This dataset includes all the data and R code needed to reproduce the analyses in a forthcoming manuscript:Copes, W. E., Q. D. Read, and B. J. Smith. Environmental influences on drying rate of spray applied disinfestants from horticultural production services. PhytoFrontiers, DOI pending.Study description: Instructions for disinfestants typically specify a dose and a contact time to kill plant pathogens on production surfaces. A problem occurs when disinfestants are applied to large production areas where the evaporation rate is affected by weather conditions. The common contact time recommendation of 10 min may not be achieved under hot, sunny conditions that promote fast drying. This study is an investigation into how the evaporation rates of six commercial disinfestants vary when applied to six types of substrate materials under cool to hot and cloudy to sunny weather conditions. Initially, disinfestants with low surface tension spread out to provide 100% coverage and disinfestants with high surface tension beaded up to provide about 60% coverage when applied to hard smooth surfaces. Disinfestants applied to porous materials were quickly absorbed into the body of the material, such as wood and concrete. Even though disinfestants evaporated faster under hot sunny conditions than under cool cloudy conditions, coverage was reduced considerably in the first 2.5 min under most weather conditions and reduced to less than or equal to 50% coverage by 5 min. Dataset contents: This dataset includes R code to import the data and fit Bayesian statistical models using the model fitting software CmdStan, interfaced with R using the packages brms and cmdstanr. The models (one for 2022 and one for 2023) compare how quickly different spray-applied disinfestants dry, depending on what chemical was sprayed, what surface material it was sprayed onto, and what the weather conditions were at the time. Next, the statistical models are used to generate predictions and compare mean drying rates between the disinfestants, surface materials, and weather conditions. Finally, tables and figures are created. These files are included:Drying2022.csv: drying rate data for the 2022 experimental runWeather2022.csv: weather data for the 2022 experimental runDrying2023.csv: drying rate data for the 2023 experimental runWeather2023.csv: weather data for the 2023 experimental rundisinfestant_drying_analysis.Rmd: RMarkdown notebook with all data processing, analysis, and table creation codedisinfestant_drying_analysis.html: rendered output of notebookMS_figures.R: additional R code to create figures formatted for journal requirementsfit2022_discretetime_weather_solar.rds: fitted brms model object for 2022. This will allow users to reproduce the model prediction results without having to refit the model, which was originally fit on a high-performance computing clusterfit2023_discretetime_weather_solar.rds: fitted brms model object for 2023data_dictionary.xlsx: descriptions of each column in the CSV data files

Context

In the dataset freMTPL2freq risk features and claim numbers were collected for 677,991 motor third-part liability policies (observed on a year).

Content

freMTPL2freq contains 11 columns (+IDpol): • IDpol The policy ID (used to link with the claims dataset). • ClaimNb Number of claims during the exposure period. • Exposure The exposure period. • Area The area code. • VehPower The power of the car (ordered categorical). • VehAge The vehicle age, in years. • DrivAge The driver age, in years (in France, people can drive a car at 18). • BonusMalus Bonus/malus, between 50 and 350: <100 means bonus, >100 means malus in France. • VehBrand The car brand (unknown categories). • VehGas The car gas, Diesel or regular. • Density The density of inhabitants (number of inhabitants per km2) in the city the driver of the car lives in. • Region The policy regions in France (based on a standard French classification)

Acknowledgements

Source: R-Package CASDatasets, Version 1.0-6 (2016) by Christophe Dutang [aut, cre], Arthur Charpentier [ctb]

Inspiration

The Swiss Actuarial Society's data science tutorials ( https://www.actuarialdatascience.org/ADS-Tutorials/ ) are build on the original dataset (see above) . This copy enables the use of notebooks (kernels) to further study this interesting topic.

Context

This dataset is used in Ewards W. Frees' book "Regression Modeling with Actuarial and Financial Applications"(Cambridge University Press 2010), Chapter 20.5 "Case Study: Swedish Automobile Claims". It can be found on the book's Web site http://research.bus.wisc.edu/RegActuaries, als well as related SAS- and R-Codes.

Content

These data were compiled by the Swedish Committee on the Analysis of Risk Premium in Motor Insurance, summarized in Hallin and Ingenbleek (1983) and Andrews and Herzberg (1985). The data are cross-sectional, describing third party automobile insurance claims for the year 1977. The outcomes of interest are the number of claims (the frequency) and sum of payments (the severity), in Swedish kroners. Outcomes are based on 5 categories of distance driven by a vehicle, broken down by 7 geographic zones, 7 categories of recent driver claims experience and 9 types of automobile. Even though there are 2,205 potential distance, zone, experience and type combinations (5 x 7 x 7 x 9 = 2,205), only n = 2,182 were realized in the 1977 data set.

Variable names: Kilometres Zone Bonus Make Insured Claims Payment

Acknowledgements

Pleas cite/acknowledge: Marc Hallin & Jean-François Ingenbleek, The Swedish automobile portfolio in 1977, Scandinavian Actuarial Journal Vol. 1983, Iss. 1, 1983 Andrews, David F., Herzberg, A.M., Data A Collection of Problems from Many Fields for the Student and Research Worker, Springer Series in Statistics, 1985

Inspiration

This upload shall enable actuarial kernels with R an Python

The welfare associated with public insurance is often difficult to quantify because the demand for coverage is unobserved and thus cannot be used to analyze welfare. However, in many settings, individuals can purchase private insurance to supplement public coverage. This paper outlines an approach to use data and variation from private complementary insurance to quantify welfare associated with counterfactuals related to compulsory public insurance. We then apply this approach using administrative data on disability insurance. Our findings suggest that public disability insurance generates substantial surplus for the sample population, and there may be gains to increasing the generosity of coverage.

We determined first- and second-line regimens, including hematopoietic stem cell transplantations, in all diffuse large B cell lymphoma (DLBCL) patients aged ≥20 yr (n = 1,888), registered at the Belgian Cancer Registry (2013–2015). Treatments were inferred from reimbursed drugs, and procedures registered in national health insurance databases. This real-world population-based study allows to assess patients usually excluded from clinical trials such as those with comorbidities, other malignancies (12%), and advanced age (28% are ≥80 yr old). Our data show that the majority of older patients are still started on first-line regimens with curative intent and a substantial proportion of them benefit from this approach. First-line treatments included full R-CHOP (44%), “incomplete” (R-)CHOP (18%), other anthracycline (14%), non-anthracycline (9%), only radiotherapy (3%), and no chemo-/radiotherapy (13%), with significant variation between age groups. The 5-year overall survival (OS) of all patients was 56% with a clear influence of age (78% [20–59 yr] versus 16% [≥85 yr]) and of the type of first-line treatments: full R-CHOP (72%), other anthracycline (58%), “incomplete” (R-)CHOP (47%), non-anthracycline (30%), only radiotherapy (30%), and no chemo-/radiotherapy (9%). Second-line therapy, presumed for refractory (7%) or relapsed disease (9%), was initiated in 252 patients (16%) and was predominantly (71%) platinum-based. The 5-year OS after second-line treatment without autologous stem cell transplantation (ASCT) was generally poor (11% in ≥70 yr versus 17% in 1 within 3 months from incidence), subsequent malignancies (HR 2.50), prior malignancies (HR 1.34), respiratory and diabetic comorbidity (HR 1.41 and 1.24), gender (HR 1.25 for males), and first-line treatment with full R-CHOP (HR 0.41) or other anthracycline-containing regimens (HR 0.72). Despite inherent limitations, patterns of care in DLBCL could be determined using an innovative approach based on Belgian health insurance data.

The Germany Property & Casualty Insurance Market is poised for steady growth, with a market size of $88.55 million in 2025 and a projected Compound Annual Growth Rate (CAGR) of 1.23% from 2025 to 2033. Key drivers of this market include increasing awareness of insurance benefits, stringent government regulations, and a rising number of catastrophic events necessitating comprehensive coverage. Trends shaping the market include the adoption of digital technologies for policy management and claims processing, as well as the integration of artificial intelligence and data analytics to personalize insurance offerings. However, the market faces restraints such as intense competition leading to price wars and the complexity of insurance products which can deter potential customers. The market is segmented by insurance type, with auto insurance, homeowners insurance, commercial property insurance, fire insurance, general liability insurance, and other types such as health and legal insurance being the primary categories. Distribution channels include direct business, agencies, banks, and other credit institutions. Leading companies in the market include Munich Re, R+V Allgemeine Versicherung AG, Zurich Insurance Group, Great Lakes Insurance, Hannover Re, AXA, Ergo Group AG, HDI Global SE, Allianz, and Generali. These firms are focusing on expanding their product portfolios and enhancing customer engagement through innovative solutions. Regionally, Germany remains a significant player within the European market, alongside France, Italy, the United Kingdom, and the Netherlands, due to its robust economic environment and high insurance penetration rates. Recent developments include: December 2022: ERGO launched a new brand claim and accompanying product campaign focusing on 'Making Insurance Easier' in all its marketing and customer communications., July 2022: Hanover Insurance introduced the Hanover i-on Sensor program to reduce business debt. Hanover Insurance Group is a leading non-life insurance company. Through strategic partnerships, Hanover's i-on-sense program provides business owners and organizations with a comprehensive suite of technology services to help prevent theft, property damage, workplace injuries, and other losses.. Key drivers for this market are: Digitalization of the Insurance Industry, Surge in Regulatory Reforms and Mandates. Potential restraints include: Data Privacy and Security Concerns, Rising Multiple Sizable Natural Catastrophes. Notable trends are: Increasing Insurance Contracts is Driving the Market.

BACKGROUND: The Health Insurance Institute of Slovenia (ZZZS) began publishing service-related data in May 2023, following a directive from the Ministry of Health (MoH). The ZZZS website provides easily accessible information about the services provided by individual doctors, including their names. The user is provided relevant information about the doctor's employer, including whether it is a public or private institution. The data provided is useful for studying the public system's operations and identifying any errors or anomalies.

METHODS: The data for services provided in May 2023 was downloaded and analysed. The published data were cross-referenced using the provider's RIZDDZ number with the daily updated data on ambulatory workload from June 9, 2023, published by ZZZS. The data mentioned earlier were found to be inaccurate and were improved using alerts from the zdravniki.sledilnik.org portal. Therefore, they currently provide an accurate representation of the current situation. The total number of services provided by each provider in a given month was determined by adding up the individual services and then assigning them to the corresponding provider.

RESULTS: A pivot table was created to identify 307 unique operators, with 15 operators not appearing in both lists. There are 66 public providers, which make up about 72% of the contractual programme in the public system. There are 241 private providers, accounting for about 28% of the contractual programme. In May 2023, public providers accounted for 69% (n=646,236) of services in the family medicine system, while private providers contributed 31% (n=291,660). The total number of services provided by public and private providers was 937,896. Three linear correlations were analysed. The initial analysis of the entire sample yielded a high R-squared value of .998 (adjusted R-squared value of .996) and a significant level below 0.001. The second analysis of the data from private providers showed a high R Squared value of .904 (Adjusted R Squared = .886), indicating a strong correlation between the variables. Furthermore, the significance level was < 0.001, providing additional support for the statistical significance of the results. The third analysis used data from public providers and showed a strong level of explanatory power, with a R Squared value of 1.000 (Adjusted R Squared = 1.000). Furthermore, the statistical significance of the findings was established with a p-value < 0.001.

CONCLUSION: Our analysis shows a strong linear correlation between contract size of the program signed and number services rendered by family medicine providers. A stronger linear correlation is observed among providers in the public system compared to those in the private system. Our study found that private providers generally offer more services than public providers. However, it is important to acknowledge that the evaluation framework for assessing services may have inherent flaws when examining the data. Prescribing a prescription and resuscitating a patient are both assigned a rating of one service. It is crucial to closely monitor trends and identify comparable databases for pairing at the secondary and tertiary levels.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

The Kidnap & Ransom (K&R) insurance market is experiencing significant growth, driven by increasing cross-border kidnappings, transnational organized crime, and heightened geopolitical instability. The market's expansion is fueled by rising awareness among individuals, businesses, and organizations about the devastating financial and reputational consequences of kidnapping and extortion. While precise figures aren't provided, a conservative estimate suggests the global K&R insurance market size was around $2 billion in 2025, considering the high-net-worth individuals and corporate clientele served. This market is expected to exhibit a Compound Annual Growth Rate (CAGR) of approximately 8-10% over the forecast period (2025-2033), reaching an estimated $4 billion by 2033. This growth is attributed to factors such as evolving kidnapping tactics, the growing digital footprint increasing vulnerability, and a wider acceptance of K&R insurance as a crucial risk mitigation strategy. The segments with the highest growth potential are likely to be business and organizational coverage due to their higher financial capacity and the increased risk they face in volatile regions. While advancements in security technology and enhanced risk mitigation strategies might counter this growth to some extent, the continued rise in high-profile kidnappings and ransom demands will sustain significant market expansion. The market is characterized by a concentration of major players like AIG, Chubb, Hiscox, and Beazley, who leverage their extensive networks and specialized expertise to offer comprehensive K&R insurance solutions. Competition is fierce, with insurers striving to differentiate themselves through innovative products, improved risk assessment methodologies, and enhanced crisis response capabilities. North America and Europe currently dominate the market, but developing regions like Asia-Pacific and the Middle East & Africa show considerable untapped potential, promising significant growth opportunities in the coming years. Regulatory changes and evolving insurance policies across various jurisdictions can significantly influence the market's trajectory. Restraints include the difficulty in accurately assessing risk, the ethical considerations surrounding ransom payments, and potential regulatory hurdles related to anti-terrorism financing laws. However, these challenges are countered by the crucial role K&R insurance plays in protecting lives and mitigating the devastating financial impact of kidnappings.

Non-life insurance comprises the assumption of risk through insurance contracts for all risks relating to illness, accidents and property damage. The industry is expected to achieve a turnover of 170.6 billion euros in 2024, which corresponds to an increase of 0.8% compared to the previous year. In 2021, industry participants were burdened by high payouts as a result of the flood disaster in the Ahr valley, which at the same time boosted demand for natural hazard insurance. In view of high inflation, the European Central Bank has recently successively raised the key interest rate, which has had a positive effect on the investments of industry participants. However, the first interest rate cut was made in June 2024, which could lead to a trend reversal if inflation eases. Since 2019, the industry has recorded average annual growth of 1.8%.Insurance companies are benefiting from the high demand for property and casualty insurance and have been able to increase their sales. Motor vehicle insurance in particular is enjoying great popularity. However, the weak economy, numerous customers switching to statutory insurance and strong competition on the market are making it difficult to acquire new customers. The area of private supplementary insurance is developing positively, although it only accounts for a very small proportion of sales. Over the past five years, the sector has benefited greatly from the rise in net disposable household income, as this has made more insurance products affordable for consumers and also increased the value of insured goods.IBISWorld forecasts average annual sales growth of 1.3% to €182.1 billion in 2029 for the period from 2024 to 2029. Future development will be characterised primarily by the use of data analysis and artificial intelligence. These methods will be used to predict extreme weather events, accidents and health developments. However, acceptance among the population is problematic here, as consumers are reluctant to accept major intrusions into their privacy. Incentives such as cheaper tariffs or support for fitness memberships are ways of counteracting the scepticism of the population. The digitalisation of the industry will increase over the next few years and companies will increasingly rely on online presences, smartphone apps and a presence on comparison portals to attract new customers.

Adaptationist models predict that individuals at higher risk of death will be calibrated to prioritize immediate over future benefits. However, operationalizing individual mortality risk in empirical studies has proven challenging. We introduce and explore a novel method of operationalizing individual mortality risk: Using the risk ratings assigned by actuaries to purchasers of individual life insurance policies. Participants, who had recently gone through underwriting as part of the insurance application process, completed self-report instruments to assess personality traits related to present-future tradeoffs and a putative fast-slow continuum of life history strategy. Study 1 (n = 270) found that insurance-based mortality risk associated negatively with a measure of slow life strategy and positively with a measure of short-term mating orientation. Study 2 (n = 402), which was preregistered, found that insurance-based mortality risk associated positively with impulsivity and negativel..., , # Mortality risk estimates from life insurance policies predict individual differences in human behavioral traits

Data were collected from two U.S. online participant samples (N = 270 and N = 402), screened to include only individuals who had purchased individual life insurance policies within the past five years. In both data sets, participants were asked for the risk ratings they had been assigned by the insurance company, and to complete self-report instruments measuring constructs relevant to psychometric life history (especially the present-future trade-off). In the second data set, participants were also asked to indicate their self-estimated lifespan, and were asked to complete three instruments measuring recalled childhood environmental harshness. R code used to analyze the data is also provided.

Description of the Data and file structure

Lukaszewski-Manson-Study-1-R.csv

Key to column headings

female: 1 = yes

age_bin: (1 = younger than 25 years, 2 = 25-29, 3 =..., We have received explicit consent from our participants to publish the de-identified data in the public domain. We have de-identified the data by removing all individually identifying information (IP addresses, and for the six in-person participants in Study 2, their names) from data files before uploading them.

In 2023, single coverage health insurance for employees cost more than 8,000 U.S. dollars for the year. this figure has increase every year since 2000, with the average annual cost of health insurance for singles being 2,471 in 2000.

Financial Lines Insurance Market Outlook

The global financial lines insurance market size was valued at approximately USD 40 billion in 2023 and is expected to grow at a compound annual growth rate (CAGR) of 5.5% from 2024 to 2032, reaching a projected market size of around USD 61 billion by 2032. The market's growth is driven by increasing awareness about the importance of financial protection among businesses and individuals, alongside the rising complexity and frequency of financial risks in an ever-evolving economic landscape. As financial markets become more interconnected and regulatory frameworks more stringent, companies and individuals alike seek comprehensive insurance solutions to safeguard their financial interests and mitigate potential liabilities.

One of the key growth factors propelling the financial lines insurance market is the escalating risk of cyber threats and data breaches. With businesses increasingly relying on digital platforms and cloud services, the potential for cyberattacks has soared, prompting a significant demand for cyber liability insurance. Organizations are becoming acutely aware of the repercussions of cyber incidents, which can lead to substantial financial losses, reputational damage, and legal liabilities. Consequently, cyber liability insurance has emerged as a critical component within the financial lines insurance portfolio, offering coverage against various cyber risks and fostering market growth. Moreover, the evolving regulatory landscape concerning data protection and privacy, such as the implementation of GDPR in Europe, has further underscored the need for robust cyber insurance coverage.

Another significant growth driver in the financial lines insurance market is the increasing complexity of corporate governance and regulatory compliance. As businesses expand globally and navigate intricate regulatory environments, the demand for directors and officers (D&O) liability insurance has surged. This type of insurance provides financial protection to executives and board members against claims arising from managerial decisions that may result in legal actions. The heightened focus on corporate responsibility, ethical governance, and shareholder activism has amplified the need for comprehensive D&O insurance policies, thereby contributing to the market's expansion. Furthermore, high-profile corporate scandals and financial misconduct cases have heightened awareness and prompted organizations to seek greater protection against potential legal and financial repercussions.

In addition to regulatory complexities, the rise in litigation and professional liabilities has spurred the growth of the financial lines insurance market. Industries such as healthcare, legal services, and consulting are witnessing a surge in claims related to professional negligence and malpractice, leading to an increased demand for professional indemnity insurance. Professionals and firms operating in these sectors are recognizing the necessity of safeguarding their financial interests against potential legal actions that can result in significant financial losses and reputational harm. The growing emphasis on accountability and the need to protect professional reputation further drive the adoption of professional indemnity insurance, contributing to the overall market expansion.

Regionally, North America remains a dominant player in the financial lines insurance market, primarily due to the presence of a well-established insurance sector and the high adoption rate of financial protection products among businesses. The region's robust economic environment and stringent regulatory frameworks have bolstered the demand for various financial lines insurance products. Meanwhile, the Asia Pacific region is expected to witness significant growth during the forecast period, owing to the rapid economic development, increasing awareness about financial risks, and the expansion of businesses in emerging markets. The rising number of startups and small enterprises in this region is also contributing to the demand for financial lines insurance, as these entities seek protection against potential risks and liabilities.

Product Type Analysis

In the financial lines insurance market, product types play a crucial role in defining the scope and applicability of coverage. Directors and Officers (D&O) Liability Insurance is a prominent segment within this market, as it provides protection to senior executives and board members against claims resulting from alleged wrongful acts in their managerial roles. The demand for D&O insurance is particularly high in industries with complex r

Analysis of ‘ Medical Cost Personal Datasets’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mirichoi0218/insurance on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

Machine Learning with R by Brett Lantz is a book that provides an introduction to machine learning using R. As far as I can tell, Packt Publishing does not make its datasets available online unless you buy the book and create a user account which can be a problem if you are checking the book out from the library or borrowing the book from a friend. All of these datasets are in the public domain but simply needed some cleaning up and recoding to match the format in the book.

Content

Columns - age: age of primary beneficiary

• sex: insurance contractor gender, female, male

• bmi: Body mass index, providing an understanding of body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9

• children: Number of children covered by health insurance / Number of dependents

• smoker: Smoking

• region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.

• charges: Individual medical costs billed by health insurance

Acknowledgements

The dataset is available on GitHub here.

Inspiration

Can you accurately predict insurance costs?

--- Original source retains full ownership of the source dataset ---

Tweedie’s compound Poisson model is a popular method to model data with probability mass at zero and nonnegative, highly right-skewed distribution. Motivated by wide applications of the Tweedie model in various fields such as actuarial science, we investigate the grouped elastic net method for the Tweedie model in the context of the generalized linear model. To efficiently compute the estimation coefficients, we devise a two-layer algorithm that embeds the blockwise majorization descent method into an iteratively reweighted least square strategy. Integrated with the strong rule, the proposed algorithm is implemented in an easy-to-use R package HDtweedie, and is shown to compute the whole solution path very efficiently. Simulations are conducted to study the variable selection and model fitting performance of various lasso methods for the Tweedie model. The modeling applications in risk segmentation of insurance business are illustrated by analysis of an auto insurance claim dataset. Supplementary materials for this article are available online.

PurposeEarly childhood developmental delay remains problematic worldwide in terms of weight and the five domains of child development, including gross motor, fine motor, cognition, language, and social domains. Based on the World Health Organization (WHO) guideline and the theoretical domain framework, this study identified five key socioeconomic factors, such as parenting time during hospitalization, parental educational level, medical spending, distance to hospital, and medical insurance coverage, to describe how these five factors are associated with improved outcomes of developmental quotient (DQ) values and the weight of infants in a tertiary hospital.MethodsIn this prospective observational study, clinical and socioeconomic data were collected. Clinical data included the weight and DQ values of infants and other data relevant to the birth of infants. A National Developmental Scale was used to observe infants in five domains and calculate the DQ values of infants. These five domains include gross motor, fine motor, cognition, language, and social domains. Parenting time during hospitalizations was observed by a research nurse. Other socioeconomic factors were reported by parents and verified with system information.ResultsA total of 75 infants' parents were approached, of which 60 were recruited. The age of infants ranged from 75 to 274 days at the first admission. Increments of their weight and DQ values improved from −0.5 to 2.5 kg and from −13 to 63, respectively. More than half of the parents (54.1%) were at the level of minimum secondary education although the results were not statistically significant. However, there was a positive correlation between weight improvement and parenting time during hospitalization (r(58) = 0.258, p < 0.05), medical spending (r(58) = 0.327, p < 0.05), distance to hospital (r(58) = 0.340, p < 0.01), but there was a negative association with medical insurance coverage (r(58) =-0.256, p < 0.05). There was also a significant relationship between the improved DQ value and distance to hospital (r(58)= 0.424, p < 0.01).ConclusionParenting time during hospitalization, medical spending, distance to hospital, and medical insurance coverage are important factors for early childhood developmental delay in relation to possible hospital intervention and improved accessibility to health services for families in rural areas. Therefore, changes in the current medical scheme are needed because a universal medical subsidy among regions will reduce the financial burden of families and provide families with more access to the necessary health services that their children need.

The research aimed to develop and test models of household savings and labour supply to evaluate how reforms to social insurance schemes would impact household behaviour, household well-being, inequality and the public finances. There was no primary data collected as part of the grant. The materials uploaded consist of code to reproduce analysis and open licence secondary data. The 1332-6709-1-SP folder contains the supplementary material for Crawford, R. & O'Dea, C, "Household Portfolios and Financial Preparedness for Retirement" work. The HealthAffairsPaper.7z contains the replication materials supporting the project French EB, McCauley J, Aragon M, Bakx P, Chalkley M, Chen SH, et al. End-of-life medical spending in last twelve months of life is lower than previously reported. Health Aff (Millwood). 2017;36(7). The inheritances_report consists of the dofile "MasterReplication.do" required to re-create the results of the report “Inheritances and inequality over the life cycle: what will they mean for younger generations?”. The data sources used are the End-user-license versions of the English Longitudinal Study of Aging and the ONS Wealth and Assets Survey data. These data are available to download from the UK Data Service Website. The only non-publicly available data used here are a series of estimates made using the Longitudinal Study and exported from the Secure Research Service (SRS). These are available from the authors on request and with permission of the SRS. The authors' are happy to give guidance in how to access the data used in the project. The IntergenAltruismPaper contains the supplementary materials for "Intergenerational Altruism and Transfers of Time and Money: A Lifecycle Perspective" by Uta Bolt, Eric French, Jamie Hentall MacCuish and Cormac O'Dea. All relevant data can be downloaded from the UK Data Service: NCDS - https://beta.ukdataservice.ac.uk/datacatalogue/series/series?id=2000032; UKTUS - https://beta.ukdataservice.ac.uk/datacatalogue/series/series?id=2000054; ELSA - https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=5050 and Family Expenditure Survey - https://beta.ukdataservice.ac.uk/datacatalogue/series/series?id=200016. The JHR_BlundellBrittonCostaDiasFrench contains the files for "The impact of health on labor supply near retirement" by Richard Blundell, University College London, Jack Britton, Institute for Fiscal Studies, Monica Costa Dias, Institute for Fiscal Studies and Eric French, University College London. The LifetimeMedicalSpending contains the code and results used in "The Lifetime Medical Spending of Retirees". Tables 1&2 are produced by the contents of the "healthtrans" directory. The operative file is "life_exp_couples3.gau", which runs in GAUSS. (c_elifeMCs.m is Matlab code that performs the same calculations for a given household configuration, but it does not produce the summary tables.) The output resides in "life_exp_couples3_021118b.out". Look for the bottom instance of the phrase "Life Expectancy Tables". The results for the oldest survivor lie to the far right of the panel for couples. Finally the MediationPaper consists of the supplemnetary materials for "The Intergenerational Elasticity of Earnings: Exploring the Mechanisms" by Uta Bolt, Eric French, Jamie Hentall MacCuish, and Cormac O'Dea Details of what each of the flders contain are in the respective ReadMe files.

The provision of 'social insurance' (the benefits governments pay to those who are ill, unemployed, disabled, poor or old), accounts for more government expenditure than any other category of public spending. This social insurance is potentially valuable to all households, not just those receiving those benefits at a given point in time. It ensures that, should households find themselves in difficult circumstances, they will be shielded from extremely low living standards. However, the provision of social insurance also brings costs. These costs are both direct (e.g. the financial cost of the transfers) and indirect (e.g. the provision of benefits reduces the incentives to work and save). Balancing these costs and benefits is a challenge for policy-makers.

Our proposed research will develop and test models of household savings and labour supply to evaluate how reforms to social insurance schemes would impact household behaviour, household well-being, inequality and the public finances.

PDF Map of FCC Form 477 provider reported maximum download speeds by census block for January - June 2020. This map seeks to highlight areas that are undeserved by terrestrial broadband (fiber/cable/dsl on the ground), with "underserved" defined as down/up speeds less than 25/3 Mbps.These data represent a static snapshot of provider reported coverage between January 2020 and June 2020. Maps also depict the locations of federally recognized tribes, Alaskan communities, ANCSA and borough boundaries.Broadband coverage is represented using provider reported speeds under the FCC Form 477 the amalgamated broadband speed measurement category based on Form 477 "All Terrestrial Broadband" as a proxy for coverage. This field is unique to the NBAM platform. These maps do not include satellite internet coverage (and may not include microwave coverage through the TERRA network for all connected areas).This map was produced by DCRA using data provided by NTIA through the NBAM platform as part of a joint data sharing agreement undertaken in the year 2021. Maps were produced using the feature layer "NBAM Data by Census Geography v4": https://maps.ntia.gov/arcgis/home/item.html?id=8068e420210542ba8d2b02c1c971fb20Coverage is symbolized using the following legend:No data avalible or no terrestrial coverage: Grey or transparent< 10 Mbps Maximum Reported Download: Red10-25 Mbps Maximum Reported Download: Orange25-50 Mbps Maximum Reported Download: Yellow50-100 Mbps Maximum Reported Download: Light Blue100-1000 Mbps Maximum Reported Download: Dark Blue_Description from layer "NBAM Data by Census Geography v4":This layer is a composite of seven sublayers with adjacent scale ranges: States, Counties, Census Tracts, Census Block Groups, Census Blocks, 100m Hexbins and 500m Hexbins. Each type of geometry contains demographic and internet usage data taken from the following sources: US Census Bureau 2010 Census data (2010) USDA Non-Rural Areas (2013) FCC Form 477 Fixed Broadband Deployment Data (Jan - Jun 2020) Ookla Consumer-Initiated Fixed Wi-Fi Speed Test Results (Jan - Jun 2020) FCC Population, Housing Unit, and Household Estimates (2019). Note that these are derived from Census and other data. BroadbandNow Average Minimum Terrestrial Broadband Plan Prices (2020) M-Lab (Jan - Jun 2020)Some data values are unique to the NBAM platform: US Census and USDA Rurality values. For units larger than blocks, block count (urban/rural) was used to determine this. Some tracts and block groups have an equal number of urban and rural blocks—so a new coded value was introduced: S (split). All blocks are either U or R, while tracts and block groups can be U, R, or S. Amalgamated broadband speed measurement categories based on Form 477. These include: 99: All Terrestrial Broadband Plus Satellite 98: All Terrestrial Broadband 97: Cable Modem 96: DSL 95: All Other (Electric Power Line, Other Copper Wireline, Other) Computed differences between FCC Form 477 and Ookla values for each area. These are reflected by six fields containing the difference of maximum, median, and minimum upload and download speed values.The FCC Speed Values method is applied to all speeds from all data sources within the custom-configured Omnibus service pop-up. This includes: Geography: State, County, Tract, Block Group, Block, Hex Bins geographies Data source: all data within the Omnibus, i.e. FCC, Ookla, M-Lab Representation: comparison tables and single speed values