100+ datasets found
  1. Saving measures due to rising costs in the UK 2022

    • statista.com
    Updated Aug 5, 2022
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    Statista (2022). Saving measures due to rising costs in the UK 2022 [Dataset]. https://www.statista.com/forecasts/1324974/saving-measures-due-to-rising-costs-in-the-uk
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    Dataset updated
    Aug 5, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jun 30, 2022 - Jul 10, 2022
    Area covered
    United Kingdom
    Description

    53 percent in the UK plan to save money in the area of current contracts and subscriptions in times of high inflation and rising energy costs, while 47 percent plan on saving money when purchasing clothes, followed by 44 percent who plan to cut back on going out (visiting bars, cafés or restaurants. These are results of the GCS Special Finance & Assets 2022.

  2. Groceries price increase in the U.S. 2021-2024, by category

    • statista.com
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    Statista, Groceries price increase in the U.S. 2021-2024, by category [Dataset]. https://www.statista.com/statistics/1301086/grocery-categories-price-increase-us/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Dec 2021 - Dec 2024
    Area covered
    United States
    Description

    Food price increases hit the egg category the hardest between December 2021 and December 2024 in the United States. The price of eggs increased by **** percent in 2024.

  3. d

    Percent of Households Burdened by Housing Costs

    • data.ore.dc.gov
    Updated Aug 20, 2024
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    City of Washington, DC (2024). Percent of Households Burdened by Housing Costs [Dataset]. https://data.ore.dc.gov/datasets/percent-of-households-burdened-by-housing-costs
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    Dataset updated
    Aug 20, 2024
    Dataset authored and provided by
    City of Washington, DC
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    ACS 1-year estimates are based on data collected over one calendar year, offering more current information but with a higher margin of error. ACS 5-year estimates combine five years of data, providing more reliable information but less current. Both are based on probability samples. Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%.

    Data Source: American Community Survey (ACS) 1- & 5-Year Estimates

    Why This Matters Housing is a basic necessity, and affordable housing is essential for individuals and families to live and thrive in DC.The rising cost of housing threatens residents’ access to safe and stable housing as well as their ability to cover other essential expenses like food, transportation, and childcare.Racial segregation, housing discrimination, and racist urban renewal programs, among other policies and practices, have meant that Black residents and residents of color in the District disproportionately experience the effects of rapidly rising housing costs. The District's Response Leading the nation in policies and investments for low-income rental households. Target of 12,000 new affordable housing units by 2025. Steps taken to preserve and expand affordable housing include the Housing Production Trust Fund, the Affordable Housing Preservation Fund, and the Home Purchasing Assistance Program, among others.

  4. a

    Home care costs in Rising City vs. state and national averages

    • aplaceformom.com
    html
    Updated Nov 14, 2024
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    (2024). Home care costs in Rising City vs. state and national averages [Dataset]. https://www.aplaceformom.com/home-care/nebraska/rising-city
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    htmlAvailable download formats
    Dataset updated
    Nov 14, 2024
    Area covered
    Rising City
    Description

    Daily, monthly, and annual costs in the table below are based on a part-time care schedule of 4 hours of care per day, 5 days per week.

  5. g

    The rising cost of living and its effects on Londoners | gimi9.com

    • gimi9.com
    Updated May 7, 2008
    + more versions
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    (2008). The rising cost of living and its effects on Londoners | gimi9.com [Dataset]. https://gimi9.com/dataset/uk_the-rising-cost-of-living-and-its-effects-on-londoners/
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    Dataset updated
    May 7, 2008
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This work looks at the spiralling cost of living and the challenges facing Londoners including the rising poverty levels in the capital. The latest update is dated August 2022. The report and public attitudes and behaviour charts (published 1 February 2022) were republished (7 April 2022) to correct a calculation error. This error was due to manual calculation.

  6. Food price index inflation rate New Zealand 2016-2025

    • statista.com
    Updated Apr 15, 2025
    + more versions
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    Statista (2025). Food price index inflation rate New Zealand 2016-2025 [Dataset]. https://www.statista.com/statistics/1334504/new-zealand-food-price-index-inflation-rate/
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    Dataset updated
    Apr 15, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2016 - Apr 2025
    Area covered
    New Zealand
    Description

    In April 2025, the food price index in New Zealand had risen by around *** percent in comparison to the same period of the previous year. The rising cost of food products contributed to the overall increasing cost of living in the country.

  7. a

    Nursing homes costs in Rising Sun, WI, over time

    • aplaceformom.com
    html
    Updated Oct 23, 2025
    + more versions
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    (2025). Nursing homes costs in Rising Sun, WI, over time [Dataset]. https://www.aplaceformom.com/nursing-homes/wisconsin/rising-sun
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    htmlAvailable download formats
    Dataset updated
    Oct 23, 2025
    Area covered
    Rising Sun
    Description

    Cost comparison table showing 2023 and 2024 median costs by location

  8. Impact of increased cost of living on adults across Great Britain: September...

    • gov.uk
    Updated Feb 20, 2023
    + more versions
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    Office for National Statistics (2023). Impact of increased cost of living on adults across Great Britain: September 2022 to January 2023 [Dataset]. https://www.gov.uk/government/statistics/impact-of-increased-cost-of-living-on-adults-across-great-britain-september-2022-to-january-2023
    Explore at:
    Dataset updated
    Feb 20, 2023
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Office for National Statistics
    Area covered
    United Kingdom
    Description

    Official statistics are produced impartially and free from political influence.

  9. 2014 sub-regional fuel poverty data: low income high costs indicator

    • gov.uk
    Updated Jun 30, 2016
    + more versions
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    Department of Energy & Climate Change (2016). 2014 sub-regional fuel poverty data: low income high costs indicator [Dataset]. https://www.gov.uk/government/statistics/2014-sub-regional-fuel-poverty-data-low-income-high-costs-indicator
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    Dataset updated
    Jun 30, 2016
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department of Energy & Climate Change
    Description

    2014 sub-regional fuel poverty data: low income high costs indicator.

  10. D

    High-Cost Claimant Prediction For Payers Market Research Report 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Oct 1, 2025
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    Dataintelo (2025). High-Cost Claimant Prediction For Payers Market Research Report 2033 [Dataset]. https://dataintelo.com/report/high-cost-claimant-prediction-for-payers-market
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    pptx, pdf, csvAvailable download formats
    Dataset updated
    Oct 1, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    High-Cost Claimant Prediction for Payers Market Outlook



    The global High-Cost Claimant Prediction for Payers market size reached USD 2.48 billion in 2024, as per our latest research, and is expected to grow at a robust CAGR of 14.2% from 2025 to 2033. By the end of the forecast period, the market is projected to achieve a value of USD 7.62 billion in 2033. The primary growth driver for this market is the escalating demand for advanced analytics and artificial intelligence tools among payers to proactively identify and manage high-cost claimants, thereby optimizing healthcare costs and improving patient outcomes.




    One of the most significant growth factors fueling the High-Cost Claimant Prediction for Payers market is the increasing pressure on healthcare payers to control rising medical expenses. High-cost claimants, typically representing less than 5% of insured populations yet accounting for over 50% of total healthcare expenditures, have become a focal point for cost containment strategies. The proliferation of large-scale healthcare data, advancements in machine learning algorithms, and the integration of predictive analytics into payer workflows have enabled organizations to identify high-risk individuals earlier and implement targeted interventions. This has not only improved financial performance for payers but also enhanced the quality of care for at-risk populations by facilitating timely and personalized care management programs.




    Another key driver is the regulatory emphasis on value-based care and population health management. Governments and industry bodies across North America, Europe, and Asia Pacific are increasingly mandating the adoption of predictive analytics to improve transparency, accountability, and efficiency in healthcare delivery. The shift from fee-for-service to value-based reimbursement models compels payers to invest in sophisticated solutions that can accurately forecast high-cost claimants and enable proactive risk stratification. Additionally, the growing prevalence of chronic diseases, an aging global population, and the expansion of health insurance coverage in emerging economies are further amplifying the need for high-cost claimant prediction solutions.




    The market is also benefiting from the rapid digital transformation of the healthcare sector. The adoption of electronic health records (EHRs), interoperability standards, and cloud-based data platforms has created a fertile environment for deploying advanced analytics and AI-powered prediction tools. Payers are leveraging these technologies to consolidate disparate data sources, enhance data accuracy, and generate actionable insights for cost management and fraud detection. As a result, the High-Cost Claimant Prediction for Payers market is witnessing increased investments from both established players and innovative startups, leading to continuous innovation and the development of more sophisticated, scalable, and user-friendly solutions.




    From a regional perspective, North America continues to dominate the market, driven by the presence of major healthcare payers, advanced IT infrastructure, and supportive regulatory frameworks. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid healthcare digitization, increasing insurance penetration, and rising investments in health analytics. Europe is also experiencing steady growth, particularly in countries with mature health insurance markets and strong government initiatives promoting predictive analytics. The Middle East & Africa and Latin America, while still nascent, are expected to witness significant adoption as healthcare systems modernize and focus shifts toward cost efficiency and quality improvement.



    Component Analysis



    The Component segment of the High-Cost Claimant Prediction for Payers market is primarily bifurcated into Software and Services. Software solutions encompass predictive analytics platforms, artificial intelligence (AI) engines, and data management tools designed to aggregate, analyze, and interpret vast datasets from claims, electronic health records, and other sources. These platforms are at the core of high-cost claimant prediction, offering features such as risk scoring, patient segmentation, and visualization dashboards. The demand for software is being propelled by its ability to automate complex data processing tasks, reduce manual errors, and deliver real-time insights, which a

  11. T

    Russia Food Inflation

    • tradingeconomics.com
    • pt.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, Russia Food Inflation [Dataset]. https://tradingeconomics.com/russia/food-inflation
    Explore at:
    excel, xml, csv, jsonAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 2002 - Oct 31, 2025
    Area covered
    Russia
    Description

    Cost of food in Russia increased 8.91 percent in October of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Russia Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  12. United States Health And Medical Insurance Market Size By Type (Preferred...

    • verifiedmarketresearch.com
    Updated Aug 12, 2025
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    VERIFIED MARKET RESEARCH (2025). United States Health And Medical Insurance Market Size By Type (Preferred Provider Organizations (PPO), Health Maintenance Organizations (HMO), High Deductible Health Plans (HDHP), Point of Service Plans (POS)), By Provider (Private, Public), By Coverage Type (Individual, Family, Corporate), By Geographic Scope And Forecast [Dataset]. https://www.verifiedmarketresearch.com/product/united-states-health-and-medical-insurance-market/
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    Dataset updated
    Aug 12, 2025
    Dataset provided by
    Verified Market Researchhttps://www.verifiedmarketresearch.com/
    Authors
    VERIFIED MARKET RESEARCH
    License

    https://www.verifiedmarketresearch.com/privacy-policy/https://www.verifiedmarketresearch.com/privacy-policy/

    Time period covered
    2026 - 2032
    Area covered
    United States
    Description

    United States Health And Medical Insurance Market was valued at USD 120 Billion in 2024 and is projected to reach USD 180 Billion by 2032, growing at a CAGR of 5.3% from 2026 to 2032.United States Health And Medical Insurance Market DynamicsThe key market dynamics that are shaping the United States health and medical insurance market include:Key Market DriversRising Healthcare Costs: The rising cost of healthcare services in the United States is a major driver of the health and medical insurance market, as individuals and employers seek coverage to reduce out-of-pocket costs. According to the United States Centers for Medicare and Medicaid Services (CMS), total national health expenditures exceeded $4.8 trillion by 2023, accounting for approximately 19% of GDP. This rising cost drives more people and corporations to enroll in health insurance to mitigate financial risks.

  13. Vegetable oils price index worldwide 2000-2025

    • statista.com
    Updated Sep 10, 2025
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    T. Ozbun (2025). Vegetable oils price index worldwide 2000-2025 [Dataset]. https://www.statista.com/topics/9262/food-inflation/
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    Dataset updated
    Sep 10, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    T. Ozbun
    Description

    The FAO vegetable oil Price Index* reached 178.32 index points in June of 2008 during the financial crisis. During the pandemic, the price index rose to 184.56 points in October of 2021. After the start of the war in Ukraine, the index jumped to over 251 points in March of 2022. As of September 2025, the index had slightly declined to 167.9 points. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page. For further information about the Russian invasion of Ukraine, please visit our dedicated page on the topic.

  14. f

    Cost prices in euros.

    • figshare.com
    xls
    Updated Nov 7, 2025
    + more versions
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    Sascha F. M. Schulten; Rosa A. Enklaar; Mirjam Weemhoff; Hugo W.F. van Eijndhoven; Sanne A.L. van Leijsen; Eddy M.M. Adang; Kirsten B. Kluivers (2025). Cost prices in euros. [Dataset]. http://doi.org/10.1371/journal.pone.0336030.t001
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    xlsAvailable download formats
    Dataset updated
    Nov 7, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Sascha F. M. Schulten; Rosa A. Enklaar; Mirjam Weemhoff; Hugo W.F. van Eijndhoven; Sanne A.L. van Leijsen; Eddy M.M. Adang; Kirsten B. Kluivers
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundPelvic organ prolapse is a common condition in females. The reported lifetime risk of undergoing pelvic organ prolapse surgery is estimated to affect up to 20% of women. Recently, a higher level of surgical success after the Manchester procedure has been shown compared to sacrospinous hysteropexy. As the costs in healthcare are rising, it is also important to consider the resources and associated cost implications of the choice between these two procedures. An economic evaluation was conducted to compare the alternative costs and benefits.MethodsAn economic evaluation alongside a randomized controlled trial (RCT) was performed from a societal and healthcare perspective at 2 years of follow-up according to the intention to treat principle. The RCT was a multicenter, randomized, open label trial, executed in 26 Dutch hospitals. 434 women were randomly assigned to the Manchester procedure or sacrospinous hysteropexy. Direct costing data were obtained from electronic case report forms and Medical Consumption Questionnaires. Indirect costing data were obtained by the Productivity Cost Questionnaire. Quality-adjusted Life Years (QALYs) were calculated from the scores on the Euroqol5D-5L questionnaire. Mean cost differences and their 95% confidence intervals (CI) were calculated.ResultsFrom the societal perspective, the Manchester procedure was significantly less expensive than sacrospinous hysteropexy, with a mean difference of 1458.34 euros (95% CI −2746.16 to −170.52). There was no significant difference in the number of QALYs gained over period of 2 years between the arms: 1.67 QALYs (95% confidence interval (95% CI) 1.63 to 1.71) for the sacrospinous hysteropexy group and 1.68 QALYs (95% CI 1.65 to 1.72) for the Manchester procedure group (p = 0.346).ConclusionsDuring two years of follow-up the Manchester procedure and sacrospinous hysteropexy showed no statistically significant different effectiveness in terms of QALYs gained against significantly higher costs for sacrospinous hysteropexy.

  15. T

    South Africa Food Inflation

    • tradingeconomics.com
    • jp.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Oct 15, 2025
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    TRADING ECONOMICS (2025). South Africa Food Inflation [Dataset]. https://tradingeconomics.com/south-africa/food-inflation
    Explore at:
    csv, excel, xml, jsonAvailable download formats
    Dataset updated
    Oct 15, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 2009 - Oct 31, 2025
    Area covered
    South Africa
    Description

    Cost of food in South Africa increased 3.90 percent in October of 2025 over the same month in the previous year. This dataset provides the latest reported value for - South Africa Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  16. R

    Russia Structure of Retail Price: Gasoline: AI-95 & Higher: Costs for...

    • ceicdata.com
    Updated Mar 9, 2019
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    CEICdata.com (2019). Russia Structure of Retail Price: Gasoline: AI-95 & Higher: Costs for Delivery [Dataset]. https://www.ceicdata.com/en/russia/structure-of-retail-price-non-food-products-annual/structure-of-retail-price-gasoline-ai95--higher-costs-for-delivery
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    Dataset updated
    Mar 9, 2019
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2010 - Dec 1, 2017
    Area covered
    Russia
    Description

    Russia Structure of Retail Price: Gasoline: AI-95 & Higher: Costs for Delivery data was reported at 2.040 % in 2017. This records an increase from the previous number of 1.990 % for 2016. Russia Structure of Retail Price: Gasoline: AI-95 & Higher: Costs for Delivery data is updated yearly, averaging 1.985 % from Dec 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 2.220 % in 2015 and a record low of 1.750 % in 2014. Russia Structure of Retail Price: Gasoline: AI-95 & Higher: Costs for Delivery data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PF002: Structure of Retail Price: Non Food Products: Annual.

  17. O

    High Cost Disbursements

    • opendata.usac.org
    • datahub.usac.org
    csv, xlsx, xml
    Updated Nov 27, 2025
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    Universal Service Administrative Company (2025). High Cost Disbursements [Dataset]. https://opendata.usac.org/High-Cost/High-Cost-Disbursements/w6qn-gx72
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    xml, xlsx, csvAvailable download formats
    Dataset updated
    Nov 27, 2025
    Dataset authored and provided by
    Universal Service Administrative Company
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    This dataset provides information about total dollars disbursed to Eligible Telecommunication Carriers (ETCs) within the High Cost Program by month and year since January 2003.

  18. a

    Memory care costs in Nutall Rise, FL, over time

    • aplaceformom.com
    html
    Updated Nov 29, 2025
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    (2025). Memory care costs in Nutall Rise, FL, over time [Dataset]. http://www.aplaceformom.com/alzheimers-care/florida/nutall-rise?destination-page=1
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Nov 29, 2025
    Area covered
    Florida, Nutall Rise
    Description

    Cost comparison table showing 2023 and 2024 median costs by location

  19. Assessing the costs of spraying communities predicted to be at high-risk of...

    • figshare.com
    xls
    Updated May 31, 2023
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    Gonzalo M. Vazquez-Prokopec; Cynthia Spillmann; Mario Zaidenberg; Ricardo E. Gürtler; Uriel Kitron (2023). Assessing the costs of spraying communities predicted to be at high-risk of domestic infestation clustering. [Dataset]. http://doi.org/10.1371/journal.pntd.0001788.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Gonzalo M. Vazquez-Prokopec; Cynthia Spillmann; Mario Zaidenberg; Ricardo E. Gürtler; Uriel Kitron
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    1Assumes all communities are visited. Blanket control is performed based on the rule of contiguity (i.e. the nearest neighbor first). Targeted control assumes only communities predicted as high-risk (from the risk map) are visited.2Refers to the city where spraying brigades are based.3Communities with prevalence of domestic infestation by T. infestans higher than 10% are slated for blanket spraying (Tintina = 66 communities and 880 houses; Quimili = 76 communities and 822 houses).4Selected from communities estimated in 3.5The total cost for a Blanket contiguous strategy was estimated to be US$69,779 and for a Targeted strategy US$35,552. Costs were based on Vazquez-Prokopec et al. 2009 [5] estimates and include cost of insecticides (US$6.9 per sprayed house), salaries (US$22 per-diem and US$11.2 wages per technician per day) and mobility (US$1 per km).

  20. T

    Luxembourg - Households without access to internet at home, because the...

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Sep 16, 2020
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    TRADING ECONOMICS (2020). Luxembourg - Households without access to internet at home, because the equipment costs are too high [Dataset]. https://tradingeconomics.com/luxembourg/households-without-access-to-internet-at-home-because-the-equipment-costs-are-too-high-eurostat-data.html
    Explore at:
    xml, csv, json, excelAvailable download formats
    Dataset updated
    Sep 16, 2020
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    Luxembourg
    Description

    Luxembourg - Households without access to internet at home, because the equipment costs are too high was 10.84% in December of 2019, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Luxembourg - Households without access to internet at home, because the equipment costs are too high - last updated from the EUROSTAT on November of 2025. Historically, Luxembourg - Households without access to internet at home, because the equipment costs are too high reached a record high of 11.00% in December of 2015 and a record low of 2.68% in December of 2013.

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Statista (2022). Saving measures due to rising costs in the UK 2022 [Dataset]. https://www.statista.com/forecasts/1324974/saving-measures-due-to-rising-costs-in-the-uk
Organization logo

Saving measures due to rising costs in the UK 2022

Explore at:
Dataset updated
Aug 5, 2022
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
Jun 30, 2022 - Jul 10, 2022
Area covered
United Kingdom
Description

53 percent in the UK plan to save money in the area of current contracts and subscriptions in times of high inflation and rising energy costs, while 47 percent plan on saving money when purchasing clothes, followed by 44 percent who plan to cut back on going out (visiting bars, cafés or restaurants. These are results of the GCS Special Finance & Assets 2022.

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