Aggregated Population Data Research Articles

BUILDING-LEVEL POPULATION ESTIMATION USING LIDAR-DERIVED BUILDING VOLUME DATA

Abstract. Population censuses serve as pivotal repositories for demographic and socioeconomic information. Conducted quinquennially in the Philippines, these censuses aggregate population data into administrative units, with the barangay as the smallest unit. However, this aggregation, when used in analyses, often presumes homogeneity within these units, potentially leading to overgeneralized results. With the advent of micro-level data, such as building-specific population counts, more nuanced spatial analyses become feasible. This study leveraged existing mathematical model to estimate residential building populations in Quezon City, utilizing 3D building information derived from elevation models, building footprints, local regulations, and land use types. The study yielded promising results, achieving a normalized absolute error (NAE) of 0.133 and an R2 value of approximately 0.976, indicating a high degree of model accuracy. However, the model also revealed systematic biases, notably underestimating populations in high-density areas and overestimating in low-density barangays. These findings underscore the complexity of factors influencing the model's performance, ranging from building digitization errors to assumptions about floor height and living area per person. The study thereby elucidates the valuable role that 3D building information can play in disaggregating population data for more granular analyses, while also highlighting areas for further model refinement to mitigate issues of overestimation and underestimation.

Experiences of Policing in Gentrifying Neighborhoods: Evidence From Chicago

Do patterns of unequal policing persist or transform within gentrifying neighborhoods? Using an original survey of Chicago residents, we assess whether gentrifiers and longtime residents experience policing differently. Building on macro-level studies which rely on aggregate population data and micro-level studies which rely on ethnographies and interviews, we conduct a meso-level study that compares the experiences and views of differently positioned residents. We find that the phenomenon of being “over-policed and under-protected” that characterizes race-class subjugated neighborhoods is replicated within gentrifying neighborhoods for longtime residents. Meanwhile, gentrifiers express less concern about crime and report fewer interactions with police. While the average gentrifier has low levels of police contact, we find some evidence that a subset of gentrifiers are more likely to call the police about quality-of-life issues compared to neighbors. Our methodological approach provides a blueprint for how survey research can provide insights on individual-level experiences and attitudes in gentrifying neighborhoods.

Long‐term trajectories of intergenerational coresidence: Evidence of living arrangements patterns in Chile

AbstractObjectiveThis study examines long‐term trajectories of intergenerational coresidence (IC) among a cohort of individuals aged 65 and 75 in Chile, focusing on their diversity, prevalence, internal dynamism, and sociodemographic characteristics.BackgroundIC is comparatively high in Latin America and is related to both family norms and social vulnerabilities. However, empirical evidence on this topic comes mostly from cross‐sectional studies using aggregated population data, which neglect the inherently dynamic nature of coresidential arrangements across the life course.MethodWe use representative life‐history data in Santiago, Chile (N = 802), sequence analysis to reconstruct long‐term trajectories, and bivariate analysis to examine their associations with sociodemographic factors.ResultsOur findings show that, first, IC in Chile may be more prevalent and diverse than suggested in previous cross‐sectional studies. Second, our findings indicate patterns of long‐term IC and stable conjugal cohabitation are compatible. Third, the results shed new light on the association of IC with particular sociodemographic groups.ConclusionThis study analyzed patterns of living arrangements among family members based both on intergenerational and conjugal cohabitation and their dynamic character throughout the life course. Our results challenge interpretations of contemporary living arrangements among family members at an international level.ImplicationsThis study shows that IC in Chile has been largely underrepresented, both in magnitude and heterogeneity. This finding informs public policies about the reality of family configurations that require specific care, housing, and financial support.

Use of Oral Polio Vaccine and the Global Incidence of Mother-to-Child Human Immunodeficiency Virus Transmission.

BackgroundMother-to-child transmission (MTCT) of human immunodeficiency virus (HIV) is an important global health issue. We hypothesized that the live attenuated poliovirus existing in oral polio vaccine (OPV) may protect uninfected neonates born to HIV-positive mothers through the stimulation of innate immune system.ObjectiveTo test the hypothesis that countries using OPV have a lower MTCT rate (due to postnatal protection provided by the vaccine) compared with those using only inactivated polio vaccine (IPV).MethodsIn an ecological study, the incidence of HIV/AIDS in children aged <1 year (IncHIV1), considered a surrogate index for MTCT rate, was compared between countries using OPV vs. IPV. The aggregated population data were retrieved for 204 countries from the Global Burden of Disease (GBD 2019) Collaborative Network website, “Our World in Data” website, the World Bank website, and the WHO Global Polio Eradication Initiative (GPEI). We used a negative binomial regression model with IncHIV1 as the dependent variable and the prevalence of HIV/AIDS in women aged 15–49 years (PrevHIV), antiretroviral therapy (ART) coverage, human development index (HDI), and the type of vaccine used in each country as independent variables. Multivariate imputation by chained equations was used to treat missing values. Analyses were performed for both the original dataset (with missing values) and the five imputed datasets.ResultsIncHIV1 and PrevHIV were available for all 204 countries; vaccine type, 194 countries; HDI, 182 countries; and ART coverage, 133 countries. One-hundred and twenty-nine countries in the original dataset had complete data for all the above-mentioned variables; the imputed datasets had complete data for all 204 countries. The results obtained from the analysis of the original dataset had no overall difference with the pooled results obtained from the analysis of the five imputed datasets. Countries with higher HDI mainly use IPV; those with lower HDI commonly use OPV. PrevHIV, HDI, and the type of vaccine were independent predictors of IncHIV1. Use of OPV compared to IPV, was independently associated with an average decrease of 17% in IncHIV1 at the median HDI of 0.75. The protection provided by OPV increased in countries with lower HDI.ConclusionsUse of OPV compared with IPV, was independently associated with lower MTCT rate.

Time-delayed collective flow diffusion models for inferring latent people flow from aggregated data at limited locations

The rapid adoption of wireless sensor devices has made it easier to record location information of people in a variety of spaces (e.g., exhibition halls). Location information is often aggregated due to privacy and/or cost concerns. The aggregated data we use as input consist of the numbers of incoming and outgoing people at each location and at each time step. Since the aggregated data lack tracking information of individuals, determining the flow of people between locations is not straightforward. In this article, we address the problem of inferring latent people flows, that is, transition populations between locations, from just aggregated population data gathered from observed locations. Existing models assume that everyone is always in one of the observed locations at every time step; this, however, is an unrealistic assumption, because we do not always have a large enough number of sensor devices to cover the large-scale spaces targeted. To overcome this drawback, we propose a probabilistic model with flow conservation constraints that incorporate travel duration distributions between observed locations. To handle noisy settings, we adopt noisy observation models for the numbers of incoming and outgoing people, where the noise is regarded as a factor that may disturb flow conservation, e.g., people may appear in or disappear from the predefined space of interest. We develop an approximate expectation-maximization (EM) algorithm that simultaneously estimates transition populations and model parameters. Our experiments demonstrate the effectiveness of the proposed model on real-world datasets of pedestrian data in exhibition halls, bike trip data and taxi trip data in New York City.

Individualised Trajectories of Patients with COVID-19 Refute Population Mortality Data

Background: COVID-19 syndrome is a world-wide pandemic with multiple international publications demonstrating mortality for those admitted to intensive care to be between 46 – 88%. The experience at Chelsea & Westminster’s hospital has been dramatically different from those reported with a mortality of 24·4%. We sought to analyse this difference retrospectively in a case-cohort study. Methods: Whole case of 16 non-survivors with 15 closely matched survivors (age and APACHE-II) were analysed and compared to UK national published data. Patients trajectories were aligned according to the lowest PaO2/FiO2 ratio. Lymphocyte count, ferritin and organ support data were also aligned to PaO2/FiO2 ratio to demonstrate trends in patient trajectories. Findings: Following alignment, patient trajectories were in opposition to aggregated population data suggesting Simpson’s paradox is present in COVID-19 mortality data; a form of confounding. Population based data suggests that patients’s respiratory function declines till death but following alignment, it is shown that respiratory function and lymphocytes recover prior to death. Importance: Data from this study suggests that the day 10 – 15 nadir in respiratory function will improve. Consideration of withdrawal of care should be based on individual cases as aggregated data is misleading and late recovery may occur. Funding Statement: None applicable. Declaration of Interests: None declared by all authors. Ethics Approval Statement: The study was approved by the Chelsea and Westminster NHS Foundation Trust clinical governance team. As the report involves routinely collected non-identifiable clinical audit data, no ethical approval was required under the UK policy framework for health and social care.

Exact and Efficient Inference for Collective Flow Diffusion Model via Minimum Convex Cost Flow Algorithm

Collective Flow Diffusion Model (CFDM) is a general framework to find the hidden movements underlying aggregated population data. The key procedure in CFDM analysis is MAP inference of hidden variables. Unfortunately, existing approaches fail to offer exact MAP inferences, only approximate versions, and take a lot of computation time when applied to large scale problems. In this paper, we propose an exact and efficient method for MAP inference in CFDM. Our key idea is formulating the MAP inference problem as a combinatorial optimization problem called Minimum Convex Cost Flow Problem (C-MCFP) with no approximation or continuous relaxation. On the basis of this formulation, we propose an efficient inference method that employs the C-MCFP algorithm as a subroutine. Our experiments on synthetic and real datasets show that the proposed method is effective both in single MAP inference and people flow estimation with EM algorithm.

Parameter estimation using aggregate data

In biomedical/physiological/ecological experiments, it is common for measurements in time series data to be collected from multiple subjects. Often it is the case that a subject cannot be measured or identified at multiple time points (often referred to as aggregate population data). Due to a lack of alternative methods, this form of data is typically treated as if it is collected from a single individual. As we show by examples, this assumption leads to an overconfidence in model parameter (means, variances) values and model based predictions. We discuss these issues in the context of a mathematical model to determine T-cell behavior with cancer chimeric antigen receptor (CAR) therapies where during the collection of data cancerous mice are sacrificed at each measurement time.

Neural Collective Graphical Models for Estimating Spatio-Temporal Population Flow from Aggregated Data

We propose a probabilistic model for estimating population flow, which is defined as populations of the transition between areas over time, given aggregated spatio-temporal population data. Since there is no information about individual trajectories in the aggregated data, it is not straightforward to estimate population flow. With the proposed method, we utilize a collective graphical model with which we can learn individual transition models from the aggregated data by analytically marginalizing the individual locations. Learning a spatio-temporal collective graphical model only from the aggregated data is an ill-posed problem since the number of parameters to be estimated exceeds the number of observations. The proposed method reduces the effective number of parameters by modeling the transition probabilities with a neural network that takes the locations of the origin and the destination areas and the time of day as inputs. By this modeling, we can automatically learn nonlinear spatio-temporal relationships flexibly among transitions, locations, and times. With four real-world population data sets in Japan and China, we demonstrate that the proposed method can estimate the transition population more accurately than existing methods.

Impact of delayed effects on human old-age mortality1.

There is growing empirical evidence supporting theories of developmental origins of health and disease (DOHaD). However, the implications of DOHaD conjectures for aggregate population patterns of human disease, disability, mortality and aging are poorly understood. We empirically test two predictions derived from a formal model of aggregate population-level impacts of DOHaD. This model predicts that populations potentially influenced by delayed effects should experience singularities in their adult mortality patterns that can be empirically detected from aggregate data. We test predictions using a large mortality database for populations in the Latin American and Caribbean region (LAC) spanning nearly one hundred years of mortality history. Results are consistent. within explicit bounds of uncertainty, with expected patterns. We find that younger cohorts in countries whose mortality decline starts more recently experience deceleration in survival gains at older ages, attenuation of the rate of aging at older ages and a decline in the association between early childhood and adult mortality. Results point to the importance of adverse early conditions for human longevity. Future research should shed light on the impact on morbidity, disability and healthy life expectancy. To our knowledge this is the first time that implications of DOHaD conjectures for populations' mortality patterns are formulated precisely and empirically tested with aggregate population data.

Population model for the decline of Homalodisca vitripennis (Hemiptera: Cicadellidae) over a ten-year period

ABSTRACTThe glassy-winged sharpshooter, Homalodisca vitripennis (Germar), is an invasive pest which presents a major economic threat to grape industries in California, because it spreads a disease-causing bacterium, Xylella fastidiosa. In this note we develop a time and temperature dependent mathematical model to analyze aggregate population data for H. vitripennis from a 10-year study consisting of biweekly monitoring of H. vitripennis populations on unsprayed citrus, during which H. vitripennis decreased significantly. This model was fitted to the aggregate H. vitripennis time series data using iterative reweighted weighted least squares (IRWLS) with assumed probability distributions for certain parameter values. Results indicate that the H. vitripennis model fits the phenological and temperature data reasonably well, but the observed population decrease may possibly be attributed to factors other than the abiotic effect of temperature. A key factor responsible for this decline but not analyzed here could be biotic, for example, potentially parasitism of H. vitripennis eggs by Cosmocomoidea ashmeadi. A biological control program targeting H. vitripennis utilizing the mymarid egg parasitoid Cosmocomoidea (formerly Gonatocerus) ashmeadi (Girault) is described.

Applying a novel population-based model approach to estimating breath alcohol concentration (BrAC) from transdermal alcohol concentration (TAC) biosensor data.

Alcohol biosensor devices have been developed to unobtrusively measure transdermal alcohol concentration (TAC), the amount of ethanol diffusing through the skin, in nearly continuous fashion in naturalistic settings. Because TAC data are affected by physiological and environmental factors that vary across individuals and drinking episodes, there is not an elementary formula to convert TAC into easily interpretable metrics such as blood and breath alcohol concentrations (BAC/BrAC). In our prior work, we addressed this conversion problem in a deterministic way by developing physics/physiological-based models to convert TAC to estimated BrAC (eBrAC), in which the model parameter values were individually determined for each person wearing a specific transdermal sensor using simultaneously collected TAC (via a biosensor) and BrAC (via a breath analyzer) during a calibration episode. We found these individualized parameter values produced relatively good eBrAC curves for subsequent drinking episodes, but our results also indicated the models were not fully capturing the dynamics of the system and variations across drinking episodes. Here, we report on a novel mathematical framework to improve our ability to model eBrAC from TAC data that uses aggregate population data instead of individualized calibration data to determine model parameter values via a random diffusion equation. We first provide the theoretical mathematical basis for our approach, and then test the efficacy of this method using datasets of contemporaneous BrAC/TAC measurements obtained by a) a single subject during multiple drinking episodes and b) multiple subjects during single drinking episodes. For each dataset, we used a set of drinking episodes to construct the population model, and then ran the model with another set of randomly selected test episodes. We compared raw TAC data to model-simulated TAC curve, breath analyzer BrAC data to model eBrAC curve with 75% credible bands, episode summary scores of peak BrAC, times of peak BrAC, and area under the drinking curve also with 75% credible intervals, and report the percent of the raw BrAC captured within the eBrAC curve credible bands. We also display results when stratifying the data based on the relationship between the raw BrAC and TAC data. Results indicate the population-based model is promising, with better fit within a single participant when stratifying episodes. This study provides initial proof-of-concept for constructing, fitting, and using a population-based model to obtain estimates and error bands for BrAC from TAC. The advancements in this study, including new applications of math, the development of a population-based model with error bars, and the production of corresponding MATLAB codes, represent a major step forward in our ability to produce quantitatively- and temporally-accurate estimates of BrAC from TAC biosensor data.

Partner market opportunities and union formation over the life course—A comparison of different measures

AbstractEven in the online dating era, individuals will seek intimate partners who live physically nearby. This paper considers the validity of different partner market measures in German NUTS‐3 regions; the often used sex ratio is contrasted with different versions of the availability ratio (AR) and versions of the partner market density (PMD). The paper discusses (a) how conceptional aspects of the partner market are reflected in the various indicators. Using aggregated population data provided by German statistical offices, (b) the partner market in Germany is described as measured by the different partner market indicators. Linking them to micro‐level data from the German Socio‐economic Panel, the paper (c) compares the effects of the various partner market measures on union formation. A simple sex ratio does not show variance over the life course or between cohorts, nor does it determine the rate of couple formation. The AR and PMD reveal more plausible distributions by age and cohort and higher criterion validity, especially when accounting for availableness according to marital status. With regard to men, the AR demonstrates the strongest impact on union formation; with regard to women, PMD seems more important.

Excess mortality for people diagnosed with type 2 diabetes in 2012 – Estimates based on claims data from 70 million Germans

Background and aimsThe hazard ratio (HR) is a meaningful concept for comparing the mortality of people with and without type 2 diabetes (T2D). Nevertheless, there is only one German study estimating age-specific HRs. Thus, this study aimed to provide population-wide age-specific HRs for Germany using a novel method based on aggregated population data. Methods and resultsWe used an illness-death model and published data on T2D prevalence and incidence as well as mortality in the German general population to estimate age-specific HRs in the year 2012 for the population aged 65–90 years.For men, the overall HR was 2.3, which decreased from 2.8 between 65 and 69 years old to 1.6 between 85 and 90 years old. For women, the overall HR was 3.0, which decreased from 4.2 to 1.7 in the same age groups, respectively. ConclusionIn Germany, men and women in 2012 with T2D aged 65–90 years experienced a three-to four-fold higher mortality compared to people without T2D, which might indicate that the excess mortality could be higher than in countries with comparable health care systems. Female sex and younger age were associated with higher excess mortality.

Interoperable and accessible census and survey data from IPUMS

The first version of the Integrated Public Use Microdata Series (IPUMS) was released to users in 1993, and since that time IPUMS has come to stand for interoperable and accessible census and survey data. Initially created to harmonize U.S. census microdata over time, IPUMS now includes microdata from the U.S. and international censuses and from surveys on health, employment, and other topics. IPUMS also provides geo-spatial data, aggregate population data, and environmental data. IPUMS supports ten data products, each disseminating an integrated data collection with a set of tools that make complex data easy to find, access, and use. Key features are record-level integration to create interoperable datasets, user-friendly interfaces, and comprehensive metadata and documentation. The IPUMS philosophy aligns closely with the FAIR principles of findability, accessibility, interoperability, and re-usability. IPUMS data have catalyzed knowledge generation across a wide range of social science and other disciplines, as evidenced by the large volume of publications and other products created by the vast IPUMS user community.

Racial/Ethnic Health Disparities Among Rural Adults-United States, 2012-2015: MMWR Surveillance Summaries / November 17, 2017 / 66(23);1-9.

Rural communities often have worse health outcomes, have less access to care, and are less diverse than urban communities. Much of the research on rural health disparities examines disparities between rural and urban communities, with fewer studies on disparities within rural communities. This report provides an overview of racial/ethnic health disparities for selected indicators in rural areas of the United States. 2012-2015. Self-reported data from the 2012-2015 Behavioral Risk Factor Surveillance System were pooled to evaluate racial/ethnic disparities in health, access to care, and health-related behaviors among rural residents in all 50 states and the District of Columbia. Using the National Center for Health Statistics 2013 Urban-Rural Classification Scheme for Counties to assess rurality, this analysis focused on adults living in noncore (rural) counties. Racial/ethnic minorities who lived in rural areas were younger (more often in the youngest age group) than non-Hispanic whites. Except for Asians and Native Hawaiians and other Pacific Islanders (combined in the analysis), more racial/ethnic minorities (compared with non-Hispanic whites) reported their health as fair or poor, that they had obesity, and that they were unable to see a physician in the past 12 months because of cost. All racial/ethnic minority populations were less likely than non-Hispanic whites to report having a personal health care provider. Non-Hispanic whites had the highest estimated prevalence of binge drinking in the past 30 days. Although persons in rural communities often have worse health outcomes and less access to health care than those in urban communities, rural racial/ethnic minority populations have substantial health, access to care, and lifestyle challenges that can be overlooked when considering aggregated population data. This study revealed difficulties among non-Hispanic whites as well, primarily related to health-related risk behaviors. Across each population, the challenges vary. Stratifying data by different demographics, using community health needs assessments, and adopting and implementing the National Culturally and Linguistically Appropriate Services Standards can help rural communities identify disparities and develop effective initiatives to eliminate them, which aligns with a Healthy People 2020 overarching goal: achieving health equity.

Rapid disc diffusion antibiotic susceptibility testing for Pseudomonas aeruginosa, Acinetobacter baumannii and Enterococcus spp.

BackgroundWe investigated the feasibility of rapid disc diffusion antibiotic susceptibility testing (rAST) with reading of inhibition zones after 6 and/or 8 h of incubation for Enterococcus faecalis, Enterococcus faecium, Pseudomonas aeruginosa and Acinetobacter baumannii. In addition, we evaluated discrimination of resistant populations from the WT populations at early timepoints and the requirement for clinical breakpoint adaptations for proper interpretation of rAST data.MethodsIn total, 815 clinical strains [E. faecalis (n = 135), E. faecium (n = 227), P. aeruginosa (n = 295) and A. baumannii (n = 158)] were included in this study. Disc diffusion plates were streaked, incubated and imaged using the WASPLabTM automation system. WT populations and non-WT populations were defined using epidemiological cut-offs.Results and conclusionsrAST at 6 and 8 h was possible for A. baumannii and enterococci with readability of inhibition zones >90%. Overall categorical agreement of rAST at 6 h with AST at 18 h was 97.2%, 97.4% and 95.3% for E. faecalis, E. faecium and A. baumannii, respectively. With few exceptions, major categorization error rates were <1% for A. baumannii, and vancomycin-resistant E. faecium were clearly separated from the WT at 6 h. For P. aeruginosa the average readability of inhibition zones was 68.9% at 8 h and we found an overall categorical agreement of 94.8%. Adaptations of clinical breakpoints and/or introduction of technical buffer zones, preferably based on aggregated population data from various epidemiological settings, are required for proper interpretation of rAST.

Racial/Ethnic Health Disparities Among Rural Adults - United States, 2012-2015.

Problem/ConditionRural communities often have worse health outcomes, have less access to care, and are less diverse than urban communities. Much of the research on rural health disparities examines disparities between rural and urban communities, with fewer studies on disparities within rural communities. This report provides an overview of racial/ethnic health disparities for selected indicators in rural areas of the United States.Reporting Period2012–2015.Description of SystemSelf-reported data from the 2012–2015 Behavioral Risk Factor Surveillance System were pooled to evaluate racial/ethnic disparities in health, access to care, and health-related behaviors among rural residents in all 50 states and the District of Columbia. Using the National Center for Health Statistics 2013 Urban-Rural Classification Scheme for Counties to assess rurality, this analysis focused on adults living in noncore (rural) counties.ResultsRacial/ethnic minorities who lived in rural areas were younger (more often in the youngest age group) than non-Hispanic whites. Except for Asians and Native Hawaiians and other Pacific Islanders (combined in the analysis), more racial/ethnic minorities (compared with non-Hispanic whites) reported their health as fair or poor, that they had obesity, and that they were unable to see a physician in the past 12 months because of cost. All racial/ethnic minority populations were less likely than non-Hispanic whites to report having a personal health care provider. Non-Hispanic whites had the highest estimated prevalence of binge drinking in the past 30 days.InterpretationAlthough persons in rural communities often have worse health outcomes and less access to health care than those in urban communities, rural racial/ethnic minority populations have substantial health, access to care, and lifestyle challenges that can be overlooked when considering aggregated population data. This study revealed difficulties among non-Hispanic whites as well, primarily related to health-related risk behaviors. Across each population, the challenges vary.Public Health ActionStratifying data by different demographics, using community health needs assessments, and adopting and implementing the National Culturally and Linguistically Appropriate Services Standards can help rural communities identify disparities and develop effective initiatives to eliminate them, which aligns with a Healthy People 2020 overarching goal: achieving health equity.

Data-mining, GIS and multicriteria analysis in a comprehensive method for bicycle network planning and design

ABSTRACTIn many Brazilian cities, the most common procedure for planning cycling networks is using aggregated population data in census tracts, which may not take into account the true origin and destination of trips. It may also not identify potential users of a particular mode of transport. This is particularly important considering that implementing cycling infrastructures should be based on the assumption that they are able to meet the users' needs. Therefore, the aim of this study is to develop and adopt an objective method to design and compare cycling networks based on data-mining of disaggregated origin-destination data, GIS resources, and multicriteria analysis techniques. The method follows three steps: 1) identifying potential users based on real user profiles, 2) designing proposed cycling networks and 3) a comparison between the networks proposed in this study and those developed by the municipality selected as a case study, considering real and potential users, as well as cost and benefit criteria. As a positive outcome, using disaggregated data allows for a reasonable estimate of the number of people served by the networks, a detailed analysis of their proximity to the infrastructure, as well as identifying potential users. Comparing cycling networks considering cost and benefit criteria shows that the chosen criteria were effective. It was also determined that the cycling network of the studied city poorly serves bicycle transport users, if compared to the proposed networks. These findings indicate that appropriate methods for planning cycling networks are still needed.

Socioeconomic status and susceptibility to alcohol-related harm

Socioeconomic status and susceptibility to alcohol-related harm