Transcatheter Aortic Valve Replacement in Low-Population Density Areas: Assessing Healthcare Access for Older Adults With Severe Aortic Stenosis.

BACKGROUND: Restricting transcatheter aortic valve replacement (TAVR) to centers based on volume thresholds alone can potentially create unintended disparities in healthcare access. We aimed to compare the influence of population density in state of Florida in regard to access to TAVR, TAVR utilization rates, and in-hospital mortality. METHODS AND RESULTS: From 2011 to 2016, we used data from the Agency for Health Care Administration to calculate travel time and distance for each TAVR patient by comparing their home address to their TAVR facility ZIP code. Travel time and distance, TAVR rates, and mortality were compared across categories of low to high population density (population per square miles of land). Of the 6531 patients included, the mean (SD) age was 82 (9) years, 43% were female and 91% were White. Patients residing in the lowest category (<50/square miles) were younger, more likely to be men, and less likely to be a racial minority. Those residing in the lowest category density faced a longer unadjusted driving distances and times to their TAVR center (mean extra distance [miles]=43.5 [95% CI, 35.6-51.4]; P<0.001; mean extra time (minutes)=45.6 [95% CI, 38.3-52.9], P<0.001). This association persisted regardless of the methods used to determine population density. Excluding uninhabitable land, there was a 7-fold difference in TAVR utilization rates in the lowest versus highest population density regions (7 versus 45 per 100 000, P-for-pairwise-comparisons <0.001) and increase in TAVR in-hospital mortality (adjusted OR, 6.13 [95% CI, 1.97-19.1]; P<0.001). CONCLUSIONS: Older patients living in rural counties in Florida face (1) significantly longer travel distances and times for TAVR, (2) lower TAVR utilization rates, and (3) higher adjusted TAVR mortality. These findings suggest that there are trade-offs between access to TAVR, its rate of utilization, and procedural mortality, all of which are important considerations when defining institutional and operator requirements for TAVR across the country.

Evaluating Population Forecast Accuracy: A Regression Approach Using County Data.

Many studies have evaluated the impact of differences in population size and growth rate on population forecast accuracy. Virtually all these studies have been based on aggregate data; that is, they focused on average errors for places with particular size or growth rate characteristics. In this study, we take a different approach by investigating forecast accuracy using regression models based on data for individual places. Using decennial census data from 1900 to 2000 for 2,482 counties in the US, we construct a large number of county population forecasts and calculate forecast errors for 10- and 20-year horizons. Then, we develop and evaluate several alternative functional forms of regression models relating population size and growth rate to forecast accuracy; investigate the impact of adding several other explanatory variables; and estimate the relative contributions of each variable to the discriminatory power of the models. Our results confirm several findings reported in previous studies but uncover several new findings as well. We believe regression models based on data for individual places provide powerful but under-utilized tools for investigating the determinants of population forecast accuracy.

Empirical Prediction Intervals for County Population Forecasts.

Population forecasts entail a significant amount of uncertainty, especially for long-range horizons and for places with small or rapidly changing populations. This uncertainty can be dealt with by presenting a range of projections or by developing statistical prediction intervals. The latter can be based on models that incorporate the stochastic nature of the forecasting process, on empirical analyses of past forecast errors, or on a combination of the two. In this article, we develop and test prediction intervals based on empirical analyses of past forecast errors for counties in the United States. Using decennial census data from 1900 to 2000, we apply trend extrapolation techniques to develop a set of county population forecasts; calculate forecast errors by comparing forecasts to subsequent census counts; and use the distribution of errors to construct empirical prediction intervals. We find that empirically-based prediction intervals provide reasonably accurate predictions of the precision of population forecasts, but provide little guidance regarding their tendency to be too high or too low. We believe the construction of empirically-based prediction intervals will help users of small-area population forecasts measure and evaluate the uncertainty inherent in population forecasts and plan more effectively for the future.