Mathis Walter S, Woods Scott, Srihari Vinod
Yale University Department of Psychiatry.
Program for Specialized Treatment Early in Psychosis (STEP), Connecticut Mental Health Center, New Haven, Connecticut.
Early Interv Psychiatry. 2018 Dec;12(6):1229-1234. doi: 10.1111/eip.12681. Epub 2018 Jun 21.
To apply spatial analytics to an underway first episode psychosis program to identify areas of significant variation in the geographical distribution of program enrollees from an underlying at-risk population.
Adaptive bandwidth kernel smoothing was used to estimate spatial density functions from program enrollee home addresses and a control population computed from US Census data. A relative risk surface derived from the ratio of these functions was used to discover under-represented areas, or areas from which fewer enrollees where produced than suggested by the underlying population density at the P < .05 level of statistical significance. As a test application of this analysis, a comprehensive list of primary care providers in the program catchment was extracted from the National Plan and Provider Enumeration System and spatially compared to the under-represented areas.
This approach identified under-represented areas containing 27.5% of the total program catchment area and 16% of the control population, yet had yielded zero program participants. These under-represented areas contained 179 primary care providers of the 2,337 in the total catchment area.
Findings of nonrandom spatial variation in program enrollment is valuable data for those evaluating the impact of and implementing improvements for recruitment to specialty clinics serving geographically-defined catchments. Positive findings from this preliminary study warrant further development of the predictive model as well as measurement of the impact on enrollment from recruitment interventions driven by these findings.
将空间分析应用于一个正在进行的首发精神病项目，以确定项目参与者在潜在风险人群中的地理分布存在显著差异的区域。
采用自适应带宽核平滑法，根据项目参与者的家庭住址和从美国人口普查数据计算得出的对照人群来估计空间密度函数。由这些函数的比值得出的相对风险面用于发现代表性不足的区域，即那些在P < 0.05的统计显著性水平下，参与者人数少于潜在人群密度所暗示人数的区域。作为该分析的一个测试应用，从国家计划和提供者枚举系统中提取了项目集水区内初级保健提供者的综合列表，并在空间上与代表性不足的区域进行比较。
这种方法识别出了代表性不足的区域，这些区域占项目总集水区面积的27.5%，占对照人群的16%，但却没有项目参与者。在整个集水区的2337名初级保健提供者中，这些代表性不足的区域包含179名。
项目招募中存在非随机空间差异的发现，对于那些评估为地理界定集水区服务的专科诊所招募效果并实施改进措施的人来说，是有价值的数据。这项初步研究的积极结果值得进一步开发预测模型，并衡量这些发现驱动的招募干预对招募的影响。
