Avoidance of representativeness in presence of effect modification

Integrating Electronic Health Record, Cancer Registry, and Geospatial Data to Study Lung Cancer in Asian American, Native Hawaiian, and Pacific Islander Ethnic Groups

BACKGROUND

A relatively high proportion of Asian American, Native Hawaiian, and Pacific Islander (AANHPI) females with lung cancer have never smoked. We used an integrative data approach to assemble a large-scale cohort to study lung cancer risk among AANHPIs by smoking status with attention to representation of specific AANHPI ethnic groups.

METHODS

We leveraged electronic health records (EHRs) from two healthcare systems-Sutter Health in northern California and Kaiser Permanente Hawai'i-that have high representation of AANHPI populations. We linked EHR data on lung cancer risk factors (i.e., smoking, lung diseases, infections, reproductive factors, and body size) to data on incident lung cancer diagnoses from statewide population-based cancer registries of California and Hawai'i for the period between 2000 and 2013. Geocoded address data were linked to data on neighborhood contextual factors and regional air pollutants.

RESULTS

The dataset comprises over 2.2 million adult females and males of any race/ethnicity. Over 250,000 are AANHPI females (19.6% of the female study population). Smoking status is available for over 95% of individuals. The dataset includes 7,274 lung cancer cases, including 613 cases among AANHPI females. Prevalence of never-smoking status varied greatly among AANHPI females with incident lung cancer, from 85.7% among Asian Indian to 14.4% among Native Hawaiian females.

CONCLUSION

We have developed a large, multilevel dataset particularly well-suited to conduct prospective studies of lung cancer risk among AANHPI females who never smoked.

IMPACT

The integrative data approach is an effective way to conduct cancer research assessing multilevel factors on cancer outcomes among small populations.

Good Practice Data Linkage (GPD): A Translation of the German Version

The data linkage of different data sources for research purposes is being increasingly used in recent years. However, generally accepted methodological guidance is missing. The aim of this article is to provide methodological guidelines and recommendations for research projects that have been consented to across different German research societies. Another aim is to endow readers with a checklist for the critical appraisal of research proposals and articles. This Good Practice Data Linkage (GPD) was already published in German in 2019, but the aspects mentioned can easily be transferred to an international context, especially for other European Union (EU) member states. Therefore, it is now also published in English. Since 2016, an expert panel of members of different German scientific societies have worked together and developed seven guidelines with a total of 27 practical recommendations. These recommendations include (1) the research objectives, research questions, data sources, and resources; (2) the data infrastructure and data flow; (3) data protection; (4) ethics; (5) the key variables and linkage methods; (6) data validation/quality assurance; and (7) the long-term use of data for questions still to be determined. The authors provide a rationale for each recommendation. Future revisions will include new developments in science and updates of data privacy regulations.

Practice of reporting social characteristics when describing representativeness of epidemiological cohort studies - A rationale for an intersectional perspective

Representativeness has been defined as the degree of similarity of a study population compared to an external population. To characterize a study population, both health-related and social or demographic features should be considered according to current guidelines. However, little guidance is given on how to describe social complexity of study populations when aiming to conclude on representativeness. We argue that sociological concepts should inform characterizations of study populations in order to increase credibility of conclusions on representativeness. The concept of intersectionality suggests to conceptualize social location as a combination of characteristics such as sex/gender and ethnicity instead of focusing on each feature independently. To contextualize advantages of integrating the concept of intersectionality when investigating representativeness, we reviewed publications that described the baseline population of selected epidemiological cohort studies. Information on the applied methods to characterize the study population was extracted, as well as reported social characteristics. Nearly all reviewed studies reported descriptive statistics of the baseline population and response proportions. In most publications, study populations were characterized according to place of residence, age and sex/gender while other social characteristics were reported irregularly. Differential patterns of representativeness were revealed in analyses that stratified social characteristics by sex/gender or age. Furthermore, the included studies did not explicitly state the theoretical approach that underlay their description of the study population. Intersectionality might be particularly fruitful when applied to descriptions of representativeness, because this concept provides an understanding of social location that has been developed based on situated experiences of people at the intersection of multiple axes of social power relations. An intersectional perspective, hence, contributes to approximate social complexity of study populations and might contribute to increase validity of conclusions on representativeness of population-based studies.

The use of random-effects models to identify health care center-related characteristics modifying the effect of antipsychotic drugs

Purpose

A case study was conducted, exploring methods to identify drugs effects modifiers, at a health care center level.

Patients and methods

Data were drawn from the Schizophrenia Outpatient Health Outcome cohort, including hierarchical information on 6641 patients, recruited from 899 health care centers from across ten European countries. Center-level characteristics included the following: psychiatrist’s gender, age, length of practice experience, practice setting and type, countries’ Healthcare System Efficiency score, and psychiatrist density in the country. Mixed multivariable linear regression models were used: 1) to estimate antipsychotic drugs’ effectiveness (defined as the association between patients’ outcome at 3 months – dependent variable, continuous – and antipsychotic drug initiation at baseline – drug A vs other antipsychotic drug); 2) to estimate the similarity between clustered data (using the intra-cluster correlation coefficient); and 3) to explore antipsychotic drug effects modification by center-related characteristics (using the addition of an interaction term).

Results

About 23% of the variance found for patients’ outcome was explained by unmeasured confounding at a center level. Psychiatrists’ practice experience was found to be associated with patient outcomes (p=0.04) and modified the relative effect of “drug A” (p<0.001), independent of center- or patient-related characteristics.

Conclusion

Mixed models may be useful to explore how center-related characteristics modify drugs’ effect estimates, but require numerous assumptions.

Representativeness of the LifeLines Cohort Study

BACKGROUND

LifeLines is a large prospective population-based three generation cohort study in the north of the Netherlands. Different recruitment strategies were adopted: recruitment of an index population via general practitioners, subsequent inclusion of their family members, and online self-registration. Our aim was to investigate the representativeness of the adult study population at baseline and to evaluate differences in the study population according to recruitment strategy.

METHODS

Demographic characteristics of the LifeLines study population, recruited between 2006-2013, were compared with the total adult population in the north of the Netherlands as registered in the Dutch population register. Socioeconomic characteristics, lifestyle, chronic diseases, and general health were further compared with participants of the Permanent Survey of Living Conditions within the region (2005-2011, N = 6,093). Differences according to recruitment strategy were assessed.

RESULTS

Compared with the population of the north of the Netherlands, LifeLines participants were more often female, middle aged, married, living in a semi-urban place and Dutch native. Adjusted for differences in demographic composition, in LifeLines a smaller proportion had a low educational attainment (5% versus 14%) or had ever smoked (54% versus 66%). Differences in the prevalence of various chronic diseases and low general health scores were mostly smaller than 3%. The age profiles of the three recruitment groups differed due to age related inclusion criteria of the recruitment groups. Other differences according to recruitment strategy were small.

CONCLUSIONS

Our results suggest that, adjusted for differences in demographic composition, the LifeLines adult study population is broadly representative for the adult population of the north of the Netherlands. The recruitment strategy had a minor effect on the level of representativeness. These findings indicate that the risk of selection bias is low and that risk estimates in LifeLines can be generalized to the general population.

Recruitment via the Internet and social networking sites: the 1989-1995 cohort of the Australian Longitudinal Study on Women's Health

Background

Faced with the challenge of recruiting young adults for health studies, researchers have increasingly turned to the Internet and social networking sites, such as Facebook, as part of their recruitment strategy. As yet, few large-scale studies are available that report on the characteristics and representativeness of the sample obtained from such recruitment methods.

Objective

The intent of the study was to describe the sociodemographic and health characteristics of a national sample of young Australian women recruited mainly through the Internet and social networking sites and to discuss the representativeness of their sociodemographic, health, and lifestyle characteristics relative to the population.

Methods

A cohort of 17,069 women (born between 1989 and 1995) was recruited in 2012-13 for the Australian Longitudinal Study on Women’s Health. Sociodemographic characteristics (percentages, means, and 95% confidence intervals) from the online survey data were compared with women aged 18-23 years from the 2011 Australian Census. Sample data were compared by age and education level with data from the 2011-13 Australian Health Survey (AHS).

Results

Compared to the Australian Census data, study participants were broadly representative in terms of geographical distribution across Australia, marital status (95.62%, 16,321/17,069) were never married), and age distribution. A higher percentage had attained university (22.52%, 3844/17,069) and trade/certificate/diploma qualifications (25.94%, 4428/17,069) compared with this age group of women in the national population (9.4% and 21.7% respectively). Among study participants, 22.05% (3721/16,877) were not in paid employment with 35.18% (5931/16,857) studying 16 or more hours a week. A higher percentage of study participants rated their health in the online survey as fair or poor (rather than good, very good, or excellent) compared with those participating in face-to-face interviews in the AHS (18.77%, 3203/17,069 vs 10.1%). A higher percentage of study participants were current smokers (21.78%, 3718/17,069 vs 16.4%) and physically active (59.30%, 10,089/17,014 were classified as sufficiently active vs 48.3%) but alcohol consumption was lower (59.58%, 9865/16,558 reported drinking alcohol at least once per month vs 65.9% in the AHS). Using self-reported height and weight to determine body mass index (BMI, kg/m²), 34.80% (5901/16,956) of the cohort were classified as overweight or obese (BMI of 25 or more), compared with 33.6% respectively using measured height and weight in the AHS.

Conclusions

Findings indicated that using the Internet and social networking sites for an online survey represent a feasible recruitment strategy for a national cohort of young women and result in a broadly representative sample of the Australian population.