Institute for International Programs, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
KEM Hospital Research Centre, Sardar Moodliar Road, Rasta Peth, Pune, India.
J Glob Health. 2020 Jun;10(1):010602. doi: 10.7189/jogh.10.010602.
Common approaches to measure health behaviors rely on participant responses and are subject to bias. Technology-based alternatives, particularly using GPS, address these biases while opening new channels for research. This study describes the development and implementation of a GPS-based approach to detect health facility visits in rural Pune district, India.
Participants were mothers of under-five year old children within the Vadu Demographic Surveillance area. Participants received GPS-enabled smartphones pre-installed with a location-aware application to continuously record and transmit participant location data to a central server. Data were analyzed to identify health facility visits according to a parameter-based approach, optimal thresholds of which were calibrated through a simulation exercise. Lists of GPS-detected health facility visits were generated at each of six follow-up home visits and reviewed with participants through prompted recall survey, confirming visits which were correctly identified. Detected visits were analyzed using logistic regression to explore factors associated with the identification of false positive GPS-detected visits.
We enrolled 200 participants and completed 1098 follow-up visits over the six-month study period. Prompted recall surveys were completed for 694 follow-up visits with one or more GPS-detected health facility visits. While the approach performed well during calibration (positive predictive value (PPV) 78%), performance was poor when applied to participant data. Only 440 of 22 251 detected visits were confirmed (PPV 2%). False positives increased as participants spent more time in areas of high health facility density (odds ratio (OR) = 2.29, 95% confidence interval (CI) = 1.62-3.25). Visits detected at facilities other than hospitals and clinics were also more likely to be false positives (OR = 2.78, 95% CI = 1.65-4.67) as were visits detected to facilities nearby participant homes, with the likelihood decreasing as distance increased (OR = 0.89, 95% CI = 0.82-0.97). Visit duration was not associated with confirmation status.
The optimal parameter combination for health facility visits simulated by field workers substantially overestimated health visits from participant GPS data. This study provides useful insights into the challenges in detecting health facility visits where providers are numerous, highly clustered within urban centers and located near residential areas of the population which they serve.
常用的健康行为测量方法依赖于参与者的回答，因此存在偏差。基于技术的替代方法，特别是使用 GPS，可以解决这些偏差，同时为研究开辟新的渠道。本研究描述了一种基于 GPS 的方法在印度浦那农村地区检测卫生机构就诊的开发和实施。
参与者是五岁以下儿童的母亲，位于瓦杜人口监测区。参与者收到预先安装了位置感知应用程序的 GPS 智能手机，该应用程序将持续记录并将参与者的位置数据传输到中央服务器。根据基于参数的方法分析数据以识别卫生机构就诊情况，该方法通过模拟练习校准了最佳阈值。在六次家庭随访中的每次随访中生成 GPS 检测到的卫生机构就诊列表，并通过提示回忆调查与参与者一起查看，确认正确识别的就诊情况。使用逻辑回归分析检测到的就诊情况，以探讨与识别假阳性 GPS 检测到的就诊情况相关的因素。
我们招募了 200 名参与者，并在六个月的研究期间完成了 1098 次随访。对于六次随访中的一次或多次 GPS 检测到的卫生机构就诊，完成了提示回忆调查。虽然该方法在校准期间表现良好（阳性预测值 (PPV) 为 78%），但在应用于参与者数据时效果不佳。在 22251 次检测到的就诊中，只有 440 次得到确认（PPV 为 2%）。随着参与者在高卫生机构密度区域花费的时间增加，假阳性就诊次数也会增加（优势比 (OR) = 2.29，95%置信区间 (CI) = 1.62-3.25）。在医院和诊所以外的机构检测到的就诊也更有可能是假阳性（OR = 2.78，95% CI = 1.65-4.67），而在距离参与者家庭较近的机构检测到的就诊也是如此，随着距离的增加，可能性降低（OR = 0.89，95% CI = 0.82-0.97）。就诊持续时间与确认状态无关。
实地工作人员模拟的卫生机构就诊最佳参数组合大大高估了参与者 GPS 数据中的就诊次数。本研究为在城市中心高度集中且位于其服务人群居住地附近的提供者众多的情况下检测卫生机构就诊情况提供了有用的见解。
