利用谷歌趋势来告知男同性恋、双性恋和其他与男性发生性行为的男性人群规模估计和空间分布：概念验证研究。
Using Google Trends to Inform the Population Size Estimation and Spatial Distribution of Gay, Bisexual, and Other Men Who Have Sex With Men: Proof-of-concept Study.
机构信息
Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada.
School of Public Health and Social Policy, Faculty of Human and Social Development, University of Victoria, Victoria, BC, Canada.
出版信息
JMIR Public Health Surveill. 2021 Nov 29;7(11):e27385. doi: 10.2196/27385.
BACKGROUND
We must triangulate data sources to understand best the spatial distribution and population size of marginalized populations to empower public health leaders to address population-specific needs. Existing population size estimation techniques are difficult and limited.
OBJECTIVE
We sought to identify a passive surveillance strategy that utilizes internet and social media to enhance, validate, and triangulate population size estimates of gay, bisexual, and other men who have sex with men (gbMSM).
METHODS
We explored the Google Trends platform to approximate an estimate of the spatial heterogeneity of the population distribution of gbMSM. This was done by comparing the prevalence of the search term "gay porn" with that of the search term "porn."
RESULTS
Our results suggested that most cities have a gbMSM population size between 2% and 4% of their total population, with large urban centers having higher estimates relative to rural or suburban areas. This represents nearly a double up of population size estimates compared to that found by other methods, which typically find that between 1% and 2% of the total population are gbMSM. We noted that our method was limited by unequal coverage in internet usage across Canada and differences in the frequency of porn use by gender and sexual orientation.
CONCLUSIONS
We argue that Google Trends estimates may provide, for many public health planning purposes, adequate city-level estimates of gbMSM population size in regions with a high prevalence of internet access and for purposes in which a precise or narrow estimate of the population size is not required. Furthermore, the Google Trends platform does so in less than a minute at no cost, making it extremely timely and cost-effective relative to more precise (and complex) estimates. We also discuss future steps for further validation of this approach.
背景
为了了解边缘化人群的空间分布和人口规模，我们必须对数据源进行三角测量，以便为公共卫生领导人提供赋权，以满足特定人群的需求。现有的人口规模估计技术既困难又有限。
目的
我们试图确定一种被动监测策略，利用互联网和社交媒体来增强、验证和三角测量男同性恋、双性恋和其他与男性发生性关系的男性（gbMSM）的人口规模估计。
方法
我们探索了 Google Trends 平台，以近似估计 gbMSM 人群分布的空间异质性。这是通过比较搜索词“gay porn”的流行度与搜索词“porn”的流行度来实现的。
结果
我们的结果表明，大多数城市的 gbMSM 人口规模占其总人口的 2%至 4%，与农村或郊区相比，大城市中心的估计值更高。这代表着与其他方法相比，人口规模估计值增加了近一倍，而其他方法通常发现总人口中有 1%至 2%是 gbMSM。我们注意到，我们的方法受到加拿大互联网使用率不均等和不同性别和性取向的色情使用频率的限制。
结论
我们认为，对于许多公共卫生规划目的而言，Google Trends 估计值可以在互联网普及程度较高的地区提供足够的城市级 gbMSM 人口规模估计值，并且在不需要人口规模精确或狭窄估计值的情况下使用。此外，Google Trends 平台可以在不到一分钟的时间内免费提供这些估计值，与更精确（和复杂）的估计值相比，它具有极高的时效性和成本效益。我们还讨论了进一步验证这种方法的未来步骤。
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