By early May 2020, the number of new COVID-19 infections started to increase rapidly in Chile, threatening the ability of health services to accommodate all incoming cases. Suddenly, ICU capacity planning became a first-order concern, and the health authorities were in urgent need of tools to estimate the demand for urgent care associated with the pandemic. In this article, we describe the approach we followed to provide such demand forecasts, and we show how the use of analytics can provide relevant support for decision making, even with incomplete data and without enough time to fully explore the numerical properties of all available forecasting methods. The solution combines autoregressive, machine learning and epidemiological models to provide a short-term forecast of ICU utilization at the regional level. These forecasts were made publicly available and were actively used to support capacity planning. Our predictions achieved average forecasting errors of 4% and 9% for one- and two-week horizons, respectively, outperforming several other competing forecasting models. The reports grew in complexity and regional coverage over time based on what decision makers deemed to be most pressing.The first cases of the COVID-19 pandemic were detected in Chile by early March 2020. A few days later, all schools were closed, and a few counties with relatively high numbers of cases were quarantined. By the end of April, the available data showed that the outbreak was kept relatively under control, with a few hundred confirmed new cases every day. However, by early May, the infection rate started to increase rapidly, threatening the ability of health services to accommodate all incoming COVID-19 cases. In the middle of May, the Chilean Society of Intensive Medicine (SOCHIMI) reported a worrisome occupation rate of ICU beds of more than 95% in the capital city of Santiago, where most of the cases were concentrated. Suddenly, ICU capacity planning became a first-order concern. On May 12th, the Instituto Sistemas Complejos de Ingeniería (ISCI), which was already working on analytics related to mobility, was urged to prepare short-term forecasts of ICU bed occupancy rates for those regions with the highest utilization rates. Within 24 hours, we submitted our first report. From then on, we prepared forecasts every two days for several weeks, and then we reduced the frequency and began reporting every four days. These reports were sent directly to the authorities –particularly those on the coronavirus response committee– and to SOCHIMI. Additionally, we published the reports on ISCI’s website  we review the relevant literature, (ii) we describe the statistical models we used and how we combined them to produce our forecasts, (iii) we discuss some adjustments we introduced to accommodate changing conditions in the spread of COVID-19 and (iv) we present our forecasting results. These sections are devoted to providing a comprehensive documentation of the underlying methods we used. Readers more interested in results and implementations can go directly to the nontechnical summary of the methodology and results, where we present a summary of the methodology, its results and its advantages over other approaches and discuss possible implementations in other regions. We conclude with a discussion on the implications of our findings and avenues for future research.The first COVID-19 cases were detected in Chile by early March 2020, and for the first two months, the number of new infections was relatively under control, with a few hundred confirmed new cases every day. However, by early May, the number of new COVID-19 cases increased rapidly, creating numerous and complex challenges for the country. A graphical illustration of the evolution of the pandemic in Chile is displayed in To increase ICU capacity, hospital management can follow a number of complementary strategies with different levels of complexity. A simple mechanism to increase hospital capacity is through the release of medical resources by rescheduling nonurgent procedures. Other strategies require more time for implementation. For example, pediatric rooms could be converted to receive adult patients, or anesthetic machines could be adapted to provide mechanical ventilation. As most of these mechanisms could be implemented within a time span of a few days, we decided to provide forecasts for a 14-day horizon. Despite generating forecasts of ICU utilization for each of those fourteen days, in the reports, we highlighted the number of beds that would be required in exactly one and two weeks.Chile is administratively divided into sixteen regions, and in terms of geographical aggregation, forecasts were produced at the regional level. The country’s population is very unevenly distributed, and the Metropolitan Region, which includes the capital city of Santiago, contains near half of the national population. Despite this heterogeneous population distribution, our decision to produce regional demand forecasts is justified for two reasons. First, consistent with the administrative division, budgets are executed at the regional level. Second, from an operational perspective, if needed, patients can be transported from one hospital to another within the region, and therefore, the capacity at the regional level provides the most useful aggregation for decision making.http://www.minciencia.gob.cl/covid19). The data series were reported at either the national, regional or county level, with only a few statistics available at a more disaggregated level. Throughout the whole process, we tried to include different covariates in the model, but the results we generated are based on the list described in To estimate the models, we used data that were publicly available. Given the crucial importance of the consequences of the pandemic for the whole nation, the Ministry of Health provided frequent epidemiological reports starting on the day of the first infection. Later, the Ministry of Science consolidated all available information and created a public repository with an extensive list of statistics reported in a time series format .The proportion of symptomatic patients requiring mechanical ventilation can change over time, and similarly, clinical criteria for releasing patients from the ICU can be adjusted dynamically depending on the actual usage of the existing capacity. This is not only because hospitals can relax nominal criteria but also because SARS-CoV-2 is a new virus that involves continuous learning by medical teams. For instance, the head of the Chilean Society of Intensive Medicine stated that “Patients initially stay in the ICU between 10 and 11 days, and now they are staying between 14 and 16 days. This is because, with everything we learned, we intubated less and selected more serious patients“; hence, the number of new cases might be more or less informative depending on the congestion of the laboratories.Despite governmental efforts to provide timely access to relevant information, a large portion of the system for generating the data was under constant stress, and therefore, the information that we had available for any single day could be lagged. Among other issues, the results of lab tests exhibited important delays worldwide  depends on their lagged values and its lagged errors, and the series are further differentiated to estimate stationary processes. The ARIMAX variant is the result of considering an additional set of exogenous explanatory variables xt. In the vector xt, we considered the whole series of new cases and the positivity rate. By introducing the backward shift operator B, the model can be expressed in a compact form as:We start with a classic autoregressive integrated moving average (ARIMA) approach . In thisϕ), the weights of the errors (θ), the weights of the exogenous variables (β), and the constant term (θ0). The model also depends on the number of lags  and the number of difference operations (d). To determine the value of , we used stepwise selection based on the AIC  range. If the lengths of stay were uniform, the fraction of patients entering the ICU on day t who were discharged within time t + k was d of moderately severe cases staying in the ICU between k1 − h1 and k1 + h1 days and more severe cases staying between k2 − h2 and k2 + h2 days. Formally speaking, the equations describing the evolution of patients over time are given by:a, d, l, m, k1, h1, k2, andh2) are parameters to be estimated. These parameters are disease-specific, and we could retrieve their values from the medical literature on SARS-CoV-2. For example, the mean duration of symptoms before hospital admission was reported to be 10±2 days [In these equations, the series of ICU utilization and the number of new symptomatic cases are the data, while . Considering that these predictions directly inform health officials about critical decisions, we visually inspected all forecasts before producing the final reports. In these inspections, in very exceptional cases, when more than one forecast dramatically deviated from the mean, we overruled our trimmed criteria and included both ARIMA and ARIMAX in the forecasting pool.When inspecting the series, we found no evidence of seasonality for any variable; therefore, all models were estimated using no seasonal components. To determine the number of observations to use in every forecast, we considered information starting from April 1st, when the accumulated number of symptomatic patients reached three thousand cases. Later, when more data were accumulated, we only considered the previous sixty days of data to estimate the models.All models were estimated using daily data. During the pandemic, the Ministry of Health provided an updated report on the evolution of the most critical variables, such as the number of new infections, the positivity rate and the number of fatal cases. All this information is uploaded to the public repository of the Ministry of Sciences and Knowledge, from which we downloaded the information automatically. The data presented very few missing values, and to address them, we used a Kalman smoothing approach . The parp, d, andq) for the ARIMA and ARIMAX models, we proceeded iteratively. If the value d was known, the model selected the orders of p and q via the AIC. For nonseasonal data, d was selected based on the successive KPSS unit-root test [To determine the optimal values of  that also showed an alarming rise in new cases. At this point, our model was completely automated to generate predictions for all regions in the country, but we only progressively added more regions as they became more worrisome. By early July, we started reporting forecasts for all sixteen regions of the country.The GMDH model was not considered in the original list of models and was only introduced on June 11th. Since then, this model was been considered in the ensemble.In early July, we identified that most models were starting to show that the rate at which additional ICU beds were going to be needed for the Metropolitan Region was somewhat slowing down. However, the ICD compartment model did not show any sign of saturation. After interviewing medical personnel, we realized that some patients were starting to be mechanically ventilated in emergency rooms (ERs), and so they were not counted in the nominal series of ICU utilization. Thus, in terms of capacity planning, we were required to report how many beds should be made available to cover both new cases and ventilated cases in emergency rooms. Therefore, we complemented the series of ICU beds with the number of patients ventilated in ERs. Notice however, that the number of patients ventilated in ERs decreased to almost zero by late July, and therefore, we did not report them in the final two reports.As laboratories reached their testing capacities, the variation in the number of reported new cases increased significantly in mid-June. As a consequence, the forecasts were less stable. To overcome this problem, we preprocessed the series of new cases and used a five-day moving average instead of the raw series.The results that we present in the next section are devoted to representing what we reported at each point in time, and they already include all methodological changes we introduced during the process.https://isci.cl/covid19/ and were generated regularly every other day, except for the last two weeks of July, when the reports were generated only twice a week. The first reports only provided forecasts for the most critical regions, but we later provided reports for the whole country. In the analysis we present here, we only consider results since May 20th, when our routines were fully automatized to generate predictions for all regions.Starting from May 16th, we generated standardized and frequent reports containing the two weeks ahead forecasts. The reports were made publicly available at The main body of each report consisted of a summary of the number of beds that were going to be required for each region for a time horizon of two weeks, followed by a graphical summary of the forecast. A very important requirement for these reports was that they had to be concise and easy to read. The crisis committee had a very short time to evaluate all the information, so our reports were tailored to consider this situation. In At the bottom of this figure, we show the forecasts provided for the models that survived the removal of the most extreme predictions, and then, in the upper part, we present the combined forecast. For all models, we presented both the predictions and the actual series of ICU occupancy. Furthermore, to facilitate the interpretation of the results, we highlighted the predicted numbers of beds that would be required in exactly seven and fourteen days. For the example presented in For a systematic evaluation of our forecasts, we decompose the analysis into two parts. We first compare the performance of each model and the ensemble in terms of their forecasting errors, and then we discuss how our ad hoc trimmed algorithm fares against other pooling criteria.best guess through a conditional trimmed mean ensemble. A visual representation of all forecasts we reported for the Metropolitan Region is displayed in From May 20th to July 28th, we produced 30 ICU utilization reports. In each report, we presented daily forecasts of the demand for ICU beds for the next two weeks in the regions considered in that instance. In every case, we generated predictions for different forecasting models, and we built our To summarize all these daily forecasts, we compute the mean absolute percentage error (MAPE) for each model, as displayed in In It is important to evaluate the performance of our forecasting approach in the context of a pandemic characterized by phases of exponential growth that can lead to large prediction errors. For example, consider the case of the U.K., where early epidemiological models initially projected approximately 500,000 deaths, a number that was updated to under 20,000 deaths just two weeks later . For an To further understand how individual models performed relative to the ensemble, we plot the series of MAPEs for all models in To complete the analysis, we discuss how our conditional trimmed mean ensemble performed against other criteria for combining forecasts. As we forced our predictions to include the ICD compartment model regardless of the value of its predictions, it is possible that our ensemble might lead to a worse performance than those of other criteria that are not subject to this restriction. By design, we were willing to sacrifice precision to gain interpretability, but it is worth exploring whether our predictions were deteriorated by considering this interpretability constraint.mean and the median forecasts. For simplicity, in these series, we only report the errors for the whole forecasting horizon with no distinction between the first and second weeks. Instead, in these plots, we highlight three stages depending on whether the series of ICU occupancy exhibited positive, neutral or negative trends; we label them ascending, plateau and descending phases, respectively. Although the definition of the exact time when the series changes its slope is somewhat discretionary , this qualitative decomposition helps us understand the role of the compartmental model in the forecasts. Summaries of the comparisons between the ensemble criteria are displayed in From In the early stages of the pandemic, our trimmed mean criteria were outperformed by the standard mean and median ensembles. However, after a few iterations, our predictions consistently exhibited the smallest errors. This result can be explained by the fact that the ICD model produced prediction errors with opposite signs that canceled out the errors induced by other models. We believe that feeding the model structural information about the clinical evolution of COVID-19 patients can provide a useful forecasting signal and provide additional support for the convenience of using combined forecasts.The epidemiological literature has offered a variety of tools for understanding the dynamics of infectious diseases. In this study, we built upon these epidemiological models, and we tailored them with the specific goal of producing accurate forecasts of ICU utilization, as these has been critical components for mitigating the negative impacts of the COVID-19 pandemic. In this regard, there are three key conditions that differentiate our forecast from traditional epidemiological models.As we only focused on patients who required hospital resources in the short term, instead of forecasting the evolution of the pandemic through the reproduction number R, we directly used the number of symptomatic cases. This information is readily available and easy to process. More importantly, the usage of the actual number of symptomatic cases instead of a projection of the infections had a material impact in terms of improving the forecasting accuracy.We were specifically interested in characterizing ICU utilization; therefore, our model was tailored to capture the most relevant dynamics of this problem. These include the persistence of bed utilization and flexible distributions for the duration of the stay of each patient. These dynamics can be captured by two simple conservation law equations that indicate the new daily requirements of beds and the number of discharged patients. The rates at which customers arrive and leave the ICU can be derived from clinical sources, or they can be estimated from the data as we do in our application.In our model, we combined the standard epidemiological approach with time series and machine learning models that bring additional flexibility to the forecast. Importantly, our results indicate that this additional flexibility is critical to obtain highly precise estimates. This is because standard epidemiological models do not properly capture dynamic variations in how patients evolve during critical care. This is particularly relevant for a new disease for which medical teams are continuously learning about improved treatments. Methodologically speaking, we show that the combination of different models can be achieved through a simple linear combination of forecasts.http://covid-19vis.cmm.uchile.cl/forecast). Furthermore, these models are rather sensitive to the underlying assumptions about the infection rates and can present more than a 500% difference between their conservative and pessimistic scenarios .Since epidemiological models do not incorporate detailed modeling of the dynamics of ICU requirements, they tend to have large forecasting errors. For the case of Chile, even the most sophisticated compartment models exhibited prediction errors that were up to three times larger than what we reported here , while the methodology we proposed led to a mean error rate of 4.11% (sd of 2.52). Some of the time series and machine learning models performed reasonably well, but they failed to anticipate changes in trends. The combined forecast in general produces the most accurate predictions and it correctly anticipates when the number of ICU beds will decrease. Thus, our analysis demonstrates that a simple combination of different forecasts can generate much better predictions in the context of planning emergency resources than those of single models.Our model may be implemented by health authorities rather easily. Indeed, the logic of our compartment model can be summarized into two flow conservation equations, and the time series and machine learning models can be estimated using standard statistical packages. All forecasts can be combined using a linear combination of the individual forecasts. To facilitate the implementation of the method for other countries or regions, in In this research, we proposed a methodology to produce short-term forecasts for ICU beds in the context of the COVID-19 epidemic in Chile. Our algorithm is based on an ensemble method that combines autoregressive neural networks, artificial neural networks and a compartment model to generate our best prediction of ICU utilization for a time horizon of fourteen days. This algorithm captures the epidemiological dynamics of the disease with a compartmental model and is complemented by time-series models that capture short-term changes in the clinical parameters. This approach resulted in very accurate predictions, with a mean error rate of 4% for the first week and 9% for the second week. An analysis of the performance over time indicates that, in relative terms, the proposed model produced larger errors earlier in the process. This can be explained by the fact that in the early stages of the pandemic, each individual model had less data to learn from. However, we believe that a more fundamental reason is that after a few iterations, different models produced complementary results; therefore, the trimmed mean we used to ensemble the forecast generated a better forecast than that of any single model in isolation. Hence, every model contributed a different key signal that increased the accuracy of the ICU bed predictions in most of our reports. In this regard, the inclusion of a compartmental model helped to generate highly precise predictions, despite being the least accurate single model overall.In terms of the application, the reports we made publicly available were a very useful tool for anticipating the availability of critical resources in hospitals. We generated consistent information to characterize the progression of the pandemic, providing health officials with a data-driven tool to make quick decisions about ICU planning. These reports enabled the Ministry of Health to implement a progressive increase in the number of beds, and this resulted in more than doubling the capacity in the most congested regions. We heard from health and science authorities and from SOCHIMI how these forecasts were useful for letting them know what was coming and so they could better focus their resources and efforts across the country. Importantly, the messages we were sending were well received because, following our interactions with authorities, we tailored the reports to ease communications.We are confident that our model contributed to better planning during a critical situation where the lives of many were at risk. However, as the COVID-19 pandemic is still a major threat in many countries around the world, we consider it important to discuss potential ideas to further improve the methodology. In our work, we used the data that were available and that we identified as having predictive power. However, the use of additional disaggregated data is likely to further improve the forecasting accuracy. For example, more detailed information on patient demographics and medical histories could further help to identify what fraction of patients might require mechanical ventilation and thus provide more detailed guidelines about focused mitigation policies.prophet forecasting model [The proposed methodology can also be improved by adding additional forecasting methods into the pool of models. Although we used a wide variety of models, there are others that we did not try. For example, the recently developed ng model  has beenng model . Our metng model .To produce our predictions, we treated different regions independently. Although this is a reasonable assumption for the case of Chile where commuting between regions was limited, it might not be a good assumption when replicating our work in other geographies. In such cases, a hierarchical model allowing for spatial correlation might be more appropriate . FinallyS1 TableList of libraries and the corresponding parameters used .(PDF)Click here for additional data file.S2 TableHistorical MAPE per Model—Valparaíso Region.(PDF)Click here for additional data file.S3 TableSelection Frequency per Model across Iterations in Chile.(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S1 FigIn the following three figures, we display the summaries of the forecasts for all regions in the country and the detailed plots for the most populated regions of Valparaíso and Bíbio.(TIF)Click here for additional data file.


A clear understanding of the macro-level contexts in which education impacts health is integral to improving national health administration and policy. In this research, we use a visual analytic approach to explore the association between education and health over a 20-year period for countries around the world.Using empirical data from the OECD and the World Bank for 26 OECD countries for the years 1995–2015, we identify patterns/associations between education and health indicators. By incorporating pre- and post-educational attainment indicators, we highlight the dual role of education as both a driver of opportunity as well as of inequality.Adults with higher educational attainment have better health and lifespans compared to their less-educated peers. We highlight that tertiary education, particularly, is critical in influencing infant mortality, life expectancy, child vaccination, and enrollment rates. In addition, an economy needs to consider potential years of life lost  as a measure of health quality.We bring to light the health disparities across countries and suggest implications for governments to target educational interventions that can reduce inequalities and improve health. Our country-level findings on NEET  rates offer implications for economies to address a broad array of vulnerabilities ranging from unemployment, school life expectancy, and labor market discouragement. The health effects of education are at the grass roots-creating better overall self-awareness on personal health and making healthcare more accessible. Is education generally associated with good health? There is a growing body of research that has been exploring the influence of education on health. Even in highly developed countries like the United States, it has been observed that adults with lower educational attainment suffer from poor health when compared to other populations . This paThe avenues through which education affects health are complex and interwoven. For one, at the very outset, the distribution and content of education changes over time . Second,Research has proposed that the relationship between education and health is attributable to three general classes of mediators: economic; social, psychological, and interpersonal; and behavioral health . EconomiWhile the positive association between education and health has been established, the explanations for this association are not . People On a universal scale, it has been accepted that several social factors outside the realm of healthcare influence the health outcomes . The difEducation enables people to develop a broad range of skills and traits  that predispose them towards improved health outcomes , ultimatThere is an iterative relationship between education and health. While poor education is associated with poor health due to income, resources, healthy behaviors, healthy neighborhood, and other socioeconomic factors, poor health, in turn, is associated with educational setbacks and interference with schooling through difficulties with learning disabilities, absenteeism, or cognitive disorders . EducatiTo inform future education and health policies effectively, one needs to observe and analyze the opportunities that education generates during the early life span of individuals. This necessitates the adoption of some fundamental premises in research. Research must go beyond pure educational attainment and consider the associated effects preceding and succeeding such attainment. Research should consider the variations brought about by the education–health association across place and time, including the drivers that influence such variations .In the current research, we analyze the association between education and health indicators for various countries using empirical data from reliable sources such as the Organization for Economic Cooperation and Development (OECD) and World Bank. While many studies explore the relationship between education and health at a conceptual level, we deploy an empirical approach in investigating the patterns and relationships between the two sets of indicators. In addition, for the educational indicators, we not only incorporate the level of educational attainment, but also look at the potential socioeconomic benefits, such as enrollment rates  and school life expectancy . We investigate the influences of educational indicators on national health indicators of infant mortality, child vaccinations, life expectancy at birth, premature mortality arising from lack of educational attainment, employment and training, and the level of national health expenditure. Our research question is:What are some key influencers/drivers in the education-health relationship at a country level?The current study is important because policy makers have an increasing concern on national health issues and on policies that support it. The effect of education is at the root level—creating better overall self-awareness on personal health and making healthcare more accessible. The paper is organized as follows: Section 2 discusses the background for the research. Section 3 discusses the research method; Section 4 offers the analysis and results; Section 5 provides a synthesis of the results and offers an integrated discussion; Section 6 contains the scope and limitations of the research; Section 7 offers conclusions with implications and directions for future research.Fundamental Cause Theory (FCT) [Research has traditionally drawn from three broad theoretical perspectives in conceptualizing the relationship between education and health. The majority of research over the past two decades has been grounded in the ry (FCT) , which pry (FCT) . Some ofry (FCT) .Human Capital Theory (HCT) that views it as a return on investment in the form of increased productivity [signaling or credentialing perspective [Education has also been conceptualized using the uctivity . Educatispective  - is adoIn the current research while we acknowledge the tenets of these theoretical perspectives, we incorporate the social mechanisms in education such as level of education, skills and abilities brought about by enrollment, school life expectancy, and the potential loss brought about by premature mortality. In this manner, we highlight the relevance of the social context in which the education and health domains are situated. We also study the dynamism of the mechanisms over countries and over time and incorporate the influences that precede and succeed educational attainment.We analyze country level education and health data from the OECD and World Bank for a period of 21 years (1995–2015). Our variables include the education indicators of adult education level; enrollment rates at various educational levels; NEET  rates; school life expectancy; and the health indicators of infant mortality, child vaccination rates, deaths from cancer, life expectancy at birth, potential years of life lost and smoking rates , school life expectancy (tertiary), and NEET  rates, affected life expectancy at birth Fig. . Our expFigure We wanted to explore if the potential years of life lost rates are affected by tertiary enrollment rates and tertiary adult education levels Fig. .Fig. 4AThe two sets of box plots in Fig. We compared the performance of tertiary education level and enrollment rates with the child vaccination rates Fig.  to assesIn this analysis Fig. , we lookIn the realm of child health, we also looked at the infant mortality rates. We explored if infant mortality rates are associated with the NEET rates in different age groups Fig. .Fig. 6AFigure We looked to see if the distribution of population without employment, education, or training (NEET) in various categories of high, medium, and low impacted the rate of deaths from cancer Fig. . Our expThe three pie charts in Fig. It is interesting to note the relationship between health expenditure and adult education levels Fig. . We expeFigure Having explored the relationship between health expenditure and adult education, we then explored the relationship between health expenditure and NEET rates of different countries Fig. . We expeIn Fig. The distribution of enrollment rate (tertiary) and life expectancy of all the countries in the sample can give an idea of the current status of both education and health Fig. . We expeFigure We wanted to see if various levels of GDP per capita influence the levels of adult education and deaths from cancer in countries Fig. .Fig. 11Figure Infant mortality is an important indicator of a country’s health status. Figure In Fig. We looked at the association between child vaccination rates and NEET rates in various countries Fig. . We expeFigure We compared the trend of average smoking rate for the years 1995–201 for the continents in the sample Fig. .Fig. 14Figure We explored if adult education levels  are associated with deaths from cancer Fig.  such thaFigure We drilled down further into the correlation between the upper-secondary education level and deaths from cancer. Figure Figure We moved our focus to the trends of tertiary school life expectancy and health expenditure from 1995 to 2015 Fig.  to checkFigure Our results reveal how interlinked education and health can be. We show how a country can improve its health scenario by focusing on appropriate indicators of education. Countries with higher education levels are more likely to have better national health conditions. Among the adult education levels, tertiary education is the most critical indicator influencing healthcare in terms of infant mortality, life expectancy, child vaccination rates, and enrollment rates. Our results emphasize the role that education plays in the potential years of life lost, which is a measure that represents the average years a person would have lived had he/she not died prematurely. In addition to mortality rate, an economy needs to consider this indicator as a measure of health quality.Other educational indicators that are major drivers of health include school life expectancy, particularly at the tertiary level. In order to improve the school life expectancy of the population, governments should control the number of youths ending up unemployed, dropping out of school, and without skills or training (the NEET rate). Education allows people to gain skills/abilities and knowledge on general health, enhancing their awareness of healthy behaviors and preventive care. By targeting promotions and campaigns that emphasize the importance of skills and employment, governments can reduce the NEET rates. And, by reducing the NEET rates, governments have the potential to address a broad array of vulnerabilities among youth, ranging from unemployment, early school dropouts, and labor market discouragement, which are all social issues that warrant attention in a growing economy.We also bring to light the health disparities across countries and suggest implications for governments to target educational interventions that can reduce inequalities and improve health, at a macro level. The health effects of education are at the grass roots level - creating better overall self-awareness on personal health and making healthcare more accessible.Our research suffers from a few limitations. For one, the number of countries is limited, and being that the data are primarily drawn from OECD, they pertain to the continent of Europe. We also considered a limited set of variables. A more extensive study can encompass a larger range of variables drawn from heterogeneous sources. With the objective of acquiring a macro perspective on the education–health association, we incorporated some dependent variables that may not traditionally be viewed as pure health parameters. For example, the variable potential years of life lost is affected by premature deaths that may be caused by non-health related factors too. Also there may be some intervening variables in the education–health relationship that need to be considered. Lastly, while our study explores associations and relationships between variables, it does not investigate causality.Both education and health are at the center of individual and population health and well-being. Conceptualizations of both phenomena should go beyond the individual focus to incorporate and consider the social context and structure within which the education–health relationship is embedded. Such an approach calls for a combination of interdisciplinary research, novel conceptual models, and rich data sources. As health differences are widening across the world, there is need for new directions in research and policy on health returns on education and vice versa. In developing interventions and policies, governments would do well to keep in mind the dual role played by education—as a driver of opportunity as well as a reproducer of inequality . ReducinOur study offers several prospects for future research. Future research can incorporate geographic and environmental variables—such as the quality of air level or latitude—for additional analysis. Also, we can incorporate data from other sources to include more countries and more variables, especially non-European ones, so as to increase the breadth of analysis. In terms of methodology, future studies can deploy meta-regression analysis to compare the relationships between health and some macro-level socioeconomic indicators . Future


Aims: The purpose of this study is to analyze the effect of the perceived coercion of people with mental illness living in a community on their therapeutic satisfaction and life satisfaction, mediated by therapeutic relationships. Methods: We evaluated several clinical variables , levels of perceived coercion, therapeutic relationships, therapeutic satisfaction, and life satisfaction in 185 people with mental illness  = 12.72, male 53.0%, female 45.9%) who live in the community and use community-based mental health programs. The data collected were analyzed to test the proposed hypotheses using structural equation modeling (SEM). Results: The correlation analysis of all variables showed that clinical variables had statistically significant correlations with therapeutic relationship, therapeutic satisfaction, and life satisfaction, but no significant correlation with perceived coercion. Furthermore, perceived coercion was found to have significant predictive power for treatment satisfaction and life satisfaction mediated by therapeutic relationship. Specifically, the lower the perceived coercion, the better the therapeutic relationship. This, in turn, has a positive effect on the therapeutic satisfaction and life satisfaction of participants. Conclusions: Based on these findings, we suggest strategies to minimize coercion in a community. The use of coercive treatment in the mental health field has led to an ethical dilemma for professionals attempting to balance therapeutic gains with respect for individuals. The main conflict arises between the position that coercive intervention is necessary for the optimal treatment of patients, and the position that treatment against an individual’s will, no matter how effective, cannot be expected to provide positive outcomes . The purCoercion has been defined as a situation in which an individual lacks autonomy, or as an event occurring when one party (an agent) exercises control over another (a target) by constraining the target’s freedom or self-determination . CoercioDue to deinstitutionalization, treatment facilities are shifting from within hospitals to within the community, and case management and assertive community treatment are being increasingly emphasized for the recovery of people with mental illness. While these community-based programs emphasize the therapeutic involvement and choice of patients, for those who do not want to participate in the program, coercion is enacted by restricting freedom and legally exerting control. In hospitals, coercion was justified to prevent harm to both the individuals themselves and to others. However, in the community the coercion is justified for considerations of welfare benefits, therapeutic benefits, and policy effectiveness through involuntary outpatient treatment orders, assertive outreach, assertive community treatment, and service based on housing programs . CommuniIndividuals receiving treatment in community settings may experience less physical coercion than those in inpatient settings; however, these patients may perceive psychological coercion from the pressure that is exerted on them by their case managers or family members to comply with treatment . Even ifMost studies on the coercion experienced by persons with mental illness in the community have focused on CTOs. Studies analyzing the effectiveness of CTOs have reported that CTOs contribute to reduced relapse, reduced hospitalization, and shorter hospital stays ,21,22. HIn Korea, community-based services have expanded substantially since the passing of the Mental Health Act in 1995 and outpatient treatment orders were legislated in 2008. However, outpatient treatment orders are rarely utilized, and mandated hospitalization still accounts for 46.1% of all hospitalizations. This is quite high compared to the corresponding rates of 17% in Germany, 13.5% in the UK, and 12% in Italy . In otheTherefore, this study analyzed the effect of the perceived coercion of persons with mental illness who use community mental health services in Korea—on their therapeutic satisfaction and their life satisfaction mediated by the therapeutic relationship. We considered therapeutic satisfaction and life satisfaction because these variables are the main factors that determine recovery from the point of view of a person who has experienced coercion rather than clinical characteristics such as readmission rate, BPRS , and treatment adherence.Institutional Review Board of Gyeongsang National University approval was received for the study (GIRB-A20-Y-0011). We recruited a total of 185 participants with mental disorders over the age of 20 years. All participants obtained written informed consent to take part in the study. The inclusion criteria for the participants with mental disorders included a diagnosis of schizophrenia or a mood disorder according to DSM-5, living within the community, and maintaining ongoing contact with the community service unit.Information on the 185 participants is shown in The average years of education was 12.07 (±2.86), and 47 (25.4%) of the participants were employed, meaning most of the participants (74.1%) were unemployed. Participants with schizophrenia accounted for 119 (64.3%) of all participants, followed by 44 with major depression (23.8%) and 13 with bipolar disorder (7.0%). Most of the participants lived either with their families  or alone .To measure perceived coercion, this study used the Perceived Coercion Scale of the MacArthur Admission Experience Survey, which has been altered to fit the Korean context by Seo et al. . The scaThe therapeutic relationship is defined as the relationship between a clinician and a patient in the process of working toward the goals of recovery with mutual trust and closeness . STAR-P Therapeutic satisfaction was measured using the scale of the Client Satisfaction Questionnaire (CSQ) which was reconstructed by Im . The scaLife satisfaction has been identified as a distinct construct representing a cognitive and global evaluation of one’s quality of life as a whole. This was measured using the Life Satisfaction Scale, which was used in the Korea Welfare Panel Study . The scaSymptoms are evaluated based on the degree to which patients have recently experienced psychiatric symptoms. To measure symptoms, we used the Colorado Symptom Index, which was adapted to the Korean context by Lee and Seo . This scInsight is the ability of patients to accept that they have a mental illness and to recognize both the symptoms of that mental illness and the need for treatment. Insight was assessed using the SAI (Schedule for the Assessment of Insight) , which wPsychosocial functioning refers to measures of physical, psychological, and social aspects that are necessary to maintain and manage a healthy life of persons with mental illness. This was measured with the Self-Health Management Scale developed by Seo  to assesStatistical analyses were performed using SPSS 26 and AMOS 26 . In order to review the basic assumptions of regression analysis before analyzing, we examined outliers, normality, and multi-collinearity. To verify the reliability of the scale, Cronbach’s α internal consistency reliability was used. Descriptive statistical analysis was conducted to examine the socio-demographic characteristics of the participants. Bivariate correlation analysis was used to analyze the correlations among the variables.Structural equation modeling (SEM), which involves a measurement model and a structural model, was used to analyze the cause and effect relations between latent variables. Observed variables are used to measure latent variables in measurement models, and the relationships between latent variables are tested in the structural model . Using aPerceived coercion, on the other hand, showed significant negative correlations with therapeutic relationship and therapeutic satisfaction. Namely, the higher the perceived coercion, the lower the therapeutic relationship (r = −0.233), and the lower the therapeutic satisfaction (r = −0.282). However, with life satisfaction, no significant correlation was found.Confirmatory factor analysis (CFA) was conducted to assess the observed variables with factor loadings less than 0.5 and to confirm the fitness of the measurement model for each of the observed variables of perceived coercion, therapeutic relationship, therapeutic satisfaction, and life satisfaction. All factor loading of the observed variables was over 0.6, as shown in The initial research model analysis results did not fit the data very well. In order to modify the initial research model, correlations were made between the within-factor measurement error of the observed variables as the modification indices suggested. The final research model was derived as shown in From The result of examining the relative influence of each variable through standardized regression weights was that the therapeutic relationship showed the greatest impact on therapeutic satisfaction. This was followed by the effect of the therapeutic relationship on life satisfaction, and the effect of the perceived coercion on the therapeutic relationship.The purpose of this study was to examine how perceived coercion in the community treatment process  influences therapeutic satisfaction and life satisfaction mediated by the therapeutic relationship. Two important findings were identified.First, the perceived coercion in the community does not have a meaningful correlation with the subject’s symptoms, psychosocial functioning, or insight. These results differ from those indicating that the perceived coercion experienced during hospitalization is higher since symptoms are more severe, psychosocial functioning is lower, and insight is lower ,23,37. HSecond, perceived coercion was shown to negatively affect the therapeutic relationship, and this in turn has significant effects on the patient’s therapeutic satisfaction and life satisfaction. This is consistent with the research results indicating that coercion negatively affects the therapeutic relationship ,39,40,41Based on these results, the authors propose strategies to minimize coercion in community-based treatment. There is no “perfect model in which coercion is absent” ; howeverThis study found that coercion perceived by the persons with mental illness in the community, as well as the coercion in the hospital, could seriously damage their quality of life. These results may contribute to increasing the interest of researchers and practitioners in community coercion, which was of relatively less interest than coercion in hospitals. However, as a limitation of this study, we identified first that longitudinal study is needed to analyze the impact of perceived coercion on quality of life. Insight develops over time; so at first, from a paternalistic point of view, the individual may perceive coercion as unpleasant but, as symptoms improve, they may eventually come to appreciate coercion. Thus, even if negative results appear in the short term, they may change over time. Therefore, the impact of coercion can be clearly confirmed when we track changes. Second, this study focused only on perceived coercion, which is a subjective coercion, and failed to analyze the relationship with objective coercion. Since perceived coercion is based on individual perception, the individual has the advantage of being able to evaluate its influence on themselves in the most meaningful way possible, but there are limitations that can be exaggerated or distorted. It is therefore necessary to consider subjective coercion and objective coercion together.


Background: Off-label use is frequent in paediatrics but that does not necessarily mean that the risk-benefit ratio is negative. Nevertheless, evidence-based data is essential for safe drug therapy. In Germany, there is no publicly available compendium providing transparent, evidence-based information for paediatric pharmacotherapy to date. This work describes the development of a web-based paediatric drug information system (PDIS) for Germany and its evaluation by health care professionals (HCP). Methods: Since 2012, a PDIS is being developed by the authors and is supported by the Federal Ministry of Health since 2016. Dosing recommendations were established based on systematic literature reviews and subsequent evaluation by clinical experts. The prototype was evaluated by HCP. Based on the results, the further development was concluded. Results: 92% of HCP believed that the PDIS could improve the quality of prescribing, as currently available information is deficient. Besides the license and formulations, dosing recommendations were the most relevant modules. A dosage calculator was the most wanted improvement. To facilitate sustainability of future development, a collaboration with the Dutch Kinderformularium was established. As of 2021, the database will be available to German HCP. Conclusion: The fundamentals for a German PDIS were established, and vital steps were taken towards successful continuation. Due to the lack of evidence from valuable clinical studies, and because drug development historically focused on adults, children have reduced access to licensed medicines leading to high off-label (up to 79%) and unlicensed use up to 15%) [5% [1]. CRegulatory efforts improving the development and accessibility of appropriate medicines for children have been initiated worldwide within the past decades. However, the expected improvement for generic drugs frequently used off-label in children has not been realised. Only six off-patent medicines were licensed via the Paediatric Use Marketing Authorisation (PUMA) route within more than ten years so far .The absence of a paediatric license does not necessarily mean that the risk-benefit ratio of a drug in a paediatric age group is negative. For some drugs and indications, data from, e.g., clinical trials are available, and a well-established, evidence-based use can be assumed. On the other hand, a paediatric license does not imply high-level evidence for the use in children and that the latest scientific data are integrated into the license ,7. FurthPaediatric drug formularies are an essential source of information regarding the use of medicines for children . They arThe aim of this work is to describe the development and history of the paediatric drug information system (PDIS) for Germany and its evaluation by German healthcare professionals (HCP).Since 2012, the Department of Paediatrics and Adolescent Medicine of the University Hospital of Erlangen, Germany, has been establishing a database providing dosing recommendations for paediatric drug therapy. In 2016, the German Federal Ministry of Health funded the “PaedDos” project within the scope of the “Action Plan 2013–2015” to improve the safety of drug therapy in Germany. PaedDos aimed at the development and set-up of an evidence-based online formulary for paediatric drugs in Germany.Standard operating procedures (SOP) for the research of drug information were issued to ensure the quality of the content provided by the PDIS. The Federal Ministry of Health grant facilitated a high scientific standard because one of the funding requirements was that dosing recommendations for off-label use must be based on systematic literature searches, including primary literature. Therefore, available data from clinical studies and case series in children have to be considered, and benefits of the drug use need to be weighed against risks before the most appropriate dosing regimen is proposed. The first 100 drug monographs included in the PDIS were based on a list of the most frequently prescribed drugs for patients under 18 years of age, according to out- and inpatient prescription data in Germany.To identify the demand for a PDIS for Germany and to adapt the database to end-users’ needs, an online user test was developed by the project team comprising of pharmacists, paediatricians, and computer scientists. A questionnaire consisting of 90 items was designed in collaboration with a usability expert. Thirty items focused on the pharmacological, pharmaceutical part of the PDIS and are subject of the present analysis. The technical development of the prototype database and corresponding parts of the online user test has already been published elsewhere .® tool (https://soscisurvey.de/). Participants gave informed consent to anonymous collection and data processing digitally prior to participating in the online user test. Demographic information including occupation, professional experience in years, and number of drug prescriptions or administration advices per day were collected.Participating paediatricians and pharmacists were recruited via e-mail or telephone. None of the participants was part of the project team nor had economic interests. During the user test, participants had full access to the web-based PDIS. The user test started with two scenarios for each occupational group . Each scFor numerical evaluation of the answers of participants, a five-point Likert scale ranging from “does not apply at all” (1) to “completely applies” (5) was utilised . The cloDue to the early involvement of HCP representatives, specific requirements of end-users could be integrated into the development process of the PDIS. By doing so, it became evident that the underlying data structure of the original database was not suitable for an extension and that the integration of a dosage calculator was challenging to realise. Therefore, an evaluation of existing paediatric drug formularies was performed with regard to possible collaboration.Since 2018, the Federal Ministry of Health is supporting the establishment of the PDIS with a second grant, intending to complete the database and provide the contents free of charge to German HCP.A well-structured, web-based prototype of a PDIS was developed by the project team aiming to support HCP as best as possible within the medication process. The heart of the database consists of structured drug monographs comprising of six information modules .Primary literature of published data regarding the use of an active substance in any paediatric subgroup is selected via structured PubMed searches. Besides, the Summary of Product Characteristics (SmPC), as well as national and international guidelines from specialist societies, are considered and incorporated into the recommendations. In a final step, the dosing recommendation is reviewed by clinical experts of the relevant division prior to publication in the PDIS.At the time of the online user test, the database contained about 100 drug monographs, all including dosing recommendations, licensing status, and references. Fifty of those monographs also included information on the remaining modules . Out of them, about ten were used for the scenarios of the online user test.n = 1, public: n = 2). Physicians in private practice had the highest professional experience and most prescriptions per day  and 75% (pharmacists). Twelve HCP took part: six paediatricians in private practice, three clinicians from children’s hospitals, and three pharmacists , Module 3 , and Module 6 (Formulations) contain all the content they need in every day clinical practice . For theThe evaluation of existing paediatric formularies revealed that the Dutch Paediatric Formulary (Kinderformularium.nl) was very close to the requirements of the German funding conditions, for example, that the majority of dosing information is based on systematic literature reviews of the primary literature. The Kinderformularium.nl presents as a publicly funded, web-based database containing more than 780 drug monographs. Besides dosing recommendations, it offers additional information, e.g., adverse events and a transparent overview of underlying references. Most importantly, the Kinderformularium.nl already had processes in place in which risk-benefit-analysis based on the best available scientific evidence are performed and reviewed by medical and pharmaceutical experts  . The DutNederlands Kenniscentrum Farmacotherapie bij Kinderen), signed a licensing agreement. Subsequently, a German database “Kinderformularium.de” was established. The content  is being translated, revised, and completed according to German requirements, including the results of the present evaluation. The dosing recommendations provided are crosschecked with national and international guidelines aiming at harmonisation across countries. New monographs are being prepared for active ingredients not available in the Netherlands but are used in Germany, e.g., ampicillin, metamizole, and cefixime. The licensing status and child-appropriate pharmaceutical formulations available in the German market are being systematically researched and implemented. Clinical experts from Germany review the drug monographs prior to publication in the PDIS.Considering the evaluation of the prototype presented in this manuscript, the Department of Paediatrics and Adolescent Medicine of the University Hospital of Erlangen and the Foundation of the Dutch Kinderformularium, NKFK  . As of 2As global initiatives, particularly PUMA, have failed so far predominantly regarding the availability of generic paediatric medicines, off-label use will remain high for the paediatric population in the future . TherefoThe evaluation of the prototype of the German PDIS demonstrated that there is a high demand for a national evidence-based paediatric formulary. Dosing recommendations were the most important information for the potential end-users as well as the licensing status and child-appropriate formulations. The prototype of the PDIS appeared adequate to provide the relevant information in terms of content and usability, but deficits still had to be eliminated. This could be achieved via cooperation with an existing formulary in the Netherlands, particularly by using the same technical infrastructure and adding the specific, national requirements in addition to the already established drug information.Retrieving evidence-based information on off-label use is time-consuming, as various sources have to be consulted. This procedure is not realistic during daily routine and is one of the reasons for the high variability of dosing regimens in the paediatric population ,9,10. ThAt the time of the online user test, the database only contained about 100 drug monographs. This number of monographs was sufficient to illustrate the type of information provided by the PDIS. However, more monographs would have been needed to evaluate the overall benefit for HCP in daily practice. The low amount of available entries was also critically remarked by Plate et al. when they reviewed the risks and benefits of the Zak-Kinderarzneimittel database in their beginnings in 2007 ,19.®. The Rote Liste® is a compendium of medicinal products in the German market. The entries are provided by the pharmaceutical companies on a voluntary basis, and all information is based on the official SmPC and package leaflet. Thus, this compendium is on the one hand not complete and on the other hand also not suitable for paediatrics, as it does not address off-label use.The database already used in clinical practice, which was most often mentioned by all three occupations, was the Rote ListeThe lack of age-appropriate medicinal products in the market is closely related to high off-label use . Age, heA Delphi survey with 31 HCP from more than six continents by Kelly et al. demonstrated that besides dosing information (licensed and off-label indications), formulations, adverse drug reactions, precautions, and drug-drug interactions should be included in a paediatric drug monograph . ParticuAnother important finding of the online user test was the high demand for a dosage calculator. Medication errors constitute a significant health problem in paediatric patients. The risk of adverse drug events following a medication error is higher than in the adult population . PaediatA web-based information platform, like the PDIS, described in this evaluation, provides rapid access to paediatric drug information, aids with dosage calculations and supports decisions on medication processes. The PDIS is applicable at all stages of drug use, providing relevant information not only on dosing but also on the administration, license, and adverse drug profile of the active substance. With its web-based design, it is available for both the out- and inpatient sector, without being dependent on any software of the hospital or practice system and recent (safety) information can be incorporated quickly. Therefore, the PDIS has the potential to improve paediatric drug therapy and to decrease medication errors as long as the provided information is reliable and based on the latest evidence. In addition, the online user test showed that HCP believe that the PDIS has the potential to improve prescribing quality and to decrease adverse drug events. However, sound evidence regarding the impact of the PDIS on medication safety in clinical practice is still lacking . PotentiThe rigorous specifications for medical devices, both from a technical and a regulatory perspective, were the main reasons why the structure behind the prototype PDIS was found to be inappropriate for further development, and an evaluation of existing paediatric formularies was performed.One of the best-known paediatric formularies is the British National Formulary for Children (BNFc), which was first published in 2005 and has been regularly updated since . DespiteThe SwissPedDose database provides freely accessible dosing recommendations for children in three languages, German, English, and French. Since the project is relatively new, the database is still incomplete. 130 drug monographs are planned to be published by 2022 . The infThe Kinderformularium.nl was considered very close to the purposes of the German formulary, which led to the establishment of a licensing agreement. The cooperation enables the provision of a well-functioning database providing paediatric drug information based on the best available evidence and may be a first step towards a European paediatric formulary. For the first time in the German history of paediatric pharmacotherapy, off-label use is being systematically investigated on a large scale and provided freely accessible to German HCP. The licensing status of the dosing recommendations and the underlying references are stated transparently in order to support HCP as best as possible.www.kinderformularium.de.We have demonstrated that there is a strong demand for a national paediatric formulary with evidence-based dosing recommendations and additional drug information for children in Germany. The evaluation of the prototype PDIS, developed with grants from the Federal Ministry of Health within the action plan to improve the safety of drug therapy in Germany, revealed promising results in terms of usability and content, but also provided limitations. These findings helped us meet the structural and content-related fundamental requirement for a national, publicly accessible PDIS. As of 2021, the web-based paediatric formulary will be available to German HCP via