Implausible Values Research Articles

Assessing feasibility and sex-related inequity in the cardiac rehabilitation quality indicators in Manitoba.

The cardiac rehabilitation quality indicators (CRQIs) developed by the Canadian Cardiovascular Society provide a means to standardize program assessment and identify sex-related inequities. No formal evaluation of the CRQIs has been conducted in Manitoba. An environmental scan for the CRQIs was performed using data in the electronic medical record at two cardiac rehabilitation (CR) sites in Winnipeg for 2016-2019 referrals. Of the 8116 referrals, 7758 (5491 males and 2267 females) had geographical access and were eligible for CR. The Manitoba Centre for Health Policy Data Quality Framework informed the data quality assessment. Thirteen CRQIs were available; four were considered high quality; nine demonstrated moderate to significant missing data. In addition to missing values, potential misclassification of risk (CR-4) and physiologically implausible and invalid dates were assessed and identified (CR-13 and CR-17). Each site had a physician medical director (CR-31) and a documented emergency response strategy (CR-32). Only high-quality data were evaluated for sex-related differences using chi-square and median tests. Women had lower enrollment (CR-3), and more women enrolled after the median of 41 days (CR-2b). Engagement with CR partners, including frontline staff, and utilizing strategies to assess and limit physiologically implausible values and dates will enhance data capture and quality.

A better performing algorithm for identification of implausible growth data from longitudinal pediatric medical records

Tracking trajectories of body size in children provides insight into chronic disease risk. One measure of pediatric body size is body mass index (BMI), a function of height and weight. Errors in measuring height or weight may lead to incorrect assessment of BMI. Yet childhood measures of height and weight extracted from electronic medical records often include values which seem biologically implausible in the context of a growth trajectory. Removing biologically implausible values reduces noise in the data, and thus increases the ease of modeling associations between exposures and childhood BMI trajectories, or between childhood BMI trajectories and subsequent health conditions. We developed open-source algorithms (available on github) for detecting and removing biologically implausible values in pediatric trajectories of height and weight. A Monte Carlo simulation experiment compared the sensitivity, specificity and speed of our algorithms to three published algorithms. The comparator algorithms were selected because they used trajectory information, had open-source code, and had published verification studies. Simulation inputs were derived from longitudinal epidemiological cohorts. Our algorithms had higher specificity, with similar sensitivity and speed, when compared to the three published algorithms. The results suggest that our algorithms should be adopted for cleaning longitudinal pediatric growth data.

The quality of the anthropometric data on children under five years of age in information systems in the State of São Paulo, Brazil

This paper involves the analysis of the quality of anthropometric data on children under five years of age in two information systems in the State of São Paulo. The sample included 2,117,108 children from the Food and Nutrition Surveillance System (SISVAN), and 748,551 from the State Milk Project (VIVALEITE). Initially, we evaluated the frequency of missing values and others outside the equipment spectrum and calculated the digit-to-weight preference index. After calculating height-for-age (HAZ), weight-for-age (WAZ), and body mass index-for-age (BAZ), we flagged the biologically implausible values (BIV) and calculated the standard deviation (SD). For each municipality, we calculated the mean and the SD of HAZ, WAZ, and BAZ; and plotted the SD values as a function of the mean. The digit-to-weight preference index was greater among children aged between 24 and 59 months in SISVAN. The frequency of BIV for HAZ (SISVAN 2.56%; VIVALEITE 0.98%) was higher than for WAZ (SISVAN 2.10%; VIVALEITE 0.18%). For HAZ, variations among municipalities were more pronounced in VIVALEITE than in SISVAN. The height variable presents low reliability in both systems. The weight variable reveals satisfactory quality in VIVALEITE and unsatisfactory quality in SISVAN.

Gestational Weight Gain and Neonatal Biometry during the COVID-19 Pandemic: A Multicenter Observational Cohort.

This study aimed to test the hypothesis that being pregnant and delivering during the coronavirus disease 2019 (COVID-19) pandemic was associated with changes in gestational weight gain (GWG) or frequency of small- (SGA) or large-for-gestational-age (LGA) neonates. Secondary analysis of a multicenter observational cohort comparing pregnant people who delivered during the COVID-19 pandemic (June-December 2020) to people who delivered prior to the pandemic (March-December 2019). Those with multiple gestations, fetuses with major congenital anomalies, implausible GWG values, unavailable body mass index (BMI), or who were severe acute respiratory syndrome coronavirus-2-positive were excluded. The primary outcome was frequency of optimal recommended GWG based on prepregnancy BMI. Neonatal outcomes included birth weight, ponderal index, and frequency of SGA, LGA, and small head circumference for live births. Multivariable regression analysis was used to assess associations between exposure to the pandemic and outcomes. A total of 10,717 pregnant people were included in our analysis. A total of 4,225 pregnant people were exposed to the pandemic and 6,492 pregnant people delivered prior to the COVID-19 pandemic. Pregnant people exposed to the pandemic were older and more likely to have gestational diabetes. The frequency of appropriate GWG was 28.0% during the pandemic and 27.6% before the pandemic (adjusted odds ratio [aOR]: 1.02, 95% confidence interval [CI]: 0.93-1.11). Excessive GWG was more likely (54.9 vs. 53.1%; aOR: 1.08, 95% CI: 1.001-1.17), and inadequate GWG was less likely during the pandemic (17.0 vs. 19.3%; aOR: 0.86, 95% CI: 0.77-0.95). The frequency of SGA was 5.4% during the pandemic and 6.1% before the pandemic (aOR: 0.90, 95% CI: 0.76-1.06), and the frequency of LGA was 16.0% during the pandemic versus 15.0% before the pandemic (aOR: 1.06, 95% CI: 0.95-1.18). Other neonatal outcomes including birth weight percentile (62.1 [35.8-83.2] vs. 60.2 [34.4-82.2]; adjusted mean difference (aMD) = 1.50, 95% CI: -0.28 to 3.29), ponderal index (2.6 g/cm3 [2.4-2.8] in both groups; aMD = 0.01, 95% CI: 0.00-0.02), and small head circumference for livebirths (<10th percentile [8.2 vs. 8.1%; aOR: 1.03, 95% CI: 0.89-1.19], <3rd percentile [3.5 vs. 3.1%; aOR: 1.16, 95% CI: 0.93-1.44]) were similar between groups as well. Being pregnant and delivering during the COVID-19 pandemic was associated with a higher likelihood of excessive GWG and a lower likelihood of inadequate GWG. · Delivering during the COVID-19 pandemic was associated with higher likelihood of excessive GWG.. · Delivering during the COVID-19 pandemic was associated with lower likelihood of inadequate GWG.. · COVID-19 pandemic was not associated with changes in frequency of SGA or LGA..

Validation of Multi-State EHR-Based Network for Disease Surveillance (MENDS) Data and Implications for Improving Data Quality and Representativeness.

Surveillance modernization efforts emphasize the potential use of electronic health record (EHR) data to inform public health surveillance and prevention. However, EHR data streams vary widely in their completeness, accuracy, and representativeness. We developed a validation process for the Multi-State EHR-Based Network for Disease Surveillance (MENDS) pilot project to identify and resolve data quality issues that could affect chronic disease prevalence estimates. We examined MENDS validation processes from December 2020 through August 2023 across 5 data-contributing organizations and outlined steps to resolve data quality issues. We identified gaps in the EHR databases of data contributors and in the processes to extract, map, integrate, and analyze their EHR data. Examples of source-data problems included missing data on race and ethnicity and zip codes. Examples of data processing problems included duplicate or missing patient records, lower-than-expected volumes of data, use of multiple fields for a single data type, and implausible values. Validation protocols identified critical errors in both EHR source data and in the processes used to transform these data for analysis. Our experience highlights the value and importance of data validation to improve data quality and the accuracy of surveillance estimates that use EHR data. The validation process and lessons learned can be applied broadly to other EHR-based surveillance efforts.

Prognostic Factors for Mortality in Adults Hospitalized With COVID-19 Infection in the Americas.

Many reports on COVID-19have highlighted an association between patient characteristics upon admission for COVID-19 and how they affect variable progressions of the disease severity and, ultimately mortality. In this cohort, we analyzed data from patients in the Americas who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, and were hospitalized. We retrieved data from identified studies selected from full-text screening to identify relevant patient demographics: age, gender, race, duration of hospital stay, and comorbidities. Data were assessed for consistency, missing values, presence of outliers, and implausible values were documented. Our analysis was performed by calculating the case fatality rates for data reported by the original authors within the studies. The primary outcome was mortality. Participants in the review were COVID-19 inpatients diagnosed through reverse-transcriptase polymerase chain reaction tests (RT-PCR) and above the age of 45 years in the North and South American continents. The study included 12,895 patients who were hospitalized with the COVID-19 infection, and an overall 14.4% in-hospital case-fatality rate was demonstrated. The prognostic factor with the most significant association for increased risk of in-hospital mortality in the elderly was having a high viral load of the COVID-19 virus with a case-fatality rate of 41.4%. In addition, being of Caucasian ethnicity and having the comorbidity of chronic obstructive pulmonary disease (COPD) were also associated with higher rates of mortality among elderly hospitalized patients with case-fatality rates of 40.8% and 35.7% respectively. Prognostic factors for COVID-19 mortality were determined by having a high viral load of the infection, being of Caucasian ethnicity, and having underlying pulmonary comorbidities such as COPD. In the future research should be undertaken to determine the underlying mechanisms behind these effects.

Identifying biologically implausible values in big longitudinal data: an example applied to child growth data from the Brazilian food and nutrition surveillance system

BackgroundSeveral strategies for identifying biologically implausible values in longitudinal anthropometric data have recently been proposed, but the suitability of these strategies for large population datasets needs to be better understood. This study evaluated the impact of removing population outliers and the additional value of identifying and removing longitudinal outliers on the trajectories of length/height and weight and on the prevalence of child growth indicators in a large longitudinal dataset of child growth data.MethodsLength/height and weight measurements of children aged 0 to 59 months from the Brazilian Food and Nutrition Surveillance System were analyzed. Population outliers were identified using z-scores from the World Health Organization (WHO) growth charts. After identifying and removing population outliers, residuals from linear mixed-effects models were used to flag longitudinal outliers. The following cutoffs for residuals were tested to flag those: -3/+3, -4/+4, -5/+5, -6/+6. The selected child growth indicators included length/height-for-age z-scores and weight-for-age z-scores, classified according to the WHO charts.ResultsThe dataset included 50,154,738 records from 10,775,496 children. Boys and girls had 5.74% and 5.31% of length/height and 5.19% and 4.74% of weight values flagged as population outliers, respectively. After removing those, the percentage of longitudinal outliers varied from 0.02% (<-6/>+6) to 1.47% (<-3/>+3) for length/height and from 0.07 to 1.44% for weight in boys. In girls, the percentage of longitudinal outliers varied from 0.01 to 1.50% for length/height and from 0.08 to 1.45% for weight. The initial removal of population outliers played the most substantial role in the growth trajectories as it was the first step in the cleaning process, while the additional removal of longitudinal outliers had lower influence on those, regardless of the cutoff adopted. The prevalence of the selected indicators were also affected by both population and longitudinal (to a lesser extent) outliers.ConclusionsAlthough both population and longitudinal outliers can detect biologically implausible values in child growth data, removing population outliers seemed more relevant in this large administrative dataset, especially in calculating summary statistics. However, both types of outliers need to be identified and removed for the proper evaluation of trajectories.

VigiNUTRI Brasil: methods of request, data extraction, treatment and consistency analysis of individual data from adolescents monitored by the Food and Nutrition Surveillance System (Sisvan Web).

To describe the methods for requesting, extracting data, processing and analyzing the consistency of anthropometric and food consumption data of adolescents monitored by Sisvan Web. Methodological study with individualized data from Sisvan web between 2008 and 2018. The modules of anthropometry and consumption, made available by the Ministry of Health, had a unique identifier for linkages. Implausible values and individuals outside the age range were excluded. Consistency analyses, with corrections for imputations and descriptive statistics, were performed using Stata 16.0 software. A database was obtained with 18,812,232 observations of anthropometric data between 2008 and 2018 and 440,534 records of food consumption between 2015 and 2018; after merging the banks, 64,976 observations were obtained. The combination of anthropometry and food consumption databases made it possible to link individual adolescent data and build a database with information for future analyzes relating to the dietary and nutritional profile of the same individual. The proposal for processing individual data from the Food and Nutrition Surveillance System (Sisvan Web) generated 18 million observations for anthropometric data and 65,000 observations after merging the anthropometry and food consumption databases. This work will enable replication of the methodology for processing data available in Sisvan Web, allowing for enhanced analyzes by healthcare teams and researchers in the field of public health. It is expected that these procedures will assist researchers, managers and healthcare professionals in handling and analyzing the information generated by Sisvan Web.

Quality of anthropometric data of children under 5 years in the Brazilian National Food and Nutrition Surveillance System, 2008-2020

The planning, monitoring, and evaluation of food and nutrition actions depend on reliable estimates based on adequate anthropometric data. The study aimed to analyze the quality of anthropometric data of children aged under 5 years in the Brazilian National Food and Nutrition Surveillance System (SISVAN) from 2008 to 2020. The sample comprised 23,453,620 children aged under 5 years. Initially, we evaluated the distribution of missing values and values outside the spectrum of the instrument, and calculated the digit preference index for weight and height. The nutritional indexes height for age (HAZ), weight for age (WAZ), and body mass index for age (BAZ) were calculated according to the World Health Organization 2006 child growth standards. Then, we identified the biologically implausible values (BIV) and calculated the standard deviation (SD) of the nutritional indexes. For each municipality, we calculated the mean and SD of HAZ and WAZ; and plotted the SD values as a function of the mean. In all Federative Units, the digit preference index reached a minimum value of 80 for height and 20 for weight. For the three nutritional indexes, there was a reduction in the frequency of BIV in the 2008-2020 period. Even after the exclusion of BIV, we identified high variability for the three nutritional indexes. The indicators evaluated showed low quality of measurement, especially in the North and Northeast regions. Our results indicate insufficient quality of anthropometric data in children aged under 5 years, and reinforce the need to invest in actions to improve the collection and recording of anthropometric information.

Frequency of anthropometric implausible values estimated from different methodologies: a systematic review and meta-analysis.

Poor anthropometric data quality affect the prevalence of malnutrition and could harm public policy planning. This systematic review and meta-analysis was designed to identify different methods to evaluate and clean anthropometric data, and to calculate the frequency of implausible values for weight and height obtained from these methodologies. Studies about anthropometric data quality and/or anthropometric data cleaning were searched for in the MEDLINE, LILACS, SciELO, Embase, Scopus, Web of Science, and Google Scholar databases in October 2020 and updated in January 2023. In addition, references of included studies were searched for the identification of potentially eligible studies. Paired researchers selected studies, extracted data, and critically appraised the selected publications. Meta-analysis of the frequency of implausible values and 95% confidence interval (CI) was estimated. Heterogeneity (I2) and publication bias were examined by meta-regression and funnel plot, respectively. In the qualitative synthesis, 123 reports from 104 studies were included, and in the quantitative synthesis, 23 studies of weight and 14 studies of height were included. The study reports were published between 1980 and 2022. The frequency of implausible values for weight was 0.55% (95%CI, 0.29-0.91) and for height was 1.20% (95%CI, 0.44-2.33). Heterogeneity was not affected by the methodological quality score of the studies and publication bias was discarded. Height had twice the frequency of implausible values compared with weight. Using a set of indicators of quality to evaluate anthropometric data is better than using indicators singly. PROSPERO registration no. CRD42020208977.

Quality of child anthropometric data from SISVAN, Brazil, 2008-2017.

To evaluate the quality of anthropometric data of children recorded in the Food and Nutrition Surveillance System (SISVAN) from 2008 to 2017. Descriptive study on the quality of anthropometric data of children under five years of age admitted in primary care services of the Unified Health System, from the individual databases of SISVAN. Data quality was annually assessed using the indicators: coverage, completeness, sex ratio, age distribution, weight and height digit preference, implausible z-score values, standard deviation, and normality of z-scores. In total, 73,745,023 records and 29,852,480 children were identified. Coverage increased from 17.7% in 2008 to 45.4% in 2017. Completeness of birth date, weight, and height corresponded to almost 100% in all years. The sex ratio was balanced and approximately similar to the expected ratio, ranging from 0.8 to 1. The age distribution revealed higher percentages of registrations from the ages of two to four years until mid-2015. A preference for terminal digits "zero" and "five" was identified among weight and height records. The percentages of implausible z-scores exceeded 1% for all anthropometric indices, with values decreasing from 2014 onwards. A high dispersion of z-scores, including standard deviations between 1.2 and 1.6, was identified mainly in the indices including height and in the records of children under two years of age and residents in the North, Northeast, and Midwest regions. The distribution of z-scores was symmetric for all indices and platykurtic for height/age and weight/age. The quality of SISVAN anthropometric data for children under five years of age has improved substantially between 2008 and 2017. Some indicators require attention, particularly for height measurements, whose quality was lower especially among groups more vulnerable to nutritional problems.

The matrix revolutions: How databases and database linkages will transform epidemiologic research.

The matrix revolutions: How databases and database linkages will transform epidemiologic research.

National data linkage assessment of live births and deaths in Mexico: Estimating under‐five mortality rate ratios for vulnerable newborns and trends from 2008 to 2019

Linked datasets that enable longitudinal assessments are scarce in low and middle-income countries. We aimed to assess the linkage of administrative databases of live births and under-five child deaths to explore mortality and trends for preterm, small (SGA) and large for gestational age (LGA) in Mexico. We linked individual-level datasets collected by National statistics from 2008 to 2019. Linkage was performed based on agreement on birthday, sex, residential address. We used the Centre for Data and Knowledge Integration for Health software to identify the best candidate pairs based on similarity. Accuracy was assessed by calculating the area under the receiver operating characteristic curve. We evaluated completeness by comparing the number of linked records with reported deaths. We described the percentage of linked records by baseline characteristics to identify potential bias. Using the linked dataset, we calculated mortality rate ratios (RR) in neonatal, infants, and children under-five according to gestational age, birthweight, and size. For the period 2008-2019, a total of 24,955,172 live births and 321,165 under-five deaths were available for linkage. We excluded 1,539,046 records (6.2%) with missing or implausible values. We succesfully linked 231,765 deaths (72.2%: range 57.1% in 2009 and 84.3% in 2011). The rate of neonatal mortality was higher for preterm compared with term (RR 3.83, 95% confidence interval, [CI] 3.78, 3.88) and for SGA compared with appropriate for gestational age (AGA) (RR 1.22 95% CI, 1.19, 1.24). Births at <28 weeks had the highest mortality (RR 35.92, 95% CI, 34.97, 36.88). LGA had no additional risk vs AGA among children under five (RR 0.92, 95% CI, 0.90, 0.93). We demonstrated the utility of linked data to understand neonatal vulnerability and child mortality. We created a linked dataset that would be a valuable resource for future population-based research.

Abstract 61: Frequency of Pediatric Lipid Screening Using a Large National Electronic Health Record System

Background: Universal lipid screening is recommended for U.S. youth aged 9-11 and 17-21 years. Our objective was to describe the prevalence of pediatric lipid screening and elevated lipid measurements among those screened, by patient characteristics. Methods: IQVIA’s Ambulatory Electronic Medical Records (version 5, release: October 2021) database, containing medical records from 100,000 physicians in the U.S., was used for this analysis. The study population included 3,131,956 patients aged 9-21 years with ≥1 valid measure of height and weight during our observation period (01/01/2018-11/30/2021). Lipid screening was defined as ≥1 valid measurement any time during the observation period. Body mass index (BMI) was calculated and categorized using pediatric percentiles (ages 9-20 years) and adult interpretations (age ≥21 years) ( Table ). After excluding biologically implausible values, lipid measurements were considered elevated if ≥1 of the following was identified: low density lipoprotein ≥130 mg/dL, very low-density lipoprotein ≥31 mg/dL, non-high-density lipoprotein ≥145 mg/dL, triglycerides ≥130 mg/dL, or total cholesterol ≥200 mg/dL. Results: A total of 354,764 (11.3%) youth had documented lipid screening. The frequency of lipid screening increased by age group (9-11 years: 9.1%; 12-16 years: 11.0%,17-21 years: 12.9%) and BMI category (underweight and healthy weight: 9.5%; overweight: 11.0%; obesity: 16.7%; severe obesity: 19.6%). Frequency of screening varied across race/ethnicity (White: 10.8%; Black 14.5%; Asian: 18.0%). Among those screened, 30.2% had ≥1 elevated measure with highest prevalence among those with obesity (44.8%) or severe obesity (48.1%). Conclusions: Our findings indicate that among youth aged 9-11y and 17-21y, lipid screening is suboptimal, and prevalence of elevated lipid levels is high among those screened. Increased pediatric lipid screening could lead to improvements in treatment, management, and prevention of adverse, long-term cardiovascular outcomes.

Analysis of the quality of prenatal data of pregnant women attended at Healthcare Services in the city of São Paulo between 2012 and 2020.

To analyze the quality of data collected during prenatal care recorded in the Integrated Health Care Management System (SIGA) of the Municipal Department of Health of São Paulo from 2012 to 2020. Descriptive study using SIGA data and the variables: maternal height (cm), weight (kg) measured throughout pregnancy, gestational age at prenatal consultation, systolic (SBP) and diastolic (DBP) blood pressure (in mmHg), and body mass index (BMI) at the beginning of pregnancy (up to 8 weeks). Quality analysis was carried out by calculating the indicators: percentage of incompleteness and zero values of all variables studied, percentage of implausible values for height, weight, BMI; preference for terminal digit of weight and height, and normality of distributions. The database of pregnant women made available for analysis included 8,046,608 records and 1,174,115 women. The percentage of incompleteness and zero values was low (<1%) in all original variables of the system. There are more records at the end of pregnancy. For the four original variables of interest in the database (weight, height, SBP, DBP), there is a clear preference for the terminal digit. The variables of interest did not present an approximately normal distribution during the evaluated period. The quality analysis showed the need for improving the standardization of information collection and recording, the rounding of measurements and the need for encouraging pregnant women to start prenatal care as soon as possible, in such a way that it is important to invest in data quality, through educational resources for professionals who work in health care.

Mean kurtosis-Curve (MK-Curve) correction improves the test-retest reproducibility of diffusion kurtosis imaging at 3 T.

Diffusion kurtosis imaging (DKI) is applied to gain insights into the microstructural organization of brain tissues. However, the reproducibility of DKI outside brain white matter, particularly in combination with advanced estimation to remedy its noise sensitivity, remains poorly characterized. Therefore, in this study, we investigated the variability and reliability of DKI metrics while correcting implausible values with a fit method called mean kurtosis (MK)-Curve. A total of 10 volunteers (four women; age: 41.4 ± 9.6 years) were included and underwent two MRI examinations of the brain. The images were acquired on a clinical 3-T scanner and included a T1-weighted image and a diffusion sequence with multiple diffusion weightings suitable for DKI. Region of interest analysis of common kurtosis and tensor metrics derived with the MK-Curve DKI fit was performed, including intraclass correlation (ICC) and Bland-Altman (BA) plot statistics. A p value of less than 0.05 was considered statistically significant. The analyses showed good to excellent agreement of both kurtosis tensor- and diffusion tensor-derived MK-Curve-corrected metrics (ICC values: 0.77-0.98 and 0.87-0.98, respectively), with the exception of two DKI-derived metrics (axial kurtosis in the cortex: ICC = 0.68, and radial kurtosis in deep gray matter: ICC = 0.544). Non-MK-Curve-corrected kurtosis tensor-derived metrics ranged from 0.01 to 0.52 and diffusion tensor-derived metrics from 0.06 to 0.66, indicating poor to moderate reliability. No structural bias was observed in the BA plots for any of the diffusion metrics. In conclusion, MK-Curve-corrected DKI metrics of the human brain can be reliably acquired in white and gray matter at 3 T and DKI metrics have good to excellent agreement in a test-retest setting.

Implementing Multiple Imputation for Missing Data in Longitudinal Studies When Models are Not Feasible: An Example Using the Random Hot Deck Approach.

Researchers often use model-based multiple imputation to handle missing at random data to minimize bias. However, constraints within the data may sometimes result in implausible values, making model-based imputation infeasible. In these contexts, we illustrate how random hot deck imputation can allow for plausible multiple imputation in longitudinal studies. Our motivating example is the Childhood Health, Activity, and Motor Performance School Study Denmark (CHAMPS-DK), a prospective cohort study that measured weekly sports participation for 1700 Danish schoolchildren. Using observed data on 4 variables (pain, activity frequency, sport, sport counts), we created a gold-standard data set without missing data. We then created a synthetic data set by setting some variable values to missing based on a prediction model that mimicked real-data missingness patterns. To create 5 imputed data sets, we matched each record with missing data to several fully observed records, generated probabilities from matched records, and sampled from these records based on the probability of each occurring. We assessed variability and agreement (kappa) between the imputed data sets and the gold-standard data set. We compare results to common model-based imputation methods. Variability across data sets appeared reasonable. The range of kappa for the random hot deck approach was moderate for activity frequency (0.65 to 0.71) and sport (0.59 to 0.85), and poor for common model-based approaches (range 0.00 to 0.11). The range of kappas for sport count was strong (0.87 to 0.97) for random hot deck imputation and weak to moderate (0.55 to 0.71) for common model-based imputation. Agreement was higher when more information was present, and when prevalence was higher for our binary variable sport. Random hot deck imputation should be considered as an alternative method when model-based approaches are infeasible, specifically where there are constraints within and between covariates.

Prevalence of High Weight Status in Children Under 2 Years in NHANES and Statewide Electronic Health Records Data in North Carolina and South Carolina.

We evaluated the prevalence of high weight status in children ages 0 to 24 months (m) using data from electronic health records (EHR) and NHANES. We also examined relationships between weight status during infancy and obesity at 24 months of age. EHR data from 4 institutions in North and South Carolina included patients born January 1, 2013-October 10, 2017 (N=147,290). NHANES data included study waves from 1999 to 2018 (unweighted N=5121). We calculated weight-for-length (WFL), weight-for-age (WFA), and body mass index (BMI), excluding implausible values, and categorized weight status (<85th, 85th to <95th, or ≥95th percentile), assessing prevalence at birth, 6, 12, 18, and 24 months. Utilizing individual, longitudinal EHR data, we used separate regression models to assess obesity risk at 24 months based on anthropometrics at birth, 6, 12, and 18 months, adjusting for sex, race/ethnicity, insurance, and health system. Prevalence of BMI ≥95th percentile in EHR data at 6, 12, 18, and 24 months were 9.7%, 15.7%, 19.6%, and 20.5%, respectively. With NHANES the prevalence was 11.6%, 15.0%, 16.0%, and 8.4%. For both, the prevalence of high weight status was higher in Hispanic children. In EHR data, high weight status at 6, 12, and 18 months was associated with obesity at 24 months, with stronger associations as BMI category increased and as age increased. High weight status is common in infants and young children, although lower at 24 months in NHANES than EHR data. In EHR data, high BMI at 6, 12, and 18 months was associated with increased risk of obesity at 24 months.

Fundamental problems in fitting spatial cluster process models

Existing methods for fitting Neyman–Scott cluster process models to spatial point pattern data often fail to converge, or converge to implausible values of the parameters, or exhibit numerical instability. These failures have been viewed as weaknesses of the particular model-fitting method. However, we show that they are attributable to fundamental flaws in the model structure: The model is not closed under convergence in distribution; the Poisson process is not included in the model; cluster scale is unidentifiable when the model is close to a Poisson process. We obtain new results about properties of cluster processes, and about the distance from a cluster process to a Poisson process. We define an index of cluster strength φ which plays an important role in the analysis. Remedies for the fundamental problems are proposed: the model is extended to include the Poisson process by allowing φ=0; unidentifiability is remedied using shrinkage estimators involving a penalty on cluster scale. To improve understanding of the fitted model in applications we propose several derived parameters. The improved fitting methods are implemented in open source R code. Simulation experiments and real data examples demonstrate the improved performance.

Posyandu Application in Indonesia: From Health Informatics Data Quality Bridging Bottom-Up and Top-Down Policy Implementation

The community’s mother and child health (MCH) and nutrition problems can be overcome through evidence-based health policy. Posyandu is an implementation of community empowerment in health promotion strategies. The iPosyandu application (app) is one of the health informatics tools, in which data quality should be considered before any Posyandu health interventions are made. This study aims to describe and assess differences in data quality based on the dimensions (completeness, accuracy, and consistency) of the secondary data collected from the app in Purwakarta Regency in 2019–2021. Obstacles and suggestions for improving its implementation were explored. This research applies a mixed-method explanatory approach. Data completeness was identified as the number of reported visits of children under five per year. Data accuracy was analyzed using WHO Z-score anthropometry and implausible Z-score values. Data consistency was measured using Cronbach’s alpha coefficient, followed by qualitative research with focus group discussions, in-depth interviews, and field observation notes. The quantitative study results found that some of the data were of good quality. The qualitative research identified the obstacles experienced using the iPosyandu app, one of them being that there were no regulations governing the use of iPosyandu to bridge the needs of the government, and provided suggestions from the field to improve its implementation.