Orthopaedic Surgical Demand Index: A Measure of Need in the United States.

BACKGROUND

Before being evaluated by a physician, more than 50% of patients will search their symptoms on the Google search engine. In fact, Google was the engine used for nearly 90% of all online searches between 2016 and 2018. These search data are stored by Google and can be investigated through google extended trends for health (GETH). The goal of this research was to use GETH to correlate Google search probabilities for elective orthopaedic procedures with the orthopaedic surgeon density in each US state to create a surgical demand index (SDI) that could be then compared between states. In addition, this study aims to assess the effects of annual income, percent minority population, and unemployment rate on that SDI.

METHODS

Google search probabilities were collected using the Google Trends Extraction Tool. Search probabilities were collected in each state for composite search terms. Data were collected in monthly intervals between 2016 and 2018 and averaged. The states were grouped into geographic regions. One-way analysis of variance and pairwise Mann-Whitney U tests were done between these regions. Linear regression analysis was conducted to assess the effect of median annual statewide income, percent minority population, and unemployment rate with SDI.

RESULTS

The analysis of variance and Mann-Whitney U tests demonstrated a difference between regions. Linear regression analysis revealed a notable effect of median income on SDI, but no effect of percent minority population or unemployment rate.

CONCLUSIONS

The Midwest and South had higher regional demand than the Northeast and West, with West Virginia being the most in need and the District of Columbia being the least in need. Annual median income had a notable negative effect on SDI, whereas percent minority population and unemployment rate had no effect. This study highlights the inequality that exists in the southern and midwestern United States and identifies one potential predictive factor of this unequal SDI.