Population Surveillance for Early Detection of Neurodegenerative Disease

Autism is a developmental condition that can be detected in early childhood. Early intervention can improve outcomes, though many children are not identified until they reach primary school. Early childhood educators are well placed to monitor children’s development and identify those who may benefit from additional supports, though implementation of standardised tools and processes is limited. The National Disability Insurance Scheme in Australia has increased the onus on educators to support families to access funded services. A workshop on evidence-informed practice in early detection for autism was provided for early childhood professionals. The theory of practice architectures informed development and analysis of pre- and post-workshop surveys to explore changes in early childhood educators’ perspectives on factors influencing universal developmental monitoring and referrals to early intervention supports using an evidence-based tool, the Social Attention and Communication Surveillance-Revised (SACS-R). Post-workshop increases in early childhood educators’ perceived knowledge and confidence are evident, though recent policy reforms present challenges. Population surveillance using SACS-R in early childhood education is effective for identification and referral for children who have autism, and capacity building for professionals to use SACS-R is recommended.

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Comparing malaria early detection methods in a declining transmission setting in northwestern Ethiopia

BMC Public Health ◽

10.1186/s12889-021-10850-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Dawn M. Nekorchuk ◽

Teklehaimanot Gebrehiwot ◽

Mastewal Lake ◽

Worku Awoke ◽

Abere Mihretie ◽

...

Keyword(s):

Public Health ◽

Early Detection ◽

Malaria Incidence ◽

Intervention Strategy ◽

Population Surveillance ◽

Detection Methods ◽

True Positive ◽

Trade Offs ◽

Wide Range ◽

High Event

Abstract Background Despite remarkable progress in the reduction of malaria incidence, this disease remains a public health threat to a significant portion of the world’s population. Surveillance, combined with early detection algorithms, can be an effective intervention strategy to inform timely public health responses to potential outbreaks. Our main objective was to compare the potential for detecting malaria outbreaks by selected event detection methods. Methods We used historical surveillance data with weekly counts of confirmed Plasmodium falciparum (including mixed) cases from the Amhara region of Ethiopia, where there was a resurgence of malaria in 2019 following several years of declining cases. We evaluated three methods for early detection of the 2019 malaria events: 1) the Centers for Disease Prevention and Control (CDC) Early Aberration Reporting System (EARS), 2) methods based on weekly statistical thresholds, including the WHO and Cullen methods, and 3) the Farrington methods. Results All of the methods evaluated performed better than a naïve random alarm generator. We also found distinct trade-offs between the percent of events detected and the percent of true positive alarms. CDC EARS and weekly statistical threshold methods had high event sensitivities (80–100% CDC; 57–100% weekly statistical) and low to moderate alarm specificities (25–40% CDC; 16–61% weekly statistical). Farrington variants had a wide range of scores (20–100% sensitivities; 16–100% specificities) and could achieve various balances between sensitivity and specificity. Conclusions Of the methods tested, we found that the Farrington improved method was most effective at maximizing both the percent of events detected and true positive alarms for our dataset (> 70% sensitivity and > 70% specificity). This method uses statistical models to establish thresholds while controlling for seasonality and multi-year trends, and we suggest that it and other model-based approaches should be considered more broadly for malaria early detection.

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Malaria Early Detection in a Declining Transmission Setting in the Amhara Region of Ethiopia

10.21203/rs.3.rs-116290/v1 ◽

2020 ◽

Author(s):

Dawn Nekorchuk ◽

Teklehaimanot Gebrehiwot ◽

Mastewal Lake ◽

Worku Awoke ◽

Abere Mihretie ◽

...

Keyword(s):

Public Health ◽

Early Detection ◽

Malaria Incidence ◽

Intervention Strategy ◽

Population Surveillance ◽

Detection Methods ◽

True Positive ◽

Amhara Region ◽

Trade Offs ◽

Wide Range

Abstract Background Despite remarkable progress in the reduction of malaria incidence, this disease remains a public health threat to a significant portion of the world’s population. Surveillance, combined with early detection algorithms, can be an effective intervention strategy to inform timely public health responses to potential outbreaks. Our main objective was to compare the potential for detecting malaria outbreaks by selected event detection methods.Methods We used historical surveillance data with weekly counts of confirmed Plasmodium falciparum (including mixed) cases from the Amhara region of Ethiopia, where there was a resurgence of malaria in 2019 following several years of declining cases. We evaluated three early detection methods to detect the 2019 malaria events: 1) the Centers for Disease Prevention and Control (CDC) Early Aberration Reporting System (EARS), 2) methods based on weekly statistical thresholds, including the WHO and Cullen methods, and 3) the Farrington algorithms.Results All of the methods and parameters evaluated performed better than a naïve random alarm generator. We also found distinct trade-offs between the percent of events detected and the percent of true positive alarms. CDC EARS and weekly statistical threshold methods had high event sensitivities (80% − 100% CDC; 57% − 100% weekly statistical) and low to moderate alarm specificities (25% − 40% CDC; 16% − 61% weekly statistical). Farrington variants had a wide range of scores (20% − 100% sensitivities; 16% − 100% specificities) and could achieve various balances between sensitivity and specificity.Conclusions Of the methods tested, we found that the Farrington improved method was most effective at maximizing both the percent of events detected and true positive alarms for our dataset (83% sensitivity, 51% specificity). This method uses statistical models to establish thresholds while controlling for seasonality and multi-year trends, and we suggest that it and other model-based approaches should be considered more broadly for malaria early detection.

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