Search Results (4)

Nonprobability sampling has been increasingly used in epidemiologic research, yet direct inference based on such samples is subject to selection bias. Current adjustment methods commonly rely on a reference probability-based survey sample that shares a set of covariates with the nonprobability sample. However, these common covariates are often limited and may bias estimates in the presence of population heterogeneity. Existing methods generally assume population homogeneity in models and fail to address such heterogeneity adequately. To overcome this limitation, we propose the Nonprobability Heterogeneity-adjusted Doubly Robust (NHDR) method, a novel inference framework that explicitly accounts for population heterogeneity during selection bias adjustment. NHDR proceeds in three stages: (1) identifying latent subpopulations via finite mixture modeling; (2) incorporating the resulting latent-class structure as a grouping variable into mixed-effects models for both the propensity score and outcome projection; and (3) constructing a doubly robust estimator that integrates these adjusted models. The key methodological contribution of NHDR is its formal integration of latent-class-based population structure into a doubly robust estimation framework, which enables more reliable inference under heterogeneous population settings. Simulation studies demonstrate that the proposed method control the coverage probabilities well in most scenarios. Under heterogeneous conditions, NHDR consistently outperforms existing methods achieving an average reduction in relative bias of approximately 1.8–4.5% and a corresponding decrease in mean squared error of about 5.1–15.5 compared to the benchmark method. We illustrate the practical utility of NHDR by applying it to estimate nine health indicators using data from the Health Monitoring SMS Survey in Guangzhou, China, with the seventh Guangdong Health Service Survey serving as the reference sample. Full article

Multimorbidity of malaria, anemia, and malnutrition (MAMM) is a condition in which an individual has two or more of these health conditions, and is becoming an emergent public health concern in sub-Saharan African countries. The independent associations of a child’s demographic variables and household socioeconomic (HSE) disparities with a child’s health outcomes have been established in the literature. However, the effects of the intersection of these factors on MAMM, while accounting for other covariates, have not been studied. Therefore, this study aimed to determine how children’s sex, age, and household socioeconomic status interact to explain the variations in MAMM among children aged 6–59 months in Nigeria. Data from the 2018 Nigeria Demographic and Health Survey and the 2018 National Human Development Report (NHDR) were used. This study included weighted samples of 10,184 children aged 6–59 months in Nigeria. A three-level multilevel mixed effect ordinal logistic regression model was used, such that individual characteristics at level 1 were nested in communities at level 2 and nested in states at level 3. Subsequently, predictive probability charts and average adjusted probability tables were used to interpret the intersectional effects. Five models were created in this scenario. Model 1 is the interaction between the child’s sex and household wealth status; model 2 is the interaction between the child’s sex and age; model 3 is the interaction between the child’s age and household wealth status; model 4 has the three two-way interactions of the child’s sex, age, and household wealth status; and model 5 includes model 4 and the three-way interactions between a child’s sex, age, and household wealth quintiles; while accounting for other covariates in each of the models. The prevalence of children with a ‘none of the three diseases’ outcome was 17.3% (1767/10,184), while 34.4% (3499/10,184) had ‘only one of the diseases’, and 48.3% (4918/10,184) had ‘two or more’ MAMMs. However, in the multivariate analyses, model 3 was the best fit compared with other models, so the two-way interaction effects of a child’s age and household wealth status are significant predictors in the model. Children aged 36–47 months living in the poorest households had a probability of 0.11, 0.18, and 0.32 of existing with MAMM above the probability of children of the same age who live in the middle class, more prosperous, and richest households, respectively, while all other covariates were held constant. Thus, the variation in the prevalence of MAMM in children of different ages differs depending on the household wealth quintile. In other words, in older children, the variations in MAMM become more evident between the richer and the poorer household quintiles. Therefore, it is recommended that policies that are geared toward economic redistribution will help bridge the disparities observed in the prevalence of multiple diseases among children aged 6–59 months in Nigeria. Full article

Developmental Dysplasia of the Hip (DDH) includes a broad spectrum of hip abnormalities. DDH requires early diagnosis and treatment; however, no international consensus on screening protocol and treatment is provided in the literature. Epidemiological studies are helpful to understand the national variation of a specific surgical procedure and compare it with that of other countries. Data provided by different countries could allow researchers to provide international guidelines for DDH screening and treatment. Limited data are reported regarding trends of hospitalization for DDH, and no public database is available. The purpose of this study was to estimate annual admissions for DDH in Italian patients from 2001 to 2016, based on the hospitalization reports. Data of this study were collected from the National Hospital Discharge Reports (SDO) reported at the Italian Ministry of Health. Descriptive statistical analyses were performed. From 2001 to 2016, 3103 hospitalizations for DDH were recorded in Italy, with a mean incidence of 2.33 (per 100,000 young inhabitants). Females of the 0–4 years old group represented the majority of patients hospitalized for DDH. Full article

Forward and backward stepwise regression (FR and BR, respectively) was applied for the structure–bioactivity prediction of angiotensin converting enzyme (ACE)-inhibitory/bitter-tasting dipeptides. The datasets used in this study consisted of 28 sequences and numerical variables reflecting dipeptides’ physicochemical nature. The data were acquired from the BIOPEP-UWM, Biological Magnetic Resonance Databank, ProtScale, and AAindex databases. The calculations were computed using STATISTICA^®13.1. FR/BR models differed in R² (0.91/0.76, respectively). The impact of C-_atC(−) and N-Molw(+) on the dual function of dipeptides was observed. Positive (+) and negative (−) correlations with log IC₅₀ are presented in parens. Moreover, C-Bur(+), N-_atH(+), and N-Pol(−) were also found to be important in the FR model. The additional statistical significance of N-bul(−), N-Bur(−), and N-Hdr(+) was reported in the BR model. These attributes reflected the composition of the dipeptides. We report that the “ideal” bitter ACE inhibitor should be composed of P, Y, F (C-end) and G, V, I, L (N-end). Functions: log R_caf. = f (observed log IC₅₀) and log R_caf. = f (predicted log IC₅₀) revealed no direct relationships between ACE inhibition and the bitterness of the dipeptides. It probably resulted from some structural discrepancies between the ACE inhibitory/bitter peptides and/or the measure of activity describing one of the two bioactivities. Our protocol can be applicable for the structure–bioactivity prediction of other bioactivities peptides. Full article