dataesr/scientific-paragraphs-categorization huggingface dataset:
Applying this estimation strategy on data from national Hospital Episode Statistics (HES) and the General Practice Patient Survey (GPPS), we analyze how this integration initiative affects primary care (GP and nurse visits) and secondary care provision and costs as well as health status. We investigate potential channels such as changes in patient ability to manage their care, support needs and care plan utilization. This paper is an attempt to provide comprehensive evidence on the effect of a popular integrated care initiative, which is being increasingly adopted. The remainder of this paper is organized as follows. Section 2 introduces background information on SICP. Sections 3 and 4 respectively present data used in this paper and the empirical method. Section 5 shows the estimation results, and Section 6 discusses our results and concludes the paper.
| The GP patient survey For measuring primary care use, health status and patient experience, we used the national GP Patient Survey (GPPS), a postal survey administered to approximately 2. 15 million registered patients from all GP practices in England. The survey has been conducted twice a year from 2012 until 2015 and annually from 2016 by NHS England and Ipsos Mori. Data were collected from January to March (first semester), and from July to September (second semester), and since 2016 only from January to March. The same patient may receive and send back the questionnaire at several points in time but never in the same year. However, those patients cannot be identified and followed through time. We use data from the 2012 to 2017 survey waves since previous waves were not comparable due to survey method changes.
| The Hospital Episode Statistics HES is a database recording all admissions, outpatient specialist visits, and emergency department attendances at NHS hospitals in England. The HES data cover many of the outcomes that the MDG was specifically designed to address. For example, MDGs aimed at reducing preventable exacerbation resulting in emergency care utilization as well as reducing length of stay by improving required health and social support outside of the hospital. We first created an individual-level data set over 6 years of data (annually, financial years 2011/2012-2017/2018-financial years start in April), counting all inpatient admissions, and emergency care attendances per individual person per year. We costed each measure of utilization using the NHS tariff applicable in that year, 8 and constructed a measure of the total cost of secondary care for each patient in each year.
We recorded multimorbidity using a dummy variable based on a count (2 or more) of 30 long-term conditions recorded in the HES admitted patient care data [see  Tonelli et al. (2015)  for list of conditions and icd-10 codes]. We minimized missing multimorbidity counts (e. g. , a year in which a patient only has outpatient contacts where multiple morbidities are not recorded) by updating data from the previous non-missing year(s). Using the HES data, we were able to distinguish secondary care use between emergency care and planned care. Planned care admissions are planned admissions for specific medical procedures, while emergency care are unplanned attendances (e. g. , coming from emergency room) that have led to hospital admissions. Specifically, we measure how many times patients received each of the following types of care by year.
| GP practice characteristics We also use GP practice time-varying characteristics provided for each year by NHS Digital such as practice size, the proportion of patients above 75 years old, the number of full-time equivalent doctors, and the level of socio-economic deprivation in the areas of residence of the practice patients (See Appendix A1 for more details). We minimize missing values in each year by updating data from the previous or next non-missing year. The only practices missing from each sample are those for which we were unable to collect practice characteristics from NHS Digital. None of them are in Salford.
5 years after (from the second semester of 2015 to the end of 2017) the implementation of the intervention that occurred between March to June 2015. For the HES data, the start of financial year 2015/2016 coincides with implementation of the intervention, giving us 3 years of pre-intervention data and 3 years of post data. This facilitates clear comparisons during the pre-period and simplifies the implementation of the triple difference-in-differences model to estimate the effect of MDGs. Second, using individual-level data from each source, we created two datasets 11  where each observation represents one of eight segments of each GP practice (combinations of presence of multimorbidity, aged over 65 years, and gender) at each time point (by survey wave and adjusting for survey weights for GPPS and annually for HES).
For example, one row of data represented the outcomes for multimorbid patients, aged 65 years and older, and male (with eight possible unique combinations of these three variables), in GP practice P at time t. Therefore, we can exploit the panel dimension of our datasets which will allow us to control for practice time-invariant characteristics that may affect our outcomes. In addition, we only keep practices that are in our dataset from 2012 to 2017 to avoid any time-varying change in composition with closing or opening practices. Note that the number of practices in the GPPS and HES data differ since not all outcomes were measured in all GP practices in England, while all practices are included in the HES data. Third, we decompose the effect by year over the 3-year post period to examine any heterogeneity in effects over time.
To limit this possibility, we systematically drop all CCGs that were included in the Vanguard New Care Models (except Salford) as they received additional funding to serve as testing prototypes for integrated health and social care services and were implemented at the same time as the Salford policy. We also use the 10 closest CCGs to Salford according to the NHS (called Rightcare CCGs by NHS England) as an alternative control group that is more homogeneous in terms of population and healthcare services than the rest of England. The NHS matches CCGs (in our case Salford), with CCGs that have similar weightings based on 12 variables. 12  Third, one limit of our analysis is the 3 years pre-period due to constraints in GPPS data consistency in older waves. In our main analysis, we maintain pre-period length consistent across our two data sources, but in a robustness check, we extend the pre-period from the HES data to financial years 2009-2014 instead of 2012-2014.
In the pre-period, GP practices systematically differ, but this difference is not statistically significant once practices are weighted. Our strategy for estimating the effect of the intervention relies on the assumption that the post period outside of Salford represents an appropriate counterfactual for those targeted by the program (those in Salford). A standard way to test that assumption is to examine whether outcomes for the two groups were trending similarly before the program was put in place. We test if there is any differentiated impact of being in the treatment (Salford and over 65) compared to the control group for each time point in the pre-period compared to the last time period before the intervention (2014 in HES data or fist semester 2015 in GPPS data). We interacted the treatment variable (Salford × over65) with the time variable in the pre-period and tested the joint hypothesis that none of the interacted terms are different from zero.
The large scope of secondary care outcomes is especially important as reduction of utilization seems to be the primary policy goal. Our results were robust to restricting the control group to CCGs that share similar socioeconomic characteristics with Salford, and to dropping GP practice that were simultaneously involved in a Health Coaching program, as well as extending the pre-period length. Our main results remained qualitatively similar in all cases, and we concluded that our main findings were not sensitive to the choice of control groups. Our analysis was, however, reliant on GPPS survey respondents. Although we use survey weights in the construction of our dataset, we cannot completely rule out the possibility that this data does not fully capture the extent of the effect of MDGs on patient experience, health status, and primary care utilization.
FINDINGS Among 11 studies performed on 3 regional and 8 national medicoadministrative databases in 11 countries, 35 552 550 pediatric patients were included. The overall risk of bias was low (10 studies were representative [90. 9%], and the prevalence denominator included nonusers of health care for 9 studies [81. 8%]). Prevalence of 1 or more POP per year ranged from 480 to 857 pediatric patients per 1000 in Sweden and France, respectively (PR, 1. 8 [95% CI, 1. 8-1. 8]). Overall, among 8 studies reporting ATC level 1 drugs, Denmark had the lowest POP prevalence (eg, systemic hormonal preparations: 9 pediatric patients per 1000 per year) and France the highest (eg, systemic hormonal preparation: 216 pediatric patients per 1000 per year). Among 8 studies reporting ATC level 2 drugs for PODs, the PR between France and Denmark was 108.
In 2008, Sturkenboom et al  18, 19  studied POPs among 675 868 pediatric patients in 3 European countries and also reported high between-country PRs (5 to 20) for antibiotics and systemic corticosteroids. Since these publications, some studies  [20] [21] [22] [23]  found an improvement or a worsening of inappropriate POPs over time in some OECD member countries. The only systematic review  24  of POPs, conducted in 2008, reported great variability in the prevalence of exposure to 1 or more drugs in pediatric patients over the course of a year (from 51% in Denmark to 70% in Greenland). However, that systematic review is more than a decade old, and the comparative evaluation was hampered by heterogeneity among studies. This heterogeneity is mainly associated with differences in information sources (eg, databases of prescription, dispensation, and sales or consumer surveys), indicators (eg, prevalence, share, prescription rate, and package number),  17  exhaustiveness of the studied population (notably, the inclusion of participants who did not use or slightly used the health care system), and information on nonprescription drugs (NPDs).
General Methodology This systematic review was registered at PROSPERO (CRD42021250699) and conducted and reported following international recommendations, including following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. 26, 27 We systematically searched for reports of the annual prevalence of prescriptions in a pediatric outpatient population (defined as patients aged <20 years) from a medicoadministrative database in an entire OECD member country or large geographic area within it. 2 We excluded articles focusing on a specific drug or therapeutic class, a short period of time (ie, <1 year), or an age class within the pediatric age group (ie, infants, preschool-age children, school-age children, or adolescents). the Emtree thesaurus, and keywords associated with drug prescription and dispensation, pediatric outpatients, and medicoadministrative databases (eTable 1 in the Supplement).
For included studies, we examined reference lists, searched the Science Citation Index and Google Scholar for studies citing the included studies, and examined the studies' first 50 related articles in PubMed. We also systematically searched institutes of public health or drug agency websites of 37 OECD member countries (eTable 2 in the Supplement). 2 No language restriction was applied. If data from different studies were from the same country, we selected the most recent study.
Risk of Bias Assessment We evaluated risk of bias (low, high, or unclear) by adapting tools proposed by Hoy et al  32  and Munn et al  33  for prevalence studies based on 2 dimensions (eTable 4 in the Supplement). Internal validity was assessed by the reliability of the data source (ie, prescription vs dispensation database) and exhaustiveness of the population to build the prevalence indicator (ie, populations including nonusers of health care vs populations excluding them). Threat to generalizability was assessed on the selection risk of the population and the use of a tool to improve the representativeness of the study population (ie, census process or random selection).
This study has several limitations. First, many OECD member countries did not report annual prevalence of dispensations and prescriptions, including countries with potentially extreme high or low POPs. This reduced the magnitude of variations found in our systematic review. Additionally, among many potential intervals to study POP prevalence (eg, year, month, or week), we selected the annual indicator to allow for homogeneity in comparisons for a period. Thus, we restricted our review to medicoadministrative database studies to capture sporadic users exhaustively, which is not possible in studies for which the prevalence is estimated for a short period. 77 An implication of this selection process was the exclusion of several studies based on different indicators in North America, Europe, and Asia.
21, 78, 79 Of note, North American studies  34, 37, 38  included in this systematic review were more than a decade old, so we excluded them from comparisons. Second, we did not investigate POP prevalence for level 3 or 4 drugs of the ATC classification. Third, POP prevalence comparisons were likely biased given the large study period, but we tried to limit this bias by not including data collected before 2010 in comparisons. Additionally, most databases included dispensations and likely underestimated POP prevalence because some prescriptions may not be dispensed if parents or adolescents do not pick up the prescribed drugs from the pharmacy. However, this homogeneity of databases was also a strength for comparisons. Fourth, these administrative databases did not collect information on indications for drug prescriptions, which.
However, sensitivity analyses had similar results as most of our main findings but with attenuated POP prevalence variations for antibiotics and psychoanaleptics. This limitation suggests a lack of harmonization in reporting of POPs among pediatric age groups and a need for consensual reporting guidelines to standardize reports and allow more robust comparisons. Seventh, we compared data from published and nonpublished studies (ie, websites) that were not externally peer reviewed. However, these websites provided complete data and criteria to evaluate risk of bias.
Pediatric Outpatient Prescriptions in Countries With Advanced Economies JAMA Network Open. 2022;5(4):e225964. doi:10. 1001/jamanetworkopen. 2022. 5964 (Reprinted) April 25, 2022 2/16 Downloaded From: https://jamanetwork. com/ on 06/25/2022 We used 2 strategies to search PubMed and Embase databases for articles published between January 1, 2000, and January 1, 2021. Search strategies involved Medical Subject Headings (MESH),.
Study Design This is a pre-post intervention study. The outcomes were scored by a medical secretary at baseline and a clinical research assistant at the 1-month visit. The pre and post tests were conducted at an interval of 30 + 7 days and before the memory consultation. Before the first assessment, participants received by mail information, and verbal consents were obtained. The Ethical Review Board of the University Hospital approved the study protocol (agreement CER-BDX-2022-17) and stated that no written consent was required. The phone interviews were not recorded, and the data collected were anonymized.
Intervention: Case Manager Pre-Consultation Interview by Phone Call In our memory clinic, clinical case management is routinely performed prior to the medical consultation, which often requires several telephone calls. The coordinating nurse is the case manager. The case manager lets the caller express their request regarding the person being assisted. Information related to the memory consultation was collected through a semi-structured interview. The interview was conducted over one or more calls and was used to collect information to determine the need for rapid care for the patient, based on the information provided by the caregiver and the general practitioner (GP). This information included the sociodemographic data of the caregiver and patient; the ethical dimension of the request is checked to know the level of consent of the patient to come for consultation.
Except for individuals with chronic pathologies registered in a national database, compulsory health insurance does not therefore reimburse all care, equipment or medication; the remainder to be paid by the patient is called the "copayment. " This is particularly high for certain types of care, acts or equipment notably dental prostheses, glasses and hearing aids. Moreover, certain physicians can charge more than the negotiated fees (the object of a contract with CNAM) and the "excess fees" are not covered by the compulsory health insurance (see Supplementary file 1). The expenses not reimbursed by compulsory health insurance (co-payment and excess fees) may be covered by complementary (or "top-up") health insurance, up to the guarantees set in the contracts taken out by the individuals. However, in 2019, 5% of French residents did not have complementary health insurance, often for lack of the financial means to pay for it.
Study Design and Study Participants We implemented a national cross-sectional study developed as part of the "Access to healthcare program" led by our laboratory. Data were collected sequentially at 101 local CPAM agencies in 5 successive survey waves over 4 years and 6 months (Supplementary file 2). Subjects who consulted their local CPAM office between January 2014 and June 2018 were requested by the CPAM agent who received them to fillin a questionnaire (Supplementary file 3). CPAM staff were trained to help people complete the questionnaire. The questionnaire was constructed by our laboratory in a qualitative and collaborative investigation process. It aimed at providing knowledge on forgoing healthcare in a population that had not been previously studied, ie, people insured under the French general compulsory health insurance scheme.
The questionnaire was tested in the Gard county by CPAM agents and volunteers insured by the CPAM, who were interviewed. Then adjustments were made taking into account the feedback from all parties, before it was deployed in all French CPAM agencies. For the present study, we included people registered with CPAM at the interview date and who answered the key question: "Have you forgone or put-off healthcare on one or more occasions in the last 12 months (yes/no)?" Those who did not reply to this question were not included in the study dataset.
Data Collected and Procedures Forgoing Healthcare Data Data were collected using the census method in which the questionnaire was systematically proposed to all persons attending the CPAM agency during the period of the survey wave. Those who agreed to participate and replied to the key question were given the questionnaire containing 24 questions organized into four sections (Supplementary file 3): • General participant characteristics: Gender, age-band, socio-professional category and family situation. • Health characteristics: Their perception of their current state of health. • Healthcare insurance: Whether they had complementary (top-up) health insurance, the type of complementary health insurance: private insurance, CMU-C or ACS, and whether they were registered with a GP. • Healthcare forgone: If they answered 'yes' to the key question they were asked for details about the type(s) of healthcare forgone, the reason for foregoing or putting it off, how long they had been forgoing or putting-off healthcare and the impact (if any) on their life.
Statistical Analyses Descriptive Analyses and Spatial Distribution of People Forgoing Healthcare Only categorical variables were collected, for which rates were calculated. The chi-square test was used for comparisons between groups with different healthcare insurance statuses. A P value <. 05 was considered as significant. Due to the low rate of missing data (<1%), a simple imputation method was used, replacing missing values by the most frequent observation. To visualize the spatial distribution of forgoing healthcare in France, a populationadjusted mean rate was calculated for each county using direct standardization methods  18  (Supplementary file 2) and presented using the maptools package  19  of R version 3. 5. 1 (R Core Team 2019). 20 dividual Determinants of Forgoing Healthcare A mixed logit multinomial regression model with a random effect on geographical location was used to analyse the association between an individual's personal characteristics (introduced as fixed effects) and the probability of forgoing healthcare for a specific reason.
To consider both socio-professional categories and type of complementary health insurance, a categorical variable with 12 modes (listed in the supplement) was created. Collinearity between the variables was tested by means of Cramer's V test. However, as associations between variables were all low to moderate (Cramer V varied from 0. 02 to 0. 5) no collinearity was considered. In addition, we performed a subgroup analysis using simply "working" and "retired or not-working" subgroups, where "not-working" included retired and unemployed participants. Statistical analyses were performed using SAS 9. 4 software (SAS Institute, Inc. , Cary, North Carolina) and R version 3. 5. 1 (R Core Team 2019). 20.
With the reductions in coverage for routine care of compulsory primary health insurance schemes complementary healthcare insurance has become indispensable. However, a part of the population does not have complementary insurance because they cannot afford the cost. People on low wages (or pensions) and the unemployed have no disposable income after paying fixed charges (rent, electricity, transport, hygiene and food) each month. Moreover, the ACS scheme provided insurance contracts that did not cover all expenses related to care. 23 ur large database enabled us to describe the complexity of factors underlying why people forgo healthcare. A simplistic view would have been to consider that financial reasons are the most prominent issues by far overwhelming all other causes. A consequence of such an oversimplification would be to underestimate significant concerns related to the geographical distribution of access to care and transport issues, difficulties in obtaining appointments with practitioners in certain medical specialities and the importance of individual psychosocial behaviours and health beliefs.
2] [33]  There is an evolving definition of the GP/primary care provider towards both their partial replacement by a network of supervised physician assistants and e-health supported by artificial intelligence. A challenge will be the capability of health systems to preserve fair and equal access to healthcare when deploying such new forms of organization. The majority of the factors we identified that could be used to predict forgoing healthcare are certainly also present in other high-income countries with similar healthcare systems, but with a different ranking depending on the subtleties of national healthcare organization. This field needs new tools to analyse international databases in order to identify the clusters of individuals, behavioural, societal and health system factors that lead to a risk of forgoing or postponing care.