The “Système National des Données de Santé” (the French National Health Data System – SNDS) consists of individual data from three databases: the inter-scheme consumption datamart, i.e. the national claims database (DCIR), the national hospital discharge database (PMSI), and the national causes-of-death register. The SNDS covers continuously around 99% of the French population, i.e. more than 67 million people.

Characteristics of subjects benefiting from cancer-related healthcare in 2010 and 2015.

BackgroundReliable epidemiological data on Alzheimer's disease are scarce. However, these are necessary to adapt healthcare policy in terms of prevention, care and social needs related to this condition. To estimate the prevalence rate in the Alpes-Maritimes on the French Riviera, with a population of one million, we present a capture-recapture procedure applied to cases of Alzheimer’s disease, based on two epidemiological surveillance systems.MethodsTo estimate the total number of patients affected by Alzheimer's disease, a capture-recapture study included a cohort of patients with Alzheimer's disease or receiving medications only eligible for use for this condition, recorded by a specific health insurance information system (Health Insurance Cohort, HIC), and those registered in the French National Alzheimer’s Data Bank (“Banque Nationale Alzheimer”, BNA) in 2010 and 2011. We applied Bayesian estimation of the Mt ecological model, taking into account age and gender as covariates, i.e. factors of inhomogeneous catchability.ResultsOverall, 5,562 patients with Alzheimer's disease were recorded, of whom only 856 were common to both information systems. Mean age and F/M sex ratio differed between BNA and HIC surveillance systems, 81 vs 84 years and 2.7 vs 3.2, respectively. A Bayesian estimation, with age and gender as covariates, yields an estimate of 15,060 cases of Alzheimer's disease [95%HPDI: 14,490–15,630] in the Alpes-Maritimes. The completeness of the HIC and BNA databases were respectively of 25.4% and 17.2%. The estimated prevalence rate among the population over 65 years old was 6.3% in 2010–2011.ConclusionsThis study demonstrates that it is possible to determine the number of subjects affected by Alzheimer's disease in a geographical unit, using available data from two existing surveillance systems in France, i.e. 15,060 cases in the Alpes-Maritimes. This is the first stage of a population-based approach in view of adapting available resources to the population’s needs.

To compare healthcare use and the number of days of sickness benefits between people with anterior cruciate ligament (ACL) injury who received physiotherapy before and after ACL reconstruction (ACLR) and those who received physiotherapy after ACLR only. Secondary aim: to measure the association between the volume of preoperative healthcare and post-ACLR recovery. Each individual’s care pathway was extracted from a section of the French National Health Data System (SNDS) database (province: Pays de La Loire). The database was queried for the codes related to sickness benefits and healthcare utilization, including physiotherapy, medical and paramedical visits and procedures, medication, and medical equipment provided up to six months before and eighteen months after the ACLR. (Registry/number: ClinicalTrials.gov/NCT05737719). Based on the timing of physiotherapy, two subcohorts were created from the database: ‘prehabilitation’ (n = 513) for those receiving physiotherapy before and after ACLR; ‘no prehabilitation’ (n = 630) for those only receiving physiotherapy after ACLR. Before ACLR, healthcare use was higher for the ‘prehabilitation’ group, including the number of medical visits (3.9 ± 2.3 vs. 3.0 ± 1.9 univariate p < 0.001), analgesia (mild opioids 60.4% vs. 49.8% univariate p < 0.001), dispensing of medical equipment (85.0% vs. 68.9% univariate p < 0.001) and sickness benefit days (52.7 ± 45.6 days vs. 33.2 ± 35.8 days, univariate p < 0.001). After ACLR, the ‘prehabilitation’ group underwent a higher number of physiotherapy sessions (46.8 ± 21.9 sessions vs 35.8 ± 19.0 sessions, p < 0.001) but had a similar number of sickness benefit days (94.7 ± 77.8 days vs 87.1 ± 69.9 days, p = 0.092). From the multivariate analysis (n = 1143): age, comorbidities, the preoperative number of sickness benefit days, and the number of physiotherapy sessions before ACLR explained 24% of the variance in days of sickness benefits after ACLR. Prehabilitation was associated with higher healthcare utilization before and after ACLR. Prehabilitation, and other preoperative variables, explained only a part of the number of days of sickness benefits after ACLR.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

This dataset is one of the sources of data visualisations available on the [Liberal Health Professionals] website(https://data.ameli.fr/pages/data-professionnels-sante-liberaux/). ### General information: The liberal health professions available in this dataset are: * the doctors (with more than twenty medical specialties); * dental surgeons** (including dentofacial orthopaedic specialists – ODF); * the women; * medical assistants with five professions: nurses, massage therapists, speech therapists, orthoptists, pedicures-podologists. They are health professionals active on 31 December of the year concerned and: * exercising their activity as a liberal; * in metropolitan France, Guadeloupe, French Guiana, Reunion, Martinique and Mayotte; * having received at least EUR 1 in fees; * whether they are contracted with the Sickness Insurance or not (when they generate a prescription reimbursed by the Sickness Insurance); * professionals in employment-retirement cumulation are counted in the workforce as long as they meet the previous conditions. This dataset presents information on the patientele of liberal healthcare professionals: * number of unique patients (active file); * number of “doctor treating” patients (only for general physicians and pediatricians). Several territorial levels are available: national level (whole France), region, department. The data are derived from the National Health Data System (NSDS). For more information (source, field, definitions of modalities), visit the Method page of this site. ### Statistical confidentiality: Out of respect for statistical confidentiality (Law of 7 June 1951) and in order that direct or indirect identification of individuals is impossible, no information on fees, prescriptions and patient care is provided when the number of liberal health professionals is strictly less than 5. The value of the indicator is then indicated by “NS” (not significant) in the dataset. ### Abbreviations present in the data: * “NS” = non-significant (application of statistical confidentiality) * “NC” = not calculated (occupation not concerned, etc.) ### Data update: The data proposed for download in the “Export” tab is updated every year (data from the whole of France since 2016).

This dataset is one of the sources of data visualisations available on the [Liberal Health Professionals] website(https://data.ameli.fr/pages/data-professionnels-sante-liberaux/). ### General information: The liberal health professions available in this dataset are: * the doctors (with more than twenty medical specialties); * dental surgeons** (including dentofacial orthopaedic specialists – ODF); * the women; * medical assistants with five professions:**nurses, massage therapists, speech therapists, orthoptists, pedicures-podologists**. They are health professionals active on 31 December of the year concerned and: * exercising their activity as a liberal; * in metropolitan France, Guadeloupe, French Guiana, Reunion, Martinique and Mayotte; * having received at least EUR 1 in fees; * whether they are contracted with the Sickness Insurance or not (when they generate a prescription reimbursed by the Sickness Insurance); * professionals in employment-retirement cumulation are counted in the workforce as long as they meet the previous conditions. This dataset presents information on the fees of liberal healthcare professionals: * amount of fees without exceeding; * amount of overruns; These amounts are presented: * in total amount (for the whole profession); * in average amount (per professional). These average fees are presented for the sub-population of professionals known as “active in their own right” (APE). The dataset also presents, for ** liberal doctors contracted in sector 2**, the exceedance rates (depending on whether or not they join Optam/Optam-CO). Several territorial levels are available: national level (whole France), region, department. The data are derived from the National Health Data System (NSDS). For more information (source, field, definitions of modalities), visit the Method page of this site. ### Statistical confidentiality: Out of respect for statistical confidentiality (Law of 7 June 1951) and in order that direct or indirect identification of individuals is impossible, no information on fees, prescriptions and patient care is provided when the number of liberal health professionals is strictly less than 5. The value of the indicator is then indicated by “NS” (not significant) in the dataset. ### Abbreviations present in the data: * “NS” = non-significant (application of statistical confidentiality) * “NC” = not calculated (occupation not concerned, etc.) ### Data update: The data proposed for download in the “Export” tab is updated every year (data from the whole of France since 2010).

According to a March 2022 survey among the French population, it was found that health professionals were the most trustworthy in terms of data privacy, with roughly ** percent of the respondents stating so. A further ** percent said they trusted banks with their data, while ** percent were confident about sharing their personal data with tax authorities, as well as with the National Health Insurance system. Social networks were the least trustworthy party, according to the survey respondents.

This dataset is one of the sources of data visualisations available on the [Liberal Health Professionals] website(https://data.ameli.fr/pages/data-professionnels-sante-liberaux/). ### General information: The liberal health professions available in this dataset are: doctors (with more than twenty medical specialties). They are health professionals active on 31 December of the year concerned and: * exercising their activity as a liberal; * in metropolitan France, Guadeloupe, French Guiana, Reunion, Martinique and Mayotte; * having received at least EUR 1 in fees; * whether they are contracted with the Sickness Insurance or not (when they generate a prescription reimbursed by the Sickness Insurance); * professionals in employment-retirement cumulation are counted in the workforce as long as they meet the previous conditions. This dataset presents information on the conventional sector of liberal doctors: * contracted in Sector 1; * contracted in Sector 2; * with membership in Optam/Optam-CO; * without membership in Optam/Optam-CO; * not agreed. Several territorial levels are available: national level (whole France), region, department. The data are derived from the National Health Data System (NSDS). For more information (source, field, definitions of modalities), visit the Method page of this site. ### Data update: The data proposed for download in the “Export” tab is updated every year (data from the whole of France since 2010).

This supplemental material provides additional methodological details, definitions, and results supporting the main analysis.

Data Source: Describes the French national health data system (SNDS), and approvals for study.

Marginal Structural Models (MSMs) with Inverse Probability Treatment Weighting (IPTW): Provides a detailed explanation of the statistical modeling framework used to estimate the causal effect of concomitant methotrexate (MTX) on TNFi persistence, including model specification, weight construction, stabilization, and truncation procedures.

Supplemental Tables I–VI:

Table I: Lists the ATC codes and definitions used to identify medication exposures.

Table II: Describes algorithms for identifying comorbidities and related diagnoses from administrative data.

Table III: Summarizes baseline characteristics of the study population across MTX exposure levels.

Table IV: Presents detailed distributions of continuous and categorical MTX exposure across dosing regimens and calculation methods over trimesters.

Table V: Reports results from sensitivity analyses assessing the robustness of the main findings under alternative definitions of MTX exposure.

Table VI: Provides subgroup analyses evaluating the impact of concomitant MTX use on adalimumab persistence.

Supplemental Figure 1: Displays a directed acyclic graph (DAG) illustrating the hypothesized relationships among biologic persistence, concomitant MTX use, and time-varying confounders.

Time trends in the prevalence of treatment with continuous positive airway pressure by gender and age group, France, 2008–2019.

Pneumococcal disease (PD) is associated with high morbidity and mortality, specifically among individuals ≥65 years of age and those with underlying medical conditions (UMCs). This retrospective cohort study estimated the clinical burden of PD in adults ≥18 years of age with or without UMCs in France. Data were obtained from the French National Health Data System for four yearly cohorts (1 January 2015–31 December 2018). Characteristics of patients with UMCs, with or without PD (UMC population), and the incidence rate and lethality rate of PD leading to hospitalization (in-patient PD population), stratified by age and risk status, were described. In the UMC population (n = 7,947,622; mean age: 65 years), the incidence rate of in-patient PD episodes was 121.98 per 100,000 person-years and was highest among individuals ≥65 years of age (138.52) and in those considered medium-risk (102.45) or high-risk (165.77). In the in-patient PD population (n = 41,885), 59.6% were ≥65 years of age; 1-year all-cause mortality following the initial in-patient PD episode was 26.5%. Individuals ≥65 years of age (regardless of risk status) had a higher risk of PD leading to hospitalization than individuals 18–64 years of age. This study shows a high burden of PD in France due to in-patient PD among adults with UMCs, particularly in those ≥65 years of age, despite their eligibility for pneumococcal vaccination. This highlights the need for higher vaccination coverage, supported by the recent extension of vaccination to all people ≥65 years of age, regardless of their health risk status.

This dataset is one of the sources of data visualisations available on the [Liberal Health Professionals] website(https://data.ameli.fr/pages/data-professionnels-sante-liberaux/). ### General information: The liberal health professions available in this dataset are: * the doctors (with more than twenty medical specialties); * dental surgeons** (including dentofacial orthopaedic specialists – ODF); * the women; * medical assistants with five professions: nurses, massage therapists, speech therapists, orthoptists, pedicures-podologists. They are health professionals active on 31 December of the year concerned and: * exercising their activity as a liberal; * in metropolitan France, Guadeloupe, French Guiana, Reunion, Martinique and Mayotte; * having received at least EUR 1 in fees; * whether they are contracted with the Sickness Insurance or not (when they generate a prescription reimbursed by the Sickness Insurance); * professionals in employment-retirement cumulation are counted in the workforce as long as they meet the previous conditions. This dataset presents information on the type of exercise of liberal health professionals: * exclusive liberal exercise; * mixed liberal exercise. Several territorial levels are available: national level (whole France), region, department. The data are derived from the National Health Data System (NSDS). For more information (source, field, definitions of modalities), visit the Method page of this site. ### Data update: The data proposed for download in the “Export” tab is updated every year (data from the whole of France since 2010).

Few studies investigated sex-related differences in care consumption after dialysis initiation. Therefore, the aim of this study was to compare the care trajectory in the first year after dialysis start between men and women by taking into account the context of dialysis initiation. All patients who started dialysis in France in 2015 were included. Clinical data of patients and context of dialysis initiation were extracted from the Renal Epidemiology and Information Network (REIN) registry. Data on care consumption in the first year after dialysis start came from the French national health data system (SNDS): hospital stays <24h, hospital stays to prepare or maintain vascular access, hospital stays >24h for kidney problems and hospital stays >24h for other problems, and consultations with a general practitioner. Variables were compared between men and women with the χ2 test and Student’s or Welch t-test and logistic regression models were used to identify the factors associated with care consumption after dialysis start. The analysis concerned 8,856 patients (36% of women). Men were less likely to have a hospital stays >24h for kidney problems than women (OR = 0.8, 95% CI = [0.7–0.9]) and less general practitioner consultations (OR = 0.8, 95% CI = [0.8–0.9]), in the year after dialysis initiation, after adjustment on patient’s characteristics. Moreover, hospital stays for vascular access preparation or maintenance were longer in women than men (median duration: 2 days [0–2] vs. 1 day [0–2], p < 0.001). In conclusion, despite greater comorbidities in men, this study found few differences in post-dialysis care trajectory between men and women.

Santé publique France's mission is to improve and protect the health of populations. During the health crisis linked to the COVID-19 epidemic, Public Health France is responsible for monitoring and understanding the dynamics of the epidemic, anticipating the different scenarios and implementing actions to prevent and limit the transmission of this virus on the national territory.

Daily hospital data relating to the COVID-19 epidemic by department and sex of the patient: number of hospitalized patients, number of people currently in intensive care or intensive care, cumulative number of people returned home, cumulative number of people who died.

For some patients, gender was not identified in the database. This can lead to a discrepancy between the H/F sum of an indicator and the total number of this indicator.

The region and iso 3166-1 codes of the zones have been added.

Warning: data under construction. May contain anomalies or missing data.

IntroductionPluriprofessional and coordinated healthcare use is recommended for Alzheimer's Disease and Related Diseases (ADRD). Despite a protective health system, France is characterized by persistent and significant social inequalities in health. Although social health inequalities are well documented, less is known about social disparities in healthcare use in ADRD, especially in France. Therefore, this study aimed to describe healthcare use according to socioeconomic deprivation among ADRD subjects and the possible potentiating role of deprivation by age.MethodsWe studied subjects identified with incident ADRD in 2017 in the French health insurance database (SNDS). We described a large extent of their healthcare use during the year following their ADRD identification. Deprivation was assessed through French deprivation index (Fdep), measured at the municipality level, and categorized into quintiles. We compared healthcare use according to the Fdep quintiles through chi-square tests. We stratified the description of certain healthcare uses by age groups (40–64 years, 65–74 years, 75–84 years, 85 years, and older), number of comorbidities (0, 1, 2–3, 4 comorbidities and more), or the presence of psychiatric comorbidity.ResultsIn total, 124,441 subjects were included. The most deprived subjects had less use of physiotherapy (28.56% vs. 38.24%), ambulatory specialists (27.24% vs. 34.07%), ambulatory speech therapy (6.35% vs. 16.64%), preventive consultations (62.34% vs. 69.65%), and were less institutionalized (28.09% vs. 31.33%) than the less deprived ones. Conversely, they were more exposed to antipsychotics (11.16% vs. 8.43%), benzodiazepines (24.34% vs. 19.07%), hospital emergency care (63.84% vs. 57.57%), and potentially avoidable hospitalizations (12.04% vs. 10.95%) than the less deprived ones.Discussion and conclusionThe healthcare use of subjects with ADRD in France differed according to the deprivation index, suggesting potential health renunciation as in other diseases. These social inequalities may be driven by financial barriers and lower education levels, which contribute to health literacy (especially for preventive care). Further studies may explore them.

Variables associated with mortality in patients with metastatic lung cancer at diagnosis (Cox model).

In 2023, Singapore dominated the ranking of the world's health and health systems, followed by Japan and South Korea. The health index score is calculated by evaluating various indicators that assess the health of the population, and access to the services required to sustain good health, including health outcomes, health systems, sickness and risk factors, and mortality rates. The health and health system index score of the top ten countries with the best healthcare system in the world ranged between 82 and 86.9, measured on a scale of zero to 100.

Global Health Security Index  Numerous health and health system indexes have been developed to assess various attributes and aspects of a nation's healthcare system. One such measure is the Global Health Security (GHS) index. This index evaluates the ability of 195 nations to identify, assess, and mitigate biological hazards in addition to political and socioeconomic concerns, the quality of their healthcare systems, and their compliance with international finance and standards. In 2021, the United States was ranked at the top of the GHS index, but due to multiple reasons, the U.S. government failed to effectively manage the COVID-19 pandemic. The GHS Index evaluates capability and identifies preparation gaps; nevertheless, it cannot predict a nation's resource allocation in case of a public health emergency.

Universal Health Coverage Index  Another health index that is used globally by the members of the United Nations (UN) is the universal health care (UHC) service coverage index. The UHC index monitors the country's progress related to the sustainable developmental goal (SDG) number three. The UHC service coverage index tracks 14 indicators related to reproductive, maternal, newborn, and child health, infectious diseases, non-communicable diseases, service capacity, and access to care. The main target of universal health coverage is to ensure that no one is denied access to essential medical services due to financial hardships. In 2021, the UHC index scores ranged from as low as 21 to a high score of 91 across 194 countries. 

Techsalerator’s Job Openings Data for France offers a thorough collection of essential information for businesses, job seekers, and labor market analysts. This dataset provides a detailed overview of job openings across various sectors in France by aggregating and categorizing data from company websites, job boards, and recruitment agencies.

To receive this dataset, please reach out to info@techsalerator.com or https://www.techsalerator.com/contact-us

Top 5 Most Utilized Data Fields

Job Posting Date: Captures the date when a job is posted. This information is crucial for tracking new opportunities and understanding recruitment trends. Job Title: Specifies the title of the job position. It helps job seekers and recruitment agencies categorize and filter openings based on industry roles and career interests. Company Name: Lists the company offering the position. This helps job seekers target their applications and enables businesses to identify competitors and market trends. Job Location: Provides the geographic location of the job within France. Job seekers can use this to find opportunities in specific regions, while employers can analyze regional talent availability and market conditions. Job Description: Details the job responsibilities, required qualifications, and other relevant information. It is essential for candidates to determine if they meet the requirements and for recruiters to clearly communicate job expectations. Top 5 Types of Job Openings in France

Information Technology (IT): Includes roles such as software developers, data analysts, cybersecurity experts, and IT project managers, driven by France’s growing tech sector. Engineering: Features positions in civil, mechanical, electrical engineering, and project management, reflecting ongoing infrastructure and technological projects. Healthcare: Covers jobs for doctors, nurses, medical technicians, and healthcare administrators, supporting France’s extensive healthcare system. Finance and Banking: Encompasses opportunities in financial analysis, accounting, risk management, and banking roles within the financial sector. Education: Offers positions for teachers, school administrators, and educational support staff across primary, secondary, and higher education institutions. Top 5 Employers with Job Openings in France

L'Oréal: A global beauty and cosmetics leader, offering roles in marketing, research and development, and operations. Dassault Systèmes: A major software company specializing in 3D design and engineering software, with opportunities in IT, software development, and project management. TotalEnergies: A key player in the energy sector, providing positions in engineering, project management, and operations related to oil, gas, and renewable energy. SNCF (Société Nationale des Chemins de fer Français): The French national railway company, offering jobs in engineering, operations, and customer service. Orange S.A.: A leading telecommunications provider, with roles in IT, network management, customer service, and technical support. Accessing Techsalerator’s Job Openings Data

To obtain Techsalerator’s Job Openings Data for France, please reach out to info@techsalerator.com with your specific needs. Techsalerator will provide a customized quote based on the number of data fields and records you require, with the dataset available for delivery within 24 hours. Ongoing access options can also be discussed as needed.

Included Data Fields

Job Posting Date Job Title Company Name Job Location Job Description Application Deadline Job Type (Full-time, Part-time, Contract) Salary Range Required Qualifications Contact Information Techsalerator’s dataset offers valuable insights into job openings and employment trends in France, making it an essential resource for businesses, job seekers, and labor market analysts who want to stay informed and make strategic decisions.

This dataset is an extraction of the National Inter-System Health Insurance System (SNIIRAM) covering all health insurance reimbursements for all schemes. It completes a less detailed dataset (expenditure excluding hospital services national version for informed users wishing to explore complementary axes. This dataset covers all the benefits covered by the compulsory sickness insurance scheme, including hospital benefits invoiced directly to the sickness insurance scheme for all the schemes. In order to preserve the anonymity of healthcare professionals and care recipients, geographical areas are limited: - 9 geographic areas (study and land use planning areas) that are groupings of administrative regions from 2009 to 2014, - 13 geographical areas (close to the newly created large administrative regions) as of 2015. Expenditures are detailed according to six lines of analysis (period, benefit, provider, recipient of care, performing health professional, prescribing health professional) and seven indicators of amount (total expenditure, reimbursement basis, amount reimbursed, overrun) and volume (enumeration, quantity, coefficient). In total, each delivery line is described by 55 variables. It is essential to refer to the description accompanying the dataset. The data can be downloaded in csv format. Monthly files are prefixed by P from 2009 to 2014 and then prefixed by A from 2015. These files are suffixed by the year and month of reimbursement of expenses. As regards health insurance expenditure, two other datasets, covering different fields, are proposed: - Health insurance expenditure excluding hospital benefits (national data): database containing all reimbursements (excluding hospital services) made by the general sickness insurance scheme in France; - Annual expenditure on health insurance: dashboards covering all reimbursements (including hospital services) made by the general sickness insurance scheme in mainland France.