Healthcare access, socioeconomic factors and late-stage cancer diagnosis: an exploratory spatial analysis and public policy implication

Abstract

Patients diagnosed with late-stage cancer have lower survival rates than those with early-stage cancer. This paper examines possible associations between several risk factors and late-stage diagnosis for four types of cancer in Illinois: breast cancer, prostate cancer, colorectal cancer, and lung cancer. Potential risk factors are composed of spatial factors and nonspatial factors. The spatial factors include accessibility to primary healthcare and distance or travel time to the nearest cancer screening facility. A set of demographic and socioeconomic variables are consolidated into three nonspatial factors by factor analysis. The Bayesian model with convolution priors is utilised to analyse the relationship between the above risk factors and each type of late-stage cancer while controlling for spatial autocorrelation. The results for breast cancer suggest that people living in neighbourhoods with socioeconomic disadvantages and cultural barriers are more likely to be diagnosed at a late stage. In regard to prostate cancer, people in regions with low socioeconomic status are also more likely to be diagnosed at a late stage. Diagnosis of late-stage colorectal or lung cancer is not significantly associated with any of the abovementioned risk factors. The results have important implications in public policy.

General practitioner (family physician) workforce in Australia: comparing geographic data from surveys, a mailing list and medicare

“…These agencies do not generally publicly release FTE GP data at scales smaller than that of the state which are of limited if any use at all in a geographic analysis [21], although very occasionally data at fine scales are released on specific request for some research [9,22] and reports [23]. Contrast this, for example, with the CIHI database in Canada and the resulting research [16,17], research with US-AMA data [10,11,51,52] in the United states or GP data in New Zealand [53,54]. …”

“…The FWE metric, unlike FTE, does not “cap” doctors providing more than a standard full-time level of services at an upper threshold, usually of 1. Thus a GP providing 20% more than a standard full time level of services will be 1.2 FWE but 1.0 FTE [10]. However, a number of different methods of calculating FWE/FTE exist.…”

“…Physician Masterfile. These datasets have been used in multiple analyses of relationships with outcomes [1,10-12]. Occasionally, surveys are also used to assess the geographical distribution of physicians [13].…”

Measurement, Optimization, and Impact of Health Care Accessibility: A Methodological Review

“…Wang and Tang (2010) developed an equity model with an objective of minimizing inequality in accessibility. Given an accessibility measure as defined in any of the Equations 1 through 3, it is known that the weighted mean of accessibility is equal to the ratio of total supply to total demand in a study area (Shen 1998), denoted by a constant a .…”

“…This section uses late-stage cancer diagnosis as an example to illustrate the impact of health care access inequality. There is an enormously rich body of literature on analyzing various risk factors of late-stage cancer (Wang, Luo, and McLaffferty 2010). In summary, the risk factors include (1) spatial access to both cancer screening facilities and primary care physicians and (2) nonspatial factors at both the individual and neighborhood levels (see Table 2).…”