Multi-stage Approach Research Articles

A Novel Approach to Pollution Source Identification in Groundwater Using Hybrid Ensemble of Wavelet Neural Network (WNN) and ARIMA

The groundwater is a significant source of readily available freshwater. However, these groundwater aquifers are susceptible to contamination by the presence of many potential pollution sources. These potential sources may be active at any location for any duration with an unknown magnitude of source fluxes. The identification of these potential sources is necessary for remediation and management. In this work, the groundwater contamination sources are identified for two probable situations, i.e., Error-free concentration measurements and erroneous concentration measurements. The proposed methodology exploits the capability of the multistage approach of recent hybrid soft computing models (ensemble of Wavelet Neural Network [WNN]; Autoregressive Integrated Moving Average [ARIMA]) to identify potential pollution sources effectively. The models are trained by the simulated concentrations for randomly generated source fluxes by Groundwater Modelling System (GMS). The multistage approach has been adopted here, which includes three-stage models. To exploit the effectiveness of WNN and ARIMA, they are used at different stages in the multistage models, thereby developing various hybrid ensembles. Total of four hybrid models are developed utilising the multi-staging approach of WNN and ARIMA models, Namely Hybrid-II, III, IVA, and IVB. Predicted results from these models were compared on the same data sets. They are compared using various performance indices, such as RMSE, NE, correlation coefficient, etc. Error values of Hybrid II ensemble (employing WNN at first stage and ARIMA at second stage) are lowest as compared to only WNN and other Hybrid models. Hence, the Hybrid-II model can be utilised to accurately predict potential contamination sources in any sample area in groundwater.

Perceived Effect of COVID-19 on the Academic Performance and Satisfaction Level of Online Teaching of Sandwich Students

The study examined the perceived effect of COVID-19 on academic performance and the satisfaction level of online teaching of sandwich students on the OLA university campus. The study was a descriptive survey design with a quantitative approach. The accessible population was made up of 150 students. Through a multi-stage sampling approach, a sample size of 109 students was chosen for the study and determined using Miller and Brewer's (2003) sample size mathematical model. Data was collected using an adapted questionnaire with an r = .727. Data on respondents' demographic characteristics, as well as data to answer the four research questions, were analysed using descriptive statistics (frequencies, percentages, means, and standard deviations) and inferential statistics (Pearson Product Moment correlation, r). The study found a negative influence of the COVID-19 pandemic on students’ academic performance who study through the sandwich mode of learning. The study found that there is a high satisfaction level of online teaching among sandwich students. However, they are faced with problems of poor internet connection and the inability to purchase internet data/bundle for online learning. Ultimately, it was discovered that the degree of pleasure with online instruction and the effect of COVID-19 on academic achievement are not significantly correlated. The Ministry of Education, the universities, and all other relevant stakeholders were advised to provide online teaching apps. Also, they should provide professional training for online teaching applications and devices, Wi-Fi facilities, affordable internet packages, and virtual resources in light of the findings.

Resilience, efficiency fluctuations, and regional heterogeneity in disaster: An empirical study on logistics

While resilience analysis can provide important insights into risk management strategies, research gaps exist in terms of logistics efficiency, regional economic environment, and spatial clustering patterns. Therefore, our study capitalizes on the dramatic variations in logistics activities among regions during COVID-19 to examine regional logistics resilience in the disaster and explore potential spatial clustering patterns. We measure regional logistics resilience through changes in performance outcomes, specifically efficiency fluctuation. To analyze spatial clustering patterns, (regional heterogeneity) presented by logistics efficiency, we propose a new multi-stage approach that integrates the data envelopment analysis with the K-medoids algorithm and analyzes data from the Chinese logistic industry. The results reveal that efficiency fluctuations exhibit distinct spatial distribution characteristics, with more pronounced negative fluctuations in areas of important logistics activities nodes and islands, and more widespread negative trends in coastal areas. Moreover, regions with high freight volumes and logistics specialization demonstrate a sustained high level and quality of logistics efficiency. Furthermore, consumption capacity and economic development appear to positively influence fluctuations in logistics efficiency. The findings hold implications for enhancing regional logistics resilience in the disaster and contribute valuable insights to regional logistics risk management.

Evaluating Maternal Health Services Within the Reproductive, Maternal, Newborn, Child Health and Adolescents (RMNCH+A) Framework Amidst the COVID-19 Pandemic in Rural India: A Comprehensive Mixed-Methods Analysis.

Background Around half of the pregnant women in India do not receive full antenatal care. During the year 2020, routine health services were further affected by COVID-19. This study was conducted to assess the effect of the pandemic on the delivery/utilization of reproductive, maternal, newborn, child health, and adolescent(RMNCH+A) services. Methodology The study, conducted in Wardha district, Maharashtra, from July to December 2020, aimed to assess maternal health. In Wardha block, 200 pregnant and postnatal women were surveyed using a multistage sampling approach. Adequate knowledge was gauged through Mother and Child Protection Card comprehension. Health system data for April to December 2020 was compared with 2019 district-wide. In-depth interviews were conducted with beneficiaries, including pregnant and post-natal women and healthcare workers. The qualitative inquiries involved medical officers, supervisory staff, community health officers, an auxiliary nurse and midwife (ANMs), Taluka Health Officers, and focus group discussions with accredited social health activists (ASHA), Anganwadi workers (AWW), and Village Health Nutrition and Sanitation Committee members. Results Essential services were delivered to both antenatal and postpartum women, though family planning services and health education were the worst affected. Among the survey respondents, 75% of the post-partum women were not using any contraceptives. District-wide coverage of post-abortion/MTP contraception fell by around 90% as compared to the previous year. The most common difficulties faced by the respondents in availing of the services were related to finances and arranging transport to visit health facilities. Conclusion Learning from the current pandemic for system strengthening, adequate manpower, and planning to prevent disruption of essential services and promoting e-health and m-health initiatives may prevent such catastrophic events in the future from affecting the delivery of routine services.

Influence of Student Councils’ Socio-Demographic Dynamics on Management of Secondary School Students’ Discipline in Kenyenya Sub-County, Kenya

Although student councils are known to be vital in assisting school administration in management of student discipline, their presence in leadership has not fully controlled cases of student indiscipline. This study sought to examine the influence of student councils’ socio-demographic dynamics on management of secondary school students’ discipline in Kenyenya Sub-County, Kenya. The study was based on the Social Cognitive Learning Theory (SCLT), Trait Theory of Leadership (TTL) and the Red-Hot-Stove Theory (RHST) and employed descriptive survey design. The study utilized interview guide designed for SCDE to collect primary data. There were questionnaires for principals, deputy principals, teachers and Student Council Leaders (SCL) to collect both qualitative and quantitative data. A sample size of one hundred and twenty-six (126) respondents corresponding to thirty per cent (30%) of the target population (422) was used to select 13 principals, 13 deputy principals, 86 teachers and 13 students’ council leaders. This study adopted a multi-stage sampling approach where stratified sampling was used to select schools while simple and purposive sampling was used to select respondents. The data was analysed based on the study objectives. Descriptive statistics were used to analyse quantitative data and the results were presented in form of frequencies, percentages, means, and standard deviations using tables, graphs, and charts. Qualitative data were analysed thematically based on the research objectives. The findings of this study indicate that the selected student councils’ socio-demographic dynamics had infinitesimal influence on management of student discipline. The study therefore recommended that the ministry of education develop policies on how learners will vet their council leaders focusing on their selected leadership dynamics

A Co-Created Tool to Help Counter Health Misinformation for Spanish-Speaking Communities in the San Francisco Bay Area.

Health misinformation, which was particularly prevalent during the COVID-19 pandemic, hampers public health initiatives. Spanish-speaking communities in the San Francisco Bay Area may be especially affected due to low digital health literacy and skepticism towards science and healthcare experts. Our study aims to develop a checklist to counter misinformation, grounded in community insights. We adopted a multistage approach to understanding barriers to COVID-19 vaccine uptake in Spanish-speaking populations in Alameda and San Francisco counties. Initial work included key informant and community interviews. Partnering with a community-based organization (CBO), we organized co-design workshops in July 2022 to develop a practical tool for identifying misinformation. Template analysis identified key themes for actionable steps, such as source evaluation and content assessment. From this, we developed a Spanish-language checklist. During formative interviews, misinformation was identified as a major obstacle to vaccine uptake. Three co-design workshops with 15 Spanish-speaking women resulted in a 10-step checklist for tackling health misinformation. Participants highlighted the need for scrutinizing sources and assessing messenger credibility, and cues in visual content that could instill fear. The checklist offers a pragmatic approach to source verification and information assessment, supplemented by resources from local CBOs. We have co-created a targeted checklist for Spanish-speaking communities to identify and counter health misinformation. Such specialized tools are essential for populations that are more susceptible to misinformation, enabling them to differentiate between credible and non-credible information.

Mapping food system drivers of the double burden of malnutrition using community-based system dynamics: a case study in Peru

BackgroundPeru is facing a double burden of malnutrition (DBM), characterized by the co-existence of undernutrition and overnutrition. Double-duty actions that concurrently target common drivers of undernutrition and overnutrition, while ensuring no unintended side effects, are recommended to effectively address the DBM. To understand these complex common mechanisms and design context-specific double-duty actions, there is a need for participatory systems approaches. This paper provides a case study of applying a community-based system dynamics approach to capture stakeholder perspectives of food system drivers of the DBM in two regions in Peru.MethodsWe implemented a multi-stage community-based system dynamics approach, which included processes for research capacity building for systems approaches, and the designing, piloting, and implementation of stakeholder workshops. A total of 36 stakeholders, representing diverse perspectives, participated in five group model building workshops. Stakeholder views are presented in a causal loop diagram that showcases the feedback mechanisms between key food system drivers of overweight and stunting in Peru.ResultsThe causal loop diagram highlights that prioritization of undernutrition over overnutrition in the policymaking process, due to Peru’s historically high levels of undernutrition, may undermine action against the DBM. It also describes potential mechanisms of unintended impacts of undernutrition policies on the DBM in Peru, including impacts related to within-family distribution and quality of food provided through food assistance programs, and unintended impacts due to regional dynamics.ConclusionsThis paper highlights the importance of a participatory approach to understand local needs and priorities when recommending double-duty actions in Peru and shares practical methodological guidance on applying participatory systems approaches in public health.

An exploration of the quality of graduates of Philippine teacher education institutions

The study evaluated the quality of graduates of Philippine Teacher Education Institutions (TEIs) through the performance of the graduates in the Board Licensure Examination for Professional Teachers (BLEPT) and the teaching performance of the beginning teachers in their first three years of teaching. A nationwide survey study employing a descriptive cross-sectional design was conducted to 2,680 respondents from 16 regions in the country, selected using a multi-stage approach. Results revealed an increasing trend in the beginning teachers’ performance in the licensure examinations from 2017 to 2019. These teachers were also consistently averaging a rating of Very Satisfactory from 2019 to 2021 in their teaching performance. Furthermore, socio-economic status, grade point average in college, and grade level taught were other factors found to be significant in relation to teaching performance. As a result, a graduate’s score on the teacher licensure exam has an impact on their success during their first three years of teaching. The dimensions of teacher quality in the country are anchored on the alignment of the outcomes and competencies in the written and tested curricula, effective program delivery, and qualified faculty which contribute to the high probability of passing the BLEPT that ultimately results in better teaching performance of beginning teachers. By focusing on these aspects, institutions can play a pivotal role in nurturing highly competent educators who make a lasting impact on the education system. The findings of this study serve as a valuable guide for educational policymakers and institutions striving to elevate the standard of teacher education across the country.

The Quality of Life of Older Adults in Rural Eastern India and Its Influencing Factors: A Cross-Sectional Study.

Aims This study aimed to assess the quality of life (QoL) of older adults in rural Odisha, India, exploring its multidimensional nature across physical, psychological, social, and environmental domains. The impact of depression and various sociodemographic factors on QoL was also investigated. Methods The research was conducted in the Tangi block of Khordha district, Odisha, encompassing 468 older adults. The World Health Organization Quality of Life Brief Version (WHOQOL-BREF) questionnaire, Geriatric Depression Scale (GDS-15), and sociodemographic questionnaire were used in data collection. Sampling employed a multistage approach, with statistical analysis utilizing Statistical Package for the Social Sciences (SPSS) version 20 (IBM SPSS Statistics, Armonk, NY), including t-tests for normally distributed data and the Mann-Whitney U test for non-normally distributed data. Results The QoL of older adults in rural Odisha showed variability, with physical and social domains exhibiting relatively positive scores compared to psychological and environmental domains. Depression significantly impacted all QoL dimensions, with the most profound effect observed in global QoL and global health. Sociodemographic factors such as employment, substance use, elder abuse, adverse life events, and poverty were identified as significant determinants of global QoL. Additionally, recreational activity, elder abuse, education, and employment significantly affected all QoL domains. Conclusions This study reveals the complex landscape of QoL of older adults in rural Odisha. The findings emphasize the need for comprehensive interventions targeting mental health, social support, and environmental conditions to enhance the overall well-being of this population. Policymakers and healthcare professionals should consider these multidimensional factors to develop effective strategies for improving the QoL of older adults in similar contexts.

SOCIAL MEDIA AND FAMILY DEVELOPMENT AMONG RESIDENTS OF EDO STATE, NIGERIA

The world today has become a global village of the advancement of the information and communication technology. Apart from the physical economy, it was created a digital economy where almost all that can take place in the physical can also take place. Does this have any impact on the development of the family? This study is aimed at investigating the relationship between online marketing, social media and family development among residents of Edo state. This study will adopt the correlational survey design. Three research questions were raised to guide the study, while three corresponding hypotheses were formulated for this study. The sample of people living in the three senatorial districts of Edo State, Nigeria, was drawn using a multi-stage sampling approach. The Impart of Digital Economy Tools Scale (IDETS) was the tool used to gather the data. The analytical works were addressed using inferential statistics, the first two hypotheses were tested using the Pearson Product Moment method, and the third hypothesis was tested using the method of multiple regression at the threshold of 0.05 percent. From the results, it was found that there is a significant relationship between online marketing and use of social media and family development; and that online marketing has a higher influence on family development. Based on the above, it was recommended that families should explore the positive and development aspect of online marketing to the advantage of family development; family members should focus more on the development of the family, which is the main idea behind internet development and use than the pathological aspect; and finally, that the government should make policies towards the regulation of internet use to control for addiction/overuse.

Combinatorial Optimization-Enriched Machine Learning to Solve the Dynamic Vehicle Routing Problem with Time Windows

With the rise of e-commerce and increasing customer requirements, logistics service providers face a new complexity in their daily planning, mainly due to efficiently handling same-day deliveries. Existing multistage stochastic optimization approaches that allow solving the underlying dynamic vehicle routing problem either are computationally too expensive for an application in online settings or—in the case of reinforcement learning—struggle to perform well on high-dimensional combinatorial problems. To mitigate these drawbacks, we propose a novel machine learning pipeline that incorporates a combinatorial optimization layer. We apply this general pipeline to a dynamic vehicle routing problem with dispatching waves, which was recently promoted in the EURO Meets NeurIPS Vehicle Routing Competition at NeurIPS 2022. Our methodology ranked first in this competition, outperforming all other approaches in solving the proposed dynamic vehicle routing problem. With this work, we provide a comprehensive numerical study that further highlights the efficacy and benefits of the proposed pipeline beyond the results achieved in the competition, for example, by showcasing the robustness of the encoded policy against unseen instances and scenarios. History: This paper has been accepted for the Transportation Science special issue on DIMACS Implementation Challenge: Vehicle Routing Problems. Funding: This work was supported by Deutsche Forschungsgemeinschaft [Grant 449261765].

Clicks and Connection: How Internet Habits and Family Bonds Impact Student Success

The internet has ingrained itself into peoples' daily lives. It is regarded as the media that is used the most extensively worldwide. The development of the internet has altered many facets of daily life, including how people engage with one another and amuse themselves thanks to the plethora of social networking sites. The goal of the current investigation was to find out how internet addiction & parent-child relationship affected secondary school children' academic performance. The study categorised academic performance as a dependent variable and parent-child relationship & internet addiction as independent variables. Study's methodology was descriptive survey. Using a multistage random selection approach, 700 Sec. School students were selected as a sample. In order to gather the data, Internet Addiction Scale by Gulati, Kurisunkal & Bakliwal (2021) and Parent-child Relationship by Nalini Rao (2016) were used. For the Academic performance measure, the researcher had to depend upon the school examination record of the respective schools. Academic performance of secondary school pupils and internet addiction were shown to differ significantly. Students in secondary schools were shown to have a substantial impact on their academic achievement from their parent-child relationship. The study's conclusions showed that there was a substantial double interaction impact between internet addiction & parent-child relationships on secondary school students' academic performance.

Wildlife harvesting from the Afaka Forest Reserve, Kaduna, Nigeria: The prosperity–posterity crossroad

This study examined the harvesting of wildlife from the Afaka Forest Reserve from the standpoint of economic benefits and sustainability. The data for this study were collected using questionnaire and interview in 2017 from eight communities around the Afaka Forest Reserve, Kaduna State, Nigeria. A multi-stage sampling approach was adopted to collect data from 134 respondents who harvest wildlife from the Reserve. Data were analyzed using descriptive statistics, gross margin analysis, and Gini Index analysis. The findings revealed that wildlife incomes from animals like rabbits, porcupines, bats, and monitor lizards contributed 18.41% of total incomes of communities living around the Afaka Forest Reserve in Kaduna State in 2015. Sixty-one percent of the respondents harvested wildlife at least once in a week. Gross margin was highest in porcupine harvesting (N26, 872.73) while the highest return per Naira invested (N8.02) accrued to harvesting of insects. The average gross margin was N10, 610.86 while average return on investment was N2.35. Wildlife income had a reducing effect on income inequality as the marginal effect of wildlife income on Gini index of total income was -0.0883 meaning a 10% increase in wildlife incomes reduces inequality by about 0.9%. The study recommends that wildlife harvesting regulations which curb indiscriminate harvesting should be enforced alongside continuous sensitization of communities on the importance of sustainable harvesting practices towards a balanced prosperity-posterity equation.

Assessing the resilience of the financial market - a multistage approach in the context of the COVID-19 pandemic

ABSTRACT The ability of companies to adapt in a short time to adverse situations is related to their resilience. This study aims to assess the resilience of companies listed in the Bucharest Stock Exchange (BSE) during the COVID-19 pandemic through a multistage approach, identifying as a proxy the stock price of their securities compared to the market trend. Testing the behavior of companies during phases of resilience (resistance, recovery, transformation) according to the evolution of the benchmark index of the BSE, and by applying the slope function and factor analysis methods, this paper shows that, in periods of crisis, the behavior of the financial market is different from normal periods.

Efficient Multistage License Plate Detection and Recognition Using YOLOv8 and CNN for Smart Parking Systems

Smart parking systems play a vital role in enhancing the efficiency and sustainability of smart cities. However, most existing systems depend on sensors to monitor the occupancy of parking spaces, which entail high installation and maintenance costs and limited functionality in tracking vehicle movement within the car park. To address these challenges, we propose a multistage learning-based approach that leverages existing surveillance cameras within the car park and a self-collected dataset of Saudi license plates. The approach combines YOLOv5 for license plate detection, YOLOv8 for character detection, and a new convolutional neural network architecture for improved character recognition. We show that our approach outperforms the single-stage approach, achieving an overall accuracy of 96.1% versus 83.9% of the single-stage approach. The approach is also integrated into a web-based dashboard for real-time visualization and statistical analysis of car park occupancy and vehicle movement with an acceptable time efficiency. Our work demonstrates how existing technology can be leveraged to improve the efficiency and sustainability of smart cities.

Vision-Language Model for Generating Textual Descriptions From Clinical Images: Model Development and Validation Study.

The automatic generation of radiology reports, which seeks to create a free-text description from a clinical radiograph, is emerging as a pivotal intersection between clinical medicine and artificial intelligence. Leveraging natural language processing technologies can accelerate report creation, enhancing health care quality and standardization. However, most existing studies have not yet fully tapped into the combined potential of advanced language and vision models. The purpose of this study was to explore the integration of pretrained vision-language models into radiology report generation. This would enable the vision-language model to automatically convert clinical images into high-quality textual reports. In our research, we introduced a radiology report generation model named ClinicalBLIP, building upon the foundational InstructBLIP model and refining it using clinical image-to-text data sets. A multistage fine-tuning approach via low-rank adaptation was proposed to deepen the semantic comprehension of the visual encoder and the large language model for clinical imagery. Furthermore, prior knowledge was integrated through prompt learning to enhance the precision of the reports generated. Experiments were conducted on both the IU X-RAY and MIMIC-CXR data sets, with ClinicalBLIP compared to several leading methods. Experimental results revealed that ClinicalBLIP obtained superior scores of 0.570/0.365 and 0.534/0.313 on the IU X-RAY/MIMIC-CXR test sets for the Metric for Evaluation of Translation with Explicit Ordering (METEOR) and the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) evaluations, respectively. This performance notably surpasses that of existing state-of-the-art methods. Further evaluations confirmed the effectiveness of the multistage fine-tuning and the integration of prior information, leading to substantial improvements. The proposed ClinicalBLIP model demonstrated robustness and effectiveness in enhancing clinical radiology report generation, suggesting significant promise for real-world clinical applications.

Preparation of spheroidized W−Cu pseudo-alloy micropowder with sub-micrometer particle structure using plasma technology

Preparation of spheroidized W−Cu pseudo-alloy micropowder with sub-micrometer particle structure using plasma technology

Identifying Pareto Fronts Reliably Using a Multistage Reference-Vector-Based Framework

Evolutionary multi-objective and many-objective optimization (EMO and EMaO) algorithms are increasingly being used to identify the true shape and location of the Paretooptimal front using a few representative well-converged and welldistributed solutions. The reason for their popularity is due to their ability to provide a better understanding of objective relationships for optimal solutions, and also to facilitate the choice of a preferred solution using an interactive or postoptimal multi-criterion decision analysis. However, since EMO and EMaO algorithms are stochastic, a single application may not provide a true representative set with a desired number of Pareto solutions reliably in repetitive runs and importantly with a well-distributed set of solutions. In this paper, we propose a multistage framework involving reference-vector based evolutionary multi-and many-objective algorithms (MuSt-EMO and MuSt-EMaO) that attempts to recursively rectify shortcomings of previous stages by careful executions of subsequent stages so that a prescribed number of well-distributed and well-converged solutions are achieved at the end. The proposed multi-stage approach is implemented to a number of popular reference vector based EMO/EMaO algorithms and is applied on various multiand many-objective test and real-world problems.

Comprehensive assessment of the psychological well-being of school going adolescent girls in Chandigarh, India

Background: Adolescence, a crucial stage in human development, involves significant physical and psychological changes. Unfortunately, these transformations often go unnoticed, leading to an increase in psychological issues among adolescents. Objective of the study was to have a comprehensive assessment of the reasons due to which the adolescents are suffering from high psychological distress in school-going adolescent girls in Chandigarh. Methods: A cross-sectional study was conducted in Chandigarh schools included 168 adolescent females aged 13 to 19. The data collection utilized a multi-stage random sampling approach. Results: The research revealed that academic pressure 147 (87.5%), financial concerns of the family 77 (45.8%), difficulties in communication with family and peers 86 (51.2%) and the negative thinking pattern (83.4%, n=140) are affecting the adolescent girls' mental well-being. The study revealed that a significant number of participants frequently experienced emotions such as nervousness, guilt, social anxiety, insecurity, discrimination, and difficulty concentrating. Additionally, a substantial portion of the group reported instances of sleeplessness 92 (54.8%) and feelings of loneliness 136 (81%). Participants also indicated engaging in coping mechanisms like over thinking, procrastination, and overeating when facing stress. Alarmingly, some individuals turned to smoking, alcohol consumption, and drug abuse as stress-relief strategies, raising concerns for the community. Conclusions: The study highlighted the fact that supportive environment at home and in schools, with open communication and counseling to address these issues. Work should be done to reduce the stigma associated with mental health issue and encourage open conversations about mental health to help adolescents feel more comfortable seeking support.

Understanding the impact of socioeconomic and health factors on geriatric depression: A comparative study in rural and urban Bangladesh.

The prevalence of depression among the elderly is a growing concern, and this study examines the differences between urban and rural areas in terms of geriatric depression. Using a two-stage random sampling approach in urban areas and a multistage random sampling approach in rural areas, the study surveyed 944 elderly individuals of both sexes. The results indicate that the prevalence of depression was high, with 52.5% of the elderly population experiencing mild to severe depression. The study found that increasing age, female gender, nuclear family structure, and involvement of housewives or others were significant factors affecting depression in urban areas, while increasing age and elderly people without spouses were significant factors in rural areas. Additionally, the study identified hearing impairment, asthma, and arthritis as risk factors for depression in rural areas, and bronchitis, heart disease, and thyroid illness as significant factors in urban areas. These findings highlight the need for policymakers to focus on addressing the mental health needs of older people, particularly women and those without spouses.