scholarly journals The John Bryden memorial lecture: improving health with the community health index and developments in record linkage

2014 ◽  
Vol 21 (4) ◽  
pp. 156-160
Author(s):  
Francis M. Sullivan
Keyword(s):  
Community Health ◽  
Record Linkage ◽  
Memorial Lecture ◽  
Health Index

Best-Link Matching of Scottish Health Data Sets

1998 ◽  
Vol 37 (01) ◽  
pp. 64-68 ◽  
Author(s):  
M. M. Douglas ◽  
D. Gardner ◽  
D. Hucker ◽  
S. W. Kendrick
Keyword(s):  
Community Health ◽  
Health Data ◽  
Data Sets ◽  
Health Index ◽  
Probability Matching ◽  
Central Register ◽  
National Patient ◽  
Degree Of Confidence ◽  
Sufficient Degree ◽  
Matching Techniques

Abstract:Methods are described used to link the Community Health Index and the National Health Service Central Register (NHSCR) in Scotland to provide a basis for a national patient index. The linkage used a combination of deterministic and probability matching techniques. A best-link principle was used by which each Community Health Index record was allowed to link only to the NHSCR record with which it achieved the highest match weight. This strategy, applied in the context of two files which each covered virtually the entire population of Scotland, increased the accuracy of linkage approximately a thousand-fold compared with the likely results of a less structured probability matching approach. By this means, 98.8% of linkable records were linked automatically with a sufficient degree of confidence for administrative purposes.

The Community Health Index-how accurate is it?

1988 ◽  
Vol 10 (4) ◽  
pp. 327-330 ◽  
Author(s):  
M. A. Roworth ◽  
I. G. Jones
Keyword(s):  
Community Health ◽  
Health Index

scholarly journals Administrative mass data linking minimal/zero false positives

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Chris Povey
Keyword(s):  
Record Linkage ◽  
False Positive ◽  
False Positives ◽  
Historical Records ◽  
Health Index ◽  
Electronic Patient Records ◽  
Acceptance Threshold ◽  
Heuristic Strategies ◽  
Probability Mass ◽  
Service Data

ABSTRACT ObjectivesPart of eHealth project in Scotland to assign a health index to all electronic patient records. One off extracts from live and historical records were posted to a record linkage department where deterministic and Newcombe probability mass matching was performed to assign the Scottish GP registration (CHI) number. These were real world administrative matches with emphasis on minimal false positives rather than maximum acceptable match rates. ApproachEarly investigations examined the causes of false positive matching. A running window of incomer match scores, instead of only the highest pair score indicated that highest pair Binit scores, even well above acceptance threshold yielded spurious matches and that lower scoring pair matches for the same incomer were more acceptable. A single threshold would not work. The customers were invited to clerically check the matches; their heuristic strategies were observed and incorporated into an automated partitioning exercise. ResultsDeterministic match rates below 70% were considered very poor. 70-80% poor, 80-85% average, 85-90% good, >90% excellent. The residual unmatched incomers were processed using Newcombe methods, then through the partitioning exercise. 90-95% (deterministic+residual) match rates were viewed as average, 95-98 % as good, 98-100% as excellent. Several deterministic match runs were passed through the residue process early in the exercise, any false positive thrown up by this caused a change in the deterministic process to eradicate errors. Roughly 1000 linkage exercises were done for the eHealth project. ConclusionsThis was a joint exercise where the linkage department delivered potential match pairs to the customer. The customer then decided on the partitions they were willing to accept. All the potential pairs were sent with a checking engine to view the outcome. Most elected to accept only deterministic matches. Some accepted linkage department advice; often the linkers would clerically flag accepted and rejected pair matches for the customers to review. There was a pilot administrative matching project to assign the health index to social service data in Scotland called eCare which started after the eHealth exercise; in both, the customers were requested to alert us with any false positives - no alerts were received. The same methods were used in the recent exercise to de-duplicate and merge all Glasgow's hospital records; the customer was very used to the methodology, so more checking work by the linker was accepted to achieve higher match rates. A method to estimate false positive rates is proposed.

scholarly journals COVID-19 Growth Rate Decreases with Social Capital

2020 ◽  
Author(s):  
Lav R. Varshney ◽  
Richard Socher
Keyword(s):  
United States ◽  
Social Capital ◽  
Growth Rate ◽  
Community Health ◽  
Human Mobility ◽  
County Level ◽  
Health Index ◽  
Negatively Associated ◽  
Social Distancing ◽  
Health Indices

AbstractBackgroundSocial capital has been associated with many public health variables including mortality, obesity, diabetes, and sexually-transmitted disease rates. However, the relationship of social capital to the spread of infectious disease like COVID-19 is lacking. The COVID-19 pandemic presents an unprecedented threat to global health and economy, for which control strategies have relied on aggressive social distancing. However, an understanding of how social capital is related to changes in human mobility patterns for adherence to social distancing is lacking.ObjectiveThis study examines the association between state- and county-level social capital indices and community health indices in the United States, and the growth rate of COVID-19 cases. It also examines changes in human mobility.MethodsUsing publicly available state- and county-specific time series data for COVID-19 cases from March 13 to March 31, we used exponential fits to determine growth rate. We obtained publicly available mobility change data, originally measured from GPS-enabled mobile devices. The design was then state- and county-level correlation analysis with social capital and community health indices from the Social Capital Project (United States Senate).ResultsIn bivariate linear correlation analyses, we find social capital and community health indices were negatively associated with COVID-19 growth rates at both the state and county levels. The correlation was strongest at the county level for the community health index: a one-unit increase in the county community health index was associated with a decrease in the COVID-19 growth rate exponent by 0.045. In further bivariate correlation analyses, we find that social capital indices were negatively associated with retail/recreation movement and positively associated with residential movement. That is, an increase in social capital is correlated with slower COVID-19 infection spread and more adherence to social distancing protocols.ConclusionOur results indicate the potential benefit of incorporating social capital concepts in planning policies to control the spread of COVID-19, e.g. different social distancing requirements in different communities. The results also indicate a need for further research into this potentially causal relationship, including examining interventions to increase social capital, community health, and institutional health.

Sign in / Sign up

Export Citation Format

Share Document