A simulation study of sample size for multilevel logistic regression models

<p>Abstract</p> <p>Background</p> <p>Many studies conducted in health and social sciences collect individual level data as outcome measures. Usually, such data have a hierarchical structure, with patients clustered within physicians, and physicians clustered within practices. Large survey data, including national surveys, have a hierarchical or clustered structure; respondents are naturally clustered in geographical units (e.g., health regions) and may be grouped into smaller units. Outcomes of interest in many fields not only reflect continuous measures, but also binary outcomes such as depression, presence or absence of a disease, and self-reported general health. In the framework of multilevel studies an important problem is calculating an adequate sample size that generates unbiased and accurate estimates.</p> <p>Methods</p> <p>In this paper simulation studies are used to assess the effect of varying sample size at both the individual and group level on the accuracy of the estimates of the parameters and variance components of multilevel logistic regression models. In addition, the influence of prevalence of the outcome and the intra-class correlation coefficient (ICC) is examined.</p> <p>Results</p> <p>The results show that the estimates of the fixed effect parameters are unbiased for 100 groups with group size of 50 or higher. The estimates of the variance covariance components are slightly biased even with 100 groups and group size of 50. The biases for both fixed and random effects are severe for group size of 5. The standard errors for fixed effect parameters are unbiased while for variance covariance components are underestimated. Results suggest that low prevalent events require larger sample sizes with at least a minimum of 100 groups and 50 individuals per group.</p> <p>Conclusion</p> <p>We recommend using a minimum group size of 50 with at least 50 groups to produce valid estimates for multi-level logistic regression models. Group size should be adjusted under conditions where the prevalence of events is low such that the expected number of events in each group should be greater than one.</p

Implications of Trauma among Male and Female Offenders

Criminal behaviour is believed to arise from a multiplicity of factors, including unemployment and poverty [1,2], low self-control [3], psychological issues [4,5], early conduct problems [6], childhood physical and sexual abuse disorder [5], and social bonding in child- and adulthood [7]. Social-structural influences like family conflict/disruption, financial resources, child-parent and school/peer attachment and abuse and neglect in childhood have lasting impressions, leading to multiple problems including delinquency and later criminal activity, substance use/abuse, mental illness and poor self-rated health [8-12]. The consequences of such behaviour include financial losses, injury, and death that together have significant personal and societal costs. Society also bears the burden of incarcerating and rehabilitating offenders; a burden that is not trivial. Direct costs of imprisonment in Canada approach $3.5 billion annually; in the US the cost is substantially higher, approaching$74 billion [13]. [...

Effect of Housing First on violence-related traumatic brain injury in adults with experiences of homelessness and mental illness: findings from the At Home/Chez Soi randomised trial, Toronto site

Objectives People experiencing homelessness have a high prevalence and incidence of traumatic brain injury (TBI) due to violence. Little is known about the effectiveness of interventions to reduce TBI in this population. This study assessed the effect of Housing First (HF) on violence-related TBI in adults with experiences of homelessness and mental illness.Design Pragmatic randomised trial.Participants 381 participants in the Toronto site of the At Home/Chez randomised trial.Intervention HF participants were provided with scattered-site housing using rent supplements and supports from assertive community treatment or intensive case management teams (n=218, 57.2%). Control participants had access to treatment as usual (TAU) in the community (n=163, 42.8%).Main outcome measures Primary outcomes were an incident physical violence-related TBI event and the number of physical violence-related TBI events during the follow-up period (January 2014 to March 2017). Interval-censored survival time regression and zero-inflated negative binomial regression were used to assess the effect of HF on primary outcomes.Results Among study participants, 9.2% (n=35) had an incident physical violence-related TBI event, and the mean physical violence-related TBI events was 0.16 (SD ±0.6). Compared with TAU participants, HF participants did not have a significantly lower risk of an incident violence-related TBI event (adjusted HR : 0.58 (95% CI, 0.29 to 1.14)), but they had a significantly lower number of physical violence-related TBI events (unadjusted incidence rate ratio (IRR): 0.22 (95% CI, 0.06 to 0.78); adjusted IRR: 0.15 (95% CI, 0.05 to 0.48)).Conclusion HF may be a useful intervention to reduce the burden of TBI due to physical violence among homeless individuals with mental illness.Trial registration number ISRCTN42520374

Ethics of health research with prisoners in Canada

Abstract Background Despite the growing recognition for the need to improve the health of prisoners in Canada and the need for health research, there has been little discussion of the ethical issues with regards to health research with prisoners in Canada. The purpose of this paper is to encourage a national conversation about what it means to conduct ethically sound health research with prisoners given the current realities of the Canadian system. Lessons from the Canadian system could presumably apply in other jurisdictions. Main text Any discussion regarding research ethics with Canadian prisoners must begin by first taking into account the disproportionate number of Indigenous prisoners (e.g., 22–25% of prisoners are Indigenous, while representing approximately 3% of the general Canadian population) and the high proportion of prisoners suffering from mental illnesses (e.g., 45% of males and 69% of female inmates required mental health interventions while in custody). The main ethical challenges that researchers must navigate are (a) the power imbalances between them, the correctional services staff, and the prisoners, and the effects this has on obtaining voluntary consent to research; and (b), the various challenges associated to protecting the privacy and confidentiality of study participants who are prisoners. In order to solve these challenges, a first step would be to develop clear and transparent processes for ethical health research, which ought to be informed by multiple stakeholders, including prisoners, the correctional services staff, and researchers themselves. Conclusion Stakeholder and community engagement ought to occur in Canada with regards to ethical health research with prisoners that should also include consultation with various parties, including prisoners, correctional services staff, and researchers. It is important that national and provincial research ethics organizations examine the sufficiency of existing research ethics guidance and, where there are gaps, to develop guidelines and help craft policy

Illicit Drug Use and Problem Gambling

Problem gambling, substance use disorders, and their cooccurrence are serious public health concerns. We conducted a comprehensive review of the literature to understand the present state of the evidence on these coaddictions. Our main focus was illicit drug use rather than misuse of legal substances. The review covers issues related to gambling as a hidden problem in the illicit drug use community; prevalence, problem gambling, and substance use disorders as kindred afflictions; problem gambling as an addiction similar to illicit drug use; risk factors and problems associated with comorbidity, and gender issues. We end with some suggestions for future research.Peer Reviewe

The weight of place: A multilevel analysis of gender, neighborhood material deprivation, and body mass index among Canadian adults

This study examined the impact of neighborhood material deprivation on gender differences in body mass index (BMI) for urban Canadians. Data from a national health survey of adults (Canadian Community Health Survey Cycles 1.1/2.1) were combined with census tract-level neighborhood data from the 2001 census. Using multilevel analysis we found that living in neighborhoods with higher material deprivation was associated with higher BMI. Compared to women living in the most affluent neighborhoods, women living in the most deprived neighborhoods had a BMI score 1.8 points higher. For women 1.65 m in height (5'4'' inches), this translated into a 4.8 kg or 11 lb difference. For men, living in affluent neighborhoods was associated with higher BMI (7 lb) relative to men living in deprived neighborhoods. The relative disadvantage for men living in pockets of affluence and women living in pockets of poverty persisted after adjusting for age, married and visible minority status, educational level, self-perceived stress, sense of belonging, and lifestyle factors, including smoking, exercise, diet, and chronic health conditions. The implication of these disparate findings for men and women is that interventions that lead to healthy weight control may need to be gender responsive. Our findings also suggest that what we traditionally have thought to be triggering factors for weight gain and maintenance of unhealthy BMI--lifestyle and behavioral factors--are not sufficient explanations. Indeed, these factors account for only a portion of the explanation of why neighborhood stress is associated with BMI. Cultural attitudes about the body that pressure women to meet the thin ideal which can lead to an unhealthy cycle of dieting and, subsequent weight gain, and the general acceptability of the heavier male need to be challenged. Education and intervention within a public health framework remain important targets for producing healthy weight.Body mass index (BMI) Neighborhoods Gender Multilevel model Material deprivation Canada Gender

Modeling the Cumulative Effects of Social Exposures on Health: Moving beyond Disease-Specific Models

The traditional explanatory models used in epidemiology are “disease specific”, identifying risk factors for specific health conditions. Yet social exposures lead to a generalized, cumulative health impact which may not be specific to one illness. Disease-specific models may therefore misestimate social factors’ effects on health. Using data from the Canadian Community Health Survey and Canada 2001 Census we construct and compare “disease-specific” and “generalized health impact” (GHI) models to gauge the negative health effects of one social exposure: socioeconomic position (SEP). We use logistic and multinomial multilevel modeling with neighbourhood-level material deprivation, individual-level education and household income to compare and contrast the two approaches. In disease-specific models, the social determinants under study were each associated with the health conditions of interest. However, larger effect sizes were apparent when outcomes were modeled as compound health problems (0, 1, 2, or 3+ conditions) using the GHI approach. To more accurately estimate social exposures’ impacts on population health, researchers should consider a GHI framework

Modeling the Cumulative Effects of Social Exposures on Health: Moving beyond Disease-Specific Models

The traditional explanatory models used in epidemiology are “disease specific”, identifying risk factors for specific health conditions. Yet social exposures lead to a generalized, cumulative health impact which may not be specific to one illness. Disease-specific models may therefore misestimate social factors’ effects on health. Using data from the Canadian Community Health Survey and Canada 2001 Census we construct and compare “disease-specific” and “generalized health impact” (GHI) models to gauge the negative health effects of one social exposure: socioeconomic position (SEP). We use logistic and multinomial multilevel modeling with neighbourhood-level material deprivation, individual-level education and household income to compare and contrast the two approaches. In disease-specific models, the social determinants under study were each associated with the health conditions of interest. However, larger effect sizes were apparent when outcomes were modeled as compound health problems (0, 1, 2, or 3+ conditions) using the GHI approach. To more accurately estimate social exposures’ impacts on population health, researchers should consider a GHI framework