Gligorić Kristina, Kamath Chaitanya, Weiss Daniel J, Bavadekar Shailesh, Liu Yun, Shekel Tomer, Schulman Kevin, Gabrilovich Evgeniy
Google Research, Mountain View, CA, USA.
Computer Science Department, Stanford University, Stanford, CA, USA.
Commun Med (Lond). 2023 Nov 3;3(1):157. doi: 10.1038/s43856-023-00384-9.
Timely access to healthcare is essential but measuring access is challenging. Prior research focused on analyzing potential travel times to healthcare under optimal mobility scenarios that do not incorporate direct observations of human mobility, potentially underestimating the barriers to receiving care for many populations.
We introduce an approach for measuring accessibility by utilizing travel times to healthcare facilities from aggregated and anonymized smartphone Location History data. We measure these revealed travel times to healthcare facilities in over 100 countries and juxtapose our findings with potential (optimal) travel times estimated using Google Maps directions. We then quantify changes in revealed accessibility associated with the COVID-19 pandemic.
We find that revealed travel time differs substantially from potential travel time; in all but 4 countries this difference exceeds 30 minutes, and in 49 countries it exceeds 60 minutes. Substantial variation in revealed healthcare accessibility is observed and correlates with life expectancy (⍴=-0.70) and infant mortality (⍴=0.59), with this association remaining significant after adjusting for potential accessibility and wealth. The COVID-19 pandemic altered the patterns of healthcare access, especially for populations dependent on public transportation.
Our metrics based on empirical data indicate that revealed travel times exceed potential travel times in many regions. During COVID-19, inequitable accessibility was exacerbated. In conjunction with other relevant data, these findings provide a resource to help public health policymakers identify underserved populations and promote health equity by formulating policies and directing resources towards areas and populations most in need.
及时获得医疗保健至关重要，但衡量医疗可及性具有挑战性。先前的研究主要集中在分析在不纳入人类移动性直接观测的最佳移动性情景下前往医疗保健机构的潜在旅行时间，这可能低估了许多人群获得护理的障碍。
我们引入了一种通过利用来自聚合和匿名化智能手机位置历史数据的前往医疗保健机构的旅行时间来衡量可及性的方法。我们测量了100多个国家前往医疗保健机构的实际旅行时间，并将我们的发现与使用谷歌地图导航估计的潜在（最佳）旅行时间并列比较。然后，我们量化了与新冠疫情相关的实际可及性变化。
我们发现实际旅行时间与潜在旅行时间有很大差异；除4个国家外，在所有国家这种差异都超过30分钟，在49个国家超过60分钟。观察到实际医疗可及性存在很大差异，并且与预期寿命（⍴ = -0.70）和婴儿死亡率（⍴ = 0.59）相关，在调整潜在可及性和财富因素后，这种关联仍然显著。新冠疫情改变了医疗保健的获取模式，尤其是对于依赖公共交通的人群。
我们基于实证数据的指标表明，在许多地区实际旅行时间超过潜在旅行时间。在新冠疫情期间，不公平的可及性加剧。结合其他相关数据，这些发现为公共卫生政策制定者提供了一种资源，以帮助他们识别服务不足的人群，并通过制定政策和将资源导向最需要的地区和人群来促进健康公平。
