Among countries with the highest number of overseas Chinese on each continent, the largest Chinese diaspora community is living in Indonesia, numbering more than ten million people. Most of these people are descendants from migrants born in China, who have moved to Indonesia a long time ago. On the contrary, a large part of overseas Chinese living in Canada and Australia have arrived in these countries only during the last two decades. China as an emigration country Many Chinese people have emigrated from their home country in search of better living conditions and educational chances. The increasing number of Chinese emigrants has benefited from loosened migration policies. On the one hand, the attitude of the Chinese government towards emigration has changed significantly. Overseas Chinese are considered to be strong supporters for the overall strength of Chinese culture and international influence. On the other hand, migration policies in the United States and Canada are changing with time, expanding migration opportunities for non-European immigrants. As a result, China has become one of the world’s largest emigration countries as well as the country with the highest outflows of high net worth individuals. However, the mass emigration is causing a severe loss of homegrown talents and assets. The problem of talent and wealth outflow has raised pressing questions to the Chinese government, and a solution to this issue is yet to be determined. Popular destinations among Chinese emigrants Over the last decades, English speaking developed countries have been popular destinations for Chinese emigrants. In 2022 alone, the number of people from China naturalized as U.S. citizens had amounted to over 27,000 people, while nearly 68,000 had obtained legal permanent resident status as “green card” recipients. Among other popular immigration destinations for Chinese riches are Canada, Australia, Europe, and Singapore.

New York was the most populous state in the union in the year 1900. It had the largest white population, for both native born and foreign born persons, and together these groups made up over 7.1 million of New York's 7.2 million inhabitants at this time. The United States' industrial centers to the north and northeast were one of the most important economic draws during this period, and states in these regions had the largest foreign born white populations. Ethnic minorities Immigration into the agricultural southern states was much lower than the north, and these states had the largest Black populations due to the legacy of slavery - this balance would begin to shift in the following decades as a large share of the Black population migrated to urban centers to the north during the Great Migration. The Japanese and Chinese populations at this time were more concentrated in the West, as these states were the most common point of entry for Asians into the country. The states with the largest Native American populations were to the west and southwest, due to the legacy of forced displacement - this included the Indian Territory, an unorganized and independent territory assigned to the Native American population in the early 1800s, although this was incorporated into Oklahoma when it was admitted into the union in 1907. Additionally, non-taxpaying Native Americans were historically omitted from the U.S. Census, as they usually lived in separate communities and could not vote or hold office - more of an effort was made to count all Native Americans from 1890 onward, although there are likely inaccuracies in the figures given here. Changing distribution Internal migration in the 20th century greatly changed population distribution across the country, with California and Florida now ranking among the three most populous states in the U.S. today, while they were outside the top 20 in 1900. The growth of Western states' populations was largely due to the wave of internal migration during the Great Depression, where unemployment in the east saw many emigrate to "newer" states in search of opportunity, as well as significant immigration from Latin America (especially Mexico) and Asia since the mid-1900s.

Total current population for China in 2025 is 1,424,381,924, a 0.06% decline from 2024.

<ul style='margin-top:20px;'>

<li>Total population for China in 2024 was <strong>1,425,178,782</strong>, a <strong>1.03% increase</strong> from 2023.</li>
<li>Total population for China in 2023 was <strong>1,410,710,000</strong>, a <strong>0.1% decline</strong> from 2022.</li>
<li>Total population for China in 2022 was <strong>1,412,175,000</strong>, a <strong>0.01% decline</strong> from 2021.</li>
</ul>Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.

This statistics shows the leading metropolitan areas in the United States in 2023 with the highest percentage of Asian population. Among the 81 largest metropolitan areas, Urban Honolulu, Hawaii was ranked first with **** percent of residents reporting as Asian in 2023.

This graph shows the population of the U.S. by race and ethnic group from 2000 to 2023. In 2023, there were around 21.39 million people of Asian origin living in the United States. A ranking of the most spoken languages across the world can be accessed here. U.S. populationCurrently, the white population makes up the vast majority of the United States’ population, accounting for some 252.07 million people in 2023. This ethnicity group contributes to the highest share of the population in every region, but is especially noticeable in the Midwestern region. The Black or African American resident population totaled 45.76 million people in the same year. The overall population in the United States is expected to increase annually from 2022, with the 320.92 million people in 2015 expected to rise to 341.69 million people by 2027. Thus, population densities have also increased, totaling 36.3 inhabitants per square kilometer as of 2021. Despite being one of the most populous countries in the world, following China and India, the United States is not even among the top 150 most densely populated countries due to its large land mass. Monaco is the most densely populated country in the world and has a population density of 24,621.5 inhabitants per square kilometer as of 2021. As population numbers in the U.S. continues to grow, the Hispanic population has also seen a similar trend from 35.7 million inhabitants in the country in 2000 to some 62.65 million inhabitants in 2021. This growing population group is a significant source of population growth in the country due to both high immigration and birth rates. The United States is one of the most racially diverse countries in the world.

The US Census Bureau defines Asian as "A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent, including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam. This includes people who reported detailed Asian responses such as: Indian, Bangladeshi, Bhutanese, Burmese, Cambodian, Chinese, Filipino, Hmong, Indonesian, Japanese, Korean, Laotian, Malaysian, Nepalese, Pakistani, Sri Lankan, Taiwanese, Thai, Vietnamese, Other Asian specified, Other Asian not specified.". 2020 Census block groups for the Wichita / Sedgwick County area, clipped to the county line. Features were extracted from the 2020 State of Kansas Census Block Group shapefile provided by the State of Kansas GIS Data Access and Support Center (https://www.kansasgis.org/index.cfm).Change in Population and Housing for the Sedgwick County area from 2010 - 2020 based upon US Census. Census Blocks from 2010 were spatially joined to Census Block Groups from 2020 to compare the population and housing figures. This is not a product of the US Census Bureau and is only available through City of Wichita GIS. Please refer to Census Block Groups for 2010 and 2020 for verification of all data Standard block groups are clusters of blocks within the same census tract that have the same first digit of their 4-character census block number. For example, blocks 3001, 3002, 3003… 3999 in census tract 1210.02 belong to Block Group 3. Due to boundary and feature changes that occur throughout the decade, current block groups do not always maintain these same block number to block group relationships. For example, block 3001 might move due to a change in the census tract boundary. Even if the block is no longer in block group 3, the block number (3001) will not change. However, the identification string (GEOID20) for that block, identifying block group 3, would remain the same in the attribute information in the TIGER/Line Shapefiles because block identification strings are always built using the decennial geographic codes.Block groups delineated for the 2020 Census generally contain between 600 and 3,000 people. Local participants delineated most block groups as part of the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated block groups only where a local or tribal government declined to participate or where the Census Bureau could not identify a potential local participant.A block group usually covers a contiguous area. Each census tract contains at least one block group and block groups are uniquely numbered within census tract. Within the standard census geographic hierarchy, block groups never cross county or census tract boundaries, but may cross the boundaries of county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian, Alaska Native, and Native Hawaiian areas.Block groups have a valid range of 0 through 9. Block groups beginning with a zero generally are in coastal and Great Lakes water and territorial seas. Rather than extending a census tract boundary into the Great Lakes or out to the 3-mile territorial sea limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore.

In the late 20th century, the number of Asians obtaining permanent resident status in the U.S. rose significantly. For decades, migration from Asia had been blocked through the Chinese Exclusion Act of 1882, which was implemented after an influx of Chinese immigrants in the middle of the century. The Act was then expanded to block almost all Asian migration in 1917, before the Immigration and Nationality Act then removed targeted restrictions. Since the 1950s, the Philippines (a former territory of the United States) has been the largest country of origin for Asian immigrants, while migration from India and China, the world's most populous countries, has also been high.

In the past four centuries, the population of the United States has grown from a recorded 350 people around the Jamestown colony of Virginia in 1610, to an estimated 331 million people in 2020. The pre-colonization populations of the indigenous peoples of the Americas have proven difficult for historians to estimate, as their numbers decreased rapidly following the introduction of European diseases (namely smallpox, plague and influenza). Native Americans were also omitted from most censuses conducted before the twentieth century, therefore the actual population of what we now know as the United States would have been much higher than the official census data from before 1800, but it is unclear by how much. Population growth in the colonies throughout the eighteenth century has primarily been attributed to migration from the British Isles and the Transatlantic slave trade; however it is also difficult to assert the ethnic-makeup of the population in these years as accurate migration records were not kept until after the 1820s, at which point the importation of slaves had also been illegalized. Nineteenth century In the year 1800, it is estimated that the population across the present-day United States was around six million people, with the population in the 16 admitted states numbering at 5.3 million. Migration to the United States began to happen on a large scale in the mid-nineteenth century, with the first major waves coming from Ireland, Britain and Germany. In some aspects, this wave of mass migration balanced out the demographic impacts of the American Civil War, which was the deadliest war in U.S. history with approximately 620 thousand fatalities between 1861 and 1865. The civil war also resulted in the emancipation of around four million slaves across the south; many of whose ancestors would take part in the Great Northern Migration in the early 1900s, which saw around six million black Americans migrate away from the south in one of the largest demographic shifts in U.S. history. By the end of the nineteenth century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. Twentieth and twenty-first century The U.S. population has grown steadily throughout the past 120 years, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. In the past century, the U.S. established itself as a global superpower, with the world's largest economy (by nominal GDP) and most powerful military. Involvement in foreign wars has resulted in over 620,000 further U.S. fatalities since the Civil War, and migration fell drastically during the World Wars and Great Depression; however the population continuously grew in these years as the total fertility rate remained above two births per woman, and life expectancy increased (except during the Spanish Flu pandemic of 1918).

Since the Second World War, Latin America has replaced Europe as the most common point of origin for migrants, with Hispanic populations growing rapidly across the south and border states. Because of this, the proportion of non-Hispanic whites, which has been the most dominant ethnicity in the U.S. since records began, has dropped more rapidly in recent decades. Ethnic minorities also have a much higher birth rate than non-Hispanic whites, further contributing to this decline, and the share of non-Hispanic whites is expected to fall below fifty percent of the U.S. population by the mid-2000s. In 2020, the United States has the third-largest population in the world (after China and India), and the population is expected to reach four hundred million in the 2050s.

In 2024, healthcare expenditures to treat diabetes in the United States came to some 404.5 billion U.S. dollars, while China, the country with the highest number of diabetics by far, spent some 169 billion U.S. dollars that year. Prevalence among adults In 2024, almost 16 percent of American adults aged 20 to 79 had diabetes mellitus, while roughly 14 percent of the adult Chinese population had the condition. Turkey and Mexico both had higher diabetes rates that year. Future of diabetes Around 11 percent of the global adult population suffered from diabetes mellitus in 2024. By 2050, the number of people with the condition is set to increase by roughly two percentage points. By that year, China and India are expected to be the countries with the highest number of diabetic adults worldwide. China is projected to have a diabetic population of approximately 168 million people, while India is estimated to have almost 157 million people suffering from the condition.

The 2008 National Asian American Survey (NAAS) contains 5,159 completed telephone interviews of self-identified Asian/Asian American residents of the United States. Interviewing began on August 12, 2008, and ended on October 29, 2008. The survey instrument included questions about political behavior and attitudes as well as personal experiences in immigration to the United States. Topics include attitudes toward government, politics and political issues, extent of political involvement, party affiliation, sources of political information, voting behavior, health and financial status, racial and ethnic identification, linked fate and discrimination, and religious and ethnic social networks. The overall length of the interview was approximately 29 minutes. The NAAS includes adults in the United States who identify any family background from countries in Asia, exclusive of countries classified as the Middle East. Survey interviews were conducted in eight languages (English, Cantonese, Mandarin, Korean, Vietnamese, Tagalog, Japanese, and Hindi) -- chosen according to the interviewee's preference -- and yielded sample sizes of at least 500 adult Asian American residents in the six largest national-origin groups. The final breakdown was 1,350 Chinese, 1,150 Asian Indian, 719 Vietnamese, 614 Korean, 603 Filipino, and 541 Japanese origin respondents, with 182 additional respondents who are either from other countries in Asia, or who identify as multi-racial or multi-ethnic. Overall, 40 percent of the sample chose English as their preferred language for the interview. The sample is weighted, using a raking procedure, to reflect the balance of gender, nativity, citizenship status, and educational attainment of the six largest national-origin groups in the United States, as well as the proportion of these national-origin groups within each state. Demographic information includes age, race, language, gender, country of birth, religion, marital status, educational level, employment status, citizenship status, household income, and size of household. Several strategies of sampling were used to collect the data. The largest number of cases were completed interviews drawn from a random selection of respondents in a listed sample of high-probability Asian Americans. This listed sample was drawn from a commercial database of voter registration and marketing, with ethnic propensity classifications based on ethnic names, surnames, and geographic density. Two additional strategies of RDD were used to select respondents, the first from a set of telephone numbers generated to maximize the probability of Filipino Americans, and a second set of telephone numbers generated for the population in general. The general population RDD yielded a very small number of completed interviews relative to contacts made by interviewers (8 out of 1,028 attempts) primarily as a result of the low incidence of the Asian American population in the United States. The sampling design was stratified to collect a disproportionately high number of respondents from "new immigrant destinations" as defined by Audrey Singer of the Brookings Institution. In their raw format, 22 percent of the cases were selected from counties in new destinations while the remaining 78 percent were representative of the United States population. Cases were weighted to account for this stratified sampling design. Additional details about sampling and weighting can be found in the book "Asian American Political Participation: Emerging Constituents and their Political Identities" (Wong, Ramakrishnan, Lee, and Junn. 2011, Publisher: Russell Sage Foundation). Post-stratification weights (NWEIGHTNATYRS) were created using a raking procedure to reflect the balance of gender, nativity, citizenship status, and educational attainment of the six largest national-origin groups in the United States, as well as the proportion of these national-origin groups within each state. More details about the weighting procedure can be found in the book "Asian American Political Participation: Emerging Constituents and their Political Identities" (Wong, Ramakrishnan, Lee, and Junn. 2011, Publisher: Russell Sage Foundation). The universe of analysis contains 5,159 completed telephone interviews of self-identified Asian/Asian American residents of the United States. This is approximately 88 percent of the United States Asian/Asian American adult population. The final breakdown was 1,350 Chinese, 1,150 Asian Indian, 719 Vietnamese, 614 Korean, 603 Filipino, and 541 Japanese origin respondents, with 182 additional respondents who are either from other countries in Asia, or who identify as multi-racial or multi-ethnic. Smallest Geographic Unit: county ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accom...

More than half of males in China are current smokers and evidence from western countries tells us that an unprecedented number of smoking-attributable deaths will occur as the Chinese population ages. We used the China Lung Cancer Policy Model (LCPM) to simulate effects of computed tomography (CT)-based lung cancer screening in China, comparing the impact of a screening guideline published in 2015 by a Chinese expert group to a version developed for the United States by the U.S. Centers for Medicare & Medicaid Services (CMS). The China LCPM, built using an existing lung cancer microsimulation model, can project population outcomes associated with interventions for smoking-related diseases. After calibrating the model to published Chinese smoking prevalence and lung cancer mortality rates, we simulated screening from 2016 to 2050 based on eligibility criteria from the CMS and Chinese guidelines, which differ by age to begin and end screening, pack-years smoked, and years since quitting. Outcomes included number of screens, mortality reduction, and life-years saved for each strategy. We projected that in the absence of screening, 14.98 million lung cancer deaths would occur between 2016 and 2050. Screening with the CMS guideline would prevent 0.72 million deaths and 5.8 million life-years lost, resulting in 6.58% and 1.97% mortality reduction in males and females, respectively. Screening with the Chinese guideline would prevent 0.74 million deaths and 6.6 million life-years lost, resulting in 6.30% and 2.79% mortality reduction in males and females, respectively. Through 2050, 1.43 billion screens would be required using the Chinese screening strategy, compared to 988 million screens using the CMS guideline. In conclusion, CT-based lung cancer screening implemented in 2016 and based on the Chinese screening guideline would prevent about 20,000 (2.9%) more lung cancer deaths through 2050, but would require about 445 million (44.7%) more screens than the CMS guideline.

BackgroundAddressing contemporary anti-Asian racism and its impacts on health requires understanding its historical roots, including discriminatory restrictions on immigration, citizenship, and land ownership. Archival secondary data such as historical census records provide opportunities to quantitatively analyze structural dynamics that affect the health of Asian immigrants and Asian Americans. Census data overcome weaknesses of other data sources, such as small sample size and aggregation of Asian subgroups. This article explores the strengths and limitations of early twentieth-century census data for understanding Asian Americans and structural racism.MethodsWe used California census data from three decennial census spanning 1920–1940 to compare two criteria for identifying Asian Americans: census racial categories and Asian surname lists (Chinese, Indian, Japanese, Korean, and Filipino) that have been validated in contemporary population data. This paper examines the sensitivity and specificity of surname classification compared to census-designated “color or race” at the population level.ResultsSurname criteria were found to be highly specific, with each of the five surname lists having a specificity of over 99% for all three census years. The Chinese surname list had the highest sensitivity (ranging from 0.60–0.67 across census years), followed by the Indian (0.54–0.61) and Japanese (0.51–0.62) surname lists. Sensitivity was much lower for Korean (0.40–0.45) and Filipino (0.10–0.21) surnames. With the exception of Indian surnames, the sensitivity values of surname criteria were lower for the 1920–1940 census data than those reported for the 1990 census. The extent of the difference in sensitivity and trends across census years vary by subgroup.DiscussionSurname criteria may have lower sensitivity in detecting Asian subgroups in historical data as opposed to contemporary data as enumeration procedures for Asians have changed across time. We examine how the conflation of race, ethnicity, and nationality in the census could contribute to low sensitivity of surname classification compared to census-designated “color or race.” These results can guide decisions when operationalizing race in the context of specific research questions, thus promoting historical quantitative study of Asian American experiences. Furthermore, these results stress the need to situate measures of race and racism in their specific historical context.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the .Technical Documentation.. section......Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the .Methodology.. section..Source: U.S. Census Bureau, 2018 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see .ACS Technical Documentation..). The effect of nonsampling error is not represented in these tables..Total includes people who reported Asian only, regardless of whether they reported one or more detailed Asian groups......Other Asian, specified. Includes respondents who provide a response of another Asian group not shown separately, such as Iwo Jiman, Maldivian, or Singaporean......Other Asian, not specified. Includes respondents who checked the "Other Asian" response category on the ACS questionnaire and did not write in a specific group or wrote in a generic term such as "Asian," or "Asiatic."..... Two or more Asian. Includes respondents who provided multiple Asian responses such as Asian Indian and Japanese; or Vietnamese, Chinese and Hmong..While the 2018 American Community Survey (ACS) data generally reflect the July 2015 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas, in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:..An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution, or the margin of error associated with a median was larger than the median itself..An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution..An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution..An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An "(X)" means that the estimate is not applicable or not available....

Chinese Medicine Needle Market Outlook

The Chinese Medicine Needle market size in 2023 is estimated to be valued at USD 500 million and is projected to grow at a compound annual growth rate (CAGR) of 7.5% to reach USD 900 million by 2032. The growth of this market is driven by increasing adoption of traditional Chinese medicine practices worldwide, rising prevalence of chronic pain and neurological disorders, and growing awareness about the benefits of acupuncture and related treatments.

One significant growth factor for the Chinese Medicine Needle market is the increasing acceptance and integration of traditional Chinese medicine (TCM) into mainstream healthcare systems globally. In countries such as the United States, Canada, and various European nations, acupuncture has gained recognition and validation through clinical research, leading to wider acceptance by medical practitioners and patients alike. This integration is further bolstered by supportive regulatory frameworks and inclusion of acupuncture treatments in insurance coverage, making these therapies more accessible to a broader population.

Another driving factor is the rising prevalence of chronic pain and neurological disorders, which has led to an increased demand for alternative treatment options. Traditional Chinese medicine, particularly acupuncture, has been recognized for its efficacy in pain management, which is a significant concern for patients dealing with conditions such as arthritis, migraines, and fibromyalgia. As the global population ages and the incidence of these disorders increases, the demand for acupuncture and other TCM treatments is expected to rise, thereby boosting the market for Chinese medicine needles.

Moreover, there is a growing awareness and preference for natural and holistic healthcare approaches, which is contributing to the expansion of the Chinese Medicine Needle market. Many individuals are seeking treatments that have fewer side effects compared to conventional pharmaceuticals. Acupuncture and related techniques offer a viable solution for those looking to manage their health conditions in a more natural way. This trend is particularly strong in regions with high levels of health consciousness and wellness culture, such as North America and Europe.

Seirin Pyonex Needles have gained significant attention in the Chinese Medicine Needle market due to their innovative design and user-friendly features. These needles are particularly favored for their precision and minimal discomfort during acupuncture treatments, making them a preferred choice among practitioners and patients alike. The unique design of Seirin Pyonex Needles allows for easy application and consistent results, enhancing the overall effectiveness of acupuncture therapy. As the demand for high-quality and reliable acupuncture needles continues to grow, Seirin Pyonex Needles are well-positioned to meet the needs of both traditional and modern healthcare practices. Their popularity is further bolstered by the increasing focus on patient comfort and safety, which are critical factors in the adoption of acupuncture as a mainstream treatment option.

In terms of regional outlook, Asia Pacific is anticipated to dominate the Chinese Medicine Needle market due to its historical roots and widespread practice of traditional Chinese medicine. Countries like China, Japan, and South Korea have a long-standing tradition of using acupuncture and related therapies, which continues to drive high demand for Chinese medicine needles in the region. Additionally, increasing governmental support for TCM practices and the presence of numerous TCM institutions and practitioners further bolster the market. North America and Europe are also expected to witness substantial growth owing to rising acceptance and integration of TCM into their healthcare systems.

Product Type Analysis

The Chinese Medicine Needle market is segmented by product type into Acupuncture Needles, Intradermal Needles, Press Needles, and Others. Acupuncture needles are the most widely used and recognized type, primarily due to their application in traditional acupuncture therapy. These needles are designed to penetrate the skin and stimulate specific points on the body, known as acupoints, to promote healing and alleviate various health conditions. The increasing popularity of acupuncture in pain management and its incorporation into both traditional and modern healthcare practices have significantly driven the demand for acupuncture needles.&

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Total includes people who reported Asian only, regardless of whether they reported one or more detailed Asian groups.Other Asian, specified. Includes respondents who provided a response of another Asian group not shown separately, such as Malay or Tai Dam.Other Asian, not specified. Includes respondents who checked the "Other Asian" response category on the ACS questionnaire and did not write in a specific group or wrote in a generic term such as "Asian," or "Asiatic." Two or more Asian. Includes respondents who provided multiple Asian responses such as Asian Indian and Japanese; or Vietnamese, Chinese and Hmong..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

The average for 2023 based on 196 countries was 0.51 percent. The highest value was in India: 17.91 percent and the lowest value was in Andorra: 0 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.Revisions added on 4/23/2020 are highlighted.Revisions added on 4/30/2020 are highlighted.Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Correction on 6/1/2020Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Reasons for undertaking this work:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-30 days + 5% from past 31-56 days - total deaths.We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source used as basis:Stephen A. Lauer, MS, PhD *; Kyra H. Grantz, BA *; Qifang Bi, MHS; Forrest K. Jones, MPH; Qulu Zheng, MHS; Hannah R. Meredith, PhD; Andrew S. Azman, PhD; Nicholas G. Reich, PhD; Justin Lessler, PhD. 2020. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine DOI: 10.7326/M20-0504.New Cases per Day (NCD) = Measures the daily spread of COVID-19. This is the basis for all rates. Back-casting revisions: In the Johns Hopkins’ data, the structure is to provide the cumulative number of cases per day, which presumes an ever-increasing sequence of numbers, e.g., 0,0,1,1,2,5,7,7,7, etc. However, revisions do occur and would look like, 0,0,1,1,2,5,7,7,6. To accommodate this, we revised the lists to eliminate decreases, which make this list look like, 0,0,1,1,2,5,6,6,6.Reporting Interval: In the early weeks, Johns Hopkins' data provided reporting every day regardless of change. In late April, this changed allowing for days to be skipped if no new data was available. The day was still included, but the value of total cases was set to Null. The processing therefore was updated to include tracking of the spacing between intervals with valid values.100 News Cases in a day as a spike threshold: Empirically, this is based on COVID-19’s rate of spread, or r0 of ~2.5, which indicates each case will infect between two and three other people. There is a point at which each administrative area’s capacity will not have the resources to trace and account for all contacts of each patient. Thus, this is an indicator of uncontrolled or epidemic trend. Spiking activity in combination with the rate of new cases is the basis for determining whether an area has a spreading or epidemic trend (see below). Source used as basis:World Health Organization (WHO). 16-24 Feb 2020. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Obtained online.Mean of Recent Tail of NCD = Empirical, and a COVID-19-specific basis for establishing a recent trend. The recent mean of NCD is taken from the most recent fourteen days. A minimum of 21 days of cases is required for analysis but cannot be considered reliable. Thus, a preference of 42 days of cases ensures much higher reliability. This analysis is not explanatory and thus, merely represents a likely trend. The tail is analyzed for the following:Most recent 2 days: In terms of likelihood, this does not mean much, but can indicate a reason for hope and a basis to share positive change that is not yet a trend. There are two worthwhile indicators:Last 2 days count of new cases is less than any in either the past five or 14 days. Past 2 days has only one or fewer new cases – this is an extremely positive outcome if the rate of testing has continued at the same rate as the previous 5 days or 14 days. Most recent 5 days: In terms of likelihood, this is more meaningful, as it does represent at short-term trend. There are five worthwhile indicators:Past five days is greater than past 2 days and past 14 days indicates the potential of the past 2 days being an aberration. Past five days is greater than past 14 days and less than past 2 days indicates slight positive trend, but likely still within peak trend time frame.Past five days is less than the past 14 days. This means a downward trend. This would be an

Acupuncture has become a widely practiced form of complementary and alternative medicine, addressing various medical conditions. Although both public and private insurers provide coverage for acupuncture under various circumstances, the lack of comprehensive clinical evidence limits the extent of this coverage. Despite disruptions caused by the pandemic, acupuncture services were considered essential, resulting in continued industry growth and increasing profit. Key growth drivers include a growing older adult population experiencing more chronic pain, expanding Medicare coverage and gains in per capita disposable income that would cover out-of-pocket costs. The need for definitive research on acupuncture's efficacy persists, yet the industry's revenue has grown at a CAGR of 6.5%. By 2024, industry revenue is expected to reach $707.5 million, reflecting a 1.9% gain in that year alone. As the population becomes more health-conscious and the insurance industry seeks cost-effective solutions, interest in acupuncture and insurance support is predicted to climb. The number of acupuncture establishments is also expected to increase and profit spurs entry. While most acupuncturists are small businesses serving local clients in urban and densely populated areas, telemedicine and remote acupuncture advancements could expand their reach. Also, new technologies, such as electroacupuncture, are introducing new therapeutic techniques that will broaden the market services and boost revenue opportunities. Looking ahead, industry profit is expected to remain stable, with new entrants intensifying competition. Comprehensive research studies, telemedicine and artificial intelligence will enhance data analytics, improving the understanding of acupuncture's benefits for various ailments. Overall, industry revenue is forecast to climb at a CAGR of 3.2% through 2029 to total $829.0 million, with profit steady at 17.5%.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Total includes people who reported Asian only, regardless of whether they reported one or more detailed Asian groups.Other Asian, specified. Includes respondents who provide a response of another Asian group not shown separately, such as Iwo Jiman, Maldivian, or Singaporean.Other Asian, not specified. Includes respondents who checked the "Other Asian" response category on the ACS questionnaire and did not write in a specific group or wrote in a generic term such as "Asian," or "Asiatic." Two or more Asian. Includes respondents who provided multiple Asian responses such as Asian Indian and Japanese; or Vietnamese, Chinese and Hmong..The 2019 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution, or the margin of error associated with a median was larger than the median itself.An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.An "(X)" means that the estimate is not applicable or not available.

In 2022, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth