Nancy Lozano-Gracia

Spatial analysis of elderly access to primary care services

BackgroundAdmissions for Ambulatory Care Sensitive Conditions (ACSCs) are considered preventable admissions, because they are unlikely to occur when good preventive health care is received. Thus, high rates of admissions for ACSCs among the elderly (persons aged 65 or above who qualify for Medicare health insurance) are signals of poor preventive care utilization. The relevant geographic market to use in studying these admission rates is the primary care physician market. Our conceptual model assumes that local market conditions serving as interventions along the pathways to preventive care services utilization can impact ACSC admission rates.ResultsWe examine the relationships between market-level supply and demand factors on market-level rates of ACSC admissions among the elderly residing in the U.S. in the late 1990s. Using 6,475 natural markets in the mainland U.S. defined by The Health Resources and Services Administrations Primary Care Service Area Project, spatial regression is used to estimate the model, controlling for disease severity using detailed information from Medicare claims files. Our evidence suggests that elderly living in impoverished rural areas or in sprawling suburban places are about equally more likely to be admitted for ACSCs. Greater availability of physicians does not seem to matter, but greater prevalence of non-physician clinicians and international medical graduates, relative to U.S. medical graduates, does seem to reduce ACSC admissions, especially in poor rural areas.ConclusionThe relative importance of non-physician clinicians and international medical graduates in providing primary care to the elderly in geographic areas of greatest need can inform the ongoing debate regarding whether there is an impending shortage of physicians in the United States. These findings support other authors who claim that the existing supply of physicians is perhaps adequate, however the distribution of them across the landscape may not be optimal. The finding that elderly who reside in sprawling urban areas have access impediments about equal to residents of poor rural communities is new, and demonstrates the value of conceptualizing and modelling impedance based on place and local context.

Spatial Hedonic Models

In this chapter, we focus on some econometric aspects related to a sub-set of hedonic house price models, which we refer to as spatial hedonic models. In these, the locational aspects of the observations are treated explicitly, and the estimation of the models is an application of spatial econometrics. As defined in Anselin (2006), spatial econometrics “consists of a sub-set of econometric methods that is concerned with spatial aspects present in cross-sectional and spacetime observations.” These methods focus in particular on two forms of so-called spatial effects in econometric models, referred to as spatial dependence and spatial heterogeneity. In this chapter we provide a review of the principles underlying the hedonic house price model, and continue to extensively discuss spatial econometric aspects due to spatial models and spatial data specific to house price applications. We review and discuss the treatment of spatial dependence (including space-time dynamics) and spatial heterogeneity with selective illustrations from the empirical literature.

A Spatial Econometric Analysis of Loss Experience in the U.S. Crop Insurance Program

Patterns in loss‐ratio experience in the U.S. corn insurance market are investigated with a spatial econometric model. The results demonstrate systematic geographically related misratings and provide estimates of the impacts of several observable factors on the magnitude of misrating in the program. The model is used to estimate actuarial cross‐subsidizations across the primary corn‐producing states and counties. The impacts of the primary factors are substantial, resulting in net premium transfers of approximately 26 percent of total premiums annually. The misratings likely have important insurance demand, welfare, and land‐use implications.

The Journey to Safety: Conflict-Driven Migration Flows in Colombia

While there is a growing econometrics literature on the modeling of conflict and the interactions with trade, there has been relatively little evidence modeling the interregional migration behavior of individuals internally displaced by conflicts. The current article models the flows of households forced to leave their residence because of violent conflicts in Colombia. Results shed light on the main determinants of what we call journey to safety. Violence appears to be one of the most relevant pushing effects together with the absence of institutions and the dissatisfaction with the provision of basic needs. Furthermore, for regions with extreme violence levels, individuals appear to be willing to relocate to more distant locations. On the destination side, most populated regions are more attractive as well as areas with a sufficient level of fulfillment of basic needs.

Spatial J-test: some Monte Carlo evidence

Researchers using spatial econometric methods generally assume a known structure for the process being modeled embedded in a spatial weights matrix. The present paper evaluates the performance of the J-test in selecting the most appropriate spatial structure in the context of a Monte Carlo study. Results suggest that the J-test performs well when used to select between different weights matrices. Increases in power are associated with the use of the full set of instruments.

The welfare benefit of a home’s location: an empirical comparison of spatial and non-spatial model estimates

This article compares results from non-spatial and new spatial methods to examine the reliability of welfare estimates (direct and multiplier effects) for locational housing attributes in Seattle, WA. In particular, we assess if OLS with spatial fixed effects is able to account for the spatial structure in a way that represents a viable alternative to spatial econometric methods. We find that while OLS with spatial fixed effects accounts for more of the spatial structure than simple OLS, it does not account for all of the spatial structure. It thus does not present a viable alternative to the spatial methods. Similar to existing comparisons between results from non-spatial and established spatial methods, we also find that OLS generates higher coefficient and direct effect estimates for both structural and locational housing characteristics than spatial methods do. OLS with spatial fixed effects is closer to the spatial estimates than OLS without fixed effects but remains higher. Finally, a comparison of the direct effects with locally weighted regression results highlights spatial threshold effects that are missed in the global models. Differences between spatial estimators are almost negligible in this study.

Predictors of Endoscopic Colorectal Cancer Screening Over Time in 11 States

ObjectivesWe study a cohort of Medicare-insured men and women aged 65+ in the year 2000, who lived in 11 states covered by Surveillance, Epidemiology, and End Results (SEER) cancer registries, to better understand various predictors of endoscopic colorectal cancer (CRC) screening.MethodsWe use multilevel probit regression on two cross-sectional periods (2000–2002, 2003–2005) and include people diagnosed with breast cancer, CRC, or inflammatory bowel disease (IBD) and a reference sample without cancer.ResultsMen are not universally more likely to be screened than women, and African Americans, Native Americans, and Hispanics are not universally less likely to be screened than whites. Disparities decrease over time, suggesting that whites were first to take advantage of an expansion in Medicare benefits to cover endoscopic screening for CRC. Higher-risk persons had much higher utilization, while older persons and beneficiaries receiving financial assistance for Part B coverage had lower utilization and the gap widened over time.ConclusionsScreening for CRC in our Medicare-insured sample was less than optimal, and reasons varied considerably across states. Negative managed care spillovers were observed, demonstrating that policy interventions to improve screening rates should reflect local market conditions as well as population diversity.

Valuing access to water - a spatial hedonic approach applied to Indian cities

An important infrastructure policy issue for rapidly growing cities in developing countries is how to raise fiscal revenues to finance basic services in a fair and efficient manner. This paper applies hedonic analysis that explicitly accounts for spatial spillovers to derive the value of improved access to water in the Indian cities of Bhopal and Bangalore. The findings suggest that by looking at individual or private benefits only, the analysis may underestimate the overall social welfare from investing in service supply especially among the poorest residents. The paper further demonstrates how policy simulations based on these estimates help prioritize spatial targeting of interventions according to efficiency and equity criteria.

Leveraging land to enable urban transformation : lessons from global experience

Around the world, in both developed and developing countries, policy makers use a variety of tools to manage and accommodate urban growth and redevelopment. Government officials have three main concerns in terms of land policy: (i) accommodating urban expansion, (ii) providing infrastructure, and (iii) managing density. Together, the planning for infrastructure and urban expansion, land use, and density policies combine to shape the spatial structure of cities. This paper reviews global experience on using land based instruments to accommodate urban development and financing infrastructure. The review suggests that urban transformation is most efficient when land markets are fluid, particularly when they are grounded in strong institutions that (i) assign and protect property rights, (ii) enable independent valuation and public dissemination of land values across uses, and (iii) enable the judicial system to handle disputes that may arise in the process.

Urbanization and Housing Investment

This paper provides the first systematic empirical assessment of the pace at which housing investment has responded to rising demand from urbanization. The assessment used National Accounts Statistics to build a data set of residential housing investment for more than 90 countries. The data set explicitly accounts for investment by households, the government, and the private sector. The analysis finds that housing investment follows an S-shaped trajectory taking off around per capita GDP of about