Understanding stunting risk factors in Kampar Regency: Insights from mothers with stunted children (qualitative study).

Objective: To assess the perceptions of mothers with stunted children regarding the factors contributing to stunting in Kampar Regency, Riau Province, Indonesia. Introduction: Stunting is intricately linked to underlying risk factors, and understanding these factors is crucial for preventing its recurrence, particularly for mothers who play a central role in caring for stunted children. Methods: The research sample consisted of mothers referred by the Perhentian Raja Community Health Center with stunted children. Purposive sampling was employed to select informants and thematic analysis was used for data analysis. Results: A total of 15 informants were interviewed, leading to the identification of five themes related to stunting risks: (1) before pregnancy, (2) during pregnancy, (3) postnatal, (4) infectious disease factors, and (5) socio-demographic factors. Conclusion: The study indicates that mothers hold diverse opinions about factors contributing to stunting, with their views being shaped by health programs and personal experiences, specifically focusing on aspects like breastfeeding and weaning foods. Additionally, it underscores that education and economic challenges introduce complexity to understanding how mothers perceive the risk factors associated with stunting.

Sequence of COVID-19 Vaccination and COVID-19 Infection and Their Association With the Development of Active Tuberculosis: A Case-Control Study.

Introduction Information regarding the cross-risk of coronavirus disease 2019 (COVID-19) and tuberculosis (TB) is still sparse. This study aimed to identify the patterns of sequence of COVID-19 vaccination and COVID-19 infection and to explore the association between COVID-19 vaccination, COVID-19 infection, and the development of active TB. Methods It was a case-control study conducted in RSUD Dr. Iskak Hospital, Tulungagung, between October 2022 and April 2023. Active cases of TB patients were compared with non-TB controls in the same hospital, with the same age and sex. Their pattern of sequence of COVID-19 vaccination and infection was investigated. Logistic regression was used to assess the association between these key variables. Results Of 296 case-control sets, 64.2% were female. The mean ± standard deviation of age was 46 ± 15.6 years. 5.7% of the cases and 6.4% of the controls had a history of COVID-19 infection, whereas 58.8% and 68.4% had been vaccinated (mostly after infection). The adjusted odds ratio (95% confidence interval) of COVID-19 infection on risk to the development of active TB was 1.45 (0.58, 3.65). Those of COVID-19 vaccination of one to four doses were 0.42 (0.17, 1), 0.98 (0.58, 1.66), 0.48 (0.25, 0.93), and 0.09 (0.01, 0.81), respectively. Conclusion It was found that there were five patterns of sequence of COVID-19 infection and COVID-19 vaccination, with the most frequent being having COVID-19 infection before COVID-19 vaccination. Our data did not support the association between COVID-19 infection and the subsequent development of active TB. On the other hand, COVID-19 vaccination has been demonstrated to increase some protection against the development of active TB.

Cross-Risk Between Tuberculosis and COVID-19 in East Java Province, Indonesia: An Analysis of Tuberculosis and COVID-19 Surveillance Registry Period 2020-2022.

INTRODUCTION: Tuberculosis (TB) and COVID-19 are highly transmissible diseases and pose a serious risk to public health. Unfortunately, information on cross-risk between the two diseases was still sparse. Our main objective was to estimate the excess risk among TB patients in getting COVID-19 infection and vice versa. METHODS: The study design was a series of analyses of existing data from TB and COVID-19 registries in East Java Province, Indonesia. The study period was from January 2020 to June 2022. Case-by-case data for this study were obtained from the registration systems for TB and COVID-19 in separate databases. In comparing risk across different groups, adjusting for differences in risk factors that influence the outcome was essential. We overcame this problem by employing a standardized morbidity ratio. RESULTS: Among 92,424 newly diagnosed TB patients, 1,326 were subsequently infected with COVID-19 during the study period, compared with 1,679 expected. The standardized morbidity ratio (95% confidence interval) was 72.61% (60.19%, 85.03%). Among 635,946 newly diagnosed COVID-19-infected patients, 987 subsequently got active TB during the study period against 1,679 expected. The standardized morbidity ratio (95% confidence interval) was 55.33% (49.24%, 61.42%). CONCLUSION: There was no evidence of excess risk in either direction, the excess risk among TB patients in getting COVID-19 infection and vice versa.

Efficacy of pretomanid-containing regiments for drug-resistant tuberculosis: A systematic review and meta-analysis of clinical trials.

Concerns regarding the rise of drug-resistant tuberculosis (DR-TB) infections and the need for new drugs with shorter treatment time and fewer side effects have been voiced by the World Health Organization (WHO). The WHO revised its guideline to treat multidrug resistant tuberculosis (MDR-TB) with a 6-month course of BPaLM (bedaquiline, pretomanid, linezolid and moxifloxacin) in 2022. However, a thorough study and meta-analysis of available evidence is required due to the limited confidence of the evidence confirming the effectiveness of pretomanid-containing regiments. The aim of this systematic review and meta-analysis was to evaluate the effectiveness of pretomanid-containing regiments in treating DR-TB patients. Data from six search engines were searched using inclusion criteria based on the PICOS framework. The keywords of pretomanid and tuberculosis or their alternatives were used. Using RoB2 Cochrane risk-of-bias tool for randomized clinical trials, data were independently extracted and the quality of the data was evaluated. Odds ratio (OR) and heterogeneity tests were used and the findings were presented in ORs and forest plots. A total of four studies with 237 patients was included in the final analysis and 204 (86%) patients had favorable outcome (cured) and 33 (14%) was not cured. Pretomanid-containing regimen (OR: 46.73; 95%CI: 11.76-185.7) and BPaLM/BPaL (OR: 41.67; 95%CI: 8.86-196.73) regimens were associated with favorable outcome (cured). This meta-analysis indicates that the pretomanid-containing regimen and the BPaLM/BPaL regimen could increase the chance to have favorable outcome in DR-TB patients.

Adversarial training and deep k-nearest neighbors improves adversarial defense of glaucoma severity detection.

Glaucoma is an eye disease that can cause irreversible blindness to people if not treated properly. Although deep learning models have shown that they can provide good results in identifying diseases from medical imagery, they suffer from the vulnerability of adversarial attacks, making them perform poorly. Several techniques can be applied to improve defense against such attacks. One of which is adversarial training (AT) which trains a deep learning model using the input's gradient used to generate noises to the input image and Deep k-Nearest Neighbor (DkNN) that enforces prediction's conformity based on nearest neighbor voting on each layer's representation. This work tries to improve the defense against adversarial attacks by combining AT and DkNN. The evaluation performed on several adversarial attacks show that given an optimum k, the combination of these two methods is able to improve most models' overall classification result on the perturbed retinal fundus image.

Augmented-syllabification of n-gram tagger for Indonesian words and named-entities.

As one of the statistical-based models, an n-gram syllabification commonly gives a high syllable error rate (SER) for Bahasa Indonesia, one of the low-resource languages, since it fails for a high out-of-vocabulary (OOV) rate. Two previous models: bigram-syllabification with flipping onsets (BFO) and a combination of bigram with backoff smoothing based on phonological similarity (CBSPS), which use augmentation methods, can reduce the OOV rate. However, there are two problems in both BFO and CBSPS. First, they use an n-gram that is applied syllable-level, instead of grapheme-level, so that they suffer on the sparsity of n-grams. Second, they rely on a procedure to detect the positions of both vowels and diphthongs. Both problems make them not capable of distinguishing diphthongs from derivative words as well as syllabifying named-entities, which have many ambiguities related to vowels and semi-vowels. In this paper, a syllabification based on an n-gram tagger, which is applied on grapheme-level and does not rely on both vowel and diphthong detections, is developed to solve both problems. Besides, three data augmentation methods are exploited to enrich the dataset. The 5-fold cross-validations (5-FCV) using both datasets of 50 k words and 15 k named-entities show that the proposed augmented-syllabification of n-gram tagger (ASnGT) model is significantly better than both BFO and CBSPS. It is also significantly better than the fuzzy k-nearest neighbor in every class (FkNNC)-based model for formal words and named-entities. However, it suffers from derivative words, where it cannot easily distinguish them from both absorption words and terms of foreign languages. Besides, it also undergoes some foreign named-entities.

Analysis of Supporting Factors Associated with Exclusive Breastfeeding Practice in the Urban Setting during the COVID-19 Pandemic.

Breastfeeding mothers have had limited access to breastfeeding support throughout the COVID-19 pandemic. This study aims to investigate breastfeeding practices during the COVID-19 period and to determine the factors associated with supporting exclusive breastfeeding. A sequential explanatory mixed methods approach was adopted, including a quantitative method in the first phase and qualitative method in the second phase. Mothers whose babies were aged over 6 months to 24 months old from July to September 2021 in Pekanbaru City were selected as research subjects. Data analysis was performed with multivariate and deductive content analysis. Of 156 participants, 97 mothers (62.2%) exclusively breastfed their babies. Of those, mothers who delivered exclusive breastfeeding worked less than eight hours per day, were aged 17-25 and had low education. Though by using exclusive breastfeeding practice as a reference, associated supports, including emotional, instrumental, appraisal and information regarding exclusive breastfeeding practice were insignificant; however, mothers who practice exclusive breastfeeding had higher information support. During the COVID-19 pandemic period, the informational support factor was found to be important to achieve the successful exclusive breastfeeding practice.

The Quality of Life of Coronavirus Disease Survivors Living in Rural and Urban Area of Riau Province, Indonesia.

This study assesses the status of health-related quality of life (HRQOL) among coronavirus survivors living in rural and urban districts in Riau province, Indonesia. The cross-sectional study was conducted among 468 and 285 Coronavirus disease (COVID-19) survivors living in rural and urban areas, respectively in August 2021. The St. George Respiratory Questionnaire (SGRQ) was used to measure the HRQOL of COVID-19 survivors. A higher total score domain corresponds to worse quality of life status. Quantile regression with the respect to 50th percentile found a significant association for the factors living in rural areas, being female, having comorbidities, and being hospitalized during treatment, with total score of 4.77, 2.43, 7.22, and 21.27 higher than in their contra parts, respectively. Moreover, having received full vaccination had the score 3.96 in total score. The HRQOL of COVID-19 survivors living in rural areas was significantly lower than in urban areas. Factors such as living in rural areas, female sex, having comorbidities, and history of symptomatic COVID-19 infection were identified as significant predictors for lower quality of life. Meanwhile, having full vaccination is a significant predictor for a better quality of life. The results of this study can provide the targeted recommendations for improvement of HRQOL of COVID-19 survivors.

Augmented words to improve a deep learning-based Indonesian syllabification.

Recent deep learning-based syllabification models generally give low error rates for high-resource languages with big datasets but sometimes produce high error rates for the low-resource ones. In this paper, two procedures: massive data augmentation and validation, are proposed to improve a deep learning-based syllabification, using a combination of bidirectional long short-term memory (BiLSTM), convolutional neural networks (CNN), and conditional random fields (CRF) for a low-resource Indonesian language. The massive data augmentation comprises four methods: transposing nuclei, swapping consonant-graphemes, flipping onsets, and creating acronyms. Meanwhile, the validation is implemented using a phonotactic-based scheme. A preliminary investigation on 50k Indonesian words informs that those augmentation methods significantly enlarge the dataset size by 12.8M valid words based on the phonotactic rules. An examination is then performed using 5-fold cross-validation. It reports that the augmentation methods significantly improve the BiLSTM-CNN-CRF model for 50k formal words and 100k named-entities datasets. A detailed investigation informs that augmenting the training set can reduce the word error rate (WER) coming from the long formal words and named entities.

Segmentation technique and dynamic ensemble selection to enhance glaucoma severity detection.

The severity of glaucoma can be observed by categorising glaucoma diseases into several classes based on a classification process. The two most suitable parameters, cup-to-disc ratio (CDR) and peripapillary atrophy (PPA), which are commonly used to identify glaucoma are utilized in this study to strengthen the classification. First, an active contour snake (ACS) is employed to retrieve both optic disc (OD) and optic cup (OC) values, which are required to calculate the CDR. Moreover, Otsu segmentation and thresholding techniques are used to identify PPA, and the features are then extracted using a grey-level co-occurrence matrix (GLCM). An advanced segmentation technique, combined with an improved classifier called dynamic ensemble selection (DES), is proposed to classify glaucoma. Because DES is generally used to handle an imbalanced dataset, the proposed model is expected to detect glaucoma severity and determine the subsequent treatment accurately. The proposed model obtains a higher mean accuracy (0.96) than the deep learning-based U-Net (0.90) when evaluated using three datasets of 250 retinal fundus images (200 training, 50 testings) based on the 5-fold cross-validation scheme.

Clustering and firm productivity spillovers in Indonesian manufacturing.

Earlier studies on foreign direct investment (FDI) and firm productivity have mainly focused on the general impacts of FDI on local firms' productivity. This current research extends the existing literature by examining the heterogeneity issue in firm productivity by clustering Indonesian manufacturing firms into nine industrial groups. Two estimation methods are applied: stochastic frontier analysis and panel data analysis, which emerge three key findings. Firstly, the impacts of FDI vary among firm clusters, with six out of nine clusters are able to grasp the positive benefits of FDI, whereas three out of nine clusters experience negative productivity effects. Secondly, the labor-intensive clusters tend to receive negative productivity effects, whereas the capital-intensives clusters receive positive productivity impacts. Thirdly, the sources of FDI-productivity vary across clusters, with scale-efficiency as the most dominant source and technology advancement as the second dominant source. These findings justify the importance of estimating firms in homogenous industrial groups to gain precision findings of productivity benefits.

Optimal control on a mathematical model to pattern the progression of coronavirus disease 2019 (COVID-19) in Indonesia.

Background: Understanding the pattern of COVID-19 infection progression is critical for health policymakers. Reaching the exponential peak of cases, flattening the curve, and treating all of the active cases are the keys to success in reducing outbreak transmission. The objective of this study was to determine the most effective model for predicting the peak of COVID-19 in Indonesia, using a deterministic model. Methods: The SEI2RS model considers five strategies for control, namely: large-scale social restriction ( u1 ), contact tracing ( u2 ), mass testing ( u3 ) , case detection and treatment ( u4 ), and the wearing of face masks ( u5 ). Three scenarios were developed, each differentiated by the controls. The model used April 10, 2020, and December 31, 2020, as the initial and final times. Results: The simulation results indicated that the peak of COVID-19 cases for scenarios 1, 2, and 3 occur on the 59th day with 33,151 cases, on the 38th day with 37,908 cases, and on the 40th day with 39,305 cases. For all of the scenarios, the decline phase shows a slow downward slope and about 8000 cases of COVID-19 still active by the end of 2020. Conclusion: The study concludes that scenario 2, which consists of large-scale social restriction (u1), contact tracing (u2), case detection and treatment (u4), and the wearing of face masks (u5), is the most rational scenario to control COVID-19 spreading in Indonesia.

The Effect of Treatment during A Haze/Post-Haze Year on Subsequent Respiratory Morbidity Status among Successful Treatment Tuberculosis Cases.

The purpose of this study was to evaluate the respiratory morbidity status within the two to three years among successful (completed/cured) treatment of tuberculosis cases during a haze year (2015) and a post-haze year (2016). The study was conducted among 133 cases of a 2015 group and 103 cases of a 2016 group between January to March 2018 in Pekanbaru city, Indonesia. The St George Respiratory Questionnaire (SGRQ) was used to assess respiratory morbidity status. A higher score corresponds to worse respiratory morbidity. Based on a directed acyclic graph, quantile regression models were constructed to assess the associations between haze/post-haze year and the SGRQ (symptom, activity, impact, and total) domains score. The subsequent respiratory morbidity status of tuberculosis (TB) cases was poorer among respondents treated during a haze year (2015). Among SGRQ domains, only the activity domain score showed significant difference, in which the median for the 2015 group was 23.7 (inter-quartile range (IQR); 17.2, 30.9) compared to 18.4 (IQR; 11.9, 24.8) for the 2016 group. The effect was limited to the 2015 group who were exposed by an average PM10 index ≥ 55 during TB treatment. This raises concern for monitoring and improving the quality of life of TB patients treated during a haze year.

A Rare Delayed Atypical Pseudoprogression in Nivolumab-Treated Non-Small-Cell Lung Cancer.

Immune checkpoint inhibitors such as Nivolumab work by preventing the inactivation of host T-cells by tumour cells, thereby allowing the T-cells to attack the tumour cells, which results in tumour tissue necrosis. We describe a 78-year-old woman with metastatic lung adenocarcinoma treated with Nivolumab after disease progression following first-line chemotherapy. Computed tomography (CT) after 3 cycles showed a smaller left lower lobe (LLL) primary and stable right lower lobe (RLL) metastatic lesion. CT after 9 cycles showed a reduced RLL mass and an increase in LLL primary. However, CT after 15 cycles showed that the RLL mass had further reduced in size but the LLL mass was significantly larger. The biopsy of the LLL lesion showed necrotic areas and reactive inflammatory changes, without residual malignancy. A repeat CT after further 4 cycles confirmed tumour regression in both the primary and the metastatic lesions. There was a prior reported case of pseudoprogression in a non-small-cell lung cancer patient who had 7 cycles of Nivolumab, and it was diagnosed during a further line of chemotherapy. Here, we report a patient with pseudoprogression during treatment with Nivolumab and at a much later time, after 15 cycles.