OBJECTIVE: To investigate the association between socioeconomic status (SES) and hospitalization for 2009 H1N1 influenza, independently of access to care and known risk factors for severe influenza illness, among New York City residents during the 2009-2010 influenza season. DESIGN: We used a 1:2 case-control study design, matching by age group and month of diagnosis. Cases were defined as laboratory-confirmed patients with 2009 H1N1 influenza who were hospitalized during their illness. Controls were defined as nonhospitalized laboratory-confirmed influenza A patients. Participants were contacted for a telephone interview to collect relevant clinical and demographic data. We used conditional logistic regression to analyze the association between SES and hospitalization. SETTING: New York City. PARTICIPANTS: Of the 171 hospitalized cases who were identified between October 2009 and February 2010, a total of 128 completed telephone interviews. A total of 640 nonhospitalized controls were contacted, and of these, 337 completed interviews. MAIN OUTCOME MEASURES: The main outcome of interest was whether or not a patient was hospitalized during his or her 2009 H1N1 influenza illness. Socioeconomic status was measured using education and neighborhood poverty. RESULTS: We identified a gradient in the odds of hospitalization for 2009 H1N1 influenza by education level among adults. This association could not be entirely explained by access to care and underlying risk factors. An inverse association between odds of hospitalization and neighborhood poverty was also identified among adults and children. CONCLUSIONS: This study suggests that individuals of lower SES were more vulnerable to severe illness during the 2009 H1N1 pandemic. Additional research is needed to help guide interventions to protect this population during future influenza pandemics.
OBJECTIVE: To investigate the association between socioeconomic status (SES) and hospitalization for 2009 H1N1 influenza, independently of access to care and known risk factors for severe influenza illness, among New York City residents during the 2009-2010 influenza season. DESIGN: We used a 1:2 case-control study design, matching by age group and month of diagnosis. Cases were defined as laboratory-confirmed patients with 2009 H1N1 influenza who were hospitalized during their illness. Controls were defined as nonhospitalized laboratory-confirmed influenza A patients. Participants were contacted for a telephone interview to collect relevant clinical and demographic data. We used conditional logistic regression to analyze the association between SES and hospitalization. SETTING: New York City. PARTICIPANTS: Of the 171 hospitalized cases who were identified between October 2009 and February 2010, a total of 128 completed telephone interviews. A total of 640 nonhospitalized controls were contacted, and of these, 337 completed interviews. MAIN OUTCOME MEASURES: The main outcome of interest was whether or not a patient was hospitalized during his or her 2009 H1N1influenza illness. Socioeconomic status was measured using education and neighborhood poverty. RESULTS: We identified a gradient in the odds of hospitalization for 2009 H1N1 influenza by education level among adults. This association could not be entirely explained by access to care and underlying risk factors. An inverse association between odds of hospitalization and neighborhood poverty was also identified among adults and children. CONCLUSIONS: This study suggests that individuals of lower SES were more vulnerable to severe illness during the 2009 H1N1 pandemic. Additional research is needed to help guide interventions to protect this population during future influenza pandemics.
Authors: Nila J Dharan; Leslie Z Sokolow; Po-Yung Cheng; Paul Gargiullo; Ken Gershman; Ruth Lynfield; Craig Morin; Ann Thomas; James Meek; Monica M Farley; Kathryn E Arnold; Arthur Reingold; Allen S Craig; William Schaffner; Nancy M Bennett; Shelley Zansky; Joan Baumbach; Sarah Lathrop; Laurie Kamimoto; David K Shay Journal: Pediatr Infect Dis J Date: 2014-06 Impact factor: 2.129
Authors: Emily K Johnson; Dillon Sylte; Sandra S Chaves; You Li; Cedric Mahe; Harish Nair; John Paget; Tayma van Pomeren; Ting Shi; Cecile Viboud; Spencer L James Journal: Popul Health Metr Date: 2021-06-14