Chronic Disease Prevalence and Other Risk Factors from Behavioral Risk Factor Surveillance Survey (BRFSS) 2018 or 2017, Census Bureau 2010 census population or annual population estimates for county 2018 or 2017, and American Community Survey (ACS) 2014-2018 or 2013-2017Health Outcomes: arthritis, current asthma, high blood pressure, cancer (excluding skin cancer), high cholesterol, chronic kidney disease, chronic obstructive pulmonary disease (COPD), coronary heart disease, diagnosed diabetes, mental health not good for >=14 days, physical health not good for >=14 days, all teeth lost and strokePreventive Service Utilization: lack of health insurance, visits to doctor for routine checkup, visits to dentist, taking medicine for high blood pressure control, cholesterol screening, mammography use for women, cervical cancer screening for women, colon cancer screening, and core preventive services use for older adults (men and women)Unhealthy Behavior Risk Factors: binge drinking, current smoking, obesity, physical inactivity, and sleeping less than 7 hoursSee original CDC Project map for PLACES (Population Level Analysis and Community Estimates) here.PLACES expands the original 500 Cities project and is a collaboration between the CDC, the Robert Wood Johnson Foundation (RWJF), and the CDC Foundation (CDCF)

A collection of data of an epidemiological study of chronic disease in the oldest old based on information collected from Kaiser Permanente facilities in Northern California (KPNC). The initial sample was drawn from the Kaiser''s active membership lists for the years 1971 and 1980. The sample was restricted to members that had a Multiphasic Health Checkup examination (MHC) within 7 years of the baseline date. The sample was stratified to attain equal numbers of observations (1,000 in each) in three sex-age cells for each cohort: 65-69, 70-79, and 80+. Each cohort was followed for 9 years through existing medical records and computerized hospitalization tapes. Mortality data was collected by matching the sampled data with state Vital Statistics data for an additional 3 years for a total follow-up time of 12 years. Part 1 of the data collections consists of Master Records, which includes information from the morbidity review, in which over 35 chronic conditions or diagnoses were abstracted from the member charts, as well as detailed diagnostic criteria for the major conditions. A prevalence review was done, which included the 4 years prior to the baseline date for these same conditions. Recurrent disease is included for the following conditions: cancers, myocardial infarction, and various forms of strokes. A detailed account of outpatient health services use, and data from the multiphasic health checkup, which was administered to each participant during the nine yearly follow-ups, are also included in the Master Records file. The labs and procedures included: chemistry, hematology, urinalysis, bacteriology, chest x-ray, GI x-ray, ultrasound, CT/MRI, mammogram, resting ECG, treadmill ECG, echocardiograms, nuclear scans, outpatient breast biopsy, cystoscopy, and cataract surgery. Inpatient utilization includes all hospitalizations, procedures done during a hospital stay, length of stay, admitting/discharge diagnosis. Part 2, Hospitalization, contains records of causes and dates of hospitalizations and discharges and nursing home admissions. There is also a section on incomplete reviews and the reasons for them. Demographic information and some lifestyle information from the multiphasic health checkup (e.g., smoking, alcohol, and Body Mass Index) are also in this file. Data Availability: These datasets have been documented extensively and are available from the ICPSR (Study No. 4219). * Dates of Study: 1971-1992 * Study Features: Longitudinal, Anthropometric Measures * Sample Size: ** 1971 cohort: 2,877 (baseline) ** 1980 cohort: 3,113 (baseline) ** 1971 & 1980: 5,990 ** Hospitalization: 14,730 Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/04219 * HSRR: http://wwwcf.nlm.nih.gov/hsrr_search/view_hsrr_record_table.cfm?TITLE_ID=381&PROGRAM_CAME=toc_with_source2.cfm

This is a bar graph comparing the prevalence of adults with high cholesterol to the prevalence of adults who have been screened for cholesterol in the past five years. This project provides model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data 2015, Census Bureau 2010 census population data, and American Community Survey (ACS) 2011-2015 estimates. More information about the methodology can be found at www.cdc.gov/500cities.

Note: This data was created by the Center for Disease Control, not the City of Rochester. This map is zoomed in to show the CDC data at the census tract level. You can zoom out to see data for all 500 cities in the data set. This map has been built to symbolize the percentage of adults in 2017 diagnosed with coronary heart disease. However, if you click on a census tract, you can see statistics for the other public health statistics mentioned below in the "Overview of the Data" section.Overview of the Data: This service provides the 2019 release for the 500 Cities Project, based on data from 2017 or 2016 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Twenty measures are based on 2017 Behavioral Risk Factor Surveillance System (BRFSS) model estimates. Seven measures (all teeth lost, dental visits, mammograms, Pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) kept 2016 model estimates, since those questions are only asked in even years. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations.Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Data sources used to generate these measures include BRFSS data (2017 or 2016), Census Bureau 2010 census population data, and American Community Survey (ACS) 2013-2017 or 2012-2016 estimates. For more information about the methodology, visit https://www.cdc.gov/500cities or contact 500Cities@cdc.gov.

BackgroundChronic diseases have emerged as a significant public health challenge owing to the escalating global demographic shift toward an aging population. Middle-aged and older individuals are particularly vulnerable to chronic illnesses owing to physiological and socioeconomic changes. By leveraging health literacy data from the Zhejiang Province, this study aimed to elucidate the correlation between health literacy levels and the prevalence of chronic diseases in this demographic cohort.MethodsIn this cross-sectional study, a stratified multistage whole-cluster random sampling method was used to select 12,116 permanent residents aged 45–69 years from 30 monitoring sites in Zhejiang Province from June to November 2023, using the National Health Literacy Monitoring Questionnaire for the Population. Multivariate regression analysis was employed to unravel the correlation between proficiency in health education and the prevention of chronic illnesses.ResultsSex, age, income, education, self-assessed health status, and smoking status emerged as significant predictors across the different models. Notably, self-assessed health and smoking statuses were identified as confounders that significantly affected the association between health literacy and chronic diseases. Furthermore, this study explored the influence of independent variables on specific chronic diseases, such as hypertension and cerebrovascular disease, with consistent patterns observed across models.ConclusionHealth literacy is instrumental in thwarting chronic diseases among middle-aged and older individuals. Those with higher levels of health literacy are less likely to suffer from chronic diseases, and high health literacy is a protective factor against hypertension and cerebrovascular disease.

This is a chart comparing the prevalence of adults with high blood pressure to the prevalence of adults taking medications for high blood pressure. This project provides model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data 2015, Census Bureau 2010 census population data, and American Community Survey (ACS) 2011-2015 estimates. More information about the methodology can be found at www.cdc.gov/500cities.

BackgroundChronic diseases have emerged as a significant public health challenge owing to the escalating global demographic shift toward an aging population. Middle-aged and older individuals are particularly vulnerable to chronic illnesses owing to physiological and socioeconomic changes. By leveraging health literacy data from the Zhejiang Province, this study aimed to elucidate the correlation between health literacy levels and the prevalence of chronic diseases in this demographic cohort.MethodsIn this cross-sectional study, a stratified multistage whole-cluster random sampling method was used to select 12,116 permanent residents aged 45–69 years from 30 monitoring sites in Zhejiang Province from June to November 2023, using the National Health Literacy Monitoring Questionnaire for the Population. Multivariate regression analysis was employed to unravel the correlation between proficiency in health education and the prevention of chronic illnesses.ResultsSex, age, income, education, self-assessed health status, and smoking status emerged as significant predictors across the different models. Notably, self-assessed health and smoking statuses were identified as confounders that significantly affected the association between health literacy and chronic diseases. Furthermore, this study explored the influence of independent variables on specific chronic diseases, such as hypertension and cerebrovascular disease, with consistent patterns observed across models.ConclusionHealth literacy is instrumental in thwarting chronic diseases among middle-aged and older individuals. Those with higher levels of health literacy are less likely to suffer from chronic diseases, and high health literacy is a protective factor against hypertension and cerebrovascular disease.

2011 to present. BRFSS combined land line and cell phone prevalence data. BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Data will be updated annually as it becomes available. Detailed information on sampling methodology and quality assurance can be found on the BRFSS website (http://www.cdc.gov/brfss). Methodology: http://www.cdc.gov/brfss/factsheets/pdf/DBS_BRFSS_survey.pdf Glossary: https://chronicdata.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factor-Surveillance-System-BRFSS-H/iuq5-y9ct/data

This map features new data from the US CDC, mapping Behavioral Risk Factors Data at the Census Tract level for the first time.For more info, see the CDC webpage on Chronic Disease and Health Promotion Data & Indicators: https://chronicdata.cdc.gov/health-area/behavioral-risk-factors.NMCDC has built the feature service that runs this map and made it available for sharing on your own AGOL map. It contains 27 adult behavioral risk factors for 206 census tracts in NM's four major cities (Albuquerque, Rio Rancho, Santa Fe and Las Cruces). Feature service information at - http://nmcdc.maps.arcgis.com/home/item.html?id=bd74a088596e48358b22ae76a32a2631#overview "The purpose of the 500 Cities Project is to provide city- and census tract-level small area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use for the largest 500 cities in the United States. These small area estimates will allow cities and local health departments to better understand the burden and geographic distribution of health-related variables in their jurisdictions, and assist them in planning public health interventions. Learn more about the 500 Cities Project(https://www.cdc.gov/500cities/about.htm)."

BackgroundHeart failure (HF) is a critical global health issue, with chronic kidney disease (CKD) as a significant contributing factor. Both primarily affect older adults, with prevalence rising substantially after age 60. This study examined global trends and disparities in CKD-associated HF among older adults from 1990 to 2021.MethodsUtilizing data from the Global Burden of Disease (GBD) 2021, the study analyzed the prevalence and years lived with disability (YLDs) of CKD-associated HF. Joinpoint regression assessed trends from 1990 to 2021 globally, regionally, and nationally. Health inequity analysis, including the slope index of inequality and health inequality concentration index, evaluated disparities across countries.ResultsFrom 1990 to 2021, the prevalence and YLDs of CKD-associated HF increased globally, with an average annual percentage change (AAPC) of 2.21% [95% confidence interval (CI), 2.17–2.25] and 2.20% (95% CI, 2.16–2.24), respectively. Males exhibited higher prevalence and YLDs but demonstrated a slower increase than females. The low-SDI region exhibited the highest burden, while the high-SDI region showed an unfavorable increase. Socioeconomic disparities were decreased but persisted. From 1990 to 2021, the inequality slope index for prevalence decreased from 143.66 (95% CI, 167.68–119.65) to 114.12 (95% CI, 151.59–76.65), whereas the health inequality concentration index improved from −0.21 (95% CI, −0.30 to −0.12) to −0.07 (95% CI, −0.14 to 0) for prevalence.ConclusionThe global burden of CKD-associated HF has increased substantially, with persistent disparities across gender and SDI levels. Strengthening preventive measures and implementing effective interventions are essential to addressing this escalating health challenge.

BackgroundGiven the rising prevalence of chronic diseases and multimorbidity among middle-aged and older individuals in China, it is crucial to explore the patterns of chronic disease multimorbidity and uncover the underlying mechanisms driving the co-existence of multiple chronic conditions.MethodsThis study analyzed data from 19,206 participants in the China Health and Retirement Longitudinal Study (CHARLS 2018). The IsingFit model was used to build the chronic disease co-morbidity network, where nodes represented diseases and edges reflected conditionally independent partial correlations. Community detection identified groups of closely related diseases using the Louvain algorithm. Multivariable linear regression with forward stepwise selection explored factors influencing chronic disease co-morbidity. A random forest model ranked these factors by importance, providing insights into relationships and key contributors.ResultsThis study identified the most frequent multimorbidity pairs in the middle-aged and older adult population as hypertension with arthritis, and digestive diseases with arthritis. Multimorbidities were classified into four subgroups: respiratory diseases, metabolic syndrome, neurological diseases, and digestive diseases. Heart disease showed centrality in the multimorbidity network, while memory-related diseases played a bridging role. Key factors associated with multimorbidity included age, gender, pain, sleep, physical activity, depression, and education. Random forest analysis revealed that age and pain had the greatest impact on multimorbidity development, offering insights for targeted prevention and management strategies.ConclusionThis study systematically analyzed multimorbidity patterns and their influencing factors in the Chinese middle-aged and older adult population. The data were examined at three levels: overall network, key influencing factors, and individual characteristics. Cardio-metabolic diseases were identified as a core component of the multimorbidity network. Advanced age, pain, and depression were found to be independent risk factors affecting the number of multimorbidities, while healthy behaviors acted as significant protective factors. The study enhances understanding of multimorbidity mechanisms and provides a scientific basis for public health interventions, emphasizing the importance of behavioral modification, health education, and social support for high-risk groups.

This is a source dataset for a Let's Get Healthy California indicator at "https://letsgethealthy.ca.gov/. This table displays the prevalence of diabetes in California. It contains data for California only. The data are from the California Behavioral Risk Factor Surveillance Survey (BRFSS). The California BRFSS is an annual cross-sectional health-related telephone survey that collects data about California residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. The BRFSS is conducted by Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. This prevalence rate does not include pre-diabetes, or gestational diabetes. This is based on the question: "Has a doctor, or nurse or other health professional ever told you that you have diabetes?" The sample size for 2014 was 8,832. NOTE: Denominator data and weighting was taken from the California Department of Finance, not U.S. Census. Values may therefore differ from what has been published in the national BRFSS data tables by the Centers for Disease Control and Prevention (CDC) or other federal agencies.

IT: Mortality Rate Attributed to Household and Ambient Air Pollution: Age-standardized: Male data was reported at 20.000 NA in 2016. IT: Mortality Rate Attributed to Household and Ambient Air Pollution: Age-standardized: Male data is updated yearly, averaging 20.000 NA from Dec 2016 (Median) to 2016, with 1 observations. IT: Mortality Rate Attributed to Household and Ambient Air Pollution: Age-standardized: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Health Statistics. Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Medical X Ray Table Market Outlook

The global Medical X Ray Table market size was valued at approximately USD 2.1 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.3% during the forecast period. The increasing prevalence of chronic diseases, the aging population, and advancements in imaging technology are some of the primary factors driving the growth of this market.

One of the significant growth factors for the Medical X Ray Table market is the rising incidence of chronic diseases such as cancer, cardiovascular conditions, and musculoskeletal disorders. As the burden of these diseases continues to escalate globally, there is an increased demand for diagnostic imaging services, which in turn fuels the need for advanced and efficient X-ray tables. Additionally, the aging global population is another critical factor contributing to the market growth. Older adults are more susceptible to conditions requiring medical imaging, thereby creating a sustained demand for high-quality X-ray tables.

Technological advancements in imaging equipment are also contributing significantly to the market's growth. Modern X-ray tables are now equipped with advanced features like motorized controls, digital imaging capabilities, and improved ergonomics, which enhance the overall efficiency and accuracy of diagnostic procedures. Moreover, these innovations help in reducing the time required for imaging, thereby increasing patient throughput and operational efficiency in healthcare settings. Enhanced imaging technologies also aid in better diagnostics, leading to improved patient outcomes, which further drives the adoption of advanced X-ray tables.

Another major factor propelling the growth of the Medical X Ray Table market is the increasing investments in healthcare infrastructure, particularly in emerging economies. Governments and private entities in regions like Asia Pacific and Latin America are investing heavily in modernizing healthcare facilities, which includes upgrading diagnostic imaging equipment. This trend is expected to continue as countries aim to improve their healthcare systems to meet the growing demand for medical services, thereby fostering market growth. Additionally, favorable reimbursement policies for diagnostic imaging procedures in developed regions like North America and Europe are also supporting market expansion.

From a regional perspective, North America holds a significant share of the Medical X Ray Table market, driven largely by the advanced healthcare infrastructure, high adoption rate of new technologies, and significant healthcare expenditure. Europe also represents a substantial portion of the market, with countries like Germany, France, and the UK leading in terms of adoption of advanced imaging technologies. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to increasing healthcare investments, rising awareness about advanced diagnostic techniques, and a growing patient population.

Product Type Analysis

The Medical X Ray Table market is segmented by product type into fixed X-ray tables and mobile X-ray tables. Fixed X-ray tables are traditionally used in clinical settings where high stability and precision are required. These tables are often preferred in large hospitals and diagnostic centers due to their robust construction and ability to integrate with advanced imaging systems. They are designed to support a wide range of diagnostic procedures, making them versatile and essential equipment in healthcare facilities.

Mobile X-ray tables, on the other hand, offer greater flexibility and convenience, especially in settings where patient mobility is an issue. These tables are increasingly being adopted in emergency departments, intensive care units, and during bedside imaging, where transporting patients to radiology departments may not be feasible. The demand for mobile X-ray tables is particularly high in ambulatory surgical centers and specialty clinics, where space constraints and the need for quick, on-the-spot imaging are critical factors. The portability of these tables also makes them ideal for use in temporary medical facilities and field hospitals.

Advancements in design and functionality have significantly enhanced the utility of both fixed and mobile X-ray tables. Modern fixed X-ray tables now come with features like floating table tops, motorized height adjustments, and integrated digital detectors, which improve the ease of use and precision in ima

ObjectiveTo study the correlation between Life’s Essential 8 (LE8) and the occurrence of chronic obstructive pulmonary disease (COPD) among US adults, as well as the association between LE8 and all-cause and cardiovascular disease (CVD) mortality among individuals with COPD.MethodsData from National Health and Nutrition Examination Survey (2005–2018 year) were analyzed. The correlation between LE8 scores and the prevalence of COPD was evaluated using logistic regression models. Additionally, the Cox proportional hazards model was applied to investigate how LE8 scores relate to the risk of mortality from all causes and cardiovascular diseases. To ensure the robustness of the findings, sensitivity analyses and subgroup analyses were performed.ResultsIn the overall population, an inverse relationship was observed between a 10-point increase in LE8 score and the risk of COPD [OR = 0.78, 95%CI (0.75 ~ 0.82), p

In 2021, it was estimated that China had about 141 million diabetics aged from 20 to 79 years, which was the highest number of any country. The figure would very likely climb to 174 million by 2045. Diabetes is one of the leading death causes across the globe.

An overview of diabetes

Diabetes mellitus, commonly known as diabetes, is an incurable chronic health condition in which dangerously high levels of glucose flood the body due to the lack of insulin production (type 1 diabetes) or the body’s inability to use insulin to regulate blood sugar levels (type 2 and gestational diabetes). Globally, the number of people suffering from this chronic disease amounted to 537 million in 2021. The largest number of diabetics were from China, followed by India and Pakistan in that year. In terms of diabetes prevalence, French Polynesia, Mauritius, and Kuwait had the highest rates. With regard to diabetes-related health expenditure, China alone spent over half of the amount spent by the entire Western Pacific region.

Key figures of diabetes in China

Back in the 1980s, less than one percent of the Chinese population was said to have diabetes. In the recent decade, the prevalence rate has jumped to an alarming level, and about one in five of all adult diabetes sufferers worldwide were in China. Records from 2021 show that most of such patients in the country fell within the age group of 20 to 79 years - mainly type 2 diabetes. Some experts point out the nation’s economic growth coupled with unhealthy diets and reduced physical activity as major risk factors which cause type 2 diabetes. It is worth noting that the awareness and control rates of diabetes were relatively low in China compared with the situations in other strong economies.

As of 2022, around **** million adults in the United States were current cigarette smokers. Although this figure is still high, it is significantly lower compared to previous years. For example, in 2011, there were almost ** million smokers in the United States. Smoking demographics in the U.S. Although smoking in the U.S. has decreased greatly over the past few decades, it is still more common among certain demographics than others. For example, men are more likely to be current cigarette smokers than women, with ** percent of men smoking in 2021, compared to ** percent of women. Furthermore, non-Hispanic whites and non-Hispanic Blacks smoke at higher rates than Hispanics and non-Hispanic Asians, with almost ** percent of non-Hispanic whites smoking in 2022, compared to just under **** percent of non-Hispanic Asians. Certain regions and states also have a higher prevalence of smoking than others, with around ** percent of adults in West Virginia considered current smokers, compared to just *** percent in Utah. The health impacts of smoking The decrease in smoking rates in the United States over the past decades is due to many factors, including policies and regulations limiting cigarette advertising, promotion, and sales, price increases for cigarettes, and widespread awareness among the public of the dangers of smoking. According to the CDC, those who smoke are *** to **** times more likely to develop coronary heart disease and stroke and around ** times more likely to develop lung cancer than nonsmokers. In fact, it is estimated that around ** percent of lung cancer deaths in the United States can be attributed to cigarette smoking, as well as ** percent of larynx cancer deaths. Cigarette smokers are also much more likely to develop chronic obstructive pulmonary disease (COPD), with around ** percent of current smokers in the U.S. living with COPD in 2021, compared to just ***** percent of those who had never smoked.

IntroductionAn aging population and the increasing prevalence of chronic conditions challenge healthcare systems in developed countries. In response, there is a growing emphasis on person-centered care, as advocated by the World Health Organization and integrated into national health strategies in countries such as the UK and Sweden. However, transitioning to person-centered care is a complex, long-term process shaped by organizational culture and care environments. These contextual factors play crucial roles in the development and sustainability of person-centered practice, significantly transforming the experiences of both older adults and staff.ObjectiveTo describe how workplace culture within an inpatient hospital department shapes person-centered care practices for older adults with chronic illnesses.MethodsA qualitative, descriptive, exploratory-observational study was performed. Data were collected through participant observation guided by the Workplace Culture Critical Analysis Tool®. In a deductive thematic content analysis, data patterns of meaning were identified. The themes were generated underpinned by the Person-Centered Practice Framework dimensions of prerequisites, the practice environment, and person-centered processes and their respective constructs.ResultsThemes related to all person-centered practice dimensions were identified. Task demands during shifts create tension between routine-oriented work and the holistic, individualized approach required for person-centeredness. The absence of systematic multiprofessional team meetings further exacerbates this issue, limiting collaborative decision-making and personalized care planning. The contrasts in some subthemes may be related to discrepancies in the care provided by different professionals.ConclusionThis study highlights the tension between routine-driven care and individualized approaches. Addressing identified challenges, such as formalizing multiprofessional meetings and enhancing reflective practices, is crucial for advancing person-centered care in this setting.

The mortality rate from influenza in the United States is by far highest among those aged 65 years and older. During the 2023-2024 flu season, the mortality rate from influenza for this age group was around 32.1 per 100,000 population. The burden of influenza The impact of influenza in the U.S. varies from season to season, but in the 2023-2024 flu season, there were an estimated 40 million cases. These cases resulted in around 470,000 hospitalizations. Although most people recover from influenza without requiring medical treatment, the disease can be deadly for young children, the elderly, and those with weakened immune systems or chronic illnesses. During the 2023-2024 flu season, around 28,000 people in the U.S. lost their lives due to influenza. Impact of vaccinations The most effective way to prevent influenza is to receive an annual vaccination at the beginning of flu season. Flu vaccines are safe and can greatly reduce the burden of the disease. During the 2022-2023 flu season, vaccinations prevented around 2,479 deaths among those aged 65 years and older. Although flu vaccines are usually cheap and easily accessible, every year a large share of the population in the U.S. still does not get vaccinated. For example, during the 2022-2023 flu season, only about 35 percent of those aged 18 to 49 years received a flu vaccination.

BackgroundPopulation aging challenges health care systems due to the high prevalence and impact of multimorbidity in older adults. Studies on multimorbidity in Shenzhen have primarily focused on the quantity of multimorbidity, lacking in-depth exploration of multimorbidity patterns.MethodsBased on baseline data from the Shenzhen aging-related disease cohort, this study analyzed information from 8,911 people aged 60 and above after excluding missing and abnormal values from interview results. Using self-organizing map combined with weighted k-means, the distribution of diseases in the population was visualized, dividing the overall population into four clusters. The study also analyzed comorbidity and association rules for each cluster.ResultThis study found a high prevalence of cardiometabolic comorbidities among the older adult in Shenzhen, reaching 15.83%, and detailed the distribution of specific comorbidity combinations. Hypertension had a high prevalence and was the most common factor in comorbidities among Shenzhen’s older adult. Additionally, hyperuricemia was included as a disease to explore its multimorbidity patterns with other chronic conditions.ConclusionThe study found that multimorbidity is prevalent among the older adult in Shenzhen and explored their patterns, suggesting that Shenzhen should enhance screening and integrated management of high-risk groups and implement public health interventions to alleviate the multimorbidity burden.