Population health information that is both accurate and comprehensive can aid program implementation, monitoring, and evaluation, resource allocation and planning. However, there are currently large gaps in the technologies and measurement methods that are available to generate this information, and this makes it difficult to address health inequities through effective policy ¹ .

The Population Health Metrics Research Consortium (PHMRC) conducted data collection to enable the development and validation of methods that measure cause-specific mortality in populations with incomplete or inadequate cause of death coding. This work produced around 12,000 verbal autopsy interviews (VAIs), in which a relative or someone familiar with the final illness of the deceased, provides information about the signs symptoms of the final illness, as well as demographic characteristics, and information on risk factor exposures (such as tobacco use), and other potentially relevant characteristics ² .

The VAI data were released in a de-identified format in September 2013, through the Global Health Data Exchange, in files that contain verbal autopsies from six sites in four countries (India, Mexico, Tanzania, and the Philippines) using a standardized VA questionnaire developed by the PHMRC. The data is organized into three parts corresponding to the questionnaire modules for each age group: neonate, child, and adult. Each VAI in the database is matched with a “gold standard” diagnoses of underlying causes of death, typically identified from medical records, and using stringent diagnostic criteria (such as laboratory, pathology, or medical imaging findings.) ³

One portion of a VAI is the “open narrative,” where the respondent has the opportunity to tell, in their own words, what happened during the illness that led to the death being investigated. This was collected as a final question in the PHMRC survey, after the structured interview, when the respondent was asked, “Could you please summarize, or tell us in your own words, any additional information about the illness and/or death of your loved one?” The full response to this question was transcribed and translated into English, and the 2013 data release included counts of stemmed keywords as variables in the final dataset, to allow researchers access to this rich source of unstructured data, while also removing any potentially personally identifiable information (PII) in that portion of the interview.

Due to research interest, we have now created redacted versions of 11,979 open narratives to allow researchers the opportunity to learn even more about how deaths are described. We hope that this database will be the source of innovations that increase our knowledge about the causes of ill health and through knowledge produce improvements in health for individuals and populations.

The process of collecting the VAIs has been described in detail previously ¹ . In this article, we provide a detailed account of the protocol used to redact personal information from the open-ended question, and therefore allow the release of the full text of the open narrative collected in the VAIs.

Study participants provided their consent to participate with the knowledge that “reports of the data … will not identify any individual person.” We chose also to redact the names of specific health facilities to avoid the risk of identifying individual health service providers indirectly, through their association with individual facilities. To retain the most information possible for future research, we replaced PII with “tags” that denote what sort of information has been redacted.

An example makes this clear: a typical text was redacted to read, “vaginal bleeding and delay to receive care at [HOSPITAL] was the main cause of death. he said that his wife arrive at the hospital at 8pm and didn’t receive any care until 8am.” Instead of including the name of the specific hospital, we redacted it to [HOSPITAL]. The tags used to replace PII are [HOSPITAL], [DOCTOR], [PATIENT], [PLACE], [PERSON], and [YEAR].

We initially planned to redact dates entirely but chose to redact only the year, to make it easier for future researchers to measure the time between events. To allow for different years, we used the tag [YEAR + n]. An example is “last november of [YEAR]-the deceased got stroke left side of his body. was hospitalized due to high blood pressure last year. january this year was his last hospitalization that leads to death. jan. 26, [YEAR+1]. experienced fast breathing, that’s why he was brought to the hospital (provincial hospital). with oxygen and ngt; got fever and cough; in coma. jan. 31, [YEAR+1]. was tried to revive around 11:00 pm to 12 midnight, but was not able to revive him. around 3:00 am (at dawn), he died.”

When a response referred to multiple different specific hospitals, we redacted the hospitals to [HOSPITAL] and [HOSPITAL2]. Subsequent distinct hospitals in same passage were redacted to [HOSPITAL3], [HOSPITAL4], etc.

We included all VAIs for which there was an open-response string available to redact, even when the response was devoid of information.

We implemented the redaction process in a spreadsheet using Excel 2010, redacted manually by a single data analyst (LH), who read each open-response and replaced each piece of PII with the appropriate tag.

This study was approved by the Human Subjects Division of the University of Washington (application number 34413). Ethical approval sought for the VAIs is stated in 1. All data were collected with informed verbal consent from participants before beginning the interview.

Data underlying the study are available on OSF: http://doi.org/10.17605/OSF.IO/XUK5Q ⁴

Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).