Technical rationality and the decentring of patients and care delivery: A critique of 'unavoidable' in the context of patient harm.

In recent decades, debate on the quality and safety of healthcare has been dominated by a measure and manage administrative rationality. More recently, this rationality has been overlaid by ideas from human factors, ergonomics and systems engineering. Little critical attention has been given in the nursing literature to how risk of harm is understood and actioned, or how patients can be subjectified and marginalised through these discourses. The problem of assuring safety for particular patient groups, and the dominance of technical forms of rationality, has seen the word 'unavoidable' used in connection with intractable forms of patient harm. Employing pressure injury policy as an exemplar, and critically reviewing notions of risk and unavoidable harm, we problematise the concept of unavoidable patient harm, highlighting how this dominant safety rationality risks perverse and taken-for-granted assumptions about patients, care processes and the nature of risk and harm. In this orthodoxy, those who specify or measure risk are positioned as having more insight into the nature of risk, compared to those who simply experience risk. Driven almost exclusively as a technical and administrative pursuit, the patient safety agenda risks decentring the focus from patients and patient care.

Impact of socioeconomic inequalities on geographic disparities in cancer incidence: comparison of methods for spatial disease mapping.

BACKGROUND: The reliability of spatial statistics is often put into question because real spatial variations may not be found, especially in heterogeneous areas. Our objective was to compare empirically different cluster detection methods. We assessed their ability to find spatial clusters of cancer cases and evaluated the impact of the socioeconomic status (e.g., the Townsend index) on cancer incidence. METHODS: Moran's I, the empirical Bayes index (EBI), and Potthoff-Whittinghill test were used to investigate the general clustering. The local cluster detection methods were: i) the spatial oblique decision tree (SpODT); ii) the spatial scan statistic of Kulldorff (SaTScan); and, iii) the hierarchical Bayesian spatial modeling (HBSM) in a univariate and multivariate setting. These methods were used with and without introducing the Townsend index of socioeconomic deprivation known to be related to the distribution of cancer incidence. Incidence data stemmed from the Cancer Registry of Isère and were limited to prostate, lung, colon-rectum, and bladder cancers diagnosed between 1999 and 2007 in men only. RESULTS: The study found a spatial heterogeneity (p < 0.01) and an autocorrelation for prostate (EBI = 0.02; p = 0.001), lung (EBI = 0.01; p = 0.019) and bladder (EBI = 0.007; p = 0.05) cancers. After introduction of the Townsend index, SaTScan failed in finding cancers clusters. This introduction changed the results obtained with the other methods. SpODT identified five spatial classes (p < 0.05): four in the Western and one in the Northern parts of the study area (standardized incidence ratios: 1.68, 1.39, 1.14, 1.12, and 1.16, respectively). In the univariate setting, the Bayesian smoothing method found the same clusters as the two other methods (RR >1.2). The multivariate HBSM found a spatial correlation between lung and bladder cancers (r = 0.6). CONCLUSIONS: In spatial analysis of cancer incidence, SpODT and HBSM may be used not only for cluster detection but also for searching for confounding or etiological factors in small areas. Moreover, the multivariate HBSM offers a flexible and meaningful modeling of spatial variations; it shows plausible previously unknown associations between various cancers.

Pediatric medical complexity algorithm: a new method to stratify children by medical complexity.

OBJECTIVES: The goal of this study was to develop an algorithm based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), codes for classifying children with chronic disease (CD) according to level of medical complexity and to assess the algorithm's sensitivity and specificity. METHODS: A retrospective observational study was conducted among 700 children insured by Washington State Medicaid with ≥1 Seattle Children's Hospital emergency department and/or inpatient encounter in 2010. The gold standard population included 350 children with complex chronic disease (C-CD), 100 with noncomplex chronic disease (NC-CD), and 250 without CD. An existing ICD-9-CM-based algorithm called the Chronic Disability Payment System was modified to develop a new algorithm called the Pediatric Medical Complexity Algorithm (PMCA). The sensitivity and specificity of PMCA were assessed. RESULTS: Using hospital discharge data, PMCA's sensitivity for correctly classifying children was 84% for C-CD, 41% for NC-CD, and 96% for those without CD. Using Medicaid claims data, PMCA's sensitivity was 89% for C-CD, 45% for NC-CD, and 80% for those without CD. Specificity was 90% to 92% in hospital discharge data and 85% to 91% in Medicaid claims data for all 3 groups. CONCLUSIONS: PMCA identified children with C-CD (who have accessed tertiary hospital care) with good sensitivity and good to excellent specificity when applied to hospital discharge or Medicaid claims data. PMCA may be useful for targeting resources such as care coordination to children with C-CD.

How health care organizations are using data on patients' race and ethnicity to improve quality of care.

CONTEXT: Racial and ethnic disparities in the quality of health care are well documented in the U.S. health care system. Reducing these disparities requires action by health care organizations. Collecting accurate data from patients about their race and ethnicity is an essential first step for health care organizations to take such action, but these data are not systematically collected and used for quality improvement purposes in the United States. This study explores the challenges encountered by health care organizations that attempted to collect and use these data to reduce disparities. METHODS: Purposive sampling was used to identify eight health care organizations that collected race and ethnicity data to measure and reduce disparities in the quality and outcomes of health care. Staff, including senior managers and data analysts, were interviewed at each site, using a semi-structured interview format about the following themes: the challenges of collecting and collating accurate data from patients, how organizations defined a disparity and analyzed data, and the impact and uses of their findings. FINDINGS: To collect accurate self-reported data on race and ethnicity from patients, most organizations had upgraded or modified their IT systems to capture data and trained staff to collect and input these data from patients. By stratifying nationally validated indicators of quality for hospitals and ambulatory care by race and ethnicity, most organizations had then used these data to identify disparities in the quality of care. In this process, organizations were taking different approaches to defining and measuring disparities. Through these various methods, all organizations had found some disparities, and some had invested in interventions designed to address them, such as extra staff, extended hours, or services in new locations. CONCLUSION: If policymakers wish to hold health care organizations accountable for disparities in the quality of the care they deliver, common standards will be needed for organizations' data measurement, analysis, and use to guide systematic analysis and robust investment in potential solutions to reduce and eliminate disparities.

Estimating inequalities in ownership of insecticide treated nets: does the choice of socio-economic status measure matter?

Research on the impact of socio-economic status (SES) on access to health care services and on health status is important for allocating resources and designing pro-poor policies. Socio-economic differences are increasingly assessed using asset indices as proxy measures for SES. For example, several studies use asset indices to estimate inequities in ownership and use of insecticide treated nets as a way of monitoring progress towards meeting the Abuja targets. The validity of different SES measures has only been tested in a limited number of settings, however, and there is little information on how choice of welfare measure influences study findings, conclusions and policy recommendations. In this paper, we demonstrate that household SES classification can depend on the SES measure selected. Using data from a household survey in coastal Kenya (n = 285 rural and 467 urban households), we first classify households into SES quintiles using both expenditure and asset data. Household SES classification is found to differ when separate rural and urban asset indices, or a combined asset index, are used. We then use data on bednet ownership to compare inequalities in ownership within each setting by the SES measure selected. Results show a weak correlation between asset index and monthly expenditure in both settings: wider inequalities in bednet ownership are observed in the rural sample when expenditure is used as the SES measure [Concentration Index (CI) = 0.1024 expenditure quintiles; 0.005 asset quintiles]; the opposite is observed in the urban sample (CI = 0.0518 expenditure quintiles; 0.126 asset quintiles). We conclude that the choice of SES measure does matter. Given the practical advantages of asset approaches, we recommend continued refinement of these approaches. In the meantime, careful selection of SES measure is required for every study, depending on the health policy issue of interest, the research context and, inevitably, pragmatic considerations.

A plan for action: key perspectives from the racial/ethnic disparities strategy forum.

CONTEXT: Racial and ethnic disparities in health care in the United States have been well documented, with research largely focusing on describing the problem rather than identifying the best practices or proven strategies to address it. METHODS: In 2006, the Disparities Solutions Center convened a one-and-a-half-day Strategy Forum composed of twenty experts from the fields of racial/ethnic disparities in health care, quality improvement, implementation research, and organizational excellence, with the goal of deciding on innovative action items and adoption strategies to address disparities. The forum used the Results Based Facilitation model, and several key recommendations emerged. FINDINGS: The forum's participants concluded that to identify and effectively address racial/ethnic disparities in health care, health care organizations should: (1) collect race and ethnicity data on patients or enrollees in a routine and standardized fashion; (2) implement tools to measure and monitor for disparities in care; (3) develop quality improvement strategies to address disparities; (4) secure the support of leadership; (5) use incentives to address disparities; and (6) create a message and communication strategy for these efforts. This article also discusses these recommendations in the context of both current efforts to address racial and ethnic disparities in health care and barriers to progress. CONCLUSIONS: The Strategy Forum's participants concluded that health care organizations needed a multifaceted plan of action to address racial and ethnic disparities in health care. Although the ideas offered are not necessarily new, the discussion of their practical development and implementation should make them more useful.

Oral health disparities in the United States.

Oral health disparities refers to the existence of differences in the incidence, prevalence, mortality, and burden of oral diseases and other adverse health conditions, as well as the use of health care services, among specific population groups in the United States. Existence of disparities in oral health status, accessing and using the oral health care delivery system, and receiving treatment depending on gender, race or ethnicity, education, income, disability, geographic location, and sexual orientation have been documented. Different states have initiated a series of steps as tools to document, assess, develop strategies, and monitor progress in efforts to eliminate or reduce oral health disparities in the United States.