scholarly journals Barriers State Agencies Face with the Dissemination of Local Health Data through Web-Based Data Query Systems (Preprint)

2019 ◽  
Author(s):  
Manik Ahuja ◽  
Robert Aseltine Jr
Keyword(s):  
Surveillance System ◽  
Contextual Factors ◽  
Local Level ◽  
Health Data ◽  
Behavioral Risk ◽  
Local Health ◽  
Data Systems ◽  
Web Based ◽  
Data Query ◽  
Query Systems

BACKGROUND Web Based Data Query Systems (WDQS) make health data at the local level easily accessible to the public health. Despite their benefits Many state and local health agencies face significant challenges with their dissemination. OBJECTIVE The purpose of this study is to identify the most significant challenges they face from the perspective of Behavioral Risk Factor Surveillance System (BRFSS) coordinators. We also seek to find an association between perceived system aspects, challenges faced, contextual factors, and overall satisfaction with state level health data systems. METHODS We surveyed Behavioral Risk Surveillance System (BRFSS) coordinators from 43 states. We surveyed participants about contextual factors and asked them to rate system aspects and challenges they face with their health data system on a Likert scale. We used two sample t-tests to compare means on participant ratings for states with and without Web Based Data Query Systems (WDQS). RESULTS Overall, 95.4% of states make health data available over the internet, while 65.1% employ a WDQS. States reported the challenge of cost of hardware/software as a greater challenge between states with WDQS than without WDQS. States rated standardization of vocabulary more favorably in states with WDQS (n=3.32; 95% CI, 2.94-3.69) versus states without WDQS (n=2.85, 95% CI, 2.47-3.22). CONCLUSIONS Securing adequate resources, and commitment to standardization are vital in the dissemination of local level health data. Factors such a receiving data in a timely manner, privacy, and political opposition are less significant of a barrier than anticipated.

scholarly journals Road traffic injuries in one local health unit in the Lazio region: results of a surveillance system integrating police and health data

2009 ◽  
Vol 8 (1) ◽  
pp. 21 ◽  
Author(s):  
Francesco Chini ◽  
Sara Farchi ◽  
Ivana Ciaramella ◽  
Tranquillo Antoniozzi ◽  
Paolo Rossi ◽  
...  
Keyword(s):  
Surveillance System ◽  
Road Traffic ◽  
Health Data ◽  
Local Health ◽  
Health Unit ◽  
Lazio Region ◽  
Road Traffic Injuries ◽  
Local Health Unit

scholarly journals COVID-19 Surveillance for Local Decision Making

2021 ◽  
pp. 003335492110182
Author(s):  
Ayaz Hyder ◽  
Anne Trinh ◽  
Pranav Padmanabhan ◽  
John Marschhausen ◽  
Alexander Wu ◽  
...  
Keyword(s):  
Decision Making ◽  
School Districts ◽  
Surveillance System ◽  
Local Health ◽  
Health Departments ◽  
Local Health Departments ◽  
Franklin County ◽  
Web Based ◽  
School Based ◽  
Data Informed

Objective Data-informed decision making is valued among school districts, but challenges remain for local health departments to provide data, especially during a pandemic. We describe the rapid planning and deployment of a school-based COVID-19 surveillance system in a metropolitan US county. Methods In 2020, we used several data sources to construct disease- and school-based indicators for COVID-19 surveillance in Franklin County, an urban county in central Ohio. We collected, processed, analyzed, and visualized data in the COVID-19 Analytics and Targeted Surveillance System for Schools (CATS). CATS included web-based applications (public and secure versions), automated alerts, and weekly reports for the general public and decision makers, including school administrators, school boards, and local health departments. Results We deployed a pilot version of CATS in less than 2 months (August–September 2020) and added 21 school districts in central Ohio (15 in Franklin County and 6 outside the county) into CATS during the subsequent months. Public-facing web-based applications provided parents and students with local information for data-informed decision making. We created an algorithm to enable local health departments to precisely identify school districts and school buildings at high risk of an outbreak and active SARS-CoV-2 transmission in school settings. Practice Implications Piloting a surveillance system with diverse school districts helps scale up to other districts. Leveraging past relationships and identifying emerging partner needs were critical to rapid and sustainable collaboration. Valuing diverse skill sets is key to rapid deployment of proactive and innovative public health practices during a global pandemic.

scholarly journals 817Official small area health statistics using the stratified reweighting method

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Ian Rayson ◽  
Sean Buttsworth
Keyword(s):  
Health Survey ◽  
Small Area ◽  
Census Data ◽  
Local Level ◽  
Health Data ◽  
Local Health ◽  
Health Statistics ◽  
Data Set ◽  
Acceptable Quality ◽  
Generalised Linear Mixed Model

Abstract Background The Australian Bureau of Statistics (ABS) presently produces health data for small population groups using a Generalised Linear Mixed Model (GLMM) method. Although this method is highly effective at producing reliable local level health data, it takes several months to compile data once it’s collected. The Stratified Reweighting Method (SRM) was investigated as an innovative efficient method for producing local level health data. Methods The SRM harnesses information from both health survey and Census data. A cluster analysis of 12 Census data items creates 13 area groups with similar population demographics. A replicated survey data set is then created where each small area is bolstered by the other small areas within its area group. The survey weights from this dataset are adjusted to match Census data of each small area across several demographic variables. A final survey weight adjustment ensures consistency of the small area predictions with national survey estimates. Results Health statistics were produced for over 20 health outcomes in the latest ABS National Health Survey; and the ABS Survey of Disability, Ageing and Carers. It was found that, compared to the GLMM method: the models had lower, but still acceptable quality; the errors of prevalence estimates were similar magnitude; and the data compilation time was reduced to within two weeks. Conclusions The SRM is an efficient approach for producing acceptable quality official local health statistics. Key messages The SRM is an innovative and efficient weight-based method using health survey and population Census data to produce official local health statistics.

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