A framework to evaluate the effects of small area variations in healthcare infrastructure on diagnostics and patient outcomes of rare diseases based on administrative data

The collective impact of rare diseases in Western Australia: an estimate using a population-based cohort

PURPOSE

It has been argued that rare diseases should be recognized as a public health priority. However, there is a shortage of epidemiological data describing the true burden of rare diseases. This study investigated hospital service use to provide a better understanding of the collective health and economic impacts of rare diseases.

METHODS

Novel methodology was developed using a carefully constructed set of diagnostic codes, a selection of rare disease cohorts from hospital administrative data, and advanced data-linkage technologies. Outcomes included health-service use and hospital admission costs.

RESULTS

In 2010, cohort members who were alive represented approximately 2.0% of the Western Australian population. The cohort accounted for 4.6% of people discharged from hospital and 9.9% of hospital discharges, and it had a greater average length of stay than the general population. The total cost of hospital discharges for the cohort represented 10.5% of 2010 state inpatient hospital costs.

CONCLUSIONS

This population-based cohort study provides strong new evidence of a marked disparity between the proportion of the population with rare diseases and their combined health-system costs. The methodology will inform future rare-disease studies, and the evidence will guide government strategies for managing the service needs of people living with rare diseases.Genet Med advance online publication 22 September 2016.

Analyzing regional variation in health care utilization using (rich) household microdata

This paper exploits rich SOEP microdata to analyze state-level variation in health care utilization in Germany. Unlike most studies in the field of the Small Area Variation (SAV) literature, our approach allows us to net out a large array of individual-level and state-level factors that may contribute to the geographic variation in health care utilization. The raw data suggest that state-level hospitalization rates vary from 65 to 165 percent of the national mean. Ambulatory doctor visits range from 90 to 120 percent of the national mean. Interestingly, in the former GDR states, doctor visit rates are significantly below the national mean, while hospitalization rates lie above the national mean. The significant state-level differences vanish once we control for individual-level socio-economic characteristics, the respondents' health status, their health behavior as well as supply-side state-level factors.

The influence of regional health care structures on delay in diagnosis of rare diseases: the case of Marfan Syndrome

INTRODUCTION

This study investigates the relative influence of the regional availability of health care resources (measured by physician densities, number of health care centers) on health care quality (measured by delay in diagnosis), based on data for the rare disease Marfan Syndrome.

METHODS

Administrative data from 389 patients with Marfan Syndrome were analyzed. Logistic regression models were applied for a dichotomous comparison of the dependent variable 'time to diagnosis' with the classifications 'immediate' and 'late' diagnosis. Physician densities of cardiologists/angiologists, ophthalmologists, orthopedists, and GPs, as well as distance to medical health care centers and sociodemographic information were entered into the models.

RESULTS

The results showed that the relationship between physician densities and probability of immediate diagnosis of Marfan Syndrome is negative linear, and quadratic for cardiologists/angiologists. This effect was significant with respect to density of cardiologists/angiologists (p=0.0097). Distance to medical health care centers was not a predictor of an immediate diagnosis.

CONCLUSION

Marfan Syndrome faces significant problems of quality of health care, as although the requisite quantity of health care resources is available, this does not affect delay in diagnosis. Information technology might foster valuable networking among physicians treating such cases along with holistic assessment of symptoms as they occur.

Does healthcare infrastructure have an impact on delay in diagnosis and survival?

INTRODUCTION

The objectives of this study were to evaluate whether healthcare infrastructure impacts delay in diagnosis, and to determine whether healthcare infrastructure and delay in diagnosis impacts survival in gastric cancer.

METHODS

Administrative data from 2175 gastric cancer patients was analyzed using two Cox proportional hazard models with (i) delay in diagnosis and (ii) survival as dependent variables. Density of general practitioners, density of gastroenterologists, characteristics of specialty treatment centers, demographic information, and comorbidities were included in the models. Differentiation was made between urban and rural areas.

RESULTS

The likelihood of being diagnosed increased with an increase in general practitioners (p<0.0001) and gastroenterologists (p<0.0001) in rural areas. In urban areas a higher density of general practitioners reduced delay in diagnosis (p=0.0262), while a higher density of gastroenterologists did not (p=0.2480). The number of gastric cancer cases performed in hospital had a positive impact on survival (p<0.0001), while outpatient infrastructure did not.

CONCLUSION

Delay in diagnosis can be reduced by higher availability of general practitioners and gastroenterologists in rural areas. Given the already very high density of physicians in urban areas there is no effect of additional gastroenterologists. As learning effects can be observed with increased hospital volumes, minimum volumes for treatment of gastric cancer may be defined.