Search

Abstract Background The purpose of this paper is to systematically review the literature on the relationship between socioeconomic status (SES) and influenza immunization and to examine how certain measures of SES may influence interpretations of this relationship. Methods We conducted a systematic review of existing peer-reviewed literature to evaluate the above relationship in the general population. Electronic databases (MEDLINE and EMBASE) were searched from January 2012 to May 2017 to identify English-language studies relevant to this review. Studies were included where influenza vaccination was explicitly reported as the dependent variable and SES as the independent variable. We limited our review to measures of SES that focus on education, income, social class, occupation, and deprivation. Studies that measured SES using other variables (e.g., race, ethnicity, geographic location, rural or urban status, or insurance status) were excluded. Studies were also excluded if they did not report on the human population or did not analyze original data. The population of interest included all age groups, levels of health status, and sociodemographic backgrounds. The review was also limited to World Bank high-income countries. Two authors independently screened full-text articles after obtaining a Kappa score of K = 0.867. The methodological quality of manuscripts was assessed using the appraisal tools developed by the Joanna Briggs Institute. Results were qualitatively reported and synthesized. Results Of the 42 articles included in this review, 52.4% (n = 22) found that higher levels of SES resulted in higher levels of influenza vaccination; 4.5% (n = 2) reported a negative association; and 14.3% (n = 6) found no association. Just over a quarter (26.2%, n = 12) of articles reported mixed results. Conclusions There was consistently a relationship between SES and influenza immunization, which varied according to how SES was measured. It is recommended that authors be explicit in defining the SES concept they are trying to capture and that they utilize multiple measures of SES (e.g., education, income, class).

Vaccination indicators are used to measure the health status of individuals or populations and to evaluate the effectiveness of vaccination programs or policies. Ensuring that vaccination indicators are clearly and consistently defined is important for effective communication of outcomes, accurate program evaluation, and comparison between different populations, times, and contexts. The purpose of this commentary is to describe commonly used vaccination indicators and to highlight inconsistencies in how childhood vaccine researchers use and define these terms. The indicators we describe are vaccine coverage, uptake, and rate; vaccination status, initiation, and completion; and up-to-date, timely, partial, and incomplete vaccination. We conclude that many vaccination indicators are not explicitly defined within published research studies and/or are used quite differently across studies. We also note that the choice of indicator in a given study is often driven by program or vaccine specific factors, may be constrained by data availability, and should be chosen to best reflect the outcome of interest. We conclude that the use of consistent language and definitions would promote more effective communication of research findings. We also propose some standardized definitions for common indicators, with the goal of provoking discussion and debate on the issue.

Abstract Background Rates of Bordetella pertussis have been increasing in Alberta, Canada despite vaccination programs. Waning immunity from existing acellular component vaccines may be contributing to this. Vaccine effectiveness can be estimated using a variety of data sources including diagnostic codes from physician billing claims, public health records, reportable disease and laboratory databases. We sought to determine if diagnostic codes from billing claims (administrative data) are adequately sensitive and specific to identify pertussis cases among patients who had undergone disease-specific laboratory testing. Methods Data were extracted for 2004–2014 from a public health communicable disease database that contained data on patients under investigation for B. pertussis (both those who had laboratory tests and those who were epidemiologically linked to laboratory-confirmed cases) in Alberta, Canada. These were deterministically linked using a unique lifetime person identifier to the provincial billing claims database, which contains International Classification of Disease version 9 (ICD-9) diagnostic codes for physician visits. We examined visits within 90 days of laboratory testing. ICD-9 codes 033 (whooping cough), 033.0 (Bordetella pertussis), 033.1 (B. parapertussis), 033.8 (whooping cough, other specified organism), and 033.9 (whooping cough, other unspecified organism) in any of the three diagnostic fields for a claim were classified as being pertussis-specific codes. We calculated sensitivity, specificity, positive (PPV) and negative (NPV) predictive values. Results We identified 22,883 unique patients under investigation for B. pertussis. Of these, 22,095 underwent laboratory testing. Among those who had a laboratory test, 2360 tested positive for pertussis. The sensitivity of a pertussis-specific ICD-9 code for identifying a laboratory-confirmed case was 38.6%, specificity was 76.9%, PPV was 16.0%, and NPV was 91.6%. Conclusion ICD-9 codes from physician billing claims data have low sensitivity and moderate specificity to identify laboratory-confirmed pertussis among persons tested for pertussis.

Abstract Background We describe the epidemiology of pertussis in Alberta, Canada by person, place, and time between 2004 and 2015, identify outbreak years, and examine vaccination coverage and vaccination timeliness. Methods We used health data from Alberta’s Communicable Disease Registry System for the period of January 1, 2004 through August 31, 2015 to identify unique cases of pertussis. Unique cases were deterministically linked to data in Alberta’s immunization repository and health care insurance plan registry. Population estimates and vaccination coverage were extracted from Alberta’s online Interactive Health Data Application. We estimated pertussis incidence rates per 100,000 persons by year, age group, gender, and health zone. Outbreak years were identified using a one-sided cumulative sum (CUSUM) analysis by comparing annual incidence rates to baseline rates. Results Over the period, 3510 cases of pertussis were confirmed by laboratory testing or epidemiological linkage. Incidence rates per 100,000 persons were highest in 2004 (20.5), 2005 (13.6), and 2015 (10.4) for all age groups. Incidence rates were highest among the youngest age groups and decreased as age groups increased. Based on CUSUM analysis, 2008 and 2012 met the criteria for outbreak years. Vaccination coverage was over 90% among the general population, however only 61% of cases received at least one dose. About 60% of cases were diagnosed 5+ years after receiving the vaccine. Approximately 87–91% of vaccinated cases did not receive the first three vaccine doses in a timely manner. Conclusion Pertussis incidence rates fluctuated over the period across all age groups. The majority of cases had no record of vaccination or were delayed in receiving vaccines. CUSUM analysis was an effective method for identifying outbreaks.