Aim: Dengue cases are on the rise in Thailand, coinciding with increases in flooding events. Studies pertaining to public knowledge, attitudes, and practices (KAP) of dengue prevention have frequently been used to better understand the public’s needs towards dengue. While these studies were conducted in different settings, it is necessary to initiate a systematic review and meta-analysis of relevant studies. Subjects and method: We conducted a systematic review and meta-analysis of prior studies in Thailand that assessed the KAP towards dengue. Eligibility criteria were established and independently used by reviewers to select nine studies for the systematic review and three for the meta-analysis. Collectively, the nine studies included 3,058 individuals and 2,519 households. Results: The overall estimate of the proportion of participants with good knowledge of dengue prevention is 35% (95% CI: 14-59%), suggesting the majority of the population in Thailand had low levels of knowledge towards dengue. The poor levels of practice in Thailand were also observed and confirmed for the majority of the population by meta-analysis, with the pooled estimate of the proportion of participants with good practice of dengue prevention being 25% (95% CI: 22-27%). In contrast, most of the studies included in the systematic review reported positive attitudes towards dengue prevention, and this finding was also affirmed by the meta-analysis, as the pooled estimate of the proportion of positive attitudes towards dengue prevention is 61% (95% CI: 43-77%). Conclusion: Despite good attitudes towards dengue prevention, poor knowledge and poor practices predominate, highlighting the need for enhanced public health campaigns to educate the public on dengue risks and prevention methods.

It is observed that, for some of the data in engineering and medical fields, the hazard rates increase to a high peak at the beginning and quickly decrease to a low level. In the context of survival analysis, such a hazard rate is called a upside-down bathtub hazard rate. In this paper, we investigated the properties of a model named exponentiated exponential-Pareto distribution. The model was recently proposed and applied to insurance data. We demonstrated that the model has upside-down bathtub-shaped hazard rates with specific choices of parameters. The theoretical properties such as moments, survival functions, and hazard functions were derived. The parameter estimation procedures were also introduced. We then briefly discussed the goodness-of-fit tests of the model with the simulations. Finally, we applied the model to a specific time-to-event data set along with a comparison of the performances with previous existing models. When compared to previous proposed models, the exponentiated exponential-Pareto model demonstrated good performance when fitting to such data sets.

Exponentiated models have been widely used in modeling various types of data such as survival data and insurance claims data. However, the exponentiated composite distribution models have not been explored yet. In this paper, we introduce an improvement of the one-parameter Inverse Gamma-Pareto composite model by exponentiating the random variable associated with the one-parameter Inverse Gamma-Pareto composite distribution function. The goodness-of-fit of the exponentiated Inverse Gamma-Pareto was assessed using three different insurance data sets. The two-parameter exponentiated Inverse Gamma-Pareto model outperforms the one-parameter Inverse Gamma-Pareto model in terms of goodness-of-fit measures for all datasets. In addition, the proposed exponentiated composite Inverse Gamma-Pareto model provides a very good fit with some well-known insurance datasets.

Waste collectors face multi-faceted health risks including exposure to musculoskeletal disorders, respiratory diseases, and workplace injuries because of their hazardous work environment. The purpose of this study was to determine the risk factors that affect health of the waste collectors across Bangladesh. Data on a cross-sectional survey obtained information from 481 waste collectors about their demographics, housing conditions, hygiene practices, security measures, and disease prevalence. Descriptive analyses and multi-level models are used. As per univariate analysis, 81.3% did not have access to clean water, 58.4% did not have access to sanitation, and 65.9% of people lived in unhealthy housing. According to bivariate analysis, there is an association between unhealthy working conditions i.e., respiratory illnesses (19.1% asthma, 29.7% cough), gastrointestinal issues (59.3% discomfort, 24.1% diarrhea), musculoskeletal disorders, and dermatological diseases, and unsafe working conditions (i.e., 60% no masks, 71% no gloves, and 75% no boots). Considering geographic clustering, multi-level modeling examined how different factors affected particular illnesses. The following were significant protective factors: better housing (80% lower odds of acute irritation), security measures (50% lower odds of gastrointestinal diseases), good hygiene (62% lower odds of dermatological diseases), and younger age (2% higher odds of respiratory diseases per year). In summary, unsafe living and working conditions greatly push the risks of illness for waste collectors. These risks to occupational health can be reduced with targeted interventions that enhance housing, hygiene, security protocols, and working conditions.