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Implementation of an electronic fingerprint-linked data collection system: a feasibility and acceptability study among Zambian female sex workers.

Wall KM, Kilembe W, Inambao M, Chen YN, Mchoongo M, Kimaru L, Hammond YT, Sharkey T, Malama K, Fulton TR, Tran A, Halumamba H, Anderson S, Kishore N, Sarwar S, Finnegan T, Mark D, Allen SA - Global Health (2015)

Bottom Line: We found that implementation of an electronic fingerprint-linked patient tracking and data collection system was feasible in this relatively resource-limited setting (false fingerprint matching rate of 1/1000 and false rejection rate of <1/10,000) and was acceptable among FSWs in a clinic setting (2% refusals).Our findings have major implications for key population research and improved health services provision.However, more work needs to be done to increase the acceptability of the electronic fingerprint-linked data capture system during field recruitment.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology, Rollins School of Public Health, Laney Graduate School, Emory University, Atlanta, GA, USA. kmwall@emory.edu.

ABSTRACT

Background: Patient identification within and between health services is an operational challenge in many resource-limited settings. When following HIV risk groups for service provision and in the context of vaccine trials, patient misidentification can harm patient care and bias trial outcomes. Electronic fingerprinting has been proposed to identify patients over time and link patient data between health services. The objective of this study was to determine 1) the feasibility of implementing an electronic-fingerprint linked data capture system in Zambia and 2) the acceptability of this system among a key HIV risk group: female sex workers (FSWs).

Methods: Working with Biometrac, a US-based company providing biometric-linked healthcare platforms, an electronic fingerprint-linked data capture system was developed for use by field recruiters among Zambian FSWs. We evaluated the technical feasibility of the system for use in the field in Zambia and conducted a pilot study to determine the acceptability of the system, as well as barriers to uptake, among FSWs.

Results: We found that implementation of an electronic fingerprint-linked patient tracking and data collection system was feasible in this relatively resource-limited setting (false fingerprint matching rate of 1/1000 and false rejection rate of <1/10,000) and was acceptable among FSWs in a clinic setting (2% refusals). However, our data indicate that less than half of FSWs are comfortable providing an electronic fingerprint when recruited while they are working. The most common reasons cited for not providing a fingerprint (lack of privacy/confidentiality issues while at work, typically at bars or lodges) could be addressed by recruiting women during less busy hours, in their own homes, in the presence of "Queen Mothers" (FSW organizers), or in the presence of a FSW that has already been fingerprinted.

Conclusions: Our findings have major implications for key population research and improved health services provision. However, more work needs to be done to increase the acceptability of the electronic fingerprint-linked data capture system during field recruitment. This study indicated several potential avenues that will be explored to increase acceptability.

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ROC plots of False Positive Matching Rate (FPMR, red) and False Negative Matching Rate (FNMR, blue) when fingerprinting RZHRG staff as a function of matching algorithm threshold. a. When fingerprinting left and right index fingers, the equal error rate (EER, where the FPMR and FNMR cross) is less than 1 %. b. When fingerprinting both index fingers and both thumbs, the ERR is zero. At a threshold of 70, the FPMR is at 1/1000 and the FNMR is 1/10,000
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Fig3: ROC plots of False Positive Matching Rate (FPMR, red) and False Negative Matching Rate (FNMR, blue) when fingerprinting RZHRG staff as a function of matching algorithm threshold. a. When fingerprinting left and right index fingers, the equal error rate (EER, where the FPMR and FNMR cross) is less than 1 %. b. When fingerprinting both index fingers and both thumbs, the ERR is zero. At a threshold of 70, the FPMR is at 1/1000 and the FNMR is 1/10,000

Mentions: Technical challenges largely concerned system and training issues (Table 1). During validation of the false fingerprint matching and false rejection rates of the system, we fingerprinted 120 RZHRG staff three times each. Despite correct fingerprinting technique, we faced challenges with both outcomes, i.e. identification numbers that were not unique and/or that were mismatching across workflows. This testing showed that collecting fingerprints from index fingers alone (Fig. 3, Panel a) was not as accurate as capturing fingerprints from both thumbs and index fingers, which gave a false fingerprint matching rate of 1/1000 and a false rejection rate of <1/10,000 (Fig. 3, Panel b). Presenting this another way, for each combination of fingers (single thumb or index, two indexes, two thumbs, two indexes and thumbs) for the 120 individuals who provided fingerprints, we filtered scores at 0.2 threshold increments. The results are presented as a series of receiver operator curves (ROC) presented in terms of sensitivity and specificity (Fig. 4). A zoomed in view of the 0.95–1.0 range of sensitivity and specificity is shown for a clearer illustration of the benefits of using multiple fingers.Table 1


Implementation of an electronic fingerprint-linked data collection system: a feasibility and acceptability study among Zambian female sex workers.

Wall KM, Kilembe W, Inambao M, Chen YN, Mchoongo M, Kimaru L, Hammond YT, Sharkey T, Malama K, Fulton TR, Tran A, Halumamba H, Anderson S, Kishore N, Sarwar S, Finnegan T, Mark D, Allen SA - Global Health (2015)

ROC plots of False Positive Matching Rate (FPMR, red) and False Negative Matching Rate (FNMR, blue) when fingerprinting RZHRG staff as a function of matching algorithm threshold. a. When fingerprinting left and right index fingers, the equal error rate (EER, where the FPMR and FNMR cross) is less than 1 %. b. When fingerprinting both index fingers and both thumbs, the ERR is zero. At a threshold of 70, the FPMR is at 1/1000 and the FNMR is 1/10,000
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4489038&req=5

Fig3: ROC plots of False Positive Matching Rate (FPMR, red) and False Negative Matching Rate (FNMR, blue) when fingerprinting RZHRG staff as a function of matching algorithm threshold. a. When fingerprinting left and right index fingers, the equal error rate (EER, where the FPMR and FNMR cross) is less than 1 %. b. When fingerprinting both index fingers and both thumbs, the ERR is zero. At a threshold of 70, the FPMR is at 1/1000 and the FNMR is 1/10,000
Mentions: Technical challenges largely concerned system and training issues (Table 1). During validation of the false fingerprint matching and false rejection rates of the system, we fingerprinted 120 RZHRG staff three times each. Despite correct fingerprinting technique, we faced challenges with both outcomes, i.e. identification numbers that were not unique and/or that were mismatching across workflows. This testing showed that collecting fingerprints from index fingers alone (Fig. 3, Panel a) was not as accurate as capturing fingerprints from both thumbs and index fingers, which gave a false fingerprint matching rate of 1/1000 and a false rejection rate of <1/10,000 (Fig. 3, Panel b). Presenting this another way, for each combination of fingers (single thumb or index, two indexes, two thumbs, two indexes and thumbs) for the 120 individuals who provided fingerprints, we filtered scores at 0.2 threshold increments. The results are presented as a series of receiver operator curves (ROC) presented in terms of sensitivity and specificity (Fig. 4). A zoomed in view of the 0.95–1.0 range of sensitivity and specificity is shown for a clearer illustration of the benefits of using multiple fingers.Table 1

Bottom Line: We found that implementation of an electronic fingerprint-linked patient tracking and data collection system was feasible in this relatively resource-limited setting (false fingerprint matching rate of 1/1000 and false rejection rate of <1/10,000) and was acceptable among FSWs in a clinic setting (2% refusals).Our findings have major implications for key population research and improved health services provision.However, more work needs to be done to increase the acceptability of the electronic fingerprint-linked data capture system during field recruitment.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology, Rollins School of Public Health, Laney Graduate School, Emory University, Atlanta, GA, USA. kmwall@emory.edu.

ABSTRACT

Background: Patient identification within and between health services is an operational challenge in many resource-limited settings. When following HIV risk groups for service provision and in the context of vaccine trials, patient misidentification can harm patient care and bias trial outcomes. Electronic fingerprinting has been proposed to identify patients over time and link patient data between health services. The objective of this study was to determine 1) the feasibility of implementing an electronic-fingerprint linked data capture system in Zambia and 2) the acceptability of this system among a key HIV risk group: female sex workers (FSWs).

Methods: Working with Biometrac, a US-based company providing biometric-linked healthcare platforms, an electronic fingerprint-linked data capture system was developed for use by field recruiters among Zambian FSWs. We evaluated the technical feasibility of the system for use in the field in Zambia and conducted a pilot study to determine the acceptability of the system, as well as barriers to uptake, among FSWs.

Results: We found that implementation of an electronic fingerprint-linked patient tracking and data collection system was feasible in this relatively resource-limited setting (false fingerprint matching rate of 1/1000 and false rejection rate of <1/10,000) and was acceptable among FSWs in a clinic setting (2% refusals). However, our data indicate that less than half of FSWs are comfortable providing an electronic fingerprint when recruited while they are working. The most common reasons cited for not providing a fingerprint (lack of privacy/confidentiality issues while at work, typically at bars or lodges) could be addressed by recruiting women during less busy hours, in their own homes, in the presence of "Queen Mothers" (FSW organizers), or in the presence of a FSW that has already been fingerprinted.

Conclusions: Our findings have major implications for key population research and improved health services provision. However, more work needs to be done to increase the acceptability of the electronic fingerprint-linked data capture system during field recruitment. This study indicated several potential avenues that will be explored to increase acceptability.

Show MeSH