Mental ill health is a significant cause of suicide and disability worldwide. It has particularly affected the construction industry. The construction labor sectors in Australia and the United Kingdom have suicide rates 2 and 3.7 times higher, respectively, than their overall national averages, which has attracted the attention of researchers and the industry. However, few studies have examined the state of construction workers' mental health. This paper systematically reviews the existing body of knowledge on mental health in the construction industry. In total, 16 journal articles met inclusion criteria, and 32 risk factors (RFs) were deduced. The foremost RFs were related to job demand and job control. A conceptual framework and checklist to aid in better understanding these RFs were developed. In assessing mental health, the primary tool used was the Depression Anxiety Stress Scale. The findings of this study help to deepen the understanding of professional mental health assessment scales and relevant RFs and protective factors as used in the construction industry. The study concludes that stronger methodologies are needed for studies into RFs and protective factors in the construction industry.

Roofers have long suffered from low back disorders (LBDs), which are a primary nonfatal injury in construction. Ergonomic studies have identified several risk factors associated with LBDs in workplaces and developed biomechanical models for general LBD risk assessments. However, these models cannot be directly used for assessments in roof workplaces because they are designed for general tasks without considering roofers' posture variance and effects of working on slanted roof surfaces. This paper examined the relationship between roofing work-related factors and LBD risk among roofers using a laboratory assessment. A pitch-configurable wood platform was built to mimic the rooftop. The maximum trunk flexion angle and normalized electromyography (EMG) signals were measured as indicators using a motion capture system and a skeletal muscle signal recording system under different settings, i.e., different roof slopes, postures, facing directions, and working paces. The results indicated the measured factors with significant effects on the LBD development and revealed unfavorable conditions (e.g., using a stooped posture to work on low-pitch rooftops at a fast pace) where the work on rooftops needs particular attention. Such information is useful for systematic understanding of roofing nonfatal LBD developments among construction professionals and may enable development of interventions and guidelines for reducing the prevalence of LBDs at roofing jobsites.

In the feasibility stage, the correct prediction of construction costs ensures that budget requirements are met from the start of a project's lifecycle. A very common approach for performing quick-order-of-magnitude estimates is based on using Location Cost Adjustment Factors (LCAFs) that compute historically based costs by project location. Nowadays, numerous LCAF datasets are commercially available in North America, but, obviously, they do not include all locations. Hence, LCAFs for un-sampled locations need to be inferred through spatial interpolation or prediction methods. Currently, practitioners tend to select the value for a location using only one variable, namely the nearest linear-distance between two sites. However, construction costs could be affected by socio-economic variables as suggested by macroeconomic theories. Using a commonly used set of LCAFs, the City Cost Indexes (CCI) by RSMeans, and the socio-economic variables included in the ESRI Community Sourcebook, this article provides several contributions to the body of knowledge. First, the accuracy of various spatial prediction methods in estimating LCAF values for un-sampled locations was evaluated and assessed in respect to spatial interpolation methods. Two Regression-based prediction models were selected, a Global Regression Analysis and a Geographically-weighted regression analysis (GWR). Once these models were compared against interpolation methods, the results showed that GWR is the most appropriate way to model CCI as a function of multiple covariates. The outcome of GWR, for each covariate, was studied for all the 48 states in the contiguous US. As a direct consequence of spatial non-stationarity, it was possible to discuss the influence of each single covariate differently from state to state. In addition, the article includes a first attempt to determine if the observed variability in cost index values could be, at least partially explained by independent socio-economic variables.