Household Clustering Research Articles

An outbreak of hepatitis A virus infection in a secondary school in England with no undetected asymptomatic transmission among students.

In June 2019 the Health Protection Team in Yorkshire and Humber, England, was notified of cases of hepatitis A virus (HAV) infection in staff at a secondary school. Investigation revealed that an earlier case worked as a food handler in the school kitchen. Indirect transmission through food from the canteen was considered the most likely route of transmission. Cases were described according to setting of exposure. Oral fluid was obtained from students for serological testing. Environmental investigations were undertaken at settings where food handling was considered a potential transmission risk. Thirty-three confirmed cases were linked to the outbreak. All of those tested (n = 31) shared the same sequence with a HAV IB genotype. The first three cases were a household cluster and included the index case for the school. A further 19 cases (16 students, 3 staff) were associated with the school and consistent with indirect exposure to the food handler. One late onset case could not be ruled out as a secondary case within the school and resulted in vaccination of the school population. Five cases were linked to a bakery where a case from the initial household cluster worked as a food server. No concerns about hygiene standards were noted at either the school or the bakery. Oral fluid samples taken at the time of vaccination from asymptomatic students (n = 219, 11-16 years-old) showed no evidence of recent or current infection. This outbreak included household and foodborne transmission but limited (and possibly zero) person-to-person transmission among secondary school students. Where adequate hygiene exists, secondary transmission within older students may not occur.

SARS-CoV-2 seroprevalence, cumulative infections, and immunity to symptomatic infection – A multistage national household survey and modelling study, Dominican Republic, June–October 2021

Population-level SARS-CoV-2 immunological protection is poorly understood but can guide vaccination and non-pharmaceutical intervention priorities. Our objective was to characterise cumulative infections and immunological protection in the Dominican Republic. Household members ≥5 years were enrolled in a three-stage national household cluster serosurvey in the Dominican Republic. We measured pan-immunoglobulin antibodies against the SARS-CoV-2 spike (anti-S) and nucleocapsid glycoproteins, and pseudovirus neutralising activity against the ancestral and B.1.617.2 (Delta) strains. Seroprevalence and cumulative prior infections were weighted and adjusted for assay performance and seroreversion. Binary classification machine learning methods and pseudovirus neutralising correlates of protection were used to estimate 50% and 80% protection against symptomatic infection. Between 30 Jun and 12 Oct 2021 we enrolled 6683 individuals from 3832 households. We estimate that 85.0% (CI 82.1-88.0) of the ≥5 years population had been immunologically exposed and 77.5% (CI 71.3-83) had been previously infected. Protective immunity sufficient to provide at least 50% protection against symptomatic SARS-CoV-2 infection was estimated in 78.1% (CI 74.3-82) and 66.3% (CI 62.8-70) of the population for the ancestral and Delta strains respectively. Younger (5-14 years, OR 0.47 [CI 0.36-0.61]) and older (≥75-years, 0.40 [CI 0.28-0.56]) age, working outdoors (0.53 [0.39-0.73]), smoking (0.66 [0.52-0.84]), urban setting (1.30 [1.14-1.49]), and three vs no vaccine doses (18.41 [10.69-35.04]) were associated with 50% protection against the ancestral strain. Cumulative infections substantially exceeded prior estimates and overall immunological exposure was high. After controlling for confounders, markedly lower immunological protection was observed to the ancestral and Delta strains across certain subgroups, findings that can guide public health interventions and may be generalisable to other settings and viral strains. This study was funded by the US CDC.

Ancient Lowland Maya neighborhoods: Average Nearest Neighbor analysis and kernel density models, environments, and urban scale.

Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method-combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups-to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization.

Do Auto-Regressive Models Protect Privacy? Inferring Fine-Grained Energy Consumption From Aggregated Model Parameters

We investigate the extent to which statistical predictive models leak information about their training data. More specifically, based on the use case of household (electrical) energy consumption, we evaluate whether white-box access to auto-regressive (AR) models trained on such data together with background information, such as household energy data aggregates (e.g., monthly billing information) and publicly-available weather data, can lead to inferring fine-grained energy data of any particular household. We construct two adversarial models aiming to infer fine-grained energy consumption patterns. Both threat models use monthly billing information of target households. The second adversary has access to the AR model for a cluster of households containing the target household. Using two real-world energy datasets, we demonstrate that this adversary can apply maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> estimation to reconstruct daily consumption of target households with significantly lower error than the first adversary, which serves as a baseline. Such fine-grained data can essentially expose private information, such as occupancy levels. Finally, we use differential privacy (DP) to alleviate the privacy concerns of the adversary in dis-aggregating energy data. Our evaluations show that differentially private model parameters offer strong privacy protection against the adversary with moderate utility, captured in terms of model fitness to the cluster.

Persistent Ascaris Transmission Is Possible in Urban Areas Even Where Sanitation Coverage Is High.

In low-income, urban, informal communities lacking sewerage and solid waste services, onsite sanitation (sludges, aqueous effluent) and child feces are potential sources of human fecal contamination in living environments. Working in informal communities of urban Maputo, Mozambique, we developed a quantitative, stochastic, mass-balance approach to evaluate plausible scenarios of localized contamination that could explain why the soil-transmitted helminth Ascaris remains endemic despite nearly universal coverage of latrines that sequester most fecal wastes. We used microscopy to enumerate presumptively viable Ascaris ova in feces, fecal sludges, and soils from compounds (i.e., household clusters) and then constructed a steady-state mass-balance model to evaluate possible contamination scenarios capable of explaining observed ova counts in soils. Observed Ascaris counts (mean = -0.01 log10 ova per wet gram of soil, sd = 0.71 log10) could be explained by deposits of 1.9 grams per day (10th percentile 0.04 grams, 90th percentile 84 grams) of child feces on average, rare fecal sludge contamination events that transport 17 kg every three years (10th percentile 1.0 kg, 90th percentile 260 kg), or a daily discharge of 2.7 kg aqueous effluent from an onsite system (10th percentile 0.09 kg, 90th percentile 82 kg). Results suggest that even limited intermittent flows of fecal wastes in this setting can result in a steady-state density of Ascaris ova in soils capable of sustaining transmission, given the high prevalence of Ascaris shedding by children (prevalence = 25%; mean = 3.7 log10 per wet gram, sd = 1.1 log10), the high Ascaris ova counts in fecal sludges (prevalence = 88%; mean = 1.8 log10 per wet gram, sd = 0.95 log10), and the extended persistence and viability of Ascaris ova in soils. Even near-universal coverage of onsite sanitation may allow for sustained transmission of Ascaris under these conditions.

Use of surveillance data to elucidate household clustering of SARS-CoV-2 in Fulton County, Georgia a major metropolitan area

BackgroundHouseholds are important for SARS-CoV-2 transmission due to high intensity exposure in enclosed spaces over prolonged durations. We quantified and characterized household clustering of COVID-19 cases in Fulton County, Georgia.MethodsWe used surveillance data to identify all confirmed COVID-19 cases in Fulton County. Household clustered cases were defined as cases with matching residential address. We described the proportion of COVID-19 cases that were clustered, stratified by age over time and explore trends in age of first diagnosed case within households and subsequent household cases.ResultsBetween 6/1/20–10/31/21, 31,449(37%) of 106,233 cases were clustered in households. Children were the most likely to be in household clusters than any other age group. Initially, children were rarely(∼ 10%) the first cases diagnosed in the household but increased to almost 1/3 in later periods.DiscussionOne-third of COVID-19 cases in Fulton County were part of a household cluster. Increasingly children were the first diagnosed case, coinciding with temporal trends in vaccine roll-out among the elderly and the return to in-person schooling in Fall 2021. Limitations include restrictions to cases with a valid address and unit number and that the first diagnosed case may not be the infection source for the household.

Spatial inequality of domestic water consumption in Mexico city

Achieving equal access to drinkable water whilst guaranteeing no discrimination constitutes a priority goal and an acquired compromise for the international agenda. However, achieving this goal has become challenging, especially in megacities. Mexico City registers 94% coverage of water supply, according to the 2020 Population and Housing Census. In 2019, per capita water consumption was 123 l/day, a supply reasonable as it is above the 100 l/day recommended by the World Health Organization to guarantee optimal access to water. However, water consumption among households is characterized by a heterogeneous spatial distribution that denotes a great inequity. The objective of this research is to evaluate the configuration of the spatial patterns that define the differences in water consumption in Mexico City's households, as well as their association with possible causal factors. This task was carried out through the spatial analysis and the use of geographic information systems. The water consumption records correspond to the year 2019 and were provided by the city's operating utility. Results confirmed the existence of two clusters of households whose location highlight areas where inequality in water consumption is present. The clusters are explained by the interaction of four factors, in order of importance: discontinuous water supply; dependence on external water sources; altitude; and the socioeconomic status of the neighborhoods. The results provide a fundamental basis for the development of water policies to mitigate inequality. Also, a methodological approach is provided to investigate water problems in large cities in developing countries, where the available literature is scarce. • Spatial analysis demonstrates the existence of a spatial distribution pattern of household consumption in Mexico City. • In Mexico City there are three clusters that show inequality in domestic water consumption. • Consumption water inequalities are mainly due to the influence of socioeconomic, demographic and drinking water supply factors.

Effect of patient-delivered household contact tracing and prevention for tuberculosis: A household cluster-randomised trial in Malawi.

Household contact tracing provides TB screening and TB preventive therapy (TPT) to contacts at high risk of TB disease. However, it is resource intensive, inconvenient, and often poorly implemented. We investigated a novel model aiming to improve uptake. Between May and December 2014, we randomised patient with TB who consented to participate in the trial to either standard of care (SOC) or intervention (PACTS) arms. Participants randomised to PACTS received one screening/triage tool (adapted from WHO integrated management of adolescent and adult illnesses [IMAI] guidelines) and sputum pots for each reported household contact. The tool guided participants through symptom screening; TPT (6-months of isoniazid) eligibility; and sputum collection for contacts. Patients randomised to SOC were managed in accordance with national guidelines, that is, they received verbal instruction on who to bring to clinics for investigation using national guidelines. The primary outcome was the proportion of adult contacts receiving treatment for TB within 3 months of randomisation. Secondary outcomes were the proportions of child contacts under age 5 years (U5Y) who were commenced on, and completed, TPT. Data were analyzed by logistic regression with random effects to adjust for household clustering. Two hundred and fourteen index TB participants were block-randomized from two sites (107 PACTS, reporting 418 contacts; and 107 SOC, reporting 420 contacts). Overall, 62.8% of index TB participants were HIV-positive and 52.1% were TB culture-positive. 250 otherwise eligible TB patients declined participation and 6 households (10 PACTS, 6 SOC) were lost to follow-up and were not included in the analysis. By three months, nine contacts (PACTS: 6, [1.4%]; SOC: 3, [0.7%]) had TB diagnosed, with no difference between groups (adjusted odds ratio [aOR]: 2.18, 95% CI: 0.60-7.95). Eligible PACTS contacts (37/96, 38.5%) were more likely to initiate TPT by 3-months compared to SOC contacts (27/101, 26.7%; aOR 2.27, 95% CI: 1.04-4.98). U5Y children in the PACTS arm (47/81 58.0%) were more likely to have initiated TPT before the 3-month visit compared to SOC children (36/89, 41.4%; aOR: 2.31, 95% CI: 1.05-5.06). A household-centred patient-delivered symptom screen and IPT eligibility assessment significantly increased timely TPT uptake among U5Y children, but did not significantly increase TB diagnosis. This model needs to be optimized for acceptability, given low participation, and investigated in other low resource settings. TRIAL REGISTRATION NUMBER: ISRCTN81659509 https://www.isrctn.com/ISRCTN81659509?q=&filters=conditionCategory:Respiratory,recruitmentCountry:Malawi,ageRange:Mixed&sort=&offset=1&totalResults=1&page=1&pageSize=10&searchType=basic-search. 19 July 2012.

Stochastic modelling of flexible load characteristics of split-type air conditioners using grey-box modelling and random forest method

Split-type air conditioners (ACs) are the major contributors to the total electricity use and peak power demand in residential buildings due to their high penetration. Accurate prediction of their energy consumption plays a significant role in demand side management (DSM), because it can help exploit the demand response (DR) potential of these ACs fully. However, the existing studies on the load characteristics modelling of split-type ACs are still not comprehensive, especially in the consideration of occupant behavior and appliance characteristics. To solve this problem, this study has developed a novel flexible load characteristics model by combining the stochastic method, grey-box modelling (i.e., equivalent thermal parameter (ETP) model) and random forest method. The stochastic method can help capture the dynamic occupants’ energy-related behaviors considering various family structures. Random forest can address the difficulties in simulating the power characteristics of variable-speed ACs under various operating conditions. With the prediction error of 2.8%, the proposed model can effectively characterize the energy performance of split-type ACs at both individual and aggregate levels. To better show the load flexibility of ACs, an investigation on DR potential of split-type ACs was also carried out by resetting temperature setpoints. The result showed that the maximum DR potentials at the scope of 1000 aggregated households are 394.54 kWh and 323.82 kWh on weekdays and weekends, respectively. The developed flexible load characteristics model of ACs can be used by utility companies for DR potential assessment and model-based DR control for a single household or building clusters.

Market access and dietary diversity: A spatially explicit multi-level analysis in Southern and Western Kenya

The risk of malnutrition, particularly micronutrient deficiency, is high in large parts of Sub-Saharan Africa for smallholder farmers. Access to diverse and nutritious food is a key component of food security, and a major development objective. It is widely accepted that good access to markets can play a key role in improving nutrition at the foodshed level. However, the magnitude and even the direction of the effect of increased market access on household dietary diversity (and thus food security) is not universal, with studies showing divergent results. One reason for these divergences may be that models do not account for place-based mediation effects, that is, farmers' local context can affect whether (and the extent to which) access to market is important to their nutrition. Drawing on household survey data from 914 Kenyan smallholder farmers from ten counties in South and West Kenya, we used a novel methodology to evaluate the role of market access in determining household dietary diversity. This methodology combines the clustering of households along places with similar characteristics and multi-level regression analysis to understand the place based variation in effects of different factors on dietary diversity. We found that, depending on how “access to market” is measured, there can be significant impacts on dietary diversity, and this is mediated by farm characteristics. For small farms with already good market access, higher diet diversity is associated with cultivating larger areas and owning larger livestock holdings, but not with easier market access. For isolated larger farms with a focus on livestock production, higher diet diversity is associated with easier market access (i.e., proximity to road), as well as greater livestock diversity. For medium-sized farms with good market access, diet diversity is mildly correlated with easier market access (i.e., proximity to road) but significantly associated with greater crop diversity. The need to account for place-based mediating effects is clearly important and highlights an exigency for greater use and development of localized models that can capture the extent to which effects might change when contexts change.

Income differences in COVID-19 incidence and severity in Finland among people with foreign and native background: A population-based cohort study of individuals nested within households.

Although intrahousehold transmission is a key source of Coronavirus Disease 2019 (COVID-19) infections, studies to date have not analysed socioeconomic risk factors on the household level or household clustering of severe COVID-19. We quantify household income differences and household clustering of COVID-19 incidence and severity. We used register-based cohort data with individual-level linkage across various administrative registers for the total Finnish population living in working-age private households (N = 4,315,342). Incident COVID-19 cases (N = 38,467) were identified from the National Infectious Diseases Register from 1 July 2020 to 22 February 2021. Severe cases (N = 625) were defined as having at least 3 consecutive days of inpatient care with a COVID-19 diagnosis and identified from the Care Register for Health Care between 1 July 2020 and 31 December 2020. We used 2-level logistic regression with individuals nested within households to estimate COVID-19 incidence and case severity among those infected. Adjusted for age, sex, and regional characteristics, the incidence of COVID-19 was higher (odds ratio [OR] 1.67, 95% CI 1.58 to 1.77, p < 0.001, 28.4% of infections) among individuals in the lowest household income quintile than among those in the highest quintile (18.9%). The difference attenuated (OR 1.23, 1.16 to 1.30, p < 0.001) when controlling for foreign background but not when controlling for other household-level risk factors. In fact, we found a clear income gradient in incidence only among people with foreign background but none among those with native background. The odds of severe illness among those infected were also higher in the lowest income quintile (OR 1.97, 1.52 to 2.56, p < 0.001, 28.0% versus 21.6% in the highest quintile), but this difference was fully attenuated (OR 1.08, 0.77 to 1.52, p = 0.64) when controlling for other individual-level risk factors-comorbidities, occupational status, and foreign background. Both incidence and severity were strongly clustered within households: Around 77% of the variation in incidence and 20% in severity were attributable to differences between households. The main limitation of our study was that the test uptake for COVID-19 may have differed between population subgroups. Low household income appears to be a strong risk factor for both COVID-19 incidence and case severity, but the income differences are largely driven by having foreign background. The strong household clustering of incidence and severity highlights the importance of household context in the prevention and mitigation of COVID-19 outcomes.

Remote bednet use monitoring to describe patterns of use and exposure to female Anopheles mosquitoes in an Ugandan cohort.

Long lasting insecticide-treated bednets (LLINs) are the most widely used tool for preventing malaria. There has been a plateau in progress in the highest burden African countries since 2015, leading to questions about the effectiveness of LLINs. In this study, remote LLIN use monitors were deployed in a cohort in Eastern Uganda to explore how LLIN use interacts with mosquito exposure. The SmartNet study included 20 households from May to October 2019. SmartNet devices recorded, every 15 min, whether an LLIN was unfurled or folded up. Unannounced visits were used to assess SmartNet accuracy. Risk factors associated with poor LLIN use were assessed using generalized linear equations. Female Anopheles exposure was estimated by combining hourly probabilities of exposure from human landing catches and measures of density from biweekly CDC light traps in participants rooms. Mosquito exposure averted by LLINs was quantified using SmartNet measurements and age-related differences were estimated using generalized linear equations, adjusting for relevant covariates and household clustering. 96 individuals contributed 5,640 SmartNet observation nights. In 126 unannounced visits, SmartNet had an area under the curve of 0.869 in classifying whether the LLIN was up or down. The rate of non-use was 13.5% of nights (95% CI: 12.6-14.3%). Compared to children under 5, non-use was 1.8 times higher (95% CI: 1.6-2.1; p < 0.001) in children 5-15 years and 2.6 times higher (95% CI: 2.2-3.1; p < 0.001) in participants aged 15-<30years. There was no difference between children under 5 years and adults > 30 years. LLIN use averted 50.3% of female Anopheles mosquito exposure (95% CI: 40.0-60.0%), with decreasing point estimates of efficacy across age groups: from 61.7% (95% CI: 42.6-80.7%) in children under 5 years to 48.0% (95% CI: 29.1-66.8%) in adults over 30. Objective monitors are accurate and can feasibly be deployed to obtain data about LLIN use. LLINs provided protection from only 50% of female Anopheles mosquito exposure in this cohort and protection was dependent upon age. In assessing the role of LLINs in malaria prevention it is crucial to consider the dynamics between mosquito exposure and LLIN use behaviors.

Impact of Age and Symptom Development on SARS-CoV-2 Transmission in Households With Children-Maryland, New York, and Utah, August 2020-October 2021.

BackgroundHouseholds are common places for spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We investigated factors associated with household transmission and acquisition of SARS-CoV-2.MethodsHouseholds with children age <18 years were enrolled into prospective, longitudinal cohorts and followed from August 2020 to August 2021 in Utah, September 2020 to August 2021 in New York City, and November 2020 to October 2021 in Maryland. Participants self-collected nasal swabs weekly and with onset of acute illness. Swabs were tested for SARS-CoV-2 using reverse transcription polymerase chain reaction. We assessed factors associated with SARS-CoV-2 acquisition using a multilevel logistic regression adjusted for household size and clustering and SARS-CoV-2 transmission using a logistic regression adjusted for household size.ResultsAmong 2053 people (513 households) enrolled, 180 people (8.8%; in 76 households) tested positive for SARS-CoV-2. Compared with children age <12 years, the odds of acquiring infection were lower for adults age ≥18 years (adjusted odds ratio [aOR], 0.34; 95% CI, 0.14–0.87); however, this may reflect vaccination status, which protected against SARS-CoV-2 acquisition (aOR, 0.17; 95% CI, 0.03–0.91). The odds of onward transmission were similar between symptomatic and asymptomatic primary cases (aOR, 1.00; 95% CI, 0.35–2.93) and did not differ by age (12–17 years vs <12 years: aOR, 1.08; 95% CI, 0.20–5.62; ≥18 years vs <12 years: aOR, 1.70; 95% CI, 0.52–5.83).ConclusionsAdults had lower odds of acquiring SARS-CoV-2 compared with children, but this association might be influenced by coronavirus disease 2019 (COVID-19) vaccination, which was primarily available for adults and protective against infection. In contrast, all ages, regardless of symptoms and COVID-19 vaccination, had similar odds of transmitting SARS-CoV-2. Our findings underscore the importance of SARS-CoV-2 mitigation measures for persons of all ages.

Who Pays Sin Taxes? Understanding the Overlapping Burdens of Corrective Taxes

Abstract We find that sin-good purchases are highly concentrated, with 10% of households paying more than 80% of taxes on alcohol and cigarettes. Total sin-tax burdens are poorly explained by demographics (including income), but are well explained by eight household clusters defined by purchasing patterns. The two most taxed clusters comprise 8% of households, pay 63% of sin taxes, are older, less educated, and lower income. Taxes on sugary beverages broaden the tax base but add to the burdens of heavily taxed households. Efforts to increase sin taxes should consider the heavy burdens borne by few households.

Profiling Risk Factors for Household and Community Spatiotemporal Clusters of Q Fever Notifications in Queensland between 2002 and 2017.

Q fever, caused by the bacterium Coxiella burnetii, is an important zoonotic disease worldwide. Australia has one of the highest reported incidences and seroprevalence of Q fever, and communities in the state of Queensland are at highest risk of exposure. Despite Australia’s Q fever vaccination programs, the number of reported Q fever cases has remained stable for the last few years. The extent to which Q fever notifications cluster in circumscribed communities is not well understood. This study aimed to retrospectively explore and identify the spatiotemporal variation in Q fever household and community clusters in Queensland reported during 2002 to 2017, and quantify potential within cluster drivers. We used Q fever notification data held in the Queensland Notifiable Conditions System to explore the geographical clustering patterns of Q fever incidence, and identified and estimated community Q fever spatiotemporal clusters using SatScan, Boston, MA, USA. The association between Q fever household and community clusters, and demographic and socioeconomic characteristics was explored using the chi-squared statistical test and logistic regression analysis. From the total 2175 Q fever notifications included in our analysis, we found 356 Q fever hotspots at a mesh-block level. We identified that 8.2% of Q fever notifications belonged to a spatiotemporal cluster. Within the spatiotemporal Q fever clusters, we found 44 (61%) representing household clusters and 20 (27.8%) were statistically significant with an average cluster size of 3 km radius. Our multivariable model shows statistical differences between cases belonging to clusters in comparison with cases outside clusters based on the type of reported exposure. In conclusion, our results demonstrate that clusters of Q fever notifications are temporally stable and geographically circumscribed, indicating a persistent common exposure. Furthermore, within individuals in household and community clusters, abattoir exposure (a traditional occupational exposure) was rarely reported by individuals.

Epidemiology and Transmission Dynamics of COVID-19 in an Urban Pediatric US Population.

Objective:This analysis summarizes observational epidemiologic data and transmission dynamics of SARS-CoV-2 among people aged <18 years to better characterize the pediatric COVID-19 pandemic.Methods:We conducted a retrospective study of public health surveillance data among children in Denver, Colorado, who were reported to have COVID-19 from March 1, 2020, through September 30, 2021. We used descriptive statistics and bivariate rate ratios (RRs) to describe demographic and clinical characteristics, transmission dynamics, case trends, and ecological associations.Results:A total of 9815 children and adolescents who had COVID-19 were reported during the study period. Adolescents aged 14-17 years had the highest incidence rate (IR) per 1000 people (IR = 107.5; 3021 of 28 108). Hispanic/Latino children had a 1.6 times higher rate of infection than non-Hispanic White children (RR = 1.57; 95% CI, 1.50-1.65; P < .001). Few hospitalizations (n = 138, 1.4%) and deaths (n = 3, 0%) occurred. Most children were symptomatic (4487 of 5499, 81.6%). Within household clusters, a large proportion of pediatric cases (n = 6136) were a secondary case (n = 3959, 64.5%), followed by index case (n = 1170, 19.1%) and co–index case (n = 1007, 16.4%). Non-Hispanic White children had an increased risk of being an index or co–index case (RR = 1.14; 95% CI, 1.06-1.23; P < .001), while Hispanic/Latino children had an increased risk of being a secondary case (RR = 1.07; 95% CI, 1.03-1.11; P < .001). From 2020 to 2021, the association between pediatric case rates and neighborhoods with higher poverty and households with ≥3 people decreased.Conclusions:Older children and those identifying as Hispanic/Latino had a disproportionate incidence of disease. A sizable proportion of children were considered index cases or co–index cases. Pediatric prevention strategies, especially vaccinations, are vital for pandemic control.

Bayesian Network Analysis of Lymphatic Filariasis Serology from Myanmar Shows Benefit of Adding Antibody Testing to Post-MDA Surveillance.

The elimination of lymphatic filariasis (LF) is achieved through repeated mass drug administration (MDA) of anti-filarial medications, which interrupts transmission and prevents new infections. Accurate transmission assessments are critical to deciding when to stop MDA. Current methods for evaluating transmission may be insufficiently sensitive, resulting in post-MDA resurgence. We, therefore, evaluated potential diagnostic testing scenarios for post-MDA surveillance. Data were used from two surveys (a household cluster and a cohort) conducted in an area of Mandalay Region, Myanmar, with ongoing transmission following several rounds of MDA. First, age- and sex-adjusted seroprevalence were estimated for the area using the household survey. Next, three Bayesian networks were built from the combined datasets to compare antigens by immunochromatic testing (ICT) and/or Og4C3 enzyme-linked immunosorbent assay (ELISA) and antibody (Ab) detection methods (Wb123 or Bm14 Ab ELISA). The networks were checked for validity and then used to compare diagnostic testing scenarios. The adjusted prevalence from the household survey for antigen, Wb123 Ab and Bm14 Ab were 4.4% (95% CI 2.6–7.3%), 8.7% (5.96–12.5%) and 20.8% (16.0–26.6%), respectively. For the three networks, the True Skill Statistic and Area Under the Receiver Operating Characteristic Curve for antigen, Wb123 and Bm14 Ab were 0.79, 0.68 and 0.55; and 0.97, 0.92 and 0.80, respectively. In the Bayesian network analysis, a positive case was defined as testing positive to one or more infection markers. A missed result was therefore the probability of a positive case having a negative test result to an alternate marker. The probability of a positive case prior to any testing scenario was 17.4%, 16.8% and 26.6% for antigen, Wb123 Ab and Bm14 Ab, respectively. In the antigen-only testing scenario, the probability of a missed positive LF result was 5.2% for Wb123 and 15.6% for Bm14 Ab. The combination of antigen plus Bm14 Ab testing reduced the probability of missing a positive LF case as measured by Wb123 Ab to 0.88%. The combination of antigen plus Wb123 Ab was less successful and yielded an 11.5% probability of a missed positive result by Bm14 Ab testing. Across scenarios, there was a greater discordance between Bm14 and both antigen and Wb123 Ab in the 1–10 age group compared to older ages. These findings suggest that the addition of Bm14 Ab improves the sensitivity of LF testing for current or past infection. The combination of antigen plus Bm14 Ab should therefore be considered for inclusion in post-MDA surveillance to improve the sensitivity of transmission surveys and prevent the premature cessation of MDA.

Households and Hamlets of the Brześć Kujawski Group

Abstract Over several decades, we have written extensively about the household as a fundamental organizational unit of the Neolithic society. Starting from our definition of the household cluster at Brześć Kujawski 3 and the detailed analysis of household activities at House 56 at Brześć Kujawski 4, we continue to consider the household as the locus of decision-making and resource allocation even as our thinking has evolved over the intervening years. At the same time, Neolithic households functioned within local concentrations of settlement in which they interacted with other such units. We characterize such local social entities as “hamlets,” adapting the definition used by the anthropologist Frank Cancian to refer to institutionalized alliances of domestic groups in which affiliation is demonstrated through residential proximity. We have studied two such hamlets of the Brześć Kujawski Group: the Osłonki-Miechowice-Konary hamlet and the Smętowo hamlet consisting of Brześć Kujawski and Pikutkowo. We characterize these hamlets to show how they extend our original conception of household-based societies to develop a robust understanding of local Neolithic communities.

The Unmeasured Burden of Febrile, Respiratory, and Diarrheal Illnesses Identified Through Active Household Surveillance in a Low Malaria Transmission Setting in Southern Zambia.

ABSTRACT.Malaria incidence has declined in southern Zambia over recent decades, leading to efforts to achieve and sustain malaria elimination. Understanding the remaining disease burden is key to providing optimal health care. A longitudinal study conducted in a rural area of Choma District, Southern Province, Zambia, assessed the prevalence of and factors associated with symptoms of non-malarial illnesses and treatment-seeking behavior. We analyzed data collected monthly between October 2018 through September 2020 from 1,174 individuals from 189 households. No incident malaria cases were detected by rapid diagnostic tests among febrile participants. Mixed-effects logistic regression identified factors associated with cough, fever, diarrhea, and treatment-seeking. Incidence rates of cough (192 of 1,000 person-months), fever (87 of 1,000 person-months), and fever with cough (37 of 1,000 person-months) were highest among adults older than 65 years. Diarrhea incidence (37 of 1,000 person-months) was highest among children younger than 5 years. For every additional symptomatic household member, one’s odds of experiencing symptoms increased: cough by 47% (95% CI, 40–55), fever by 31% (95% CI, 23–40), diarrhea by 31% (95% CI, 17–46), and fever with cough by 112% (95% CI, 90–137), consistent with household clustering of illnesses. However, between 35% and 75% of participants did not seek treatment for their symptoms. Treatment-seeking was most common for children 5 to 9 years old experiencing diarrhea (adjusted odds ratio, 3.61; 95% CI, 1.42–9.18). As malaria prevalence reduces, respiratory and diarrheal infections persist, particularly among young children but, notably, also among adults older than 65 years. Increasing awareness of the disease burden and treatment-seeking behavior are important for guiding resource re-allocation as malaria prevalence declines in this region.

Developing a population data science approach to assess increased risk of COVID-19 associated with attending large events

IntroductionIn summer 2021, as rates of COVID-19 decreased and social restrictions were relaxed, live entertainment and sporting events were resumed. In order to inform policy on the safe re-introduction of spectator events, a number of test events were organised in Wales, ranging in setting, size and audience. ObjectivesTo design and test a method to assess whether test events were associated with an increase in risk of confirmed COVID-19, in order to inform policy. MethodsWe designed a cohort study with fixed follow-up time and measured relative risk of confirmed COVID-19 in those attending two large sporting events. First, we linked ticketing information to individual records on the Welsh Demographic Service (WDS), a register of all people living in Wales and registered with a GP, and identified NHS numbers for attendees. Where NHS numbers were not found we used combinations of other identifiers such as email, name, postcode and/or mobile number. We then linked attendees to routine SARS-CoV-2 test data to calculate positivity rates in people attending each event for the period one to fourteen days following the event. We selected a comparison cohort from WDS for each event, individually matched by age band, gender and locality of residence. As many people attended events in family groups we explored the possibility of also matching on household clusters within the comparison group. Risk ratios were then computed for the two events. ResultsWe successfully assigned NHS numbers to 91% and 84% of people attending the two events respectively. Other identifiers were available for the remainder. Only a small number of attendees (&lt;10) had a record of confirmed COVID-19 following attendance at each event (14 day cumulative incidence: 36 and 26 per 100,000, respectively). There was no evidence of significantly increased risk of COVID-19 at either event. However, the event that didn't include pre-event testing in their mitigations, had a higher risk ratio (3.0 compared to 0.3). ConclusionsWe demonstrate the potential for using population data science methods to inform policy. We conclude that, at that point in the epidemic, and with the mitigations that were in place, attending large outdoor sporting events did not significantly increase risk of COVID-19. However, these analyses were carried out between epidemic waves when background incidence and testing rate was low, and need to be repeated during periods of greater transmission. Having a mechanism to identify attendees at events is necessary to calculate risk and feasibility and acceptability of data sharing should be considered.