State and Local Public Health Departments in the United States Governmental public health departments are responsible for creating and maintaining conditions that keep people healthy. A local health department may be locally governed, part of a region or district, be an office or an administrative unit of the state health department, or a hybrid of these. Furthermore, each community has a unique "public health system" comprising individuals and public and private entities that are engaged in activities that affect the public's health. (Excerpted from the Operational Definition of a functional local health department, National Association of County and City Health Officials, November 2005) Please reference http://www.naccho.org/topics/infrastructure/accreditation/upload/OperationalDefinitionBrochure-2.pdf for more information. Facilities involved in direct patient care are intended to be excluded from this dataset; however, some of the entities represented in this dataset serve as both administrative and clinical locations. This dataset only includes the headquarters of Public Health Departments, not their satellite offices. Some health departments encompass multiple counties; therefore, not every county will be represented by an individual record. Also, some areas will appear to have over representation depending on the structure of the health departments in that particular region. Town health officers are included in Vermont and boards of health are included in Massachusetts. Both of these types of entities are elected or appointed to a term of office during which they make and enforce policies and regulations related to the protection of public health. Visiting nurses are represented in this dataset if they are contracted through the local government to fulfill the duties and responsibilities of the local health organization. Since many town health officers in Vermont work out of their personal homes, TechniGraphics represented these entities at the town hall. This is denoted in the [DIRECTIONS] field. Effort was made by TechniGraphics to verify whether or not each health department tracks statistics on communicable diseases. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard HSIP fields populated by TechniGraphics. Double spaces were replaced by single spaces in these same fields. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based on this field, the oldest record dates from 11/18/2009 and the newest record dates from 01/08/2010.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

The dataset provides training information extracted from Home Care Registry (HCR) application. HCR is a web-based registry of all personal care and home health aides who have successfully completed a personal care or home health aide training program approved by either the New York State Department of Health (NYSDOH) or the New York State Education Department (NYSED). This registry is the central repository of the individuals who have successfully completed State-approved education or training programs for Home Health Aides and Personal Care Aides. The Training Programs are the sources for most of the training information available in HCR. This dataset is refreshed on monthly basis.

In 2023, Alabama and Michigan had the highest rate of Medicare Advantage (MA) penetration, meaning that ** percent of Medicare beneficiaries in these three states were enrolled in MA plans rather than traditional Medicare plans. The national average was ** percent that year. This statistic depicts the leading 10 U.S. states by percentage of Medicare beneficiaries enrolled in a Medicare Advantage plan in 2024.

Children accounted for 36.5 percent of Medicaid enrollees in 2021, which was the largest share of all enrollment groups. The elderly and persons with disabilities had the smallest shares, but together they accounted for more than half of all Medicaid expenditure.

Medicaid expenditures per enrollee Medicaid is a joint federal and state health care program in the United States. The program provides medical coverage to millions of Americans and supports a variety of enrollment groups, particularly senior citizens and individuals with disabilities. Medicaid per enrollee spending is significantly higher for these two groups because they require more frequent and costly long-term care in the community and nursing homes. In 2022 of the total U.S. health expenditure on home health care, Medicaid paid one-third.

Millions of Americans are uninsured The United States has a multi-payer health care system, meaning that some Americans will be covered by private health insurance, and others will be covered by a government program such as Medicaid. However, approximately 27.6 million people in the U.S. had no health insurance in 2021, and should they require health care, they would have to pay the full price out of their own pocket. This becomes a real problem for many because the United States has the most expensive health care system in the world.

Children accounted for **** percent of Medicaid enrollees in 2022, which was the largest share of all enrollment groups. The elderly and persons with disabilities had the smallest shares, but together they accounted for ************** of all Medicaid expenditure. Medicaid expenditures per enrollee Medicaid is a joint federal and state health care program in the United States. The program provides medical coverage to millions of Americans and supports a variety of enrollment groups, particularly senior citizens and individuals with disabilities. Medicaid per enrollee spending is significantly higher for these two groups because they require more frequent and costly long-term care in the community and nursing homes. In 2022 of the total U.S. health expenditure on home health care, Medicaid paid one-third. Millions of Americans are uninsured The United States has a multi-payer health care system, meaning that some Americans will be covered by private health insurance, and others will be covered by a government program such as Medicaid. However, approximately **** million people in the U.S. had no health insurance in 2021, and should they require health care, they would have to pay the full price out of their own pocket. This becomes a real problem for many because the United States has the most expensive health care system in the world.

The item published is a dataset that provides the raw data and original code to generate Figure 4 in the research paper, Correlative non-destructive techniques to investigate ageing and orientation effects in automotive Li-ion pouch cells, https://doi.org/10.5522/04/c.6868027 of which I am first author. The measurements and following data analysis took place between January 2022 – November 2022.

The figure illustrates the ultrasonic mapping measurements of pouch cells that have been extracted from electric vehicles and have been aged in real-world conditions. The degradation of the cells was measured using four different complementary characterisation measurement techniques, one of which was ultrasonic mapping.

The ultrasonic mapping measurements were performed using an Olympus Focus PX phased-array instrument (Olympus Corp., Japan) with a 5 MHz 1D linear phased array probe consisting of 64 transducers. The transducer had an active aperture of 64 mm with an element pitch (centre-to-centre distance between elements) of 1 mm. The cell was covered with ultrasonic couplant (Fannin UK Ltd.), prior to every scan to ensure good acoustic transmission. The transducer was moved along the length of each cell at a fixed pressure using an Olympus GLIDER 2-axis encoded scanner with the step size set at 1 mm to give a resolution of ca. 1 mm2. Due to the large size of the cells, the active aperture of the probe was wide enough to cover 1/3 the width, meaning that three measurements for each cell were taken and the data was combined to form the colour maps.

Data from the ultrasonic signals were analysed using FocusPC software. The waveforms recorded by the transducer were exported and plotted using custom Python code to compare how the signal changes at different points in the cell. For consistency, a specific ToF range was selected for all cells, chosen because it is where the part of the waveform, known as the ‘echo-peak’, is located.74 The echo-peak is useful to monitor as it is where the waveform has travelled the whole way through the cell and reflected from the back surface, so characterising the entire cell. The maximum amplitude of the ultrasonic signal within this ToF range, at each point, are combined to produce a colour map. The signal amplitude is a percentage proportion of 100 where 100 is the maximum intensity of the signal, meaning that the signal has been attenuated the least as it travels through the cell, and 0 is the minimum intensity. The intensity is absolute and not normalised across all scans, meaning that an amplitude values on different cells can be directly compared. The Pristine cell is a second-generation Nissan Leaf pouch, different to the first-generation aged cells of varying orientation. The authors were not able to acquire an identical first-generation pristine Nissan Leaf cell. Nonetheless, it was expected that the Pristine cell would contain a uniform internal structure regardless of the specific chemistry and this would be identified in an ultrasound map consisting of a single colour (or narrow colour range).

This dataset provides annual data on paediatric TB patients notified, first reported in the 2020 TB report. The notification is based on the diagnosing Primary Health Institution (PHI), meaning it reflects the cases initially recorded at the diagnosing facility. It also contains details of net paediatric TB patients notified (since 2021 report) based on the current PHI rather than the diagnosing facility. Unlike gross notifications, this dataset accounts for patient movement, meaning it includes only those TB patients currently in the facility, district, or state after adjustments for transfers in and out. The number of paediatric TB patients who have started treatment after notificatiion has also been captured in the dataset (started in 2021 report).

The North American and Caribbean region spent some 439 billion U.S. dollars on health care for people with diabetes in 2024. At that time, health care expenditures due to diabetes were the lowest in Africa and Southeast Asia. Global healthcare expenditure In 2024, diabetes-related health expenditure was by far the highest in the United States, with roughly 404.5 billion U.S. dollars, followed by China with 169 billion and Brazil with 45 billion U.S. dollars. Globally, an estimated one trillion U.S. dollars was spent on diabetes-related healthcare in 2024, meaning around 40 percent of the global expenditures for the treatment of diabetes was spent in the United States. Global healthcare spending for the condition is projected to grow to an estimated 1.04 trillion U.S. dollars by 2050. Diabetes-related mortality In 2024, around 1.2 million people died as a result of diabetes before the age of 80 in the Western Pacific, while the Europe saw around 433 thousand diabetes-related deaths. The Western Pacific also has the highest number of people between the ages of 20 and 79 with undiagnosed diabetes: in 2024, there were about 107.6 million undiagnosed diabetes cases in the Western Pacific region, while approximately 45.6 million Southeast Asians had undiagnosed diabetes.

NOTE: This dataset is no longer being updated but is being kept for historical reference. For current data on respiratory illness visits and respiratory laboratory testing data please see Influenza, COVID-19, RSV, and Other Respiratory Virus Laboratory Surveillance and Inpatient, Emergency Department, and Outpatient Visits for Respiratory Illnesses. This is the place to look for important information about how to use this dataset, so please expand this box and read on! This is the source data for some of the metrics available at https://www.chicago.gov/city/en/sites/covid-19/home/reopening-chicago.html#reopeningmetrics. For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19. The National Syndromic Surveillance Program (NSSP), a collaboration among CDC, federal partners, local and state health departments, and academic and private sector partners, is used to capture information during an Emergency Department (ED) visit. ED data can include information that are collected before cases are diagnosed or laboratory results are confirmed, providing an early warning system for infections, like COVID-19. This dataset includes reports of COVID-19-Like illness (CLI) and COVID-19 diagnosed during an ED visit. CLI is defined as fever and cough or shortness of breath or difficulty breathing with or without the presence of a coronavirus diagnosis code. Visits meeting the CLI definition that also have mention of flu or influenza are excluded. This dataset also includes ED visits among persons who have been diagnosed or laboratory confirmed to have COVID-19. During the initial months of the COVID-19 pandemic COVID-19 diagnoses counts are artificially low, due to varying eligibility requirements and availability of testing. Over the course of the COVID-19 pandemic, public health best practices migrated from focusing on CLI to focusing on diagnosed cases. This dataset originally contained only CLI columns. In June 2021, the diagnosis columns were added, back filled to the start of the pandemic but with the caveat noted above. Roughly simultaneously, updating of the CLI columns was discontinued, although previously existing data were kept. Reflecting the new columns, the name of the dataset was changed from “COVID-Like Illness (CLI) Emergency Department Visits” to “COVID-Like Illness (CLI) and COVID-19 Diagnosis Emergency Department Visits” at the same time. Data Source: Illinois Hospital Emergency Departments reporting to CDPH through the National Syndromic Surveillance Project (NSSP)

The average number of hospital beds available per 1,000 people in the United States was forecast to continuously decrease between 2024 and 2029 by in total 0.1 beds (-3.7 percent). After the eighth consecutive decreasing year, the number of available beds per 1,000 people is estimated to reach 2.63 beds and therefore a new minimum in 2029. Depicted is the number of hospital beds per capita in the country or region at hand. As defined by World Bank this includes inpatient beds in general, specialized, public and private hospitals as well as rehabilitation centers.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the average number of hospital beds available per 1,000 people in countries like Canada and Mexico.

These data contain counts of vaccine preventable disease cases among California residents by county, disease, and year.

The California Department of Public Health (CDPH) maintains a mandatory, passive reporting system for a list(1) of communicable disease cases and outbreaks. The CDPH Immunization Branch conducts surveillance for vaccine preventable diseases. Health care providers and laboratories are mandated to report cases or suspected cases of these communicable diseases to their local health department (LHD). LHDs are also mandated to report these cases to CDPH.

Materials and Methods

Case data sources and inclusion criteria

Data were extracted on communicable disease cases with an estimated onset or diagnosis date from 2001 through the last year indicated, from California Confidential Morbidity Reports and/or Laboratory Reports that were submitted to CDPH and which met the surveillance case definition for that disease.(2) Because of inherent delays in case reporting and depending on the length of follow-up of clinical, laboratory and epidemiologic investigation, cases with eligible onset dates may be added or rescinded after the date of this report.

Definitions

In general, we defined a case as laboratory and/or clinical evidence of infection or disease in a person that satisfied the communicable disease surveillance case definition published by the United States (US) Centers for Disease Control and Prevention (CDC) or by the Council of State and Territorial Epidemiologists (CSTE) at the time the case was reported.

Limitations

Completeness of reporting

The numbers of disease cases in this report are likely to underestimate the true magnitude of disease. Among factors that may contribute to under-reporting are: delays in notification, limited collection or appropriate testing of specimens, health care seeking behavior among ill persons, limited resources and competing priorities in LHDs, and lack of reporting by clinicians and laboratories. Among factors that may contribute to changes in reporting are disease severity, the availability of new or less expensive diagnostic tests, changes in the case definition by CDC or CDPH, changes in mandatory reporting requirements, recent media or public attention, and active surveillance activities. Differential reporting practices among LHDs may also result in inconsistent reporting of patient information.

References

1. California Code of Regulations, Title 17, Sections 2500 and 2505 https://www.cdph.ca.gov/Programs/CID/DCDC/CDPH%20Document%20Library/ReportableDiseases.pdf

2. Center for Disease Control and Prevention, National Notifiable Diseases Surveillance System https://ndc.services.cdc.gov/

Epidemics are associated with increased burden of psychological distress. However, the role of boredom on mental health during epidemic periods has seldom been explored. This study attempted to examine the effect of state boredom on psychological outcomes, and the role of media use and meaning in life among the indirectly exposed Chinese adults in the initial phase of the COVID-19 outbreak. An online survey was administered to 917 Chinese adults on 28 January 2020 (1 week after the official declaration of person-to-person transmission of the coronavirus). Self-report questionnaires were used to assess state boredom, anxiety, depression, stress, media use and meaning in life. Moderated mediation analysis was conducted. Our results indicated that the effect of state boredom on anxiety and stress, but not depression, were mediated by media use and that sense of meaning in life modified this association. Meaning in life served as a risk factor, rather than a protective factor for the negative psychological outcomes when people experienced boredom. The association between boredom and media use was significant for high but not low meaning in life individuals. These findings demonstrated that boredom and media use were associated with an increased burden or psychological distress in the sample. It is important to pay attention to the possible negative impact of boredom and media use during COVID-19, and find more ways to cope with boredom, especially those with high presence of meaning in life.

A. SUMMARY This dataset represents San Francisco COVID-19 related deaths by day. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals for previous days may increase or decrease. More recent data is less reliable.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death data is provided by the California Department of Public Health.

It takes time to process this data. Because of this, death totals may increase or decrease over time.

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET This dataset shows new deaths and cumulative deaths by date of death. New deaths are the count of deaths on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths up to the date listed.

Use the Deaths by Population Characteristics Over Time dataset to see deaths by different subgroups including race/ethnicity, age, and gender.

E. CHANGE LOG

• 9/11/2023 – on this date, we began using an updated definition of a COVID-19 death to align with the California Department of Public Health. This change was applied to COVID-19 deaths retrospectively beginning on 1/1/2023. More information about the recommendation by the Council of State and Territorial Epidemiologists that motivated this change can be found https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">here.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.

This dataset contains two tables on the percent of household overcrowding (> 1.0 persons per room) and severe overcrowding (> 1.5 persons per room) for California, its regions, counties, and cities/towns. Data is from the U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) and U.S. Census American Community Survey (ACS). The table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity: Healthy Communities Data and Indicators Project of the Office of Health Equity. Residential crowding has been linked to an increased risk of infection from communicable diseases, a higher prevalence of respiratory ailments, and greater vulnerability to homelessness among the poor. Residential crowding reflects demographic and socioeconomic conditions. Older-adult immigrant and recent immigrant communities, families with low income and renter-occupied households are more likely to experience household crowding. A form of residential overcrowding known as "doubling up"—co-residence with family members or friends for economic reasons—is the most commonly reported prior living situation for families and individuals before the onset of homelessness. More information about the data table and a data dictionary can be found in the About/Attachments section.The household crowding table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf
The format of the household overcrowding tables is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

This is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. Adult smoking prevalence in California, males and females aged 18+, starting in 2012. Caution must be used when comparing the percentages of smokers over time as the definition of ‘current smoker’ was broadened in 1996, and the survey methods were changed in 2012. Current cigarette smoking is defined as having smoked at least 100 cigarettes in lifetime and now smoking every day or some days. Due to the methodology change in 2012, the Centers for Disease Control and Prevention (CDC) recommend not conducting analyses where estimates from 1984 – 2011 are compared with analyses using the new methodology, beginning in 2012. This includes analyses examining trends and changes over time. (For more information, please see the narrative description.) The California Behavioral Risk Factor Surveillance System (BRFSS) is an on-going telephone survey of randomly selected adults, which collects information on a wide variety of health-related behaviors and preventive health practices related to the leading causes of death and disability such as cardiovascular disease, cancer, diabetes and injuries. Data are collected monthly from a random sample of the California population aged 18 years and older. The BRFSS is conducted by Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. The survey has been conducted since 1984 by the California Department of Public Health in collaboration with the Centers for Disease Control and Prevention (CDC). In 2012, the survey methodology of the California BRFSS changed significantly so that the survey would be more representative of the general population. Several changes were implemented: 1) the survey became dual-frame, with both cell and landline random-digit dial components, 2) residents of college housing were eligible to complete the BRFSS, and 3) raking or iterative proportional fitting was used to calculate the survey weights. Due to these changes, estimates from 1984 – 2011 are not comparable to estimates from 2012 and beyond. Center for Disease Control and Policy (CDC) and recommend not conducting analyses where estimates from 1984 – 2011 are compared with analyses using the new methodology, beginning in 2012. This includes analyses examining trends and changes over time.Current cigarette smoking was defined as having smoked at least 100 cigarettes in lifetime and now smoking every day or some days. Prior to 1996, the definition of current cigarettes smoking was having smoked at least 100 cigarettes in lifetime and smoking now.

This data visualization presents county-level provisional counts for drug overdose deaths based on a current flow of mortality data in the National Vital Statistics System. County-level provisional counts include deaths occurring within the 50 states and the District of Columbia, as of the date specified and may not include all deaths that occurred during a given time period. Provisional counts are often incomplete and causes of death may be pending investigation resulting in an underestimate relative to final counts (see Technical Notes). The provisional data presented on the dashboard below include reported 12 month-ending provisional counts of death due to drug overdose by the decedent’s county of residence and the month in which death occurred. Percentages of deaths with a cause of death pending further investigation and a note on historical completeness (e.g. if the percent completeness was under 90% after 6 months) are included to aid in interpretation of provisional data as these measures are related to the accuracy of provisional counts (see Technical Notes). Counts between 1-9 are suppressed in accordance with NCHS confidentiality standards. Provisional data presented on this page will be updated on a quarterly basis as additional records are received. Technical Notes Nature and Sources of Data Provisional drug overdose death counts are based on death records received and processed by the National Center for Health Statistics (NCHS) as of a specified cutoff date. The cutoff date is generally the first Sunday of each month. National provisional estimates include deaths occurring within the 50 states and the District of Columbia. NCHS receives the death records from the state vital registration offices through the Vital Statistics Cooperative Program (VSCP). The timeliness of provisional mortality surveillance data in the National Vital Statistics System (NVSS) database varies by cause of death and jurisdiction in which the death occurred. The lag time (i.e., the time between when the death occurred and when the data are available for analysis) is longer for drug overdose deaths compared with other causes of death due to the time often needed to investigate these deaths (1). Thus, provisional estimates of drug overdose deaths are reported 6 months after the date of death. Provisional death counts presented in this data visualization are for “12 month-ending periods,” defined as the number of deaths occurring in the 12 month period ending in the month indicated. For example, the 12 month-ending period in June 2020 would include deaths occurring from July 1, 2019 through June 30, 2020. The 12 month-ending period counts include all seasons of the year and are insensitive to reporting variations by seasonality. These provisional counts of drug overdose deaths and related data quality metrics are provided for public health surveillance and monitoring of emerging trends. Provisional drug overdose death data are often incomplete, and the degree of completeness varies by jurisdiction and 12 month-ending period. Consequently, the numbers of drug overdose deaths are underestimated based on provisional data relative to final data and are subject to random variation. Cause of Death Classification and Definition of Drug Deaths Mortality statistics are compiled in accordance with the World Health Organizations (WHO) regulations specifying that WHO member nations classify and code causes of death with the current revision of the International Statistical Classification of Diseases and Related Health Problems (ICD). ICD provides the basic guidance used in virtually all countries to code and classify causes of death. It provides not only disease, injury, and poisoning categories but also the rules used to select the single underlying cause of death for tabulation from the several diagnoses that may be reported on a single death certificate, as well as definitions, tabulation lists, the format of the death certificate, and regul

In 2024, about ** percent of respondents in the United States that the term "fresh" best defines healthy food to them. This was the most frequently chosen definition. The term "low in sugar" was the third most frequently chosen definition for healthy foods, with nearly ** percent of respondents choosing this definition.

Healthy eating patterns in the U.S.

High-protein and mindful eating are the two most common eating patterns among U.S. consumers. These two eating patterns are followed by about ** and ** percent of surveyed consumers, respectively. Other popular eating patterns included intermittent fasting, calorie-counting, and clean eating. Among those consumers who follow a specific eating pattern or diet, more than ** percent stated that their motivation is to protect their long-term health or to prevent future health conditions. Additionally, eating healthier is the most commonly made change by Americans who adjust their diet to try to manage or reduce stress, as stated by more than half of consumers who have tried to reduce their stress levels.

Sugar consumption in the U.S.

Excessive sugar consumption is one of the things that may prevent people from having a healthy diet. Over 11 million metric tons of sugar are consumed by Americans annually. The consumption of sugar in the U.S. has steadily increased during the last 14 years. The annual sugar consumption is now about ********* metric tons higher than it was in 2010/11. Nonetheless, approximately ********** of American consumers state that they are trying to limit sugars in their diet. Moreover, about ** percent of consumers state that they attempt to avoid sugar completely. Trying to improve the diet in general is the most common reason for limiting or avoiding sugars among U.S. consumers. Avoiding gaining weight, preventing a health condition, and losing weight are also among the top reasons.