Record linkage study of the pathogen‐specific burden of respiratory viruses in children

Abstract

BackgroundReliance on hospital discharge diagnosis codes alone will likely underestimate the burden of respiratory viruses.ObjectivesTo describe the epidemiology of respiratory viruses more accurately, we used record linkage to examine data relating to all children hospitalized in Western Australia between 2000 and 2012.Patients/MethodsWe extracted hospital, infectious disease notification and laboratory data of a cohort of children born in Western Australia between 1996 and 2012. Laboratory records of respiratory specimens collected within 48 hours of admission were linked to hospitalization records. We calculated the frequency and rates of virus detection. To identify groups where under‐ascertainment for respiratory viruses was greatest, we used logistic regression to determine factors associated with failure to test.Results and conclusionsNine percentage of 484 992 admissions linked to a laboratory record for respiratory virus testing. While 62% (n = 26 893) of laboratory‐confirmed admissions received respiratory infection diagnosis codes, 38% (n = 16 734) had other diagnoses, notably viral infection of unspecified sites. Of those tested, incidence rates were highest for respiratory syncytial virus (247 per 100 000 child‐years) followed by parainfluenza (63 per 100 000 child‐years). Admissions among older children and those without a respiratory diagnosis were associated with failure to test for respiratory viruses. Linked data can significantly enhance diagnostic codes when estimating the true burden of disease. In contrast to current emphasis on influenza, respiratory syncytial virus and parainfluenza were the most common viral pathogens among hospitalized children. By characterizing those failing to be tested, we can begin to quantify the under‐ascertainment of respiratory viruses.

Linking administrative data sets of inpatient infectious diseases diagnoses in far North Queensland: a cohort profile

“…4 Data-linkage studies incorporating pathology data have tested the precision of infectious diseases diagnosis in comparison with public health communicable diseases notifications systems 15 and hospital discharge coding. 13 These studies both demonstrated underascertainment of childhood respiratory tract diseases.…”

“…Australian infectious diseases cohort studies have involved: organ specific infections such as respiratory viral infections, 13 infections such as Q fever 12 and S. aureus bacteraemia 14 as well as specific patients such as asplenics 16 and haematology-oncology. 17 The value of Australian patient cohorts for infectious diseases research is further shown by the multiple studies deriving from the 45 and up study of ageing, 18 Triple I Western Australian birth cohort 15 and Victorian Post-Splenectomy Registry.…”

Assessing the burden of respiratory syncytial virus disease in Australia

“…Western Australia is unique in its ability to use record linkage for combining ICD‐coded hospitalisation data with routine laboratory data. This has allowed us to identify laboratory‐confirmed RSV hospitalisations of children with various ICD‐10‐AM codes, including the nonspecific codes B34.9, J06.9, J22, and J45.9 . Our findings indicate that the number of RSV‐related hospitalisations, particularly of children, was probably underestimated by Saravanos and colleagues because of their limited range of ICD‐10‐AM codes.…”

Use of data linkage to improve communicable disease surveillance and control in Australia: existing practices, barriers and enablers

“…10,11,13 In the communicable diseases setting, data linkage can be used to evaluate public health interventions and guide policy development. 14 In Australia, data linkage methodologies have been used to quantify case ascertainment in communicable disease registers; 15 to improve estimates of selected communicable diseases; [16][17][18][19] to better describe the burden of selected communicable diseases among Aboriginal and Torres Strait Islander people; [3][4][5]20 to improve communicable disease morbidity and mortality estimates; [21][22][23] and to evaluate immunisation programs. [24][25][26][27] These are all important examples of how data linkage can be applied to support communicable disease research in Australia, and results from these studies are often used to inform policy making and program improvement.…”