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This study investigates the projected impact of air pollution on mortality and Disability-Adjusted Life Years (DALYs) across SAARC countries. Utilizing Time Series and Machine Learning methodologies such as Autoregressive Integrated Moving Average, Exponential Smoothing, and Neural Network, the research aims to accurately forecast the mortality and DALYs attributed to air pollution from 2020 to 2030. Statistical analyses reveal a consistent upward trend in deaths and DALYs during the forecasting period, primarily driven by Ambient Particulate Matter Pollution (APM) and Ambient Ozone Pollution (AOP). Comparing the predictive accuracy of the models, Neural Network outperformed other methods, as indicated by Root Mean Square Error (RMSE) values. Specifically, the study finds that deaths and DALYs due to Ambient Particulate Matter pollution are least prevalent in the Maldives, while India and Pakistan exhibit the highest rates, and deaths and DALYs due to Ambient Ozone pollution are lowest in the Maldives and highest in Bangladesh and Pakistan. Moreover, deaths and DALYs attributed to Household Air Pollution (HAP) are lowest in Pakistan and highest in Nepal. These findings underscore the urgent need for air pollution control measures and informed policymaking in SAARC countries to mitigate the escalating health burden associated with air pollution., (© 2024. The Author(s).)

Hepatitis C virus (HCV) transmission remains a significant public health concern. It is well documented globally, however, Nowshera district, Pakistan, is lacking such profile. This study aims to explore the relationship between HCV infection and several risk factors, including socio-demographic, clinical and personal life-style factors. This study using a cross-sectional design, examined 606 randomly selected individuals visiting the Pathology department at Qazi Hussain Medical Complex and District Headquarter (DHQ) Hospital Nowshera between May 1, 2022 and Jun 30, 2023. This research investigation employed a methodical approach involving formal interviews in conjunction with structured questionnaires to gather comprehensive information related to socio-demographic characteristics, clinical history, and personal hygiene practices. We collected 10 ml of blood samples and tested the separated serum to identify markers using the Immuno-Chromatographic Test (ICT) and the Enzyme-Linked Immunosorbent Assay (ELISA). IBM SPSS Statistics version 27 was used for data analysis. To measure the association between dependent variable and independent variables, a chi-square and risk analysis was carried out; multiple logistic regression was employed for modeling the risk factors associated with independent variable. A statistical significance level was established at a P-value of less than 0.05. In present study, which included 606 participants, 12% were found to be infected with HCV. Importantly it revealed that facial shave at barber (adjusted odds ratio, aOR = 40.65, p = 0.000) represents the most prevalent mode of HCV transmission. Additionally, a strong association was observed between HCV infection and history of past surgery (RR = 2.98, p = 0.001), HCV infected family member (aOR = 4.28, p = 0.001), workplace injuries (aOR = 6.68, p = 0.000), history of hospital admission (RR = 2.09), practicing ear and nose piercing (aOR = 5.01, p = 0.001), dental treatment (RR = 2.31) and the frequency of injections (aOR = 8.607, p = 0.000). These findings underscore the pressing need for targeted interventions. This study highlights the paramount importance of understanding the modes of HCV transmission and their associated risk factors. The results emphasize the need for educational initiatives, both within the healthcare sector and among the general public, to combat HCV transmission effectively. By implementing sterilization procedures and increasing awareness, we can make significant strides in reducing the burden of HCV infection. Moreover, proactive measures within families can help contain the spread of the virus, ultimately contributing to improved public health., (© 2024. The Author(s).)

This study investigates the impact of air pollution on health outcomes in Middle Eastern countries, a region facing severe environmental challenges. As such, these are important in an effort to add up to policy-level as well as interventional changes that can be put in practice in the area of public health. Numeration analysis and association with health parameters was carried out by using Analytical tools such as, AIR Data, ARIMA,ANN, SVM and Exponential smoothing. Amongst the models, Support Vector Machine came again on top, with high accuracy yielding Mean Absolute Percentage Error of approximately 1%. Mortality of Air pollution in Qat from the case of Mortality of Air Pollution in Qatar is 959 while Auto regressive Integrated Moving average is 11.096, Exponential Smoothing 9.892 and Artificial Neural Networks are the source of inspiration for the development of this paper 4.61. The above perceptions indicate that there is need to adapt modeling strategies depending on the context and establish that it is possible to implement ML models in public health planning basket. This paper publishes the methodological frameworks for the purpose of modeling and analysis of the EHDs and serves as policy prescription for the policy makers to intending to reduce the effects of air borne pollution on health., (© 2024. The Author(s).)

This paper presents a thorough evaluation of health outcomes linked to water-related challenges in Islamic nations across East Asia and Central Asia from 2020 to 2030. It has been examined carefully that the trajectory of deaths and disability-adjusted life years associated with unsafe water sources, lack of sanitation, and absence of handwashing facilities is showing a potential rise in negative health impacts due to water pollution. The direct health influences of water-related problems are thoughtful. The increase in deaths and DALYs due to poor water quality and sanitation leads to a higher occurrence of waterborne diseases such as cholera, diarrhea, and dysentery. These conditions not only cause instant health disasters but also subsidize to long-term health issues which include chronic gastrointestinal disorders and malnutrition that is particularly among susceptible populations like children and the elderly. Employing various predictive models including autoregressive integrated moving average, exponential smoothing, support vector machines, and neural networks. The study evaluates their predictive capabilities by using mean absolute percentage error. Support vector machines is found to be the most accurate in forecasting deaths and disability-adjusted life years which is outperforming autoregressive integrated moving average, exponential smoothing, and neural networks. This research aims to inform stakeholders by providing insights into effective strategies for improving water resource management and public health interventions in the targeted regions., (© 2024. The Author(s).)

This research presents a new adaptive exponentially weighted moving average control chart, known as the coefficient of variation (CV) EWMA statistic to study the relative process variability. The production process CV monitoring is a long-term process observation with an unstable mean. Therefore, a new modified adaptive exponentially weighted moving average (AAEWMA) CV monitoring chart using a novel function hereafter referred to as the "AAEWMA CV" monitoring control chart. the novelty of the suggested AAEWMA CV chart statistic is to identify the infrequent process CV changes. A continuous function is suggested to be used to adapt the plotting statistic smoothing constant value as per the process estimated shift size that arises in the CV parametric values. The Monte Carlo simulation method is used to compute the run-length values, which are used to analyze efficiency. The existing AEWMA CV chart is less effective than the proposed AAEWMA CV chart. An industrial data example is used to examine the strength of the proposed AAEWMA CV chart and to clarify the implementation specifics which is provided in the example section. The results strongly recommend the implementation of the proposed AAEWMA CV control chart., (© 2024. The Author(s).)

Demographic health surveys (DHS) contain in-depth information about the demographic characteristics and the factors affecting them. However, fertility rates which are the important indicators of population growth have been estimated by utilizing the design-based approaches. Model-based approach, on the other hand, facilitates efficient predictive estimates for these rates by utilizing the demographic and other family planning related characters. In this article, we first attempt to observe the effect of various socio-demographic and family planing related factors on births counts by fitting different regression models to Pakistan Demographic Health Survey 2017-2018 data under classical as well as Bayesian frameworks. The births occurred during the time periods of 1-year, 3-years and 5-years are taken as the responses and modeled using different non-linear models. The model-based approach is then used for estimation of the fertility measures including age-specific fertility rates, total fertility rate, general fertility rate, and gross reproduction rate for ever-married women in Pakistan. The performance of the model-based estimators is examined using a bootstrapped sampling algorithm. While the age-specific fertility rates are over-estimated for some age groups and under-estimated for others. The model-based fertility estimates are recommended for estimating the demographic indicators at national and sub-national levels when survey data contains incomplete or missing responses., (© 2024. The Author(s).)