How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia.

Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners; however, accuracy in these datasets are evaluated at the spatial scale of model input data which is generally courser than the neighbourhood or cell-level scale of many applications. We simulate a realistic synthetic 2016 population in Khomas, Namibia, a majority urban region, and introduce several realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate the synthetic populations by census and administrative boundaries (to mimic census data), resulting in 32 gridded population datasets that are typical of LMIC settings using the WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these gridded population datasets using the original synthetic population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells. These were driven by the averaging of population densities in large areal units before model training. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy (as done in some new WorldPop-Global-Constrained datasets). It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales within cities.

Modelling the distribution of health-related quality of life of advanced melanoma patients in a longitudinal multi-centre clinical trial using M-quantile random effects regression.

Health-related quality of life assessment is important in the clinical evaluation of patients with metastatic disease that may offer useful information in understanding the clinical effectiveness of a treatment. To assess if a set of explicative variables impacts on the health-related quality of life, regression models are routinely adopted. However, the interest of researchers may be focussed on modelling other parts (e.g. quantiles) of this conditional distribution. In this paper, we present an approach based on quantile and M-quantile regression to achieve this goal. We applied the methodologies to a prospective, randomized, multi-centre clinical trial. In order to take into account the hierarchical nature of the data we extended the M-quantile regression model to a three-level random effects specification and estimated it by maximum likelihood.

Mixed hidden Markov quantile regression models for longitudinal data with possibly incomplete sequences.

Quantile regression provides a detailed and robust picture of the distribution of a response variable, conditional on a set of observed covariates. Recently, it has be been extended to the analysis of longitudinal continuous outcomes using either time-constant or time-varying random parameters. However, in real-life data, we frequently observe both temporal shocks in the overall trend and individual-specific heterogeneity in model parameters. A benchmark dataset on HIV progression gives a clear example. Here, the evolution of the CD4 log counts exhibits both sudden temporal changes in the overall trend and heterogeneity in the effect of the time since seroconversion on the response dynamics. To accommodate such situations, we propose a quantile regression model, where time-varying and time-constant random coefficients are jointly considered. Since observed data may be incomplete due to early drop-out, we also extend the proposed model in a pattern mixture perspective. We assess the performance of the proposals via a large-scale simulation study and the analysis of the CD4 count data.

Longitudinal analysis of the strengths and difficulties questionnaire scores of the Millennium Cohort Study children in England using M-quantile random-effects regression.

Multilevel modelling is a popular approach for longitudinal data analysis. Statistical models conventionally target a parameter at the centre of a distribution. However, when the distribution of the data is asymmetric, modelling other location parameters, e.g. percentiles, may be more informative. We present a new approach, M-quantile random-effects regression, for modelling multilevel data. The proposed method is used for modelling location parameters of the distribution of the strengths and difficulties questionnaire scores of children in England who participate in the Millennium Cohort Study. Quantile mixed models are also considered. The analyses offer insights to child psychologists about the differential effects of risk factors on children's outcomes.

Emotional and behavioural resilience to multiple risk exposure in early life: the role of parenting.

Ecological and transactional theories link child outcomes to neighbourhood disadvantage, family poverty and adverse life events. Traditionally, these three types of risk factors have been examined independently of one another or combined into one cumulative risk index. The first approach results in poor prediction of child outcomes, and the second is not well rooted in ecological theory as it does not consider that distal risk factors (such as poverty) may indirectly impact children through proximal risk factors (such as adverse life events). In this study, we modelled simultaneously the longitudinal effects of these three risk factors on children's internalising and externalising problems, exploring the role of parenting in moderating these effects. Our sample followed 16,916 children (at ages 3, 5 and 7 years; N = 16,916; 49% girls) from the UK Millennium Cohort Study. Parenting was characterised by quality of parent-child relationship, parental involvement in learning and parental discipline. Neighbourhood disadvantage, family poverty and adverse events were all simultaneously related to the trajectories of both outcomes. As expected, parenting moderated risk effects. Positive parent-child relationship, rather than greater involvement or authoritative discipline, most consistently 'buffered' risk effects. These findings suggest that a good parent-child relationship may promote young children's emotional and behavioural resilience to different types of environmental risk.

Robust small area prediction for counts.

A new semiparametric approach to model-based small area prediction for counts is proposed and used for estimating the average number of visits to physicians for Health Districts in Central Italy. The proposed small area predictor can be viewed as an outlier robust alternative to the more commonly used empirical plug-in predictor that is based on a Poisson generalized linear mixed model with Gaussian random effects. Results from the real data application and from a simulation experiment confirm that the proposed small area predictor has good robustness properties and in some cases can be more efficient than alternative small area approaches.

Hierarchical modeling of indoor radon concentration: how much do geology and building factors matter?

Radon is a natural gas known to be the main contributor to natural background radiation exposure and only second to smoking as major leading cause of lung cancer. The main concern is in indoor environments where the gas tends to accumulate and can reach high concentrations. The primary contributor of this gas into the building is from the soil although architectonic characteristics, such as building materials, can largely affect concentration values. Understanding the factors affecting the concentration in dwellings and workplaces is important both in prevention, when the construction of a new building is being planned, and in mitigation when the amount of Radon detected inside a building is too high. In this paper we investigate how several factors, such as geologic typologies of the soil and a range of building characteristics, impact on indoor concentration focusing, in particular, on how concentration changes as a function of the floor level. Adopting a mixed effects model to account for the hierarchical nature of the data, we also quantify the extent to which such measurable factors manage to explain the variability of indoor radon concentration.

Outlier robust model-assisted small area estimation.

Small area estimation with M-quantile models was proposed by Chambers and Tzavidis (). The key target of this approach to small area estimation is to obtain reliable and outlier robust estimates avoiding at the same time the need for strong parametric assumptions. This approach, however, does not allow for the use of unit level survey weights, making questionable the design consistency of the estimators unless the sampling design is self-weighting within small areas. In this paper, we adopt a model-assisted approach and construct design consistent small area estimators that are based on the M-quantile small area model. Analytic and bootstrap estimators of the design-based variance are discussed. The proposed estimators are empirically evaluated in the presence of complex sampling designs.

Cognitive ability, neighborhood deprivation, and young children's emotional and behavioral problems.

PURPOSE: To examine if cognitive ability moderates the effect of area (neighborhood) deprivation on young children's problem behavior. METHODS: Data from the first two sweeps of the Millennium Cohort Study (MCS) in the UK were used. Children were clustered in small areas in nine strata in the UK and were aged 9 months at Sweep 1 and 3 years at Sweep 2. Neighborhood deprivation was measured with the Index of Multiple Deprivation at Sweep 1. Overall and specific problem behavior was measured with the Strengths and Difficulties Questionnaire at Sweep 2. To explore moderator specificity we used three indices of ability (verbal cognitive ability, non-verbal cognitive ability, and attainment of developmental milestones). Adjustment was made for child's age and sex, and for Sweep 1 family adversity (number of adverse life events), family structure, mother's social class and psychological distress, and family socio-economic disadvantage. RESULTS: We found both support for our main hypothesis, and evidence for specificity. Neighborhood deprivation was, even after adjustment for covariates, significantly associated with children's peer problems. However, verbal and non-verbal cognitive ability moderated this association. CONCLUSIONS: Neighborhood deprivation was related to peer problems even at preschool age. Although the effect of neighborhood deprivation on externalizing problems was mediated by family poverty and parental socio-economic position and although its effect on internalizing problems was mediated by parental mental health, its effect on difficulties with peers was independent of both parental and child characteristics. Cognitive ability moderated the effect of neighborhood deprivation on preschoolers' peer relationships difficulties.

Non-verbal reasoning ability and academic achievement as moderators of the relation between adverse life events and emotional and behavioural problems in early adolescence: the importance of moderator and outcome specificity.

This study was carried out to model the functional form of the effect of contextual risk (number of adverse life events) on emotional and behavioural problems in early adolescence, and to test how intelligence and academic achievement compare as moderators of this effect. The effect of number of adverse life events on emotional and behavioural problems was non-quadratic. Intelligence rather than academic achievement moderated the association between contextual risk and children's emotional and behavioural problems. However, the interaction effect was significant only on peer problems. These findings suggest that both moderator and outcome specificity should be considered when evaluating the role of intellectual competence in the association between contextual risk and children's emotional and behavioural problems.

Area and family effects on the psychopathology of the Millennium Cohort Study children and their older siblings.

BACKGROUND: To model and compare contextual (area and family) effects on the psychopathology of children nested in families nested in areas. METHOD: Data from the first two sweeps of the UK's Millennium Cohort Study were used. The final study sample was 9,630 children clustered in 6,052 families clustered in 1,681 Lower-layer Super Output Areas. The mean age of the children at Sweep 2 was 4.96 (SD = 2.76) years. Contextual risk was measured at area level with the Index of Multiple Deprivation (Sweep 1), and at family level with the number of proximal (Sweep 2) and distal (Sweep 1) adverse life events experienced. Psychopathology was measured at Sweep 2 with the Strengths and Difficulties Questionnaire. RESULTS: At baseline, both proximal and distal family risk and area risk predicted broad psychopathology, although the most parsimonious was the proximal family risk model, and both the family-level and the area-level variability were significant. The area risk/broad psychopathology association remained significant even when family risk was controlled, but not when family socioeconomic status was controlled. The full model added parenting and paternal and maternal psychopathology. When parental qualifications were excluded from the family-level contextual controls the effect of area risk remained significant on both externalizing and internalizing psychopathology. CONCLUSIONS: The effect of area on child psychopathology operated via the socioeconomic characteristics of the child's family, not just the adverse characteristics of the neighbors. Multiple family risk predicted child psychopathology directly and independently, and not because it was associated with family socioeconomic status. Family socioeconomic status explained the association between area risk and broad psychopathology.

Adverse life events, area socioeconomic disadvantage, and psychopathology and resilience in young children: the importance of risk factors' accumulation and protective factors' specificity.

Few studies on resilience in young children model risk appropriately and test theory-led hypotheses about its moderation. This study addressed both issues. Our hypothesis was that for preschool children's emotional/behavioral adjustment in the face of contextual risk protective factors should be located in the cognitive domain. Data were from the first two sweeps of the UK's Millennium Cohort Study. The final study sample was 4,748 three-year-old children clustered in 1,549 Lower layer Super Output Areas in nine strata. Contextual risk was measured at both area (with the Index of Multiple Deprivation) and family (with proximal and distal adverse life events experienced) level. Moderator variables were parenting, verbal and non-verbal ability, developmental milestones, and temperament. Multivariate multilevel models-that allowed for correlated residuals at both individual and area level-and univariate multilevel models estimated risk effects on specific and broad psychopathology. At baseline, proximal family risk, distal family risk and area risk were all associated with broad psychopathology, although the most parsimonious was the proximal family risk model. The area risk/broad psychopathology association remained significant even after family risk was controlled but not after family level socioeconomic disadvantage was controlled. The cumulative family risk was more parsimonious than the specific family risks model. Non-verbal ability moderated the effect of proximal family risk on conduct and emotional problems, and developmental milestones moderated the effect of proximal family risk on conduct problems. The findings highlight the importance of modeling contextual risk appropriately and of locating in the cognitive domain factors that buffer its effect on young children's adjustment.

Modeling risks: effects of area deprivation, family socio-economic disadvantage and adverse life events on young children's psychopathology.

BACKGROUND: The effects of contextual risk on young children's behavior are not appropriately modeled. AIMS: To model the effects of area and family contextual risk on young children's psychopathology. METHOD: The final study sample consisted of 4,618 Millennium Cohort Study (MCS) children, who were 3 years old, clustered in lower layer super output areas in nine strata in the UK. Contextual risk was measured by socio-economic disadvantage (SED) at both area and family level, and by distal and proximal adverse life events at family level. Multivariate response multilevel models that allowed for correlated residuals at both individual and area level, and univariate multilevel models estimated the effect of contextual risk on specific and broad psychopathology measured by the Strengths and Difficulties Questionnaire. RESULTS: The area SED/broad psychopathology association remained significant after family SED was controlled, but not after maternal qualifications and family adverse life events were added to the model. Adverse life events predicted psychopathology in all models. Family SED did not predict emotional symptoms or hyperactivity after child characteristics were added to the model with the family-level controls. CONCLUSIONS: Area-level SED predicts child psychopathology via family characteristics; family-level SED predicts psychopathology largely by its impact on development; and adverse life events predict psychopathology independently of earlier adversity, SED and child characteristics, as well as maternal psychopathology, parenting and education.

Psychopathology and prosocial behavior in adolescents from socio-economically disadvantaged families: the role of proximal and distal adverse life events.

The study investigated if proximal contextual risk (number of adverse life events experienced in the last year) or distal contextual risk (number of adverse life events experienced before the last year) is a better predictor of adolescent psychopathology and prosocial behavior. It also tested for the specificity, accumulation and gradient of contextual risk in psychopathology and prosocial behavior, and for the interaction between proximal and distal contextual risk in psychopathology and prosocial behavior. The sample was 199 11-18 year old children from a socio-economically disadvantaged area in North-East London. The Strengths and Difficulties Questionnaire (SDQ), which measures four difficulties (hyperactivity, emotional symptoms, conduct problems, and peer problems) and prosocial behavior, was used. Confounders were age, gender, and maternal educational qualifications. To model the relationship between the five SDQ scales and contextual risk multivariate response regression models and multivariate response logistic regression models that allow the error terms of the scale specific models to be correlated were fitted. This study highlighted the importance of proximal contextual risk in predicting both broad and externalizing psychopathology, and the importance of considering risk accumulation rather than specificity in predicting psychopathology. By showing that the number of proximal adverse life events experienced had a steady, additive effect on broad and externalizing psychopathology, it also highlighted the need to protect adolescents experiencing current risk from further risk exposure. By showing that the number of distal adverse life events experienced did not affect the proximal risk's impact on either broad or externalizing psychopathology, it highlighted the need to protect all adolescents, irrespective of experience of early life adversities, from risk.