- Population
The population rate of change of High Bridge, NJ was -1.07% in 2018. The population rate of change of Turnersville, NJ was -1.26% in 2018.
Population
Population Change
Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API -
Demographics and Population Datasets Involving High Bridge, NJ or Turnersville, NJ
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Percent Of Middle School Students (grades 7-8) Who Smoke Cigarettes, New Jersey, by year: Beginning 2014
healthdata.nj.gov | Last Updated 2017-08-30T17:19:32.000ZRatio: Percentage of middle school (7th-8th grade) students who have used cigarettes on one or more days in the 30 days preceding the survey. Definition: Percentage of middle school (grades 7-8) students who have used cigarettes on one or more days in the 30 days preceding the survey. Data Source: NJDHS DMHAS NJ Middle School Risk and Protective Factor Survey History: FEB 2017 - Data source for this indicator changed to New Jersey Youth Tobacco Survey (YTS) starting with 2014 data. Previous data years were based on PRIDE survey data, New Jersey Department of Human Services. MAR 2017 - Baseline year changed from 2010 to 2014, since YTS and PRIDE data are not comparable. - 2020 targets modified to reflect a 10% improvement over the 2014 baseline for total population and all racial/ethnic groups
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New York State Population Data: Beginning 2003
health.data.ny.gov | Last Updated 2024-03-07T16:13:08.000ZPopulation data file is provided as an additional reference file when interpreting vital statistics death rates. The population data is derived from the corresponding release of the NCHS annual estimates of "Bridged Race Vintage" which are consistent with the Bureau of the Census estimates from "Vintage" (released in the summer). For more information, check out: http://www.health.ny.gov/statistics/vital_statistics/. The "About" tab contains additional details concerning this dataset.
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PPP Cares Act Loan Totals to New Jersey Businesses
data.nj.gov | Last Updated 2023-03-24T14:05:51.000ZThe Paycheck Protection Program (PPP) loans provide small businesses with the resources they need to maintain their payroll, hire back employees who may have been laid off, and cover applicable overhead. This data set includes businesses in New Jersey who received PPP funding, how much funding the employer received & how many jobs the employer claims they saved. The NAICS (National Industry Classification) was provided by the loan recipient. Note: As per SBA, The Paycheck Protection Program (PPP) ended on May 31, 2021 so no updates has been made on this dataset. Please see attached document on landing page for more details.
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Late-Stage Female Breast Cancer Incidence Rate (cases per 100,000 females), New Jersey, by year: Beginning 2010
healthdata.nj.gov | Last Updated 2019-05-10T17:35:07.000ZRate: Number of new cases of breast cancer (per 100,000) diagnosed at the regional or distant stage among females. Definition: Age-adjusted incidence rate of invasive breast cancer per 100,000 female population. Data Sources: (1) NJ State Cancer Registry, Dec 31, 2015 Analytic File, using NCI SEER*Stat ver 8.2.1 (www.seer.cancer.gov/seerstat) (2) NJ population estimates as calculated by the NCI's SEER Program, released January 2015, http://www.seer.cancer.gov/popdata/download.html.
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Percentage of high school students who played video or computer games or used a computer for something that was not school work more than 3 hours per day, New Jersey: Beginning 2009 (odd years only)
healthdata.nj.gov | Last Updated 2020-09-18T15:52:54.000ZDefinition: The percentage of students who play video/computer games and use the internet for a specified number of hours per day on an average school day Data Source: Student Health Survey, Office of Student Support Services, New Jersey Department of Education History: MAR 2017 - Chart and table titles corrected to read as "More Than 3 Hours Per Day." They were erroneously labeled previously as "2 or Less Hours Per Day." - All 2010-2014 data were updated for total population and all racial/ethnic groups - 2020 targets modified to reflect a 10% improvement over the 2010 baseline for total population and all racial/ethnic groups
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NCHS - Drug Poisoning Mortality by County: United States
data.cdc.gov | Last Updated 2022-03-30T13:15:49.000ZThis dataset describes drug poisoning deaths at the county level by selected demographic characteristics and includes age-adjusted death rates for drug poisoning from 1999 to 2015. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2015 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Estimate does not meet standards of reliability or precision. Death rates are flagged as “Unreliable” in the chart when the rate is calculated with a numerator of 20 or less. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Estimates should be interpreted with caution. Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year during 1999–2015. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates are unavailable for Broomfield County, Colo., and Denali County, Alaska, before 2003 (6,7). Additionally, Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. County boundaries are consistent with the vintage 2005-2007 bridged-race population file geographies (6).
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NCHS - Drug Poisoning Mortality by State: United States
data.cdc.gov | Last Updated 2022-03-28T19:47:01.000ZThis dataset describes drug poisoning deaths at the U.S. and state level by selected demographic characteristics, and includes age-adjusted death rates for drug poisoning from 1999 to 2015. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2015 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Estimate does not meet standards of reliability or precision. Death rates are flagged as “Unreliable” in the chart when the rate is calculated with a numerator of 20 or less. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Estimates should be interpreted with caution. Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year during 1999–2015. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates are unavailable for Broomfield County, Colo., and Denali County, Alaska, before 2003 (6,7). Additionally, Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. County boundaries are consistent with the vintage 2005-2007 bridged-race population file geographies (6).