We consider age-structured models with an imposed refractory period between births. These models can be used to formulate alternative population control strategies to China's one-child policy. By allowing any number of births, but with an imposed delay between births, we show how the total population can be decreased and how a relatively older age distribution can be generated. This delay represents a more "continuous" form of population management for which the strict one-child policy is a limiting case. Such a policy approach could be more easily accepted by society. Our analyses provide an initial framework for studying demographics and how social constraints influence population structure.

This dataset includes the raw population data for 1981 China and 2000 Japan, and some Matlab code files used to process such raw data and produce predictions.

We conducted a benchmarking analysis of 16 summary-level data-based MR methods for causal inference with five real-world genetic datasets, focusing on three key aspects: type I error control, the accuracy of causal effect estimates, replicability, and power.

The datasets used in the MR benchmarking study can be downloaded here:

"dataset-GWASATLAS-negativecontrol.zip": the GWASATLAS dataset for evaluation of type I error control in confounding scenario (a): Population stratification

"dataset-NealeLab-negativecontrol.zip": the Neale Lab dataset for evaluation of type I error control in confounding scenario (a): Population stratification;

"dataset-PanUKBB-negativecontrol.zip": the Pan UKBB dataset for evaluation of type I error control in confounding scenario (a): Population stratification;

"dataset-Pleiotropy-negativecontrol": the dataset used for evaluation of type I error control in confounding scenario (b): Pleiotropy;

"dataset-familylevelconf-negativecontrol.zip": the dataset used for evaluation of type I error control in confounding scenario (c): Family-level confounders;

"dataset_ukb-ukb.zip": the dataset used for evaluation of the accuracy of causal effect estimates;

"dataset-LDL-CAD_clumped.zip": the dataset used for evaluation of replicability and power;

Each of the datasets contains the following files:

"Tested Trait pairs": the exposure-outcome trait pairs to be analyzed;

"MRdat" refers to the summary statistics after performing IV selection (p-value < 5e-05) and PLINK LD clumping with a clumping window size of 1000kb and an r^2 threshold of 0.001.

"bg_paras" are the estimated background parameters "Omega" and "C" which will be used for MR estimation in MR-APSS.

Note:

Supplemental Tables S1-S7.xlxs provide the download link for the original GWAS summary-level data for the traits used as exposures or outcomes.

The formatted dataset after quality control can be accessible at our GitHub website (https://github.com/YangLabHKUST/MRbenchmarking).

The details on quality control of GWAS summary statistics, formatting GWASs, and LD clumping for IV selection can be found on the MR-APSS software tutorial on the MR-APSS website (https://github.com/YangLabHKUST/MR-APSS).

R code for running MR methods is also available at https://github.com/YangLabHKUST/MRbenchmarking.

Our study on saplings was conducted in six forested sites in three southern Michigan counties: Ingham Co. (three sites), Gratiot Co. (two sites), and Shiawassee Co. (one site), with 10 to 60 km between sites.Data set one - on the fate and density of emerald ash borer larvae and associated parasitoids on ash saplings from both biocontrol-release and non-release control plots in southern Michigan during the three-year study (2013–2015). Data set one was used for calculations and associated analyses for of the parameters presented in Figure 1, 2, 3, and 4.Data set two - on ash tree abundance (per 100 m2) and healthy conditions (or crown classes) at the six study sites in southern Michigan observed in summer 2015. Data set two was used for estimation of tree density (Figure 5) and healthy condition (or crown classes).Resources in this dataset:Resource Title: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: Sapling Data 2013-2015 FINAL.xlsx Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: MI Ash Transect 2015 - All trees.xlsx Resource Description: Data on ash abundance and healthy conditions from transect surveyResource Title: Data Dictionary - EAB biocontrol in ash saplings. File Name: EAB_data_dictionary.csvResource Title: 2013-2014 data sorted. File Name: 2013-2014_data_sorted_EAB.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: 2014-2015 data sorted. File Name: 2014-2015_data_sorted_EAB.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: 2015-2016 data sorted. File Name: 2015-2016_data_sorted_EAB.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: Combined: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: Emerald ash borer biocontrol in ash saplings the potential for early stage recovery of North American ash trees.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition) All 3 sets (2013-2016) combined into a CSV for visualization purposesResource Title: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: MI Ash Transect 2015 - All trees.csv Resource Description: Data on ash abundance and healthy conditions from transect survey (CSV version for data visualization)Resource Title: Estimates of the net population growth rate of emerald ash borer on saplings from life tables constructed from Dataset One. File Name: DUAN J Data on EAB Life Tables Calculation for Saplings 2013-2015.xlsx Resource Description: This life table of emerald ash borer on saplings was constructed from Dataset One and used to estimate the next population growth rate according to method described in Duan et al. (2014, 2017)Resource Title: Estimates of the net population growth rate of emerald ash borer on saplings from life tables constructed from Dataset One. File Name: EAB_Life_Tables_Calculation_for_Saplings_2013-2015.csv Resource Description: CSV version of the data - This life table of emerald ash borer on saplings was constructed from Dataset One and used to estimate the next population growth rate according to method described in Duan et al. (2014, 2017)

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information. If you have questions about underlying source data, contact PLACES at places@cdc.gov. For questions about metadata or this extracted data contact CAFÉ (climatecafe@bu.edu). "This dataset contains model-based census tract estimates. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 40 measures: 12 for health outcomes, 7 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, 3 for health status, and 7 for health-related social needs. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2020 population data, and American Community Survey 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places." [Quote from CDC PLACES Data]

Large areas of forests are annually damaged or destroyed by outbreaking insect pests. Understanding the factors that trigger and terminate such population eruptions has become crucially important, as plants, plant-feeding insects, and their natural enemies may respond differentially to the ongoing changes in the global climate. In northernmost Europe, climate-driven range expansions of the geometrid moths Epirrita autumnata and Operophtera brumata have resulted in overlapping and increasingly severe outbreaks. Delayed density-dependent responses of parasitoids are a plausible explanation for the ten-year population cycles of these moth species, but the impact of parasitoids on geometrid outbreak dynamics is unclear due to a lack of knowledge on the host ranges and prevalences of parasitoids attacking the moths in nature. To overcome these problems, we reviewed the literature on parasitism in the focal geometrid species in their outbreak range, and then constructed a DNA barcode reference library for all relevant parasitoid species based on reared specimens and sequences obtained from public databases. The combined parasitoid community of E. autumnata and O. brumata consists of 32 hymenopteran species, all of which can be reliably identified based on their barcode sequences. The curated barcode library presented here opens up new opportunities for estimating the abundance and community composition of parasitoids across populations and ecosystems based on mass barcoding and metabarcoding approaches. Such information can be used for elucidating the role of parasitoids in moth population control, possibly also for devising methods for reducing the extent, intensity, and duration of outbreaks. Methods

Literature review of parasitoid communities Laboratory rearing of larvae and parasitoids DNA extraction PCR Sanger sequencing Sequence editing and alignment (Geneious Prime 2020.1) Phylogeny reconstruction by a) midpoint-rooted Neighbor-joining based on Kimura 2-parameter distances (Mega X) and b) maximum-likelihood analysis (RAxML v.8) based on a GTR+G substitution model and separated codon positions 1+2 vs. 3 Species delimitation analyses (bPTP)

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

The effective population size (Ne) is a fundamental parameter in population genetics that determines the relative strength of selection and random genetic drift, the effect of migration, levels of inbreeding, and linkage disequilibrium. In many cases where it has been estimated in animals, Ne is on the order of 10-20% of the census size. In this study, we use 12 microsatellite markers and 14,888 single nucleotide polymorphisms (SNPs) to empirically estimate Ne in Aedes aegypti, the major vector of yellow fever, dengue, chikungunya, and Zika viruses. We used the method of temporal sampling to estimate Ne on a global dataset made up of 46 samples of Ae. aegypti that included multiple time points from 17 widely distributed geographic localities. Our Ne estimates for Ae. aegypti fell within a broad range (~25-3,000), and averaged between 400 and 600 across all localities and time points sampled. Adult census size (Nc) estimates for this species range between one and five thousand, so the Ne/Nc ratio is about the same as for most animals. These Ne values are lower than estimates available for other insects and have important implications for the design of genetic control strategies to reduce the impact of this species of mosquito on human health.

This dataset contains county-level social determinants of health (SDOH) measures from the American Community Survey 5-year data for the entire United States—50 states and the District of Columbia. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. These measures complement existing PLACES measures, including PLACES SDOH measures (e.g., health insurance, routine check-up). These data can be used together with PLACES data to identify which health and SDOH issues overlap in a community to help inform public health planning. To access spatial data, please use the ArcGIS Online service: https://cdcarcgis.maps.arcgis.com/home/item.html?id=d51009ea78b54635be95c6ec9955ec17.

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information. This dataset was created by the US Centers for Disease Control and Prevention (CDC) and is hosted and maintained here: https://data.cdc.gov/500-Cities-Places/PLACES-Local-Data-for-Better-Health-Census-Tract-D/em5e-5hvn/about_data If you have questions about underlying source data, contact PLACES at places@cdc.gov. For questions about metadata or this extracted data contact CAFÉ (climatecafe@bu.edu). CDC describes the dataset as follows: "This dataset contains model-based census tract estimates. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 36 measures: 13 for health outcomes, 9 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, and 3 for health status. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2021 or 2020 data, Census Bureau 2010 population data, and American Community Survey 2015–2019 estimates. The 2023 release uses 2021 BRFSS data for 29 measures and 2020 BRFSS data for seven measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places." [Quote from CDC PLACES Data - 2023 Release]

Averages of Pre-intervention characteristics of China, SynthChina, and the comparator.

Long term population projections by sex and single year of age for York Local Authority area. These unrounded estimates are published based on ONS estimates designed to enable and encourage further calculations and analysis. However, the estimates should not be taken to be accurate to the level of detail provided. More information on the accuracy of the estimates is available in the Quality and Methodology document The estimates are produced using a variety of data sources and statistical models, including some statistical disclosure control methods, and small estimates should not be taken to refer to particular individuals. The estimated resident population of an area includes all those people who usually live there, regardless of nationality. Arriving international migrants are included in the usually resident population if they remain in the UK for at least a year. Emigrants are excluded if they remain outside the UK for at least a year. This is consistent with the United Nations definition of a long-term migrant. Armed forces stationed outside of the UK are excluded. Students are taken to be usually resident at their term time address. The population estimates reflect boundaries in place as of the reference year. Please note that “age” 999 comprises data for ages 90 and above. Source and Licence: Adapted from data from the Office for National Statistics licensed under the Open Government Licence v.1.0.

This dataset provides data at the county level for the contiguous United States. It includes daily Global Horizontal Irradiance (GHI) data from 1991-2012 provided by the Environmental Remote Sensing group at the Rollins School of Public Health at Emory University. Please refer to the metadata attachment for more information. These data are used by the CDC's National Environmental Public Health Tracking Network to generate sunlight and ultraviolet (UV) measures. Learn more about sunlight and UV on the Tracking Network's website: https://ephtracking.cdc.gov/showUVLanding. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking. Problems or Questions? Email trackingsupport@cdc.gov.

This dataset provides data at the county level for the contiguous United States. It includes daily Ozone Monitoring Instrument (OMI) Population-Weighted Ultraviolet (UV) irradiance data from October 2004-2015 provided by the Environmental Remote Sensing group at the Rollins School of Public Health at Emory University. Please refer to the metadata attachment for more information. These data are used by the CDC's National Environmental Public Health Tracking Network to generate sunlight and UV measures. Learn more about sunlight and UV on the Tracking Network's website: https://ephtracking.cdc.gov/showUVLanding. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking. Problems or Questions? Email trackingsupport@cdc.gov.

This dataset contains model-based census tract-level estimates for the PLACES project 2020 release. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. The dataset includes estimates for 27 measures: 5 chronic disease-related unhealthy behaviors, 13 health outcomes, and 9 on use of preventive services. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population data, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS because the relevant questions are only asked every other year in the BRFSS. More information about the methodology can be found at www.cdc.gov/places.

The dataset contains data of a nationwide age-frequency matched case-control study in Germany, 2012-2013, which was performed to identify underlying conditions and foods asscociated with sporadic listeriosis. It covers anonymous sociodemografic information, information on underlying conditions, and >60 food items. Data for control subjects were obtained from a population-based random telephone sample, which was generated according to the method by Gabler and Häder and considered telephone numbers not registered in telephone books.

Life Table Data: Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per year for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia Experimentalis et Applicata 116(2): 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis.
Matrix Model Data: Life table data were used to provide parameters for population matrix models. Matrix models contain information about stage-specific rates for development, survival and reproduction. The model can be used to estimate overall population growth rate and can also be analyzed to determine which life stages contribute the most to changes in growth rates. Resources in this dataset:Resource Title: Matrix model data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: MatrixModelData.xlsxResource Description: Life table data were used to provide parameters for population matrix models. Matrix models contain information about stage-specific rates for development, survival and reproduction. The model can be used to estimate overall population growth rate and can also be analyzed to determine which life stages contribute the most to changes in growth rates. Resource Title: Data Dictionary: Life table data. File Name: DataDictionary_LifeTableData.csvResource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.xlsxResource Description: Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis. Resource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.csvResource Description: CSV version of the data. Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis.

This dataset contains place-level (incorporated and census-designated places) social determinants of health (SDOH) measures from the American Community Survey 5-year data for the entire United States—50 states and the District of Columbia. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. These measures complement existing PLACES measures, including PLACES SDOH measures (e.g., health insurance, routine check-up). These data can be used together with PLACES data to identify which health and SDOH issues overlap in a community to help inform public health planning. To access spatial data, please use the ArcGIS Online service: https://cdcarcgis.maps.arcgis.com/home/item.html?id=d51009ea78b54635be95c6ec9955ec17.

US Tobacco Use Trends by Age and State

Age and State-wise Trends in US Tobacco Use 2011-2016

By Throwback Thursday [source]

About this dataset

The US Tobacco Use 2011-2016 dataset provides comprehensive information on tobacco use trends in the United States from 2011 to 2016. The data is derived from the CDC Behavioral Risk Factor Survey, which collects data on tobacco use across different age groups and states. The dataset includes variables such as age group, year of data collection, type of tobacco product used, state abbreviation where the data was collected, and the corresponding percentage or number representing the tobacco use data. Additionally, it specifies the unit of measurement for the data value (e.g., percentage or number). This dataset aims to offer valuable insights into patterns of tobacco use in different demographic segments and geographical locations within the United States over a six-year period

How to use the dataset

Step 1: Familiarize yourself with the columns: - Year: Represents the year in which the data was collected. - State Abbreviation: Indicates the abbreviation of the state where the data was collected. - Tobacco Type: Specifies the type of tobacco product used. - Data Value: Represents either a percentage or a number that represents tobacco use data. - Data Value Unit: Indicates whether the measurement is a percentage or a number. - Age Group: Specifies which age group corresponds to each piece of tobacco use data.

Step 2: Identify your area of interest: Consider what specific information you are looking for within this dataset. For example, if you want to examine trends in cigarette smoking among young adults (age group), select relevant columns like Year, State Abbreviation, Data Value (percentage/number), etc. By narrowing down your focus, you can analyze specific trends efficiently.

Step 3: Filter and sort your data: Use filtering features provided by spreadsheet software or coding languages (e.g., Python) to extract only relevant information based on your area of interest. You can filter by year(s), state(s), age group(s), or type(s) of tobacco product used using logical operators such as equal (=) and not equal (!=). This way, you can obtain a subset of data that meets your criteria for analysis conveniently.

Step 4: Analyze trends over time: Utilize line charts or bar graphs to visualize changes in tobacco use percentages or numbers over the years. This will allow you to identify any significant patterns or fluctuations, observing whether there are any consistent trends across different states or age groups.

Step 5: Compare tobacco use between states: To assess the differences in tobacco use across various states, aggregate and compare the data using statistical measures such as averages, medians, and standard deviations. By identifying states with higher or lower tobacco use rates, you can gain insights into potential factors affecting these patterns (e.g., state-specific regulations, cultural norms).

Step 6: Explore variations by age group: Investigate how tobacco use varies among different age groups. Compare percentages/

Research Ideas

• Analyzing trends in tobacco use by age and state: This dataset provides information on tobacco use in the United States from 2011 to 2016, allowing for the analysis of trends over time and differences between states. Researchers or policymakers can use this information to examine changes in tobacco consumption rates and identify patterns or factors influencing tobacco use across different age groups and states.
• Comparing the effectiveness of tobacco control measures: With this dataset, it is possible to assess how different tobacco control measures implemented by states have impacted tobacco consumption rates. By comparing data on tobacco use with specific policies, such as smoke-free laws or increased taxation, researchers can evaluate the effectiveness of these interventions and guide future public health initiatives.
• Investigating disparities in tobacco use: By examining data on age, state, and type of tobacco product used, it is possible to explore disparities in smoking prevalence across different demographic groups and geographic areas. This dataset can be used to identify populations that are more susceptible to smoking or are experiencing higher rates of cigarette usage compared to other groups. This information can inform targeted interventions aimed at reducing these disparities and promoting healthier behaviors among vulnerable populations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

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PurposeAround 5% of United States (U.S.) population identifies as Sexual and Gender Diverse (SGD), yet there is limited research around cancer prevention among these populations. We present multi-pronged, low-cost, and systematic recruitment strategies used to reach SGD communities in New Mexico (NM), a state that is both largely rural and racially/ethnically classified as a “majority-minority” state.MethodsOur recruitment focused on using: (1) Every Door Direct Mail (EDDM) program, by the United States Postal Services (USPS); (2) Google and Facebook advertisements; (3) Organizational outreach via emails to publicly available SGD-friendly business contacts; (4) Personal outreach via flyers at clinical and community settings across NM. Guided by previous research, we provide detailed descriptions on using strategies to check for fraudulent and suspicious online responses, that ensure data integrity.ResultsA total of 27,369 flyers were distributed through the EDDM program and 436,177 impressions were made through the Google and Facebook ads. We received a total of 6,920 responses on the eligibility survey. For the 5,037 eligible respondents, we received 3,120 (61.9%) complete responses. Of these, 13% (406/3120) were fraudulent/suspicious based on research-informed criteria and were removed. Final analysis included 2,534 respondents, of which the majority (59.9%) reported hearing about the study from social media. Of the respondents, 49.5% were between 31-40 years, 39.5% were Black, Hispanic, or American Indian/Alaskan Native, and 45.9% had an annual household income below $50,000. Over half (55.3%) were assigned male, 40.4% were assigned female, and 4.3% were assigned intersex at birth. Transgender respondents made up 10.6% (n=267) of the respondents. In terms of sexual orientation, 54.1% (n=1371) reported being gay or lesbian, 30% (n=749) bisexual, and 15.8% (n=401) queer. A total of 756 (29.8%) respondents reported receiving a cancer diagnosis and among screen-eligible respondents, 66.2% reported ever having a Pap, 78.6% reported ever having a mammogram, and 84.1% reported ever having a colonoscopy. Over half of eligible respondents (58.7%) reported receiving Human Papillomavirus vaccinations.ConclusionStudy findings showcase effective strategies to reach communities, maximize data quality, and prevent the misrepresentation of data critical to improve health in SGD communities.

Objective: Case-control study designs are commonly used in retrospective analyses of Real-World Evidence (RWE). Due to the increasingly wide availability of RWE, it can be difficult to determine whether findings are robust or the result of testing multiple hypotheses. Materials and Methods: We investigate the potential effects of modifying cohort definitions in a case-control association study between depression and Type 2 Diabetes Mellitus (T2D). We used a large (>75 million individuals) de-identified administrative claims database to observe the effects of minor changes to the requirements of glucose and hemoglobin A1c tests in the control group. Results: We found that small permutations to the criteria used to define the control population result in significant shifts in both the demographic structure of the identified cohort as well as the odds ratio of association. These differences remain present when testing against age and sex-matched controls. Discussion: Analyses of RWE need to be carefully designed to avoid issues of multiple testing. Minor changes to control cohorts can lead to significantly different results and have the potential to alter even prospective studies through selection bias. Conclusion: We believe this work offers strong support for the need for robust guidelines, best practices, and regulations around the use of observational RWE for clinical or regulatory decision making.