Risk adjustment for hospital use using social security data: cross sectional small area analysis

Objectives: To identify demographic and socioeconomic determinants of need for acute hospital treatment at small area level. To establish whether there is a relation between poverty and use of inpatient services. To devise a risk adjustment formula for distributing public funds for hospital services using, as far as possible, variables that can be updated between censuses. Design: Cross sectional analysis. Spatial interactive modelling was used to quantify the proximity of the population to health service facilities. Two stage weighted least squares regression was used to model use against supply of hospital and community services and a wide range of potential needs drivers including health, socioeconomic census variables, uptake of income support and family credit, and religious denomination. Setting: Northern Ireland. Main outcome measure: Intensity of use of inpatient services. Results: After endogeneity of supply and use was taken into account, a statistical model was produced that predicted use based on five variables: income support, family credit, elderly people living alone, all ages standardised mortality ratio, and low birth weight. The main effect of the formula produced is to move resources from urban to rural areas. Conclusions: This work has produced a population risk adjustment formula for acute hospital treatment in which four of the five variables can be updated annually rather than relying on census derived data. Inclusion of the social security data makes a substantial difference to the model and to the results produced by the formula.

Risk adjustment in aging societies

Background In Switzerland, age is the predominant driver of solidarity transfers in risk adjustment (RA). Concerns have been voiced regarding growing imbalances in cost sharing between young and old insured due to demographic changes (larger fraction of elderly >65 years and rise in average age). Particularly young adults aged 19–25 with limited incomes have to shoulder increasing solidarity burdens. Between 1996 and 2011, monthly intergenerational solidarity payments for young adults have doubled from CHF 87 to CHF 182, which corresponds to the highest absolute transfer increase of all age groups. Results By constructing models for age-specific RA growth and for calculating the lifetime sum of RA transfers we investigated the causes and consequences of demographic changes on RA payments. The models suggest that the main driver for RA increases in the past was below average health care expenditure (HCE) growth in young adults, which was only half as high (average 2% per year) compared with older adults (average 4% per year). Shifts in age group distributions were only accountable for 2% of the CHF 95 rise in RA payments. Despite rising risk adjustment debts for young insured the balance of lifetime transfers remains positive as long as HCE growth rates are greater than the discount rate used in this model (3%). Moreover, the life-cycle model predicts that the lifetime rate of return on RA payments may even be further increased by demographic changes. Nevertheless, continued growth of RA contributions may overwhelm vulnerable age groups such as young adults. We therefore propose methods to limit the burden of social health insurance for specific age groups (e.g. young adults in Switzerland) by capping solidarity payments. Conclusions Taken together, our mathematical modelling framework helps to gain a better understanding of how demographic changes interact with risk adjustment and how redistribution of funds between age groups can be controlled without inducing further selection incentives. Those methods can help to construct more equitable systems of health financing in light of population aging.

Evidence that risk adjustment is unnecessary in estimates of the User cost of money

Investors value the special attributes of monetary assets (e.g., exchangeability, liquidity, and safety) and pay a premium for holding them in the form of a lower return rate -- The user cost of holding monetary assets can be measured approximately by the difference between the returns on illiquid risky assets and those of safer liquid assets -- A more appropriate measure should adjust this difference by the differential risk of the assets in question -- We investigate the impact that time non-separable preferences has on the estimation of the risk-adjusted user cost of money -- Using U.K. data from 1965Q1 to 2011Q1, we estimate a habit-based asset pricing model with money in the utility function and find that the risk adjustment for risky monetary assets is negligible -- Thus, researchers can dispense with risk adjusting the user cost of money in constructing monetary aggregate indexes

Risk modelling in quality clinical registries : Monitoring lesion treatment failure rate in percutaneous coronary interventions

Aims: This paper describes the development of a risk adjustment (RA) model predictive of individual lesion treatment failure in percutaneous coronary interventions (PCI) for use in a quality monitoring and improvement program. Methods and results: Prospectively collected data for 3972 consecutive revascularisation procedures (5601 lesions) performed between January 2003 and September 2011 were studied. Data on procedures to September 2009 (n = 3100) were used to identify factors predictive of lesion treatment failure. Factors identified included lesion risk class (p < 0.001), occlusion type (p < 0.001), patient age (p = 0.001), vessel system (p < 0.04), vessel diameter (p < 0.001), unstable angina (p = 0.003) and presence of major cardiac risk factors (p = 0.01). A Bayesian RA model was built using these factors with predictive performance of the model tested on the remaining procedures (area under the receiver operating curve: 0.765, Hosmer–Lemeshow p value: 0.11). Cumulative sum, exponentially weighted moving average and funnel plots were constructed using the RA model and subjectively evaluated. Conclusion: A RA model was developed and applied to SPC monitoring for lesion failure in a PCI database. If linked to appropriate quality improvement governance response protocols, SPC using this RA tool might improve quality control and risk management by identifying variation in performance based on a comparison of observed and expected outcomes.

Development and use of risk adjusted statistical process control tools for the monitoring and improvement of clinical outcomes in interventional cardiology

This thesis explored the development of statistical methods to support the monitoring and improvement in quality of treatment delivered to patients undergoing coronary angioplasty procedures. To achieve this goal, a suite of outcome measures was identified to characterise performance of the service, statistical tools were developed to monitor the various indicators and measures to strengthen governance processes were implemented and validated. Although this work focused on pursuit of these aims in the context of a an angioplasty service located at a single clinical site, development of the tools and techniques was undertaken mindful of the potential application to other clinical specialties and a wider, potentially national, scope.

Option-Implied Preferences Adjustments and Risk-Neutral Density Forecasts

The main objective of this paper is to analyse the value of information contained in prices of options on the IBEX 35 index at the Spanish Stock Exchange Market. The forward looking information is extracted using implied risk-neutral density functions estimated by a mixture of two-lognormals and three alternative risk-adjustments: the classic power and exponential utility functions and a habit-based specification that allows for a counter-cyclical variation of risk aversion. Our results show that at four-week horizon we can reject the hypothesis that between October 1996 and March 2000 the risk-neutral densities provide accurate predictions of the distributions of future realisations of the IBEX 35 index at a four-week horizon. When forecasting through risk-adjusted densities the performance of this period is statistically improved and we no longer reject that hypothesis. All risk-adjusted densities generate similar forecasting statistics. Then, at least for a horizon of four-weeks, the actual risk adjustment does not seem to be the issue. By contrast, at the one-week horizon risk-adjusted densities do not improve the forecasting ability of the risk-neutral counterparts.

External validation of the Hospital-patient One-year Mortality Risk (HOMR) model for predicting death within 1 year after hospital admission

BACKGROUND Predicting long-term survival after admission to hospital is helpful for clinical, administrative and research purposes. The Hospital-patient One-year Mortality Risk (HOMR) model was derived and internally validated to predict the risk of death within 1 year after admission. We conducted an external validation of the model in a large multicentre study. METHODS We used administrative data for all nonpsychiatric admissions of adult patients to hospitals in the provinces of Ontario (2003-2010) and Alberta (2011-2012), and to the Brigham and Women's Hospital in Boston (2010-2012) to calculate each patient's HOMR score at admission. The HOMR score is based on a set of parameters that captures patient demographics, health burden and severity of acute illness. We determined patient status (alive or dead) 1 year after admission using population-based registries. RESULTS The 3 validation cohorts (n = 2,862,996 in Ontario, 210 595 in Alberta and 66,683 in Boston) were distinct from each other and from the derivation cohort. The overall risk of death within 1 year after admission was 8.7% (95% confidence interval [CI] 8.7% to 8.8%). The HOMR score was strongly and significantly associated with risk of death in all populations and was highly discriminative, with a C statistic ranging from 0.89 (95% CI 0.87 to 0.91) to 0.92 (95% CI 0.91 to 0.92). Observed and expected outcome risks were similar (median absolute difference in percent dying in 1 yr 0.3%, interquartile range 0.05%-2.5%). INTERPRETATION The HOMR score, calculated using routinely collected administrative data, accurately predicted the risk of death among adult patients within 1 year after admission to hospital for nonpsychiatric indications. Similar performance was seen when the score was used in geographically and temporally diverse populations. The HOMR model can be used for risk adjustment in analyses of health administrative data to predict long-term survival among hospital patients.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

Ventricular conduction and long-term heart failure outcomes and mortality in African Americans: insights from the Jackson Heart Study.

BACKGROUND: QRS prolongation is associated with adverse outcomes in mostly white populations, but its clinical significance is not well established for other groups. We investigated the association between QRS duration and mortality in African Americans. METHODS AND RESULTS: We analyzed data from 5146 African Americans in the Jackson Heart Study stratified by QRS duration on baseline 12-lead ECG. We defined QRS prolongation as QRS≥100 ms. We assessed the association between QRS duration and all-cause mortality using Cox proportional hazards models and reported the cumulative incidence of heart failure hospitalization. We identified factors associated with the development of QRS prolongation in patients with normal baseline QRS. At baseline, 30% (n=1528) of participants had QRS prolongation. The cumulative incidences of mortality and heart failure hospitalization were greater with versus without baseline QRS prolongation: 12.6% (95% confidence interval [CI], 11.0-14.4) versus 7.1% (95% CI, 6.3-8.0) and 8.2% (95% CI, 6.9-9.7) versus 4.4% (95% CI, 3.7-5.1), respectively. After risk adjustment, QRS prolongation was associated with increased mortality (hazard ratio, 1.27; 95% CI, 1.03-1.56; P=0.02). There was a linear relationship between QRS duration and mortality (hazard ratio per 10 ms increase, 1.06; 95% CI, 1.01-1.12). Older age, male sex, prior myocardial infarction, lower ejection fraction, left ventricular hypertrophy, and left ventricular dilatation were associated with the development of QRS prolongation. CONCLUSIONS: QRS prolongation in African Americans was associated with increased mortality and heart failure hospitalization. Factors associated with developing QRS prolongation included age, male sex, prior myocardial infarction, and left ventricular structural abnormalities.

The impact of the nursing workforce on patient outcomes in intensive care

Introduction: Evidence from studies conducted mainly in the US and mainland Europe suggests that characteristics of the workforce, such as nurse patient ratios and workload (measured in a number of different ways) may be linked to variations in patient outcomes across health care settings (Carmel and Rowan 2001). Few studies have tested this relationship in the UK thus questions remain about whether we are justified in extrapolating evidence from studies conducted in very different health care systems. Objectives: To investigate whether characteristics of the nursing workforce affect patient mortality UK Intensive Care Units. Data: Patient data came from the case mix programme, Intensive Care National Audit and Research Centre (ICNARC), while information about the units came from a survey of all ICUs in England (Audit Comission 1998). The merged data set contained information on 43,859 patients in 69 units across England. ICNARC also supplied a risk adjustment variable to control for patient characteristics that are often the most important determinants of survival. Methods: Multivariate multilevel logistic regression. Findings: Higher numbers of direct care nurses and lower scores on measures of workload(proportion of occupied beds at the time the patient was admitted and mean daily transfers into the unit) were associated with lower mortality rates. Furthermore, the effect of the number of direct care nurses was greatest on the life chances of the patients who were most at risk of dying. Implications: This study has wide implications for workforce policy and planning because it shows that the size of the nursing workforce is associated with mortality (West et al 2006). Few studies have demonstrated this relationship in the UK. This study has a number of strengths and weaknesses and further research is required to determine whether this relationship between the nursing workforce and patient outcomes is causal.

Desigualdades socioeconómicas na utilização de cuidados de saúde na população com 65 ou mais anos de idade em Portugal : dados do 4.º Inquérito Nacional de Saúde 2005-2006

RESUMO - INTRODUÇÃO: A equidade em cuidados de saúde constitui uma prioridade das políticas de saúde, tendo vários estudos descrito uma iniquidade que geralmente favorece os indivíduos com maior rendimento e nível educacional. Este estudo visa caracterizar as desigualdades socioeconómicas na utilização de cuidados de saúde na população com 65 ou mais anos de idade, dadas as suas características, maior vulnerabilidade e crescente peso demográfico na população. METODOLOGIA: Através de dados do INS, procedeu-se à análise univariada e multivariada por regressão linear múltipla para avaliação das desigualdades socioeconómicas na utilização de cuidados de saúde em 8698 indivíduos. RESULTADOS: Identifica-se um padrão de desigualdade na utilização de cuidados de saúde – indivíduos com maior rendimento e nível de escolaridade utilizam em média mais consultas de especialidade; ocorrendo o inverso nas consultas de CSP. Com ajustamento pela necessidade, através do estado de saúde auto-reportado, observa-se um padrão de iniquidade no sexo masculino relativamente às consultas em geral e consultas de CSP. DISCUSSÃO E CONCLUSÕES: A iniquidade na utilização de cuidados de saúde, apesar de não constituir a única causa, pode determinar maior iniquidade em saúde, pelo que é relevante o seu estudo. Os resultados alcançados podem ser justificados pelas características do SNS, assim como pelas isenções de taxas moderadoras, rede social, outros indicadores económicos, ou ainda pelo próprio contexto de vida do individuo. Torna-se fundamental prosseguir a investigação acerca da equidade, assim como promover uma ampla reflexão sobre os desafios futuros do sistema de saúde, que permitam preservar a sua sustentabilidade e princípios fundadores.

Ajustamento pelo risco : da conceptualização à operacionalização

RESUMO - As organizações de saúde, em geral, e os hospitais, em particular, são frequentemente reconhecidos por terem particularidades e especificidades que conferem uma especial complexidade ao seu processo produtivo e à sua gestão (Jacobs, 1974; Butler, 1995). Neste sentido, na literatura hospitalar emergem alguns temas como prioritários tanto na investigação como na avaliação do seu funcionamento, nomeadamente os relacionados com a produção, com o financiamento, com a qualidade, com a eficiência e com a avaliação do seu desempenho. O estado da arte da avaliação do desempenho das organizações de saúde parece seguir a trilogia definida por Donabedian (1985) — Estrutura, Processo e Resultados. Existem diversas perspectivas para a avaliação do desempenho na óptica dos Resultados — efectividade, eficiência ou desempenho financeiro. No entanto, qualquer que seja a utilizada, o ajustamento pelo risco é necessário para se avaliar a actividade das organizações de saúde, como forma de medir as características dos doentes que podem influenciar os resultados de saúde. Como possíveis indicadores de resultados, existem a mortalidade (resultados finais), as complicações e as readmissões (resultados intermédios). Com excepção dos estudos realizados por Thomas (1996) e Thomas e Hofer (1998 e 1999), praticamente ninguém contesta a relação entre estes indicadores e a efectividade dos cuidados. Chamando, no entanto, a atenção para a necessidade de se definirem modelos de ajustamento pelo risco e ainda para algumas dificuldades conceptuais e operacionais para se atingir este objectivo. Em relação à eficiência técnica dos hospitais, os indicadores tradicionalmente mais utilizados para a sua avaliação são os custos médios e a demora média. Também neste domínio, a grande maioria dos estudos aponta para que a gravidade aumenta o poder justificativo do consumo de recursos e que o ajustamento pelo risco é útil para avaliar a eficiência dos hospitais. Em relação aos sistemas usados para medir a severidade e, consequentemente, ajustar pelo risco, o seu desenvolvimento apresenta, na generalidade, dois tipos de preocupações: a definição dos suportes de recolha da informação e a definição dos momentos de medição. Em última instância, o dilema que se coloca reside na definição de prioridades e daquilo que se pretende sacrificar. Quando se entende que os aspectos financeiros são determinantes, então será natural que se privilegie o recurso quase exclusivo a elementos dos resumos de alta como suporte de recolha da informação. Quando se defende que a validade de construção e de conteúdo é um aspecto a preservar, então o recurso aos elementos dos processos clínicos é inevitável. A definição dos momentos de medição dos dados tem repercussões em dois níveis de análise: na neutralidade económica do sistema e na prospectividade do sistema. O impacto destas questões na avaliação da efectividade e da eficiência dos hospitais não é uma questão pacífica, visto que existem autores que defendem a utilização de modelos baseados nos resumos de alta, enquanto outros defendem a supremacia dos modelos baseados nos dados dos processos clínicos, para finalmente outros argumentarem que a utilização de uns ou outros é indiferente, pelo que o processo de escolha deve obedecer a critérios mais pragmáticos, como a sua exequibilidade e os respectivos custos de implementação e de exploração. Em relação às possibilidades que neste momento se colocam em Portugal para a utilização e aplicação de sistemas de ajustamento pelo risco, verifica-se que é praticamente impossível a curto prazo aplicar modelos com base em dados clínicos. Esta opção não deve impedir que a médio prazo se altere o sistema de informação dos hospitais, de forma a considerar a eventualidade de se utilizarem estes modelos. Existem diversos problemas quando se pretendem aplicar sistemas de ajustamento de risco a populações diferentes ou a subgrupos distintos das populações donde o sistema foi originalmente construído, existindo a necessidade de verificar o ajustamento do modelo à população em questão, em função da sua calibração e discriminação.

Efectividade e volume: uma aplicação a hospitais portugueses

RESUMO - Contexto: a avaliação da qualidade como tema potencialmente importante para utentes e prestadores de cuidados de saúde. A taxa de mortalidade como medida de resultados com um adequado ajustamento do risco. A existência de determinadas características estruturais do hospital às quais está associada uma menor mortalidade. Objectivos: identificar diferenças no desempenho e na taxa de mortalidade dos hospitais e investigar que características estruturais justificam essas diferenças. Metodologia: foram seleccionados os episódios de internamento das doenças de maior mortalidade hospitalar. A medida de desempenho considerada foi a comparação entre a mortalidade observada e a mortalidade esperada, calculada a partir da escala preditiva da mortalidade do Disease Staging, recalibrada para Portugal. A medida de desempenho foi analisada por hospital, doença e grupo de doenças. A ordenação dos hospitais pelo desempenho foi comparada com a ordenação dos hospitais pela taxa de mortalidade observada. O desempenho dentro de cada hospital foi analisado para um grupo de doenças seleccionadas. A relação entre o valor da medida de desempenho e as variáveis «número de episódios», «índice tecnológico» e «gravidade dos doentes tratados» foi analisada através da regressão linear para o conjunto dos episódios e para cada doença e grupo de doenças. Resultados: foram incluídos 379 074 episódios, agrupados em 21 doenças e 8 grupos de doenças e tratados em 81 hospitais. A taxa de mortalidade observada foi de 12%. Existiam diferenças no desempenho por hospital, alguns dos quais se destacam pelo seu melhor/pior nível de desempenho. Foram observadas as limitações da taxa de mortalidade bruta como instrumento de análise do desempenho, no contexto de hospitais com diferentes níveis de risco dos doentes tratados. Para além disso, evidenciou-se que a análise do hospital como um todo ou em cada uma das partes tem resultados distintos, dada a existência de diferentes níveis de desempenho dentro do hospital. Finalmente, verificou- se que a relação entre volume e desempenho, quando existe, é, na quase totalidade dos casos, não linear e inversa à referida na literatura.