL dan bisa terjadi secara fisiologis. Pada kasus kehamilan dengan trombositopenia, ada kalanya diperlukan terminasi kehamilan melalui operasi seksio sesarea. Manajemen anestesi dengan pembiusan umum dikhawatirkan berdampak buruk pada janin akibat obat atau agen anestesi yang digunakan, sedangkan manjemen anestesi dengan pembiusan regional dikhawatirkan berdampak buruk pada ibu yaitu risiko terjadinya neuraksial hematom dengan komorbid trombositopenia. Pemilihan teknik manajemen anestesi perioperatif didasarkan pada penilaian klinis pasien dan jumlah hitung trombosit dengan rentang batasan minimal yang aman untuk dilakukan tindakan regional anestesi (neuraksial) adalah 75.000 /µL–80.000 /µL. Pada kasus yang dilakukan manajemen anestesi dengan pembiusan umum, dapat dipertimbangkan dilakukan induksi dengan pemberian opioid untuk menekan dan menumpulkan rangsang simpatis saat dilakukan laringoskopi intubasi yang bertujuan mencegah komplikasi seperti perdarahan intra-serebral. Trombositopenia pada kehamilan dapat memperberat kehamilan itu sendiri namun pada umumnya persalinan berjalan lancar dan memberikan hasil akhir yang baik. Kolaborasi antara interdisiplin secara komprehensif dan holistik diperlukan untuk menangani kasus ini mulai dari perencanaan tindakan, tatalaksana dan pencegahan komplikasi pada ibu dan janin. Tidak diragukan lagi, kasus gravida dengan trombositopenia merupakan tantangan unik bagi tim anestesi. Dengan terus berkembangnya ilmu dan penelitian dibidang ini, masih perlu dibuat panduan dan batasan yang jelas terkait manajemen perioperatif pada pasien gravida dengan trombositopenia