Estimates 9.1 Download
Roselee Pando
The Census Bureau's Population Estimates Program (PEP) produces estimates of the population for the United States, states, metropolitan and micropolitan statistical areas, counties, cities, towns, as well as for Puerto Rico and its municipios.
This web page provides weekly, preliminary estimates of the cumulative in-season numbers of flu illnesses, medical visits, hospitalizations, and deaths in the United States. It is not possible to know the exact number of people who have experienced an influenza illness in the United States because not everyone who experiences an influenza illness will seek medical care or be tested for influenza. Given this, not all influenza illnesses will be identified through our surveillance systems. This is why we use mathematical models to estimate the impact of influenza on the population. CDC has estimated the burden of flu since 2010 using a mathematical model that is based on data collected through the Influenza Hospitalization Surveillance Network (FluSurv-NET), a network that covers approximately 9% of the U.S. population.
Each week CDC calculates a lower estimate and an upper estimate of flu-related hospitalizations that have occurred since the beginning of the season (October 1, 2023). These are updated each week and are compared to end-of-season estimates of flu-related hospitalizations from previous flu seasons.
Second, rates of lab-confirmed flu-related hospitalizations were adjusted for the frequency of flu testing and the sensitivity of flu diagnostic assays. However, data on testing practices during the current flu season are not available in real-time. To make these estimates, CDC uses data on testing practices from the past flu seasons as a proxy. If more testing is being done compared to past flu seasons, these estimates may be inflated. Preliminary in-season burden estimates are finalized when data on contemporary testing practices become available, and the estimates may decrease if testing has increased.
The cumulative burden of flu is an estimate of the number of people who have been sick, seen a healthcare provider, been hospitalized, or died as a result of flu within a certain timeframe. The in-season preliminary burden estimates are provided weekly during flu season when sufficient data are available to make an estimate, and end-of-season preliminary estimates are given at the end of each flu season. End-of-season preliminary estimates will be updated year-to-year and are considered final when all data are available (usually within two years of the preliminary estimate).
Preliminary estimates of the cumulative burden of seasonal flu are based on crude rates of lab-confirmed flu-related hospitalizations, reported through the Influenza Hospitalization Surveillance Network (FluSurv-NET), which are adjusted for the frequency of flu testing during recent prior seasons and the sensitivity of flu diagnostic tests. Rates of hospitalization are then multiplied by previously estimated ratio of hospitalizations to symptomatic illnesses, and frequency of seeking medical care to calculate symptomatic illnesses, medical visits, hospitalizations, and deaths associated with seasonal flu, respectively.
The in-season estimates of flu burden are preliminary and change week-by-week as new flu hospitalizations are reported to CDC. New reports include both new admissions that have occurred during the reporting week and also patients admitted in previous weeks that have been newly reported to CDC.
Impacts were estimated following the Adding It Up methodology, details of which are available elsewhere.14 Briefly, using the most recent data for each country on contraceptive need and method use, we estimated the annual number of unintended pregnancies by multiplying the number of women using a contraceptive method by the age- and method-specific use-failure rates, and multiplying the number of women with unmet need for contraception by the pregnancy rate for women with an unmet need.14 We then adjusted these age-specific estimates of unintended pregnancy so that the total number of unintended pregnancies aligned with an external model-based estimate for each country.15 To estimate the effect of health services on cause-specific maternal and newborn deaths, we used national data on service coverage levels together with information on effectiveness of interventions from the Lives Saved Tool, a mathematical modeling tool that estimates the effects of service coverage change on mortality in LMICs.16 The data used in this analysis are annual estimates, and the reference year is 2019.
In the first of our two hypothetical scenarios, we estimated the impact that a 10% decline in the proportion of women receiving sexual and reproductive health services would have on unintended pregnancy and maternal and newborn mortality over a 12-month period. Although the changes to service provision could be greater than that, a 10% proportional decline illustrates the major effect a conservative reduction in service coverage might have. We assumed the net demand for contraceptives and the need for pregnancy-related and newborn services would not change; we did this both to simplify the analysis and because no data exist on the change in demand for services during this pandemic. Likewise, we estimated each outcome independently, and did not account for potential synergistic effects, such as the increased demand for pregnancy-related and newborn health services that would result from a decline in contraceptive use and an increase in the number of unintended pregnancies. This illustrative scenario is, therefore, likely a conservative estimate of the potential effects of sexual and reproductive health service disruptions. In addition, although we focused on provision of contraceptive, pregnancy-related and newborn care, there are other sexual and reproductive health services that would likely be affected but were not included in these estimates, including treatment for HIV and other STIs.
15. Bearak JM et al., Pregnancies, abortions, and pregnancy intentions: a protocol for modeling and reporting global, regional and country estimates, Reproductive Health, 2019, 16:36, -019-0682-0.
The report finds direct damages to buildings and infrastructure comes to more than US$135 billion across the following most affected areas: housing (37 percent), transport (26 percent), energy (8 percent), commerce and industry (8 percent), and agriculture (6 percent). Energy, housing and transport sectors have seen the greatest increase in direct damages, since the RDNA1 estimates. Agricultural damage is also significantly higher, reflecting both increased asset destruction and more precise data.
Disease outcomes include both acute illness and chronic disease that sometimes follow these acute illnesses. These 15 pathogens account for over 95 percent of the illnesses and deaths from foodborne illnesses acquired in the United States for which the U.S. Centers for Disease Control and Prevention (CDC) can identify a pathogen cause. These estimates build on CDC estimates of the incidence of foodborne disease; peer-reviewed synthesis of data on medical costs, and economic, medical and epidemiological literature; and publicly available data on wages. In 2018 dollars, the economic burden of these pathogens was about $17.6 billion, an increase of about $2 billion, or 13 percent, over the 2013 ERS estimate of $15.5 billion.
This data product provides Federal agencies such as USDA's Food Safety and Inspection Service (FSIS) with a set of consistent, peer-reviewed estimates of the costs of foodborne illness that can be used in analyzing the impact of Federal regulation. It also provides other stakeholders and the general public with a means of understanding the relative impact of different foodborne infections in the United States. Cost estimates of foodborne illnesses have been used in the past to help inform food-safety policy discussions, and these updated cost estimates will provide a foundation for economic analysis of food safety policy.
RCW 43.62.030 states that the Office of Financial Management shall annually determine the April 1 populations of all cities and towns of the state. OFM population estimates for cities and towns are used in state program administration and in the allocation of selected state revenues (RCW 43.62.020). Population estimates for counties are used to allocate revenues as specified in RCW 36.13.100 and RCW 43.62.030.
If you represent a city or town and need information about the April 1 population estimates program or how to connect to the Population Estimate System (PES), see April 1 population estimates program information.
This report outlines the key findings of the 2019 Point-In-Time (PIT) count and Housing Inventory Count (HIC) conducted in January 2019. Specifically, this report provides 2019 national, state, and CoC-level PIT and HIC estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth.
EPA estimates methane emissions from the oil and natural gas industry in its annual Inventory of U.S. Greenhouse Gas Emissions and Sinks. Charts summarizing the 2021 methane emissions by oil and gas industry segment are presented below.
Benefit estimates depend on your date of birth and on your earnings history. For security, the "Quick Calculator" does not access your earnings record; instead, it will estimate your earnings based on information you provide. So benefit estimates made by the Quick Calculator are rough.
You must be at least age 22 to use the form at right.
Lack of a substantial earnings history will cause retirement benefit estimates to be unreliable. Enter your date of birth (month/day/year format) / / Enter earnings in the current year: $
Your annual earnings must be earnings covered by Social Security. If you entered 0, we assume you are now retired. Enter the last year in which you had covered earnings and the amount of such earnings.
Year: Earnings: $ Future retirement date option If you have decided upon a retirement date, enter the month number and year in which you plan to retire. Month Year By "retirement date," we mean the month in which you intend to stop working. We assume that this is also the month for which you want benefits to begin. However, if you enter a date before you are eligible for benefits, we will assume you want to start receiving benefits at the earliest possible age (age 62).