Spatial Errors in Automated Geocoding of Incident Locations in Australian Suicide Mortality Data.

BACKGROUND: There is increasing interest in the spatial analysis of suicide data to identify high-risk (often public) locations likely to benefit from access restriction measures. The identification of such locations, however, relies on accurately geocoded data. This study aims to examine the extent to which common completeness and positional spatial errors are present in suicide data due to the underlying geocoding process. METHODS: Using Australian suicide mortality data from the National Coronial Information System for the period of 2008-2017, we compared the custodian automated geocoding process to an alternate multiphase process. Descriptive and kernel density cluster analyses were conducted to ascertain data completeness (address matching rates) and positional accuracy (distance revised) differences between the two datasets. RESULTS: The alternate geocoding process initially improved address matching from 67.8% in the custodian dataset to 78.4%. Additional manual identification of nonaddress features (such as cliffs or bridges) improved overall match rates to 94.6%. Nearly half (49.2%) of nonresidential suicide locations were revised more than 1,000 m from data custodian coordinates. Spatial misattribution rates were greatest at the smallest levels of geography. Kernel density maps showed clear misidentification of hotspots relying solely on autogeocoded data. CONCLUSION: Suicide incidents that occur at nonresidential addresses are being erroneously geocoded to centralized fall-back locations in autogeocoding processes, which can lead to misidentification of suicide clusters. Our findings provide insights toward defining the nature of the problem and refining geocoding processes, so that suicide data can be used reliably for the detection of suicide hotspots. See video abstract at, http://links.lww.com/EDE/B862.

Differential privacy for public health data: An innovative tool to optimize information sharing while protecting data confidentiality.

Coronavirus disease 2019 (COVID-19) has highlighted the need for the timely collection and sharing of public health data. It is important that data sharing is balanced with protecting confidentiality. Here we discuss an innovative mechanism to protect health data, called differential privacy. Differential privacy is a mathematically rigorous definition of privacy that aims to protect against all possible adversaries. In layperson's terms, statistical noise is applied to the data so that overall patterns can be described, but data on individuals are unlikely to be extracted. One of the first use cases for health data in Australia is the development of the COVID-19 Real-Time Information System for Preparedness and Epidemic Response (CRISPER), which provides proof of concept for the use of this technology in the health sector. If successful, this will benefit future sharing of public health data.

Development of the COVID-19 Real-Time Information System for Preparedness and Epidemic Response (CRISPER), Australia.

Accurate and current information has been highlighted across the globe as a critical requirement for the COVID-19 pandemic response. To address this need, many interactive dashboards providing a range of different information about COVID-19 have been developed. A similar tool in Australia containing current information about COVID-19 could assist general practitioners and public health responders in their pandemic response efforts. The COVID-19 Real-time Information System for Preparedness and Epidemic Response (CRISPER) has been developed to provide accurate and spatially explicit real-time information for COVID-19 cases, deaths, testing and contact tracing locations in Australia. Developed based on feedback from key users and stakeholders, the system comprises three main components: (1) a data engine; (2) data visualization and interactive mapping tools; and (3) an automated alert system. This system provides integrated data from multiple sources in one platform which optimizes information sharing with public health responders, primary health care practitioners and the general public.

Protecting the privacy of individual general practice patient electronic records for geospatial epidemiology research.

BACKGROUND: General practitioner (GP) practices in Australia are increasingly storing patient information in electronic databases. These practice databases can be accessed by clinical audit software to generate reports that inform clinical or population health decision making and public health surveillance. Many audit software applications also have the capacity to generate de-identified patient unit record data. However, the de-identified nature of the extracted data means that these records often lack geographic information. Without spatial references, it is impossible to build maps reflecting the spatial distribution of patients with particular conditions and needs. Links to socioeconomic, demographic, environmental or other geographically based information are also not possible. In some cases, relatively coarse geographies such as postcode are available, but these are of limited use and researchers cannot undertake precision spatial analyses such as calculating travel times. METHODS: We describe a method that allows researchers to implement meaningful mapping and spatial epidemiological analyses of practice level patient data while preserving privacy. RESULTS: This solution has been piloted in a diabetes risk research project in the patient population of a practice in Adelaide. CONCLUSIONS AND IMPLICATIONS: The method offers researchers a powerful means of analysing geographic clinic data in a privacy-protected manner.

Using binning to maintain confidentiality of medical data.

Biomedical informatics in general and pharmacogenomics in particular require a research platform that simultaneously enables discovery while protecting research subjects' privacy and information confidentiality. The development of inexpensive DNA sequencing and analysis technologies promises unprecedented database access to very specific information about individuals. To allow analysis of this data without compromising the research subjects' privacy, we must develop methods for removing identifying information from medical and genomic data. In this paper, we build upon the idea that binned database records are more difficult to trace back to individuals. We represent symbolic and numeric data hierarchically, and bin them by generalizing the records. We measure the information loss due to binning using an information theoretic measure called mutual information. The results show that we can bin the data to different levels of precision and use the bin size to control the tradeoff between privacy and data resolution.