In 2024, around 35 percent of college or university students surveyed in the United States reported they had been diagnosed with anxiety at some point in their life. This statistic shows the percentage of college students in the U.S. who reported having ever been diagnosed by a healthcare or mental health professional with select ongoing or chronic conditions, as of fall 2024.

This statistic shows the percentage of seniors in developed countries with a select number of chronic health conditions as of 2021, by country. According to the data, among the elderly in Canada, 30 percent had 3 or more chronic health conditions and 15 percent had no chronic health conditions.

Surveys from 2020 to 2022 found that around 44 percent of adults in the United States with a graduate degree had not been diagnosed with a chronic health condition, compared to just 30 percent of those with no postsecondary education. This statistic shows the percentage of adults in the United States who had not been diagnosed with a chronic health condition in 2020-2022, by education level.

(Source: CMS Medicare Chronic Conditions Public Use File, January 2021)

Distribution of observed frequencies, percentages, mean and standard deviation of responses to each item on the HLS-EU-PT-Q16 scale (N = 1979).

Distribution of observed frequencies, percentages, mean and standard deviation of responses to each item on the Navigational Health Literacy scale (N = 1979).

In 2022, around 5.8 percent of children in the U.S. experienced chronic school absenteeism in the past 12 months due to health-related problems. This statistic depicts the percentage of children in the United States aged 5 to 17 who faced chronic school absenteeism due to health-related issues in the past 12 months in 2022, by age group.

Objectives: Digital interventions can offer accessible and scalable treatment for chronic conditions, though often focus separately on physical or mental health. People accessing digital health services may live with multiple conditions or experience overlapping symptoms. This study aimed to describe the breadth and characteristics of chronic health conditions and self-reported disability among routine users of a digital mental health service, and to examine related motivations to engage with digital mental health interventions. Methods: A cross-sectional survey of adults registered with a digital mental health service in the Australian community (THIS WAY UP) was conducted. Participant demography, chronic health conditions, self-reported disability and motivations for accessing digital treatment were collected and analyzed descriptively. Results: 366 participants responded (77% female, mean age 50 ± 15 years). 71.6% of participants (242/338) reported ≥1 chronic health condition and one-third reported multimorbidity (112/338, 33.1%). Chronic pain, musculoskeletal and connective tissue disorders were most common. 26.9% of respondents (90/334) reported a disability, most commonly physical disabilities. 95% of those with chronic conditions reported negative mental health effects and 46% reported heightened interest in digital mental health treatments because of their condition. Primary motivations for digital service use were receiving a recommendation from a health professional and service accessibility. Discussion: People who access digital mental health services in routine care report high rates of heterogenous chronic illness and related disability. There is interest in accessible digital treatments to support mental health at scale among people who live with varied chronic conditions and disabilities. Heterogenous chronic health conditions and disability are prevalent among people who engage with digital mental health interventions in the community.Approximately three-quarters of people (72%) who access digital mental health interventions have at least one chronic condition, and approx. one quarter (27%) have a disability.The accessibility of digital mental health treatments appealed to people with chronic conditions and/or disabilities.Digital mental health services may have a role to play in supporting mental health and wellbeing at scale among people with varied, disabling chronic conditions. Heterogenous chronic health conditions and disability are prevalent among people who engage with digital mental health interventions in the community. Approximately three-quarters of people (72%) who access digital mental health interventions have at least one chronic condition, and approx. one quarter (27%) have a disability. The accessibility of digital mental health treatments appealed to people with chronic conditions and/or disabilities. Digital mental health services may have a role to play in supporting mental health and wellbeing at scale among people with varied, disabling chronic conditions.

In Portugal, in 2024, **** percent of people with chronic diseases or long-standing health problems (i.e., a health problem that has lasted or is likely to last for at least six months) had no level of education. As numbers show, the share of people with chronic diseases or long-standing health problems tends to diminish with an increased level of education.

The indicator measures the standardised death rate of chronic diseases. Chronic diseases included in the indicator are malignant neoplasms, diabetes mellitus, ischaemic heart diseases, cerebrovascular diseases, chronic lower respiratory diseases and chronic liver diseases (International Classification of Diseases (ICD) codes C00 to C97, E10 to E14, I20 to I25, I60 to I69 and J40 to J47). Death due to chronic diseases is considered premature if it occurs before the age of 65. The rate is calculated by dividing the number of people under 65 dying due to a chronic disease by the total population under 65. Data on causes of death (COD) refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. The data are presented as standardised death rates, meaning they are adjusted to a standard age distribution in order to measure death rates independently of different age structures of populations. This approach improves comparability over time and between countries. The standardised death rates used here are calculated on the basis of the standard European population referring to the residents of the countries.

Modelled estimates of chronic diseases including: arthritis, asthma, circulatory system diseases, chronic obstructive pulmonary disease, type 2 diabetes, high cholesterol, hypertension disease, mental and behavioural problems for males and females, musculoskeletal system diseases, respiratory system diseases, and arthritis for 2011-13 by by SA2. SA2 data for this indicator are derived from Population Health Area (PHA) data. PHAs are comprised of one or more whole SA2s. A full listing of SA2s and what percentage of their corresponding PHA has been allocated to them can be found at the following link: http://publichealth.gov.au/AURIN/phidu_PHA_to_SA2_concordance.xls

ObjectiveThe first year of university is a particularly stressful period and can impact academic performance and students’ health. The aim of this study was to evaluate the health and lifestyle of undergraduates and assess risk factors associated with psychiatric symptoms.Materials and methodsBetween September 2012 and June 2013, we included all undergraduate students who underwent compulsory a medical visit at the university medical service in Nice (France) during which they were screened for potential diseases during a diagnostic interview. Data were collected prospectively in the CALCIUM database (Consultations Assistés par Logiciel pour les Centres Inter-Universitaire de Médecine) and included information about the students’ lifestyle (living conditions, dietary behavior, physical activity, use of recreational drugs). The prevalence of psychiatric symptoms related to depression, anxiety and panic attacks was assessed and risk factors for these symptoms were analyzed using logistic regression.ResultsA total of 4,184 undergraduates were included. Prevalence for depression, anxiety and panic attacks were 12.6%, 7.6% and 1.0%, respectively. During the 30 days preceding the evaluation, 0.6% of the students regularly drank alcohol, 6.3% were frequent-to-heavy tobacco smokers, and 10.0% smoked marijuana. Dealing with financial difficulties and having learning disabilities were associated with psychiatric symptoms. Students who were dissatisfied with their living conditions and those with poor dietary behavior were at risk of depression. Being a woman and living alone were associated with anxiety. Students who screened positively for any psychiatric disorder assessed were at a higher risk of having another psychiatric disorder concomitantly.ConclusionThe prevalence of psychiatric disorders in undergraduate students is low but the rate of students at risk of developing chronic disease is far from being negligible. Understanding predictors for these symptoms may improve students’ health by implementing targeted prevention campaigns. Further research in other French universities is necessary to confirm our results.

Ratio: Percentage of high school student survey respondents who have used cigarettes on one or more days in the 30 days preceding the survey.

Definition: Percentage of high school (9th-12th grade) students who have used cigarettes on one or more days in the 30 days preceding the survey

Data Source:

1) Youth Risk Behavior Surveillance System, National Center for Chronic Disease Prevention and Health Promotion

2) Youth Tobacco Survey, Office of Tobacco Control, New Jersey Department of Health *This survey is conducted every 2 or 3 years

In 2021, around 26 percent of older millennials in the United States reported having chronic migraine headaches, while only around 16 percent of adults in the general public reported the same. This statistic illustrates the percentage of adults and older millennials in the United States with select chronic medical conditions in 2021.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of obesity, inactivity and inactivity/obesity-related illnesses. Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.The analysis incorporates data relating to the following:Obesity/inactivity-related illnesses (asthma, cancer, chronic kidney disease, coronary heart disease, depression, diabetes mellitus, hypertension, stroke and transient ischaemic attack)Excess weight in children and obesity in adults (combined)Inactivity in children and adults (combined)The analysis was designed with the intention that this dataset could be used to identify locations where investment could encourage greater levels of activity. In particular, it is hoped the dataset will be used to identify locations where the creation or improvement of accessible green/blue spaces and public engagement programmes could encourage greater levels of outdoor activity within the target population, and reduce the health issues associated with obesity and inactivity.ANALYSIS METHODOLOGY1. Obesity/inactivity-related illnessesThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to:- Asthma (in persons of all ages)- Cancer (in persons of all ages)- Chronic kidney disease (in adults aged 18+)- Coronary heart disease (in persons of all ages)- Depression (in adults aged 18+)- Diabetes mellitus (in persons aged 17+)- Hypertension (in persons of all ages)- Stroke and transient ischaemic attack (in persons of all ages)This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.For each of the above illnesses, the percentage of each MSOA’s population with that illness was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of patients registered with each GP that have that illness The estimated percentage of each MSOA’s population with each illness was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with each illness, within the relevant age range.For each illness, each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have that illnessB) the NUMBER of people within that MSOA who are estimated to have that illnessAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have that illness, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from an illness, and where those people make up a large percentage of the population, indicating there is a real issue with that illness within the population and the investment of resources to address that issue could have the greatest benefits.The scores for each of the 8 illnesses were added together then converted to a relative score between 1 – 0 (1 = worst, 0 = best), to give an overall score for each MSOA: a score close to 1 would indicate that an area has high predicted levels of all obesity/inactivity-related illnesses, and these are areas where the local population could benefit the most from interventions to address those illnesses. A score close to 0 would indicate very low predicted levels of obesity/inactivity-related illnesses and therefore interventions might not be required.2. Excess weight in children and obesity in adults (combined)For each MSOA, the number and percentage of children in Reception and Year 6 with excess weight was combined with population data (up to age 17) to estimate the total number of children with excess weight.The first part of the analysis detailed in section 1 was used to estimate the number of adults with obesity in each MSOA, based on GP-level statistics.The percentage of each MSOA’s adult population (aged 18+) with obesity was estimated, using GP-level data (see section 1 above). This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of adult patients registered with each GP that are obeseThe estimated percentage of each MSOA’s adult population with obesity was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of adults in each MSOA with obesity.The estimated number of children with excess weight and adults with obesity were combined with population data, to give the total number and percentage of the population with excess weight.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have excess weight/obesityB) the NUMBER of people within that MSOA who are estimated to have excess weight/obesityAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have excess weight/obesity, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from excess weight/obesity, and where those people make up a large percentage of the population, indicating there is a real issue with that excess weight/obesity within the population and the investment of resources to address that issue could have the greatest benefits.3. Inactivity in children and adultsFor each administrative district, the number of children and adults who are inactive was combined with population data to estimate the total number and percentage of the population that are inactive.Each district was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that district who are estimated to be inactiveB) the NUMBER of people within that district who are estimated to be inactiveAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the district predicted to be inactive, compared to other districts. In other words, those are areas where a large number of people are predicted to be inactive, and where those people make up a large percentage of the population, indicating there is a real issue with that inactivity within the population and the investment of resources to address that issue could have the greatest benefits.Summary datasetAn average of the scores calculated in sections 1-3 was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer the score to 1, the greater the number and percentage of people suffering from obesity, inactivity and associated illnesses. I.e. these are areas where there are a large number of people (both children and adults) who are obese, inactive and suffer from obesity/inactivity-related illnesses, and where those people make up a large percentage of the local population. These are the locations where interventions could have the greatest health and wellbeing benefits for the local population.LIMITATIONS1. For data recorded at the GP practice level, data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Levels of obesity, inactivity and associated illnesses: Summary (England). Areas with data missing’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children, we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of

Ratio: Percentage of high school student survey respondents who have used cigarettes on one or more days in the 30 days preceding the survey.

Definition: Percentage of high school (9th-12th grade) students who have used cigarettes on one or more days in the 30 days preceding the survey

Data Source:

1) Youth Risk Behavior Surveillance System, National Center for Chronic Disease Prevention and Health Promotion

2) Youth Tobacco Survey, Office of Tobacco Control, New Jersey Department of Health *This survey is conducted every 2 or 3 years

The Chronic Disease Management Platform market is experiencing robust growth, driven by the rising prevalence of chronic conditions globally, escalating healthcare costs, and the increasing adoption of telehealth solutions. The market's expansion is fueled by several key trends, including the development of sophisticated remote patient monitoring (RPM) technologies, the integration of artificial intelligence (AI) for personalized care, and the growing demand for convenient and accessible healthcare options. Furthermore, the increasing focus on value-based care models is incentivizing healthcare providers to adopt these platforms to improve patient outcomes and reduce overall healthcare expenditures. While data limitations prevent precise quantification, a reasonable estimation, based on industry reports showing similar markets expanding at 15-20% CAGR, suggests a market size around $15 billion in 2025, potentially reaching $30 billion by 2030. This growth, however, faces certain restraints. High initial investment costs, concerns regarding data security and privacy, and the need for robust infrastructure and digital literacy among both patients and healthcare providers pose challenges to wider market penetration. Nonetheless, ongoing technological advancements, coupled with favorable regulatory landscapes in several regions, are expected to mitigate these challenges and propel continued market growth. The segmentation of the market reflects various platform types, such as those focused on diabetes, cardiovascular diseases, or mental health, and is further diversified by deployment models (cloud-based, on-premise) and end-users (hospitals, clinics, home healthcare providers). The competitive landscape is characterized by a mix of established players and innovative startups. Companies like Humana and Omada Health represent established players leveraging their existing healthcare networks and patient bases. Meanwhile, smaller, agile companies such as EveryDose and Pathmate are focusing on niche applications and leveraging technological advancements to gain market share. The geographical distribution likely shows higher penetration in developed regions like North America and Europe, driven by higher healthcare spending and technological adoption rates, while emerging markets are expected to witness significant growth in the coming years as healthcare infrastructure improves and digital literacy increases. The market's future trajectory hinges on sustained investment in research and development, increased collaboration between stakeholders, and the continued development of user-friendly, secure, and effective platforms that address the diverse needs of both patients and healthcare providers.

This is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. This table displays the percentage of women ages 18-44 who have received preventative services. 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 the Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. The column percentages are weighted to the 2010 California Department of Finance (DOF) population statistics. Population estimates were obtained from the CA DOF for age, race/ethnicity, and sex. 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.

Utilising digital health technologies to aid individuals in managing chronic diseases offers a promising solution to overcome health service barriers such as access and affordability. However, their effectiveness depends on adoption and sustained use, influenced by user preferences.

This study quantifies the preferences of individuals with chronic heart disease for features of a mobile health app to self-navigate their disease condition.

We conducted an unlabelled online choice survey among adults over 18 with chronic heart disease living in Australia, recruited via an online survey platform. Four app attributes—ease of navigation, monitoring of blood pressure and heart rhythm, health education, and symptom diary maintenance—were systematically chosen through a multi-stage process. This process involved a literature review, stakeholder interviews, and expert panel discussions. Participants chose a preferred mobile app out of three alternatives, app A, app B, or neither. A D-optimal design was developed using Ngene software, informed by Bayesian priors derived from pilot survey data. Latent class model (LCM) analysis was conducted using Nlogit software. We also estimated attribute importance and anticipated adoption rates for three app versions.

The dataset contains hospitalization counts and rates, statewide and by county, for 10 ambulatory care sensitive conditions plus 4 composite measures. Hospitalizations due to these medical conditions are potentially preventable through access to high-quality outpatient care. The conditions include: diabetes short-term complications; diabetes long-term complications; chronic obstructive pulmonary disease (COPD) or asthma in older adults (age 40 and over); hypertension; heart failure; community-acquired pneumonia; urinary tract infection; uncontrolled diabetes; asthma in younger adults (age 18-39); and lower-extremity amputation among patients with diabetes. The composite measures include overall, acute conditions, chronic conditions, and diabetes (new, 2016). The data provides a good starting point for assessing quality of health services in the community. The data does not measure hospital quality. Note: In 2015, HCAI (formerly OSHPD) only released the first three quarters of data due to a change in the reporting of diagnoses from ICD-9-CM to ICD-10-CM codes, effective October 1, 2015. Due to the significant differences resulting from the code change, the ICD-9-CM data is distinguished from the ICD-10-CM data in the data file beginning in 2016.