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While incidence studies based on hospitalisation counts are commonly used for public health decision-making, no standard methodology to define hospitals' catchment population exists. We conducted a review of all published community-acquired pneumonia studies in England indexed in PubMed and assessed methods for determining denominators when calculating incidence in hospital-based surveillance studies. Denominators primarily were derived from census-based population estimates of local geographic boundaries and none attempted to determine denominators based on actual hospital access patterns in the community. We describe a new approach to accurately define population denominators based on historical patient healthcare utilisation data. This offers benefits over the more established methodologies which are dependent on assumptions regarding healthcare-seeking behaviour. Our new approach may be applicable to a wide range of health conditions and provides a framework to more accurately determine hospital catchment. This should increase the accuracy of disease incidence estimates based on hospitalised events, improving information available for public health decision making and service delivery planning.
To determine the 180-day cumulative incidence of culture-confirmed Staphylococcus aureus infections after elective pediatric surgeries.
Design:
Retrospective cohort study utilizing the Premier Healthcare database (PHD).
Setting:
Inpatient and hospital-based outpatient elective surgical discharges.
Patients:
Pediatric patients <18 years who underwent surgery during elective admissions between July 1, 2010, and June 30, 2015, at any of 181 PHD hospitals reporting microbiology results.
Methods:
In total, 74 surgical categories were defined using ICD-9-CM and CPT procedure codes. Microbiology results and ICD-9-CM diagnosis codes defined S. aureus infection types: bloodstream infection (BSI), surgical site infection (SSI), and other types (urinary tract, respiratory, and all other). Cumulative postsurgical infection incidence was calculated as the number of infections divided by the number of discharges with qualifying elective surgeries.
Results:
Among 11,874 inpatient surgical discharges, 180-day S. aureus infection incidence was 1.79% overall (1.00% SSI, 0.35% BSI, 0.45% other). Incidence was highest among children <2 years of age (2.76%) and lowest for those 10–17 years (1.49%). Among 50,698 outpatient surgical discharges, incidence was 0.36% overall (0.23% SSI, 0.05% BSI, 0.08% others); it was highest among children <2 years of age (0.57%) and lowest for those aged 10–17 years (0.30%). MRSA incidence was significantly higher after inpatient surgeries (0.68%) than after outpatient surgeries (0.14%; P < .0001). Overall, the median days to S. aureus infection was longer after outpatient surgery than after inpatient surgery (39 vs. 31 days; P = .0116).
Conclusions:
These findings illustrate the burden of postoperative S. aureus infections in the pediatric population, particularly among young children. These results underscore the need for continued infection prevention efforts and longer-term surveillance after surgery.
To assess the 180-day incidence of Staphylococcus aureus infections following orthopedic surgeries using microbiology cultures.
Design:
Retrospective observational epidemiology study.
Setting:
National administrative hospital database.
Patients:
Adult patients with an elective admission undergoing orthopedic surgeries in the inpatient and hospital-based outpatient settings discharged between July 1, 2010, and June 30, 2015.
Methods:
Patients were identified from 181 hospitals reporting microbiology results to the Premier Healthcare Database. Orthopedic surgeries were defined using International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) procedure and current procedural terminology (CPT) codes. Microbiology cultures and ICD-9/10 diagnosis codes identified surgical site infections (SSIs), bloodstream infections (BSIs), and other infections associated postoperatively (eg, respiratory and urinary tract infections).
Results:
Among 359,268 inpatient orthopedic surgical encounters, the S. aureus infection incidence was 1.13%: SSI, 0.68%; BSI, 0.28%; and other types, 0.17%. Among 292,011 outpatient encounters, the S. aureus incidence was 0.78%: SSI, 0.55%; BSI, 0.12%; and other types, 0.11%. Methicillin-resistant S. aureus (MRSA) infections accounted for 46% and 44% in the respective settings. Plastic/hand-limb reattachment and amputation had the highest overall S. aureus incidence in both settings. S. aureus was the most commonly isolated microorganism among culture-confirmed SSIs (48.0%) and BSIs (35.0%), followed by other Enterobacteriaceae (14.0%) for SSIs and Escherichia spp (12.5%) for BSIs.
Conclusions:
These findings suggest that S. aureus infections continue to be an important contributor to the burden of postoperative infections after inpatient and outpatient orthopedic procedures.
We briefly describe 2 systems that provided disaster-related mortality surveillance during and after Hurricane Sandy in New York City, namely, the New York City Health Department Electronic Death Registration System (EDRS) and the American Red Cross paper-based tracking system.
Methods
Red Cross fatality data were linked with New York City EDRS records by using decedent name and date of birth. We analyzed cases identified by both systems for completeness and agreement across selected variables and the time interval between death and reporting in the system.
Results
Red Cross captured 93% (41/44) of all Sandy-related deaths; the completeness and quality varied by item, and timeliness was difficult to determine. The circumstances leading to death captured by Red Cross were particularly useful for identifying reasons individuals stayed in evacuation zones. EDRS variables were nearly 100% complete, and the median interval between date of death and reporting was 6 days (range: 0-43 days).
Conclusions
Our findings indicate that a number of steps have the potential to improve disaster-related mortality surveillance, including updating Red Cross surveillance forms and electronic databases to enhance timeliness assessments, greater collaboration across agencies to share and use data for public health preparedness, and continued expansion of electronic death registration systems. (Disaster Med Public Health Preparedness. 2014;8:489-491)
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