Quantifying social contact patterns in Minnesota during stay-at-home social distancing order.

SARS-CoV-2 is primarily transmitted through person-to-person contacts. It is important to collect information on age-specific contact patterns because SARS-CoV-2 susceptibility, transmission, and morbidity vary by age. To reduce the risk of infection, social distancing measures have been implemented. Social contact data, which identify who has contact with whom especially by age and place are needed to identify high-risk groups and serve to inform the design of non-pharmaceutical interventions. We estimated and used negative binomial regression to compare the number of daily contacts during the first round (April-May 2020) of the Minnesota Social Contact Study, based on respondent's age, gender, race/ethnicity, region, and other demographic characteristics. We used information on the age and location of contacts to generate age-structured contact matrices. Finally, we compared the age-structured contact matrices during the stay-at-home order to pre-pandemic matrices. During the state-wide stay-home order, the mean daily number of contacts was 5.7. We found significant variation in contacts by age, gender, race, and region. Adults between 40 and 50 years had the highest number of contacts. The way race/ethnicity was coded influenced patterns between groups. Respondents living in Black households (which includes many White respondents living in inter-racial households with black family members) had 2.7 more contacts than respondents in White households; we did not find this same pattern when we focused on individual's reported race/ethnicity. Asian or Pacific Islander respondents or in API households had approximately the same number of contacts as respondents in White households. Respondents in Hispanic households had approximately two fewer contacts compared to White households, likewise Hispanic respondents had three fewer contacts than White respondents. Most contacts were with other individuals in the same age group. Compared to the pre-pandemic period, the biggest declines occurred in contacts between children, and contacts between those over 60 with those below 60.

Analyzing the demographic, spatial, and temporal factors influencing social contact patterns in U.S. and implications for infectious disease spread.

BACKGROUND: Diseases such as COVID-19 are spread through social contact. Reducing social contacts is required to stop disease spread in pandemics for which vaccines have not yet been developed. However, existing data on social contact patterns in the United States (U.S.) is limited. METHOD: We use American Time Use Survey data from 2003-2018 to describe and quantify the age-pattern of disease-relevant social contacts. For within-household contacts, we construct age-structured contact duration matrices (who spends time with whom, by age). For both within-household and non-household contacts, we also estimate the mean number and duration of contact by location. We estimate and test for differences in the age-pattern of social contacts based on demographic, temporal, and spatial characteristics. RESULTS: The mean number and duration of social contacts vary by age. The biggest gender differences in the age-pattern of social contacts are at home and at work; the former appears to be driven by caretaking responsibilities. Non-Hispanic Blacks have a shorter duration of contact and fewer social contacts than non-Hispanic Whites. This difference is largely driven by fewer and shorter contacts at home. Pre-pandemic, non-Hispanic Blacks have shorter durations of work contacts. Their jobs are more likely to require close physical proximity, so their contacts are riskier than those of non-Hispanic Whites. Hispanics have the highest number of household contacts and are also more likely to work in jobs requiring close physical proximity than non-Hispanic Whites. With the exceptions of work and school contacts, the duration of social contact is higher on weekends than on weekdays. Seasonal differences in the total duration of social contacts are driven by school-aged respondents who have significantly shorter contacts during the summer months. Contact patterns did not differ by metro status. Age patterns of social contacts were similar across regions. CONCLUSION: Social contact patterns differ by age, race and ethnicity, and gender. Other factors besides contact patterns may be driving seasonal variation in disease incidence if school-aged individuals are not an important source of transmission. Pre-pandemic, there were no spatial differences in social contacts, but this finding has likely changed during the pandemic.

Exploring Strategies for Investigating the Mechanisms Linking Climate and Individual-Level Child Health Outcomes: An Analysis of Birth Weight in Mali.

The goal of this article is to consider data solutions to investigate the differential pathways that connect climate/weather variability to child health outcomes. We apply several measures capturing different aspects of climate/weather variability to different time periods of in utero exposure. The measures are designed to capture the complexities of climate-related risks and isolate their impacts based on the timing and duration of exposure. Specifically, we focus on infant birth weight in Mali and consider local weather and environmental conditions associated with the three most frequently posited potential drivers of adverse health outcomes: disease (malaria), heat stress, and food insecurity. We focus this study on Mali, where seasonal trends facilitate the use of measures specifically designed to capture distinct aspects of climate/weather conditions relevant to the potential drivers. Results indicate that attention to the timing of exposures and employing measures designed to capture nuances in each of the drivers provides important insight into climate and birth weight outcomes, especially in the case of factors impacted by precipitation. Results also indicate that high temperatures and low levels of agricultural production are consistently associated with lower birth weights, and exposure to malarious conditions may increase likelihood of nonlive birth outcomes.

High Burden of Non-communicable Diseases among a Young Slum Population in Haiti.

The objective of this study was to characterize the demographics and population health of four slum communities in Port-au-Prince, Haiti, including population density and the burden of communicable and non-communicable diseases. Four urban slums were surveyed using a population-representative design between July and October 2016. A multistage cluster area random sampling process was used to identify households and individuals for the survey. Household surveys included rosters of residents, household characteristics, adult and child deaths in the past year, child health, and healthcare access and utilization. Individual surveys of two randomly sampled adults from each household included sociodemographic data, maternal health, and adult health. Additionally, blood pressure, height, weight, and psychological distress were measured by study staff. Data were weighted for complex survey design and non-response. A total of 525 households and 894 individuals completed the survey (96% household and 90% individual response rate, respectively). The estimated population density was 58,000 persons/km2. Across slums, 55% of all residents were female, and 38% were adolescents and youth 10-24 years. Among adults, 58% were female with median age 29 years (22-38). The most common adult illnesses were severe psychological distress (24%), hypertension (20%), history of physical injury/trauma (10%), asthma (7%), history of cholera (4%), and history of tuberculosis (3%). Ten percent of adults had obesity (BMI > 30 kg/m2), and 7% currently smoked. The most common under-5 diseases during the last 3 months were respiratory and gastrointestinal illnesses (50% and 28%, respectively). One-third of households reported needing medical care for a child in the past year but not being able to access it, largely due to financial constraints. Unique features of these slums are a population structure dominated by adolescents and youth, a high proportion of females, and a high burden of non-communicable diseases including hypertension and psychological distress. Screening, diagnostic, and disease management interventions are urgently needed to protect and promote improved population health outcomes in these slum communities.

Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation.

The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models.Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites.In this paper we discuss the deficiencies of existing spatial population datasets and their limitations on epidemiological analyses. We review sources of detailed, contemporary, freely available and relevant spatial demographic data focusing on low income regions where such data are often sparse and highlight the value of incorporating these through a set of examples of their application in disease studies. Moreover, the importance of acknowledging, measuring, and accounting for uncertainty in spatial demographic datasets is outlined. Finally, a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications is put forward.

Psychological distress among a population-representative sample of residents of four slum neighborhoods in Port-au-Prince, Haiti.

BACKGROUND: Almost one billion people live in slum environments across low- and middle-income countries. Little is known about the mental health status of slum residents or its associations with living conditions. METHODS: A cross-sectional, population-representative survey was conducted among 892 adults in four slum communities in Port-au-Prince. Psychological distress was assessed with the Kessler Psychological Distress Scale (K-6). Log-binomial regression modeled the association of sociodemographic variables, living conditions, and material hardship and severe psychological distress [SPD]. RESULTS: Eighty-six percent of adults reported psychological distress (24% severe and 62% moderate). Reliance on an outdoor drinking water source (versus bottled water) and a pit toilet (versus a flush toilet) were marginally positively associated with SPD (adjusted prevalence ratio [aPR]=1.42, 95% confidence interval [CI]: 1.00-2.02 and aPR=1.74, 95% CI: 0.96-3.15, respectively). The prevalence of SPD was higher among women (versus men, aPR=1.66, 95% CI: 1.26-2.19), residents who had foregone healthcare to afford food (versus those who had never done so, aPR=1.60, 95% CI: 1.16-2.45), and persons who drank alcohol at least twice a week (versus monthly or less, aPR=1.73, 95% CI: 1.22-2.45). LIMITATIONS: Data were cross-sectional and lacked information on potential risk factors such as exposure to trauma. CONCLUSIONS: Psychological distress was highly prevalent and associated with poor living conditions. Prospective studies on the mechanisms through which slum living conditions are associated with psychological distress are needed. Research should also assess the feasibility and acceptability of implementation strategies to increase access to mental health screening and treatment for slum residents.

Hypertension prevalence and risk factors among residents of four slum communities: population-representative findings from Port-au-Prince, Haiti.

OBJECTIVES: The aim of this study was to estimate the prevalence of hypertension and its risk factors among adults in four slum communities in Port-au-Prince. METHODS: Cluster area random sampling was used to select adults for a health and demographic survey, including anthropometric measurements. Hypertension was defined as SBP at least 140 mmHg and/or DBP at least 90 mmHg, or current hypertension treatment, and was age-standardized to WHO world population. Correlates of hypertension were tested using sex-stratified logistic regression. RESULTS: Overall, 20.3% of adults had hypertension (28.5% age-standardized), including 22.3% of men and 18.9% of women. Three percent of participants reported current hypertension treatment, and 49.5% of them had their hypertension controlled. Overweight/obesity (BMI ≥25) was the most common risk factor (20.6% among men, 48.5% among women), while smoking was less common (11.8 and 3.9%, respectively). Increasing age and hypertension prevalence in immediate surroundings were associated with greater odds of hypertension. Among men, having in-migrated in the 3 years prior (versus ≥3 years) was also associated with hypertension [adjusted odds ratio (aOR)=3.32, 95% confidence interval (95% CI): 1.79-6.17], as was overweight and obesity (aOR = 1.90, 95% CI: 1.09-3.33, and aOR = 5.73, 95% CI: 2.49-13.19, respectively) and nonreceipt of needed medical care in the preceding 6 months (aOR = 2.82, 95% CI: 1.35-5.88) among women. CONCLUSION: Hypertension prevalence was high across the age spectrum, in addition to substantial levels of overweight/obesity and unmet healthcare needs. It is important to better understand the possible effects of intraurban migration and environmental risk factors on hypertension and ensure that the benefits of increasingly cost-effective prevention and treatment programmes extend to slum residents.

Effects of Birth Month on Child Health and Survival in Sub-Saharan Africa.

Birth month is broadly predictive of both under-5 mortality rates and stunting throughout most of sub-Saharan Africa (SSA). Observed factors, such as mother's age at birth and educational status, are correlated with birth month but are not the main factors underlying the relationship between birth month and child health. Accounting for maternal selection via a fixed-effects model attenuates the relationship between birth month and health in many SSA countries. In the remaining countries, the effect of birth month may be mediated by environmental factors. This study found that birth month effects on mortality typically do not vary across age intervals; the differential mortality rates by birth month are evident in the neonatal period and continue across age intervals. The male-to-female sex ratio at birth did not vary by birth month, which suggests that in utero exposures are not influencing fetal loss, and that therefore the birth month effects are not likely a result of selective survival during the in utero period. In one-third of the sample, the birth month effects on stunting diminished after the age of 2 years; therefore, some children were able to catch up. Policies to improve child health should target pregnant women and infants and must take seasonality into account.

Impact of birth seasonality on dynamics of acute immunizing infections in Sub-Saharan Africa.

We analyze the impact of birth seasonality (seasonal oscillations in the birth rate) on the dynamics of acute, immunizing childhood infectious diseases. Previous research has explored the effect of human birth seasonality on infectious disease dynamics using parameters appropriate for the developed world. We build on this work by including in our analysis an extended range of baseline birth rates and amplitudes, which correspond to developing world settings. Additionally, our analysis accounts for seasonal forcing both in births and contact rates. We focus in particular on the dynamics of measles. In the absence of seasonal transmission rates or stochastic forcing, for typical measles epidemiological parameters, birth seasonality induces either annual or biennial epidemics. Changes in the magnitude of the birth fluctuations (birth amplitude) can induce significant changes in the size of the epidemic peaks, but have little impact on timing of disease epidemics within the year. In contrast, changes to the birth seasonality phase (location of the peak in birth amplitude within the year) significantly influence the timing of the epidemics. In the presence of seasonality in contact rates, at relatively low birth rates (20 per 1000), birth amplitude has little impact on the dynamics but does have an impact on the magnitude and timing of the epidemics. However, as the mean birth rate increases, both birth amplitude and phase play an important role in driving the dynamics of the epidemic. There are stronger effects at higher birth rates.