Measures of social deprivation that predict health care access and need within a rational area of primary care service delivery

It is internationally recognized that health and health care access inequities vary along social gradients. However, targeted health resource allocation can reduce the range of disparities (Marmot 2006). Using geographic measures of the social determinants of health to guide allocation of health resources is supported by an international consensus and substantial research (Banks et al. 2006). Examples of this work can be found in the United Kingdom (UK) (Noble et al. 2008) and New Zealand (White et al. 2008). Socioeconomic inequities and the health disparities they produce are comparably worse in the United States compared with other OECD countries (Banks et al. 2006; Schoen et al. 2009), indicating that U.S. policies designed to reduce them are inadequate. However, the United States is in the process of revising decades-old geographic measures of workforce shortage and medical underservice and has an opportunity to join these Commonwealth countries in meeting this basic tenet of the World Health Organization Committee on the Social Determinants of Health. In contrast to the United States, many OECD countries use geographic patient communities, whether assigned or self-selected, to create rational service areas that are used for monitoring population health outcomes and adjusting health care resource allocation. The United States also lacks a system of population health accountability and its methods of assigning additional resources to underserved communities, both the Health Professional Shortage Area (HPSA) and the Medically Underserved Areas (MUA), are 35 years old. The HPSA designation criteria used by Federal health agencies are based on physician to population ratios, with some adjustment for area of high needs as measured by poverty, infant mortality, or fertility; the MUA designation uses a composite index that includes physician to population ratios, poverty, infant mortality, and elderly population (Federal Register 2010). The effort now under way to revise MUA and HPSA criteria is revealing real gaps in the science of population risk assessment, particularly for small areas and measuring socioeconomic gradients, and a lack of consensus about rational service areas. Examining how we might improve how we identify socioeconomic gradients in health care need and access may go some way to addressing the health disparities observed in the United States. Understanding how socioeconomic status (SES) influences the use and access of health services, and how the use of measures of SES to guide the distribution of resources can reduce health disparities is bedded in a large body of literature and theory (Penchasky and Thomas 1981; Andersen 1995; Field 2000; Hendryx et al. 2002; Wang and Luo 2005; McGrail and Humphreys 2009). The relationship between health care need, demand, supply, and access is complex. Health need can be understood to mean the requirement for health services, deemed reasonable, or expected within society, taking into account factors such as the socioeconomic, age, and health profile of a community. Demand reflects how services are used by the population, and not necessarily the underlying need. An imbalance between need, demand, and supply can result in health care access inequity (Field 2000) and consequent poor health outcomes (Andersen 1995; Hendryx et al. 2002). Poor health care access may be measured by self report, inferred through rates of avoidable hospitalization (as an indirect measure of primary health care access) or by poor health outcomes such as morbidity and mortality rates. It is often those most marginalized and disadvantaged who suffer a greater burden of ill health and risks for ill health yet are less likely to be able to act on this and access needed care (Hart 1971; Schofield et al. 2008). Consequently, this creates a demand for more costly, reactive care rather than ideal preventative care (Dixon-Woods et al. 2006) and produces wide disparities. This suggests that measures capturing area-level disadvantage and social deprivation may be useful tools for identifying areas to which resources could be allocated to improve delivery of health services and potentially reduce health disparities. In addition to providing universal coverage, the UK allocates resources based on geographic indices of specific social deprivation (e.g., the Townsend index and Index of Multiple Deprivation) (Townsend 1987; Carstairs 1995; Noble et al. 2008) and despite having a range of social classes similar to those in the United States, they realize a much narrower range of health disparities (Banks et al. 2006). The United States could benefit from better geographic assignment of health resources as in the United Kingdom. In the literature, two general approaches have been taken in developing area-level measures of social deprivation for resource allocation. There are those that measure social deprivation alone (Krieger et al. 2003, 2005; Eibner and Sturm 2006; Adhikari 2008; White et al. 2008), while others have constructed composite measures of resource need, including supply, social deprivation, demographics, and health status (Field 2000; Wang and Luo 2005; McGrail and Humphreys 2009). Other work in the United States has examined a range of small area measures of socioeconomic disadvantage at the census tract or block group level for health monitoring, but this has not yet been applied to practice or policy at a national level (Diez Roux 2001; Krieger et al. 2003; Bird et al. 2010). A challenge of developing useful measures of social deprivation is identifying the geographic definitions around which it is to be built. The vast majority of small area measures of social deprivation and health need have been based on political or administrative boundaries, or less commonly through modeling using geographic information systems (GISs) based on physical distance. As mentioned previously, many OECD countries use geographic patient communities to create rational service areas that are used for monitoring population health outcomes and adjusting health care resource allocation. Rational service areas have also been used by Federal health agencies as the underpinning geography used for HPSAs and MUAs. However, monitoring and evaluation of these programs have been hindered by a lack of consistency and complete coverage between and across counties in defining the underpinning geography. In response to this, HRSA commissioned the Dartmouth group to develop rational service areas for primary care that overcame these limitations (Goodman et al. 2003). Primary care service areas (PCSAs) are utilization-based service areas for the United States and reflect the travel of Medicare beneficiaries to primary care clinicians. As such, this creates a nationwide geography based on natural patterns of care-seeking behavior by a population with generally good access to care. Some evidence suggests that PCSAs are generalizable to younger populations based on tests with Medicaid and commercial claims (Goodman and Wright-Slaughter 2004). PCSAs offer approximations of primary health care service areas that cover the entirety of the United States that are potentially useful for identifying health care need, allocating resources, and evaluating the impact. The aim of this article is to determine whether a measure of social deprivation can be identified that has a statistically significant relationship with health care access and health outcomes within a rational area of primary care service. We begin by constructing a social deprivation index (SDI) that can be applied nationally to small geographies. We then incorporate the SDI with other measures of access and need into regression models to examine the robustness of our measure. Finally, our index is compared with a simple measure of poverty, the single measures of social deprivation used in current underservice designations, to determine whether a composite measure improves prediction of health outcomes. We hope that such a measure might be a useful contribution to efforts to monitor population health and perhaps guide the allocation of resources in ways that could reduce disparities and improve outcomes.