Introduction The study explores the development and usability of a web-based mobile health app called Simple Health, aimed at improving the lifestyle of desk-based workers. Simple Health integrates multiple lifestyle factors, ensuring a holistic approach to workplace health promotion. Multi-component interventions yield greater behaviour change and long-term health benefit. Simple Health utilizes auto mated, evidence-based coaching to support goal-setting, performance tracking, and behaviour reinforcement, making it more scalable, cost-effective, and accessible for workplace implementation. Mostly, mobile health apps focus on just one behavior like increasing steps or reducing screen time. However, health behaviors are interconnected. Without combining physical activity, dietary habits, and sedentary behavior, these apps can’t achieve long-term impact. This gap led to the creation of Simple Health—an all-in-one solution tailored for desk-based workers. Therefore, the study is aimed to develop Simple Health, an evidence and theory-based mHealth web app designed to promote healthy eating, increase PA, and reduce sedentary time among sedentary employees. The study also evaluated the app’s usability. Methods The study was conducted in two phases. In first phase, the web app was developed and in second phase, they tested whether it is easy for employees to use. The app was built using two behavioural theories: social cognitive theory to boost self-confidence and motivation, and the ecological model to include workplace and environmental influences. Some important features were included: users could set goals, log their behaviors, receive daily reminders via the LINE app, and get personalised feedback. There were also educational booklets, motivational tools, and fun team challenges. In the testing phase, 8 office workers used the app for 5 days. They found it mostly easy to use. The logging feature and personalized advice were the most popular. Feedback suggested a few improvements, such as syncing with fitness trackers and better menu design. Results In phase 2, 8 sedentary employees, aged between 28 and 61, tested the app over 5 days. 87.5% of them found the app easy to use. On average, they spent about 11 minutes daily logging their health behaviours. The most frequently used feature was the behaviour logging tool, which helped users track diet, steps, and sitting time. Personalized advice and reminders via LINE were appreciated. However, users suggested improvements such as: better goal-setting flexibility, simpler navigation, and integration with fitness trackers for automatic syncing. Discussion Simple Health is both usable and engaging for sedentary employees. By combining diet, activity, and sedentary tracking, it stands out from existing workplace wellness apps. Theories like Social Cognitive Theory and the Ecological Model strengthened user engagement by encouraging self-belief and accounting for workplace environment. While feedback was positive, there is a need for longer trials, more participants, and improvements like automatic syncing and more interactive features. Simple health combines multiple health behaviors, which is based on proven theories and includes team-based motivation & gamification as well as educational and practical tools. Major limitation of this study is its small sample size where only 8 participants were included. The testing period was short, not enough to know long-term effects. It has Manual data entry and no automatic syncing with devices like Fitbit, making it less easier to use. Some navigation issues and limited goal-setting flexibility were also reported. Conclusion Simple Health is a promising mHealth intervention for office workers. It is easy to use, supports multiple health goals, and encourages collaboration through team-based features. It is grounded in theory, user-friendly, and has strong potential for helping desk workers live healthier lives, if further improved and tested on a larger scale. Simple health can be scaled to larger workplace programs. With improvements, it can become a cost-effective, practical solution for improving employee health. Future research should focus on long-term effectiveness and broader workplace settings.

World Health Organization (WHO) defines disability as impairment, limitation, or restriction in activity caused mainly by health issues and environmental factors. Worldwide, about one billion (15%) individuals face some form of disability. India accounts for 26.8 million differently-abled population. According to the Census 2011 and the 76th round of the National Sample Survey (NSS) estimates the prevalence of disability in India was 2.2%, and the prevalence of disabilities continues to rise gradually with age and is highest in individuals above 60 years. According to previous studies, ischemic heart disease and stroke are the leading causes of Disability adjusted life years (DALY) among 50-75 years age group. Non-communicable diseases (NCDs) like cardiovascular and musculoskeletal disorders account for 66.5% of disability-adjusted life years (DALYs) in low and middle-income countries. International Classification of Functioning, Disability and Health (ICF) has classified disability into the hearing, visual, speech, mental and locomotor. A person with a disability (PwD) generally experiences adverse socioeconomic outcomes, poverty and physiologic stress, and inequity in access to essential resources such as education, health care facilities, employment, and social participation. The study aimed to estimate the prevalence of disability in India, determine the associated factors and assess the pattern and geographical distribution using data from NFHS-5. Methods This study is based on secondary data analysis on the NFHS-5 dataset, and a total of 28,43,917 participants of all age groups were included in the study. The independent variables for assessing the prevalence of disability were sociodemographic and health-seeking behaviours characteristics. Some of the covariates are age, marital status, education, BPL card holder and health-seeking behaviour. Disability is the dependent variable. The statistical analysis was done using STATA 16. The “svyset” command was used to declare the dataset as survey type and to estimate the population’s weighted proportion. The burden of disability and its predictors were estimated using the weighted prevalence. Multivariable regression was done after checking for collinearity among the variables using the variance inflation factor. The overall prevalence of disabilities and the nationwide prevalence of locomotor, mental, and speech disabilities, which are most prevalent, was assessed using QGIS 3.2.1 version. It was performed to determine the regional differences in disabilities. Results The sample characteristics reveal that respondent’s mean age was between 30.82 ± 20.62 years, 50.41% were females, 75.83% were rural residents and 49.9% were married. The overall prevalence of disability was 0.93%; and 5.11% of households have one or more people with disability (PwD) across all age groups in India. The prevalence was highest in the age group of 75 years and above at 1.96%. Respondents aged 75 years and above had twenty-six times the prevalence of disability compared with 0–14 years. Disability was 58% more among males than females. The prevalence of disability was highest in the poorest wealth quintile that is 55%. The prevalence pattern of different disabilities across educational statuses in India shows that higher educational attainment is associated with a higher prevalence of locomotor and visual disabilities, and a lower prevalence of mental and speech disabilities as well. The prevalence of different disabilities across the population in India shows that of the total, locomotor disabilities accounted for 44.73% followed by mental disabilities 20.07% and speech disabilities account for 13.74%. The prevalence pattern of different disabilities across age groups in India shows that the preponderance of locomotor disability is highest among the 60–74 years age group. Overall prevalence pattern of disability in India indicates that it is more prevalent in Lakshadweep (1.68%), followed by Tamil Nadu (1.26%) and Karnataka (1.22%); the prevalence of locomotor disability was highest in Delhi (58.5%), followed by Punjab (55.51%) and Madhya Pradesh (53.47%); distribution of mental disability was highest in Lakshadweep (41.24%), followed by Mizoram (38.12%) and Goa (37.1%); and the highest prevalence of speech disability is in Sikkim (37%), followed by Tripura (22.66%) and Jharkhand (22.12%). Discussion The study shows disparities in the prevalence of disability types by age, gender, educational status, region, wealth index, caste, and treatment facility. Findings suggest that men are more prone to have any disability as compared to women. Despite government initiatives disability is higher among those with lower levels of education. People living in poverty may work under hazardous conditions associated with adverse health outcomes, including disability; limited access to healthcare and education, which puts them at a greater risk of developing disabilities. The findings of the study show higher prevalence of locomotor disability. Most people with disabilities chose to obtain medical care from NGOs or Trusts. The prevalence of disability varied according to region. Topographically the southern part was found to be a potential domain for disability in this study. Conclusion The study concludes that the overall prevalence of disability in India is 0.93%; and 5.11% of households have one or more people with disability (PwD). Locomotor disability is the most common type of disability among the population. More intervention strategies should be planned, considering factors like education, health promotion and caste so that the services provided by the government can be available and accessible to everyone in need.

Globally, one in seven adolescents experiences a mental disorder, accounting for 15% of the global burden of disease in this age group, yet these remain largely unrecognised and untreated. The reasons for the increase in mental disorders among young people are complex and influenced by many factors, including the use of social media. A recent survey shows that 97% of adolescents use at least one social media platform. Studies have linked increased social media use with depression, anxiety, and psychological distress, while others have found that social media can increase social support, strengthen bonds, and reduce social isolation and loneliness. And a recent model identified that social comparison, social feedback and self-reflection are 3 key mechanisms of why social media may be related to self-esteem. There is some evidence that social media use encourages young people to engage in more negative social comparison behaviors, which can lead to low self-esteem and, in turn, mental health problems. Social media use can also reduce self-esteem by facilitating cyberbullying and social exclusion. Another potential mediator in the link between social media and mental health problems could be feelings of social connectedness to peers; it could lead to negative interactions, including cyberbullying, which could result in lower feelings of peer connectedness. Exploring these mediating factors could help us understand how social media can impact mental health and inform prevention strategies and interventions to improve mental health. Therefore, this study aims to examine the longitudinal relationship between social media use and adolescent mental health and whether this relationship is mediated by self-esteem and peer connectedness. Methods The study used data from the UK Longitudinal Household Survey: Understanding Society (USoc), a nationally representative study that has interviewed all household members annually since 2009. This study used data from waves 1-10, which are from 2009 to 2019. The study included 3228 participants who were adolescents aged 10-15. Mental health problems and social media use are the outcome and explanatory variables of this study, respectively. To explore the association between social media use and mental health initially, unadjusted and adjusted multilevel linear regressions were conducted. Further, a mediation model was proposed for the association between social media use and mental health by self-esteem and peer connectedness. To investigate these mediation pathways, path analysis with structural equation modelling (SEM) was used, and Monte Carlo test was used to examine the significance of indirect effects. Results According to the results, mental health problems, as measured on the SDQ, increased by 0.96 points for every unit increase in active social media use, it thereby suggests that those who spend a lot of time on social media might have poorer mental health, but the results were no longer significant when adjusted for covariates. Mental health scores at 14-15 years of age increased by 0.34 for every additional year they took part in the survey, therefore it shows that taking part in the survey at baseline (12-13 years) more recently was related to poorer mental health problems 2 years later. The study reveals no longitudinal association between active social media use and mental health, but the unadjusted analysis shows that 68% of the effect of social media use was mediated by self-esteem. It also reveals that more active social media use was associated with lower self-esteem, which in turn was associated with more mental health problems. Discussion The study found that more time spent on active social media use at 12-13 years of age was not associated with mental health problems at 14-15 years of age for UK adolescents. Although the unadjusted results suggested that more time spent on social media was associated with more mental health problems. The study explored the mediating roles of self-esteem and peer connectedness, where only self-esteem was found to be a significant mediator between social media use and mental health. The study also found some indication that more active social media use was associated with feeling less happy with friendships, but happiness with friends was not further associated with poorer mental health. The strength of this study is that it was based on a nationally representative sample of adolescents in the United Kingdom with a longitudinal design. However, this study was limited by the relatively small sample size in the complete case analysis and may have been underpowered. The analysis was based on self-reported data, the measures used to assess social media use, self-esteem, and social connectedness may also have been too crude. Lastly, the study only focused on those with data at 12- to 13-year-olds and 14- to 15-year-olds, which may limit the generalizability of the findings. Conclusion The study highlights the importance of longitudinal research in understanding these relationships, as the findings have implications for how clinicians, parents, caregivers, policymakers and young people approach this issue. The study found little evidence to suggest that more time spent on social media was associated with later mental health problems in UK adolescents. Although it suggests that self-esteem might play a role in the relationship between social media use and mental health. Further, more research is needed to explore how different types of social media use affect mental health and who is most affected by social media use to develop more targeted prevention strategies and interventions to improve young people’s mental health.

Cancer is a significant public health challenge, ranking as a leading cause of death worldwide and in high-income countries. Despite extensive research into modifiable risk factors for cancer, such as tobacco, alcohol, and diet, the influence of psychosocial factors, particularly loneliness and social isolation, remains relatively unexplored. And loneliness and social isolation has been recognised as the potential determinants of health. Globally loneliness shows a marked increase with one in four people feeling very or fairly lonely. According to the previous studies, 27% of men and 34% of women in Denmark report feelings of loneliness. Social isolation was associated with a 23% increased risk of cancer mortality, highlighting the urgent need to understand implication for cancer prevention and care and the importance of social support networks in motivating people to engage in screening. Despite these insights, the relationship between loneliness, social isolation, and participation in cancer screening remains poorly understood. Therefore, the study aims to identify barriers to cancer screening among lonely and isolated people in Denmark and to explore strategies to improve their participation. Methods It is a qualitative study conducted in Denmark. The data was collected between August to October 2023, and purposive sampling was employed for recruitment of participants. They were recruited via organisations, local associations, SHGs, municipal services which were aimed at reducing loneliness and social isolation. The participants included were people with lived experiences of loneliness &/or social loss. There were 32 participants among which 20 were women and 12 were men withing the age range 47 to 74 years. Questions were framed in a hypothetical third-person perspective, rather than direct inquiries. Participants were provided with an information sheet, a consent form, and a short questionnaire assessing various background information and the 3 item Loneliness scale, derived from UCLA loneliness scale. The data analysis was rigorously conducted through Malterud’s systematic text condensation (STC) method. The interview was structured into six individual interviews and six group interviews. Individual interviews averaged 61 minutes in length, and group interviews averaged 50 minutes took comprehensive measures to ensure participants’ dignity, respect, and emotional safety. Given the potential for distress, FA assured the participants that they could pause or terminate interviews and offered follow-up support for debriefing or counselling if needed. Results Most participants in the study were either retired or receiving social income, 14 were married or in a relationship, and five reported having no children. Among the participants, two were cancer survivors. Additionally, 20 participants disclosed having chronic medical conditions. Eight participants’ scores indicated moderate loneliness, while nine experienced severe loneliness. The study has derived 3 main themes to describe the important aspect among lonely and isolated people to engage in cancer screening, first is the invisible barrier to and motivations for screening participation, second is ambivalence towards self-sampling, and third is the power of presence. Most participants had undergone cancer screening at least once, but regular participation and awareness were lacking. The participants revealed that their struggles with loneliness and/or isolation often intersected with their healthcare decision-making process, presenting an invisible barrier to screening participation. A participant had colorectal cancer in their family, which is hereditary, so despite contending with feelings of demotivation stemming from experiences with loneliness and/or isolation, the reality of heightened cancer risk and the association with death motivated these participants into action. Other participants suggested that straightforward communication could be far more effective for raising awareness than advertisement. Adopting humorous and positive tone, and hearing cancer survival stories with early detection could increase motivation. Participants revealed high familiarity with self-sampling for colorectal cancer screening program, but were less aware about self-sampling for cervical cancer screening programme and also raised concerns about accuracy of self-sampling and suggested that providing more on the benefits and reliability of self-sampling methods and guidance from their GPs could help ease the scepticism. A main obstacle to participation in cancer screening among the participants was their lack of self-motivation. Many participants described a need for external motivation or a “push” to initiate action towards participation. The influence of personal networks, including caregivers, support helpers, or support groups, was often cited as crucial in providing that necessary push. Many participants also mentioned that once they had started participating, things became easier and that it was just a matter of getting started. Besides that, dependence on external motivation and support systems acted as a motivation mostly among females compared to males. Discussion The study revealed that while most participants had engaged in cancer screening at least once, regular participation was uncommon. The major obstacles in regular participation was the self-motivation in reaching out and scheduling or travelling to appointments and consistent engagement. The impact of close relationships were such, that people who were in relationship, relied on their partners and those living alone expressed a need for more support and engagement. The participants suggests that, pre-booked appointments for screening, reminders from their GPs or personalised outreach could improve awareness and motivation. Additionally, self-sampling was generally received positively, due to its convenience and privacy. The study also highlights the need for strategies that focus on the emotional and psychological barriers rooted in loneliness and isolation. One of the notable strengths of this research was its ability to engage with populations typically considered hard to reach. The study made efforts to ensure participants’ comfort, facilitated open and nuanced discussions, which was crucial for the sensitive nature of the research topic. The findings of this study are applicable across nations. The study utilised an indirect approach to discuss loneliness. Although indirect approach was ethically sensitive and considerate, it may have constrained the depth of insight into direct experiences of loneliness. Secondly, some nuances and intensities of loneliness could be underrepresented due to the cautious framing of questions. Lastly the oversimplification of cultural context could influence the expression and interpretation of loneliness. To increase the motivation and engagement for participation in cancer screening among those feeling lonely and/or isolated, future interventions should prioritize awareness and social support. Awareness strategies should offer clear, accessible information. Additionally, tapping into social networks and community organisations for spreading information, community outreach efforts for motivation and engagement, expanding access to self-sampling screening options and face-to-face interaction are some of the strategies to increase participation. Conclusion This study reveals a paradox that high interest in cancer screening does not lead to regular participation. A multifaceted strategy enhancing social networks and providing clear, accessible information can increase participation and better integrate lonely or isolated populations into preventive healthcare. This approach might be helpful not only for increasing screening participation rates but also for integrating vulnerable populations more fully into preventive healthcare initiatives.

According to the WHO, there were 210 million confirmed cases and 4.4 million deaths as per August 2021, and the estimates greatly surpass the data. Most patients with COVID-19, lead a normal life after acute infection, but some patients report ongoing health issues. Although, the estimates and data for that are unknown, some reports indicate that 10 to 20% of covid patients experience lingering symptoms. In October 2021, the World Health Organisation, defined Long COVID, as the presence of symptoms, 3 months after the SARS-CoV infection, with a minimum duration of 2 months, which cannot be explained by alternative diagnosis. It represents a significant health challenge due to its high prevalence, its great impact on the quality of life, and the lack of knowledge for its cause of development, pre-disposing factors, treatment and prevention. Long Covid can affect any organ system, including the central and peripheral nervous system, cardiovascular, respiratory systems, or even the digestive system. Previous studies reveal that rather than the severity of the disease, patient profile have a larger impact on the quality of life and risks of certain disease, especially females with chronic fatigue and to a lesser extent people with obesity, develop symptoms for Long COVID. The hypothesis of the study is that long covid would be more influenced by a certain patient profile than by the severity of the acute infection. The aim of the study is to assess health related quality of life, 1year after a hospital admission due to COVID infection and the factors that may influence it. Methods The study was performed in the city of Castellon, Spain. It is a retrospective observational study, which took place between March 2021 to February 2022, with 486 participants aged above 18 years. The participants were admitted in hospital for COVID infection during March 2020 to February 2022, and their cases were confirmed through RT-PCR or antigen tests. The variables undertaken to determine the quality of life are demographic variables, medical history, clinical outcomes, laboratory test results, treatment and vaccination. For the outcome variable, the 36-item Short Form Survey (SF-36) on health-related quality of life questionnaire was undertaken. The SF-36 evaluates quality of life through 8 domains, namely, physical functioning, physical role, bodily pain, general health, energy/vitality, social functioning, emotional role and mental health. For each domain, higher scores indicate better quality of life in that domain. The statistical analysis in the study was performed using SPSS, where the quantitative variables were described through means and medians and qualitative variables were described through absolute and relative frequencies. To test the associations between the outcome variable that is SF-36 and quantitative variables, pearson correlation was performed and for associations with qualitative variables, Mann-Whitney U Test was performed. Further to correct multiple comparisons, Bonferroni test was performed. Finally, a multivariate analysis was performed between relevant explanatory variables and quality of life variables/SF-39, using multiple linear regression. Results The results reveal that variables like age is associated with worse outcomes for physical functioning, and Charlson comorbidity index is associated with worse outcomes for physical functioning and general health. The qualitative variables found to be associated with SF-36 and included in the multivariate analysis are female sex that continued to show a significant and negative association with all domains of SF-36; chronic fatigue showed negative associations with physical functioning, bodily pain, general health, energy, and emotional role, and obesity showed a smaller influence and found to be related to worse outcomes in physical functioning and physical role. Discussion The worst quality of life outcomes was obtained in the domains of general health, vitality, and mental state. Hospital admission for the disease produced a long-term deterioration in quality of life. It is observed that quality of life in practically all domains, is especially compromised for a very specific patient profile, that is among females, people with chronic fatigue and to a lesser extent people with obesity. And in contrast, the severity of disease did not appear to have an impact on the subsequent quality of life. Other determinants like age and age-adjusted Charlson comorbidity index were also associated with worse outcomes, although in multivariate analysis, age maintained its negative effect on physical functioning and age-adjusted Charlson comorbidity index showed negative outcomes on physical functioning and general health. Strengths of the study are: 1. analysing the impact of psychological and psychiatric conditions and not just the physical aspects, on quality of life; 2. reporting lab results during acute phase of infection and; 3. analysing the use of corticosteroids. Limitations of the study are: 1. The retrospective nature or the lack of estimation of size calculation; 2. absence of a control group; 3. lack of reference or expected values of SF-36 test for a population similar to the study; 4. included patients who were infected in the early stages of the pandemic, the protective, effect of vaccination prior to infection could be assessed. Conclusion Patients who required admission for COVID-19 in 2020 and early 2021 continued to show a diminished quality of life 1 year after hospital discharge, especially in the domains of general health, vitality, and mental health. The main factors that may influence this would be female sex, a history of chronic fatigue, and, to a lesser extent, obesity.

According to the World Health Organisation (WHO) Report on Hearing, 1.57 billion people worldwide, had hearing loss in 2019, i.e. 20.3% of the world population. By 2050, this population will rise up to 2.45 billion. One of the major causes of hearing loss is older age. Over 58% of moderate or higher-grade hearing loss is experienced by adults above the age of 60 years, and with increasing retirement age in many countries, hearing loss can be a barrier against productivity. Unaddressed hearing loss can pose a negative impact on various aspects of life and society, like communication, employment, mental health, cognition, interpersonal relationships as well as on the economy. The global economic costs of hearing loss in 2019, were estimated over $981 billion, which included costs related to healthcare, education, productivity losses and societal costs. The hypothesis for the study is that hearing loss impacts the work outcomes of people specifically in communicative jobs e.g. teaching. The aim of this study was to investigate the association of hearing loss with the work ability and sick leaves among teachers. Methodology The data used here was employed from a similar study titled “Hearing loss & Sustainable Employability Study”, collected between April 2014-June 2015. It is a convenience sample based on an online survey, where teachers were recruited via schools, educational sector organisations and trade unions. The online survey included the Dutch National Hearing Test (NHT) which is an online speech-in-noise screening test for hearing loss. The test calculates the lowest volume at which a person can correctly repeat 50% of spoken words, and is called Speech Reception Threshold (SRT) and a higher SRT score indicates a greater degree of hearing loss. The outcome variable that is work ability score (WAS) compared, current work ability with lifetime best, on a range of 0 to 10. The statistical analysis was conducted in IBM SPSS. To show the baseline characteristics of teachers, Kruskal Wallis and Chi-square test was employed. To examine the associations of hearing loss and work ability, poisson regression analysis was employed adjusting for the covariates. Results The results reveal that among 737 teachers, 69% of them had good hearing ability, 20% of them had insufficient hearing and 12% of them had poor hearing ability. Compared to teachers with insufficient and good hearing, teachers with poor hearing were at a higher risk, where 66% of them were older people over 56 years of age; 73% of them had poor to moderate work ability and 47% of them had been on sick leave and they reported on average 5.6 sick leave days. Discussion The study investigates the association between hearing loss with work ability and sick leave among teachers, which is a profession with high auditory demands. As per the results, teachers with insufficient hearing experience poorer work ability than good hearing teachers. Poor work ability is a potential predictor of future work participation problems, such as sick leave days and early work exit into disability pension. Similar to the previous study findings from this author, the current study also reveals that hearing loss poses a potential threat to the workers in communicative jobs, and therefore it demands more attention from the occupational health professionals. The study also found that teachers with poor hearing had higher prevalence of sick leave than good hearing teachers, and the results are in contrast with other study findings as it included the sick leave of normally hearing workers in the analysis. A strength of this study is its focus on hearing loss, specifically among teachers, which is a high auditory demanding profession. Another strength is the use of NHT to determine hearing status. It enabled to classify the teachers in good, insufficient, and poor hearing category. The limitations of this study are: 1. Its cross-sectional design is a limitation of the study; 2. use of a convenience sample, because the sample lacks the information of non-participant teachers and its characteristics; 3. use of self-reported data, because such kind of data might have recall and information bias. WHO has proposed key public health interventions for Ear & Hearing Care (EHC) provision across the life course and summarised it in this acronym called HEARING. As part of these interventions, WHO also recommends hearing screening, for all adults from the age of 50 onwards, through an application called “hearWHO” app. WHO suggests earlier detection and intervention in hearing loss. Conclusion Hearing loss might be a risk factor for work participation, particularly among older workers in communicative jobs. Public health professionals should take these findings into account in their efforts to maintain a healthy workforce. Periodic hearing screening of teachers could identify hearing loss and enable timely work accommodations and audiological rehabilitation to maintain work participation and prevent premature work exit in disability pension.

Out-of-Pocket Expenditure (OOPE) in healthcare refers to the money people pay directly from their own pockets for medical services, such as doctor visits, medicines, and hospital stays. Treatment of NCDs is expensive which can be a financial burden for the rural poor in India especially those residing in rural areas having low incomes, and substantial out-of-pocket (OOP) expenditures can push a large portion of the population below the poverty line. Economically poorer households in states such as Bihar and Odisha face significantly higher OOP expenditures for hospitalization in PHCs compared to economically developed states such as Tamil Nadu. Medicine constitutes the significant portion of healthcare expenditure and previous studies shed light on the challenges of OOPE on healthcare, specifically access to medicines. The provision of low-cost generic medicine is suggested as an effective way to escape OOP expenditures, but it has not been that effective so far. An easy accessibility of medicine would require public sector commitment, adequate public sector financing, efficient distribution, low taxes, and a culture of rational use of medicines. There is a need for strategies to protect vulnerable populations from financial hardships in India, particularly in the State of Odisha. The study aims to analyse OOPEs on medicines in Odisha comparing households through wealth index and demographic factors. Methods It is a cross-sectional household survey where six districts have been purposively selected with varied characteristics where, Rayagada and Kalahandi (south) are tribal areas with economically poor and vulnerable to healthcare services; Angul and Keonjhar (North) are industrial region with better economic condition; and, Khordha and Kendrapara are coastal & capital region with high PCI. The sample size was calculated via the following formula: n = z2 pq/d.2 (where z = 1.96, p = prevalence of OOP expenditure on medicines (55.8%), q = 1-p, d = design effect (0.035)). One hundred fifty-five households from each block were surveyed per the estimated sample size. Two blocks were selected from each district: one block, which is district headquarters, and another block, which is situated remotely from the district headquarters. The share of medicine in total monthly household expenditure was chosen as the dependent variable. Wealth index, occupation, social groups, medicine expenditure categories, regions, localities, illnesses, and insurance coverage served as independent variables. Results The main occupation that helped households earn a livelihood was daily wage labour (27.7%). Among the 902 surveyed households, 268 (29.7%) suffered from either acute or chronic illnesses or both. The share of medicine in total monthly household expenditure was chosen as the dependent variable. High share of medicine (> 50%) was recorded in the north and south Odisha. Households in South Odisha faced extreme medication expenses (32.6%), greater than the other regions. Moreover, only 14.7% of the households with chronic illnesses paid high amounts for medicines. According to the unadjusted odds ratio (UOR) analysis, wealthy households were 0.246 times less likely to spend more than 50% of their total expenditure on medicine than poor households. The rural–urban disparity shows that urban areas are 0.102 times less likely to face the effect of a high percentage of total household expenditure. Discussion The present study contributes to and extends the field of OOP expenditure studies by putting together acute and chronic illnesses with household expenditure. It provides a perspective of looking at OOP expenditure from wealth index categories within a selected geographical region. Considering wealth, expenditure categories, social groups, geographical location, rural–urban factors, and acute illness in the model, wealth and expenditure categories emerged as the significant factors contributing to medicine expenses in a household. When medicine expenses increase beyond INR 5000 (USD 59.84), they constitute more than 50% of total expenditure, which is critical for pushing households into deprivation. The rural‒urban divide regarding the share of medicine in total household expenses is still visible—a study by Vasudevan et al. showed that rural areas are more prone to higher OOP than are urban areas. However, the gap has narrowed in the current era, as indicated by the present study. Acute illnesses may contribute to high medicine expenditures in the household budget. However, chronic diseases have no such relationship with the household budget. In contrast, chronic illness medicine costs are well comprehended and accommodated in the monthly budget. Conclusion The data revealed that socioeconomic inequality exists in the population. It has direct implications for access to medicine and health. OOP has different effects on the three wealth categories of households. A wealthy household can cope with high medicine expenditures, but a middle-class and poor household can easily break down due to the burden of medicine costs. Rural‒urban areas, regional differences, and acute illnesses are prompts to act upon to control OOP in medicines immediately.

The development and recent increase in the application of epigenetic clocks in healthcare research has primarily been focused among the European, African, or Hispanic individuals. The use of epigenetic clocks is limited among the east Asian population despite having notable differences in epigenetic clocks among various ethnic groups. If the biological age is higher than chronological age, it is called Epigenetic Age Acceleration (EAA). It is typically derived by regressing the epigenetic age of clocks on chronological age, assesses whether individuals are aging faster or slower than their chronological age. EAA indicates higher risk of chronic diseases, mortality, and adverse health outcomes and thereby indicates faster aging. First-generation epigenetic clocks, such as Horvath’s and Hannum’s clocks, estimate chronological age based on DNA methylation patterns, while second-generation clocks, like PhenoAge, GrimAge, and DunedinPACE, focus on biological age by incorporating clinical biomarkers, aging pace, and mortality risk. Second-generation clocks outperform first-generation clocks in predicting lifespan and health outcomes. The study assessed the performance of epigenetic clocks in the East Asian population and explored the association of EAAs from each clock with health outcomes, health behaviors, and the time to onset of chronic diseases, and confirmed these findings in an independent South Korean cohort. Methods The present study classified participants into case and control groups for type 2 diabetes (T2D) and hypertension using diagnostic history, fasting glucose levels, and blood pressure measurements. DNA methylation data were analyzed from 1,925 KARE participants and 822 HEXA participants, combining data from Illumina 450K and EPIC BeadChips with batch effects corrected using the ComBat function. Epigenetic age was estimated using eight clocks (four first-generation and four second-generation) and evaluated for epigenetic age acceleration (EAA) by regressing epigenetic age on chronological age. Statistical analyses, including t-tests, Pearson correlations, regression, and survival analyses, were performed using R, with visualizations created via ggplot2. The study prioritized principal component-based clocks to minimize noise and improve accuracy. Results The findings show no significant association of first-generation epigenetic clocks with chronic diseases or health markers. On the contrary, second-generation clocks demonstrated strong correlations with adverse health outcomes, such as increased risk of type 2 diabetes, hypertension, and elevated levels of ALT, AST, TG, and hs-CRP, while showing decreased levels of HDL, FEV1% PRED, and FVC% PRED. Males exhibited faster epigenetic aging across most clocks. Environmental factors like smoking and high BMI accelerated aging, while regular exercise and higher socioeconomic status slowed it. Findings were validated in an independent cohort, reinforcing the utility of second-generation clocks in health prediction for East Asians​. Discussion The findings of the study depict higher accuracy of second-generation epigenetic clocks in predicting health outcomes compared to first-generation clocks. Second-generation clocks, such as PCGrimAge and DunedinPACE, were strongly associated with chronic diseases, mortality risk, and environmental factors, highlighting their relevance for healthspan and lifespan predictions. While first-generation clocks primarily estimate chronological age, second-generation clocks incorporate health biomarkers and lifestyle data, making them more robust in assessing biological aging. Lifestyle factors, like smoking and high BMI, accelerated epigenetic aging, while exercise and higher socioeconomic status slowed it. Despite the study's focus on East Asians, the findings highlighted the general utility of second-generation clocks across populations. Conclusion Overall, this study underscores the utility of epigenetic clocks in the East Asian population. The study evaluated different epigenetic clocks and confirmed that the epigenetic age from second-generation clocks provide valuable health predictions, while also suggesting potential for slowing aging through healthier lifestyles. The findings demonstrate the potential of second-generation clocks as tools for early detection of health risks and guiding interventions, particularly in East Asian populations. It suggests that epigenetic clocks developed for different ancestries can be valuable for individuals of East Asian populations. The study underscores the need for population-specific clocks, given that most existing clocks were developed in non-Asian populations. Impact of the research Although the study validated results using an independent cohort, certain limitations remain, including the inability to analyze lung function and smoking pack-years, the lack of statistical significance for psychosocial stress on epigenetic age acceleration (EAA), and insufficient follow-up cases for type 2 diabetes and hypertension to confirm their impact on disease onset. The findings emphasize the need for larger sample sizes and a comprehensive interpretation of aging indicators using multiple epigenetic clocks for a deeper understanding of their associations with health outcomes.