Yukiko Asada

Equity in health services use and intensity of use in Canada

BackgroundThe Canadian health care system has striven to remove financial or other barriers to access to medically necessary health care services since the establishment of the Canada Health Act 20 years ago. Evidence has been conflicting as to what extent the Canadian health care system has met this goal of equitable access. The objective of this study was to examine whether and where socioeconomic inequities in health care utilization occur in Canada.MethodsWe used a nationally representative cross-sectional survey, the 2000/01 Canadian Community Health Survey, which provides a large sample size (about 110,000) and permits more comprehensive adjustment for need indicators than previous studies. We separately examined general practitioner, specialist, and hospital services using two-part hurdle models: use versus non-use by logistic regression, and the intensity of use among users by zero-truncated negative binomial regression.ResultsWe found that lower income was associated with less contact with general practitioners, but among those who had contact, lower income and education were associated with greater intensity of use of general practitioners. Both lower income and education were associated with less contact with specialists, but there was no statistically significant relationship between these socioeconomic variables and intensity of specialist use among the users. Neither income nor education was statistically significantly associated with use or intensity of use of hospitals.ConclusionOur study unveiled possible socioeconomic inequities in the use of health care services in Canada.

A population health framework for setting national and state health goals.

WITH THE APPOINTMENT OF THE US DEPARTment of Health and Human Services Advisory Committee on National Health Promotion and Disease Prevention for 2020, the process for setting national health goals in 2009 for the coming decade is under way. The Healthy People 2010 goals and objectives have served as the framework for establishing outcomes for virtually every public health planning process in the United States from National Institutes of Health grants to federal health programs and to state and local health plans. Although an initial process produced a Draft Model with 4 guiding principles and a proposal for a smaller number of objectives for Healthy People 2020, a specific framework has not yet been decided and will be established after a series of public hearings. This Commentary proposes a population health guiding framework for national and state planning processes, including both broad overall goals as well as a prioritized set of policies and interventions aligned with the multiple determinants of health. The ultimate purpose of population health policy is to improve the health of individuals and populations by investments in the determinants of health through policies and interventions that influence these determinants. Without careful attention to the outcomes, attention to determinants and policies could proceed without reference to the ultimate goals and become ends instead of means to an end. A shortcoming of this step of broader goal setting is that it is often framed in general terms without quantification, so it is not likely that the impact of making progress on some objectives can be assessed. Healthy People 2010 devoted significant attention to the 467 objectives in 28 focus areas, but the 2 broad goals of “increasing quality and years of healthy life” and “eliminating disparities” did not have specified quantitative targets. Although the “Healthy People in Healthy Communities” model in Healthy People 2010 contains health determinant categories, the focus areas are presented alphabetically rather than by determinant. The FIGURE is a model that could be a starting point for a framework more precisely aligned to a population health perspective. The right side represents a way of conceptualizing broad population health outcomes. Previous health improvement frameworks have identified both increasing the overall population mean, as well as reducing and eliminating disparities within the population. Within disparities, multiple domains could be policy targets such as race/ ethnicity, socioeconomic status, sex, and geographic location. In addition, such outcomes should include both length of life (mortality) and health-related quality of life. Although it is possible to combine all 4 quadrants into a single summary measure, considering them separately is important because different patterns of determinants will probably produce different changes in each of them. Each quadrant in the Figure is arbitrarily sized equally, and similarly the domain bars within the disparity quadrants are depicted as equal. It is probably not the case that each quadrant or domain should receive equal weight. This is not an empirical issuebut ratheroneof social valuation fordifferentnations, states, or other population groups to decide. The point of presenting them this way is to encourage such consideration as a component of goal setting, which has been done occasionally. For example, the World Health Report 2000 weighted the mean and disparity equally based on a survey of about 1000 respondents. Similarly in a State Health Report Card for Wisconsin, equal weighting was primarily used, although the method used for summarizing disparities across domains resulted in slight variation from equality. The Figure’s left-hand side represents the determinants of the population health outcomes represented on the Figure’s left side. Based on the Evans-Stoddart model, these determinants are divided into 5 categories. For example, medical care includes prevention, treatment, and management of disease. Examples of individual behaviors are smoking, exercise, and eating habits. The social environment includes socioeconomic factors, most often measured by income, educational level, and occupation, while the physical environment consists of air and water quality as well as the built environment, ie, the constructed structures such as buildings, roads, parks, and other physical infrastructure that make up communities. Genetics refers to inher-

Inequity in access to cancer care: a review of the Canadian literature

Despite the policy and research attention on ensuring equitable access—equal access for equal need—to health care, research continues to identify inequities in access to cancer services. We conducted a literature review to identify the current state of knowledge about inequity in access to cancer health services in Canada in terms of the continuum of care, disease sites, and dimensions of inequity (e.g., income). We searched MEDLINE, CINAHL, and Embase for studies published between 1990 and 2009. We retrieved 51 studies, which examine inequity in access to cancer services from screening to end-of-life care, for multiple cancer types, and a variety of socioeconomic, geographic, and demographic factors that may cause concern for inequity in Canada. This review demonstrates that income has the most consistent influence on inequity in access to screening, while age and geography are most influential for treatment services and end-of-life care, even after adjusting for patient need. Our review also reports on methods used in the literature and new techniques to explore. Equitable access to cancer care is vitally important in all health systems. Obtaining information on the current status of inequities in access to cancer care is a critical first step toward action.

A framework for measuring health inequity

Background: Health inequality has long attracted keen attention in the research and policy arena. While there may be various motivations to study health inequality, what distinguishes it as a topic is moral concern. Despite the importance of this moral interest, a theoretical and analytical framework for measuring health inequality acknowledging moral concerns remains to be established. Study objective: To propose a framework for measuring the moral or ethical dimension of health inequality—that is, health inequity. Design: Conceptual discussion. Conclusions: Measuring health inequity entails three steps: (1) defining when a health distribution becomes inequitable, (2) deciding on measurement strategies to operationalise a chosen concept of equity, and (3) quantifying health inequity information. For step (1) a variety of perspectives on health equity exist under two categories, health equity as equality in health, and health inequality as an indicator of general injustice in society. In step (2), when we are interested in health inequity, the choice of the measurement of health, the unit of time, and the unit of analysis in health inequity analysis should reflect moral considerations. In step (3) we must follow principles rather than convenience and consider six questions that arise when quantifying health inequity information. This proposed framework suggests various ways to conceptualise the moral dimension of health inequality and emphasises the logical consistency from conception to measurement.

Assessment of the health of Americans: the average health-related quality of life and its inequality across individuals and groups

BackgroundThe assessment of population health has traditionally relied on the populations average health measured by mortality related indicators. Researchers have increasingly recognized the importance of including information on health inequality and health-related quality of life (HRQL) in the assessment of population health. The objective of this study is to assess the health of Americans in the 1990s by describing the average HRQL and its inequality across individuals and groups.MethodsThis study uses the 1990 and 1995 National Health Interview Survey from the United States. The measure of HRQL is the Health and Activity Limitation Index (HALex). The measure of health inequality across individuals is the Gini coefficient. This study provides confidence intervals (CI) for the Gini coefficient by a bootstrap method. To describe health inequality by group, this study decomposes the overall Gini coefficient into the between-group, within-group, and overlap Gini coefficient using race (White, Black, and other) as an example. This study looks at how much contribution the overlap Gini coefficient makes to the overall Gini coefficient, in addition to the absolute mean differences between groups.ResultsThe average HALex was the same in 1990 (0.87, 95% CI: 0.87, 0.88) and 1995 (0.87, 95% CI: 0.86, 0.87). The Gini coefficient for the HALex distribution across individuals was greater in 1995 (0.097, 95% CI: 0.096, 0.099) than 1990 (0.092, 95% CI: 0.091, 0.094). Differences in the average HALex between all racial groups were the same in 1995 as 1990. The contribution of the overlap to the overall Gini coefficient was greater in 1995 than in 1990 by 2.4%. In both years, inequality between racial groups accounted only for 4–5% of overall inequality.ConclusionThe average HRQL of Americans was the same in 1990 and 1995, but inequality in HRQL across individuals was greater in 1995 than 1990. Inequality in HRQL by race was smaller in 1995 than 1990 because race had smaller effect on the way health was distributed in 1995 than 1990. Analysis of the average HRQL and its inequality provides information on the health of a population invisible in the traditional analysis of population health.

On the choice of absolute or relative inequality measures.

CONTEXT In a recent article in this journal, Sam Harper and his colleagues (2010) call for increased awareness and open dialogue of moral judgments underlying health inequality measures. They recommend that analysts use relative inequality measures when concerned only about health inequality but use absolute inequality measures when also concerned about other issues, such as the overall level of population health and the level of health for each group in the population. METHODS Using a simple, hypothetical example, this commentary shows that the relationships among inequality, the absolute level for each group, and the overall level in the population are more complex than suggested by the analysis by Harper and his colleagues. FINDINGS First, analysts must make the choice of absolute or relative inequality measures, separately, for single- and multiple-population cases. Second, in the single-population cases, analysts can use both relative and absolute inequality measures when concerned only about health inequality independent of other considerations. Third, in almost all real-world multiple-population cases, when using either the absolute or relative inequality measure, the assessment of health inequality is influenced by the absolute level of health for each group. CONCLUSIONS The choice between absolute and relative inequality measures is not about the independent normative significance of inequality, as Harper and his colleagues suggest. In choosing between absolute and relative measures, future work needs to integrate an empirical examination of values, a moral assessment of values, and a technical understanding of inequality measures.

Need-based resource allocation: different need indicators, different results?

BackgroundA key policy objective in most publicly financed health care systems is to allocate resources according to need. Many jurisdictions implement this policy objective through need-based allocation models. To date, no gold standard exists for selecting need indicators. In the absence of a gold standard, sensitivity of the choice of need indicators is of concern. The primary objective of this study was to assess the consistency and plausibility of estimates of per capita relative need for health services across Canadian provinces based on different need indicators.MethodsUsing the 2000/2001 Canadian Community Health Survey, we estimated relative per capita need for general practitioner, specialist, and hospital services by province using two approaches that incorporated a different set of need indicators: (1) demographics (age and sex), and (2) demographics, socioeconomic status, and health status. For both approaches, we first fitted regression models to estimate standard utilization of each of three types of health services by indicators of need. We defined the standard as average levels of utilization by needs indicators in the national sample. Subsequently, we estimated expected per capita utilization of each type of health services in each province. We compared these estimates of per capita relative need with premature mortality in each province to check their face validity.ResultsBoth approaches suggested that expected relative per capita need for three services vary across provinces. Different approaches, however, yielded different and inconsistent results. Moreover, provincial per capita relative need for the three health services did not always indicate the same direction of need suggested by premature mortality in each province. In particular, the two approaches suggested Newfoundland had less need than the Canadian average for all three services, but it had the highest premature mortality in Canada.ConclusionSubstantial differences in need for health care may exist across Canadian provinces, but the direction and magnitude of differences depend on the need indicators used. Allocations from models using survey data lacked face validity for some provinces. These results call for the need to better understand the biases that may result from the use of survey data for resource allocation.

Preferred and Actual Location of Death: What Factors Enable a Preferred Home Death?

BACKGROUND Fulfillment of patient preferences for location of dying is of continued end-of-life care interest. Of those voicing a preference, most prefer home. However the majority of deaths occur in an institutional setting. OBJECTIVES The study objective was to report on the congruence between the last preferred and actual location of death among adult Nova Scotians who died from chronic disease, and to identify individual, illness-related, and environmental factors associated with achieving a preferred home death. METHODS The study employed a population-based mortality follow-back telephone survey interview. Subjects were eligible death certificate identified informants (next-of-kin) of adults (aged 18+) (n = 1316) who died of advanced chronic diseases in the Canadian province of Nova Scotia between June 2009 and May 2011 who were knowledgeable about the decedents care over the last month of life. Congruence was assessed as to whether or not the decedent died in their preferred death location. Among decedents preferring a home death, individual, illness-related, and environmental risk factor multivariable analyses were used to identify predictors of home death achievement. RESULTS Among all who voiced a preference (n = 606), 52% died in their preferred location (kappa: 0.29). Factors contributing independently to achievement of a preferred home death were emotional needs being met, nursing and family physician home visits, palliative care program involvement, and being at home for the majority of the last month. CONCLUSIONS This study identifies elements of primary and integrated care that address the gap between preferred and actual place of care.

Is Health Inequality Across Individuals of Moral Concern

The history of the documentation of health inequality is long. The way in which health inequality has customarily been documented is by comparing differences in the average health across groups, for example, by sex or gender, income, education, occupation, or geographic region. In the controversial World Health Report 2000, researchers at the World Health Organization criticized this traditional practice and proposed to measure health inequality across individuals irrespective of individuals’ group affiliation. They defended its proposal on the moral grounds without clear explanation. In this paper I ask: is health inequality across individuals of moral concern, and, if so, why? Clarification of these questions is crucial for meaningful interpretation of health inequality measured across individuals. Only if there was something morally problematic in health inequality across individuals, its reduction would be good news. Specifically, in this paper I provide three arguments for the moral significance of health inequality across individuals: (a) health is special, (b) health equity plays an important and unique role in the general pursuit of justice, and (c) health inequality is an indicator of general injustice in society. I then discuss three key questions to examine the validity of these arguments: (i) how special is health?, (ii) how good is health as an indicator?, and (iii) what do we mean by injustice? I conclude that health inequality across individuals is of moral interest with the arguments (b) and (c).

Summarizing Social Disparities in Health

Context Reporting on health disparities is fundamental for meeting the goal of reducing health disparities. One often overlooked challenge is determining the best way to report those disparities associated with multiple attributes such as income, education, sex, and race/ethnicity. This article proposes an analytical approach to summarizing social disparities in health, and we demonstrate its empirical application by comparing the degrees and patterns of health disparities in all fifty states and the District of Columbia (DC). Methods We used the 2009 American Community Survey, and our measure of health was functional limitation. For each state and DC, we calculated the overall disparity and attribute-specific disparities for income, education, sex, and race/ethnicity in functional limitation. Along with the state rankings of these health disparities, we developed health disparity profiles according to the attribute making the largest contribution to overall disparity in each state. Findings Our results show a general lack of consistency in the rankings of overall and attribute-specific disparities in functional limitation across the states. Wyoming has the smallest overall disparity and West Virginia the largest. In each of the four attribute-specific health disparity rankings, however, most of the best- and worst-performing states in regard to overall health disparity are not consistently good or bad. Our analysis suggests the following three disparity profiles across states: (1) the largest contribution from race/ethnicity (thirty-four states), (2) roughly equal contributions of race/ethnicity and socioeconomic factor(s) (ten states), and (3) the largest contribution from socioeconomic factor(s) (seven states). Conclusions Our proposed approach offers policy-relevant health disparity information in a comparable and interpretable manner, and currently publicly available data support its application. We hope this approach will spark discussion regarding how best to systematically track health disparities across communities or within a community over time in relation to the health disparity goal of Healthy People 2020.