Hannah Kemp

Bio: Hannah Kemp is an academic researcher. The author has contributed to research in topics: Population & Medicaid. The author has an hindex of 4, co-authored 10 publications receiving 48 citations.

Information-Seeking Patterns During the COVID-19 Pandemic Across the United States: Longitudinal Analysis of Google Trends Data.

Abstract: Background: The COVID-19 pandemic has impacted people’s lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States. Objective: We aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with—or precede—real-life events? Methods: We analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states. Results: The data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor’s appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others. Conclusions: COVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool.

The Pace and Pulse of the Fight against Coronavirus across the US, A Google Trends Approach

Abstract: Background The coronavirus pandemic has impacted our lives at unprecedented speed and scale - including how we eat and work, what we worry about, how much we move, and our ability to earn. Traditional surveys in the area of public health can be expensive, time-consuming, and rapidly go out of date. Analyzing big data sets (such as electronic patient records, surveillance systems) is very complex. Google Trends is an alternative approach which has been used before to analyze health behaviors, but most research on COVID-19 using this data, so far, looks at a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the US. Objective We use Google Trends to provide both insights into, and potential indicators of, important changes in information-seeking patterns during pandemics like COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Does search data correlate with - or precede - real-life events? Methods We analyzed searches on 39 terms related to COVID-19, falling into six themes: Social & Travel; Care Seeking; Government Programs; Health Programs; News & Influence; Outlook & Concerns. We generated data sets at the national level (covering Jan 1, 2016 - April 15, 2020) and state level (covering Jan 1, 2020 - April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states during March 1st to April 15th, 2020; and Principal Component Analyses (PCA) to extract search patterns across states. Results Data showed high demand for information corresponded with increasing searches for "coronavirus" linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often happened well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on coronavirus care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor's appointment, health insurance/ Medicare/ Medicaid. Finally, concerns vary across the country - some search terms were more popular in some regions than in others. Conclusions COVID-19 is unlikely to be the last pandemic the US faces. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions (NPIs) and recommend the development of a real-time dashboard as a decision-making tool.

Bringing Greater Precision to Interactions Between Community Health Workers and Households to Improve Maternal and Newborn Health Outcomes in India.

Abstract: INTRODUCTION Community health workers (CHWs) play a key role in the health of mothers and newborns in low- and middle-income countries. However, it remains unclear by what actions and messages CHWs enable good outcomes and respectful care. METHODS We collected a uniquely linked set of questions on behaviors, beliefs, and care pathways from recently delivered women (n=5,469), their husbands (n=3,064), mothers-in-law (n=3,626), and CHWs (accredited social health activists; n=1,052) in Uttar Pradesh, India. We used logistic regression to study associations between CHW actions and household behaviors during antenatal, delivery, and postnatal periods. RESULTS Pregnant women who were visited earlier in pregnancy and who received multiple visits were more likely to perform recommended health behaviors including attending multiple checkups, consuming iron and folic acid tablets, and delivering in a health facility (ID), compared to women visited later or receiving fewer visits, respectively. Counseling the woman was associated with higher likelihood of attending 3+ checkups and consuming 100+ iron and folic acid tablets, whereas counseling the husband and mother-in-law was associated with higher rates of ID. Certain behavior change messages, such as the danger of complications, were associated with more checkups and ID, but were only used by 50%-80% of CHWs. During delivery, 57% of women had the CHW present, and their presence was associated with respectful care, early initiation of breastfeeding, and exclusive breastfeeding, but not with delayed bathing or clean cord care. The newborn was less likely to receive delayed bathing if the CHW incorrectly believed that newborns could be bathed soon after birth (which is believed by 30% of CHWs). CHW presence was associated with health behaviors more strongly for home than facility deliveries. Home visits after delivery were associated with higher rates of clean cord care and exclusive breastfeeding. Counseling the mother-in-law (but not the husband or woman) was associated with exclusive breastfeeding. CONCLUSION We identified potential ways in which CHW impact could be improved, specifically by emphasizing the importance of home visits, which household members are targeted during these visits, and what messages are shared. Achieving this change will require training CHWs in counseling and behavior change and providing supervision and modern tools such as apps that can help the CHW keep track of her beneficiaries, suggest behavior change strategies, and direct attention to households that stand to gain the most from support.

Social distancing across vulnerability, race, politics, and employment: How different Americans changed behaviors before and after major COVID-19 policy announcements

Abstract: Background As states reopen in May 2020, the United States is still trying to curb the spread of the COVID-19 pandemic. To appropriately design policies and anticipate behavioral change, it is important to understand how different Americans’ social distancing behavior shifts in relation to policy announcements according to individual characteristics, and community vulnerability. Methods This cross-sectional study used Unacast’s social distancing data from February 24th - May 10th, 2020 to study how social distancing changed before and after: 1) The World Health Organization’s declaration of a global pandemic, 2) White House announcement of “Opening Up America Again” (OUAA) guidelines, and 3) the week of April 27 when several states reopened. To measure intention to social distance, we assessed the difference between weekday and weekend behavior as more individuals have more control over weekend leisure time. To investigate social distancing’s sensitivity to different population characteristics, we compared social distancing time-series data across county vulnerability as measured by the COVID-19 Community Vulnerability Index (CCVI) which defines vulnerability across socioeconomic, household composition, minority status, epidemiological, and healthcare-system related factors. We also compared social distancing across population groupings by race, 2016 presidential election voting choice, and employment sectors. Results Movement reduced significantly throughout March reaching peak reduction on April 12th (-56.1%) prior the enactment of any reopening policies. Shifts in social distancing began after major announcements but prior to specific applied policies: Following the WHO declaration, national social distancing significantly increased on weekdays and weekends (-18.6% and -41.3% decline in mobility, respectively). Social distancing significantly declined on weekdays and weekends after OUAA guidelines (i.e. before state reopening) (+1.1% and +5.3% increase in mobility, respectively) with additional significant decline after state reopening (+10.0% and +20.9% increase in mobility, respectively). Social distancing was significantly greater on weekends than weekdays throughout March, however, the trend reversed by early May with significantly less social distancing on weekends, suggesting a shift in intent to social distance during leisure time. In general, vulnerable counties social distanced less than non-vulnerable counties, and had a greater difference between weekday and weekend behavior until state reopening. This may be driven by structural barriers that vulnerable communities face, such as higher rates of employment in particular sectors. At all time periods studied, the average black individual in the US social distanced significantly more than the average white individual, and the average 2016 Clinton voter social distanced significantly more than the average 2016 Trump voter. Social distancing behavior differed across industries with three clusters of employment sectors. Conclusion Both signaling of a policy change and implementation of a policy are important factors that seem to influence social distancing. Behaviors shifted with national announcements prior to mandates, though social distancing further declined nationwide as the first states reopened. The variation in behavioral drivers including vulnerability, race, political affiliation, and employment industry demonstrates the need for targeted policy messaging and interventions tailored to address specific barriers for improved social distancing and mitigation.

Increasing Uptake of Social Distancing during COVID-19: Behavioral Drivers and Barriers among US Population Segments

Abstract: Social distancing is critical to fighting COVID-19 and is expected to continue for some time Understanding social distancing behavioral drivers can inform prec

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

COVID-19 Outcomes Among Persons Living With or Without Diagnosed HIV Infection in New York State.

Abstract: Importance New York State has been an epicenter for both the US coronavirus disease 2019 (COVID-19) and HIV/AIDS epidemics. Persons living with diagnosed HIV may be more prone to COVID-19 infection and severe outcomes, yet few studies have assessed this possibility at a population level. Objective To evaluate the association between HIV diagnosis and COVID-19 diagnosis, hospitalization, and in-hospital death in New York State. Design, Setting, and Participants This cohort study, conducted in New York State, including New York City, between March 1 and June 15, 2020, matched data from HIV surveillance, COVID-19 laboratory-confirmed diagnoses, and hospitalization databases to provide a full population-level comparison of COVID-19 outcomes between persons living with diagnosed HIV and persons living without diagnosed HIV. Exposures Diagnosis of HIV infection through December 31, 2019. Main Outcomes and Measures The main outcomes were COVID-19 diagnosis, hospitalization, and in-hospital death. COVID-19 diagnoses, hospitalizations, and in-hospital death rates comparing persons living with diagnosed HIV with persons living without dianosed HIV were computed, with unadjusted rate ratios and indirect standardized rate ratios (sRR), adjusting for sex, age, and region. Adjusted rate ratios (aRRs) for outcomes specific to persons living with diagnosed HIV were assessed by age, sex, region, race/ethnicity, transmission risk, and CD4+T-cell count–defined HIV disease stage, using Poisson regression models. Results A total of 2988 persons living with diagnosed HIV (2109 men [70.6%]; 2409 living in New York City [80.6%]; mean [SD] age, 54.0 [13.3] years) received a diagnosis of COVID-19. Of these persons living with diagnosed HIV, 896 were hospitalized and 207 died in the hospital through June 15, 2020. After standardization, persons living with diagnosed HIV and persons living without diagnosed HIV had similar diagnosis rates (sRR, 0.94 [95% CI, 0.91-0.97]), but persons living with diagnosed HIV were hospitalized more than persons living without diagnosed HIV, per population (sRR, 1.38 [95% CI, 1.29-1.47]) and among those diagnosed (sRR, 1.47 [95% CI, 1.37-1.56]). Elevated mortality among persons living with diagnosed HIV was observed per population (sRR, 1.23 [95% CI, 1.07-1.40]) and among those diagnosed (sRR, 1.30 [95% CI, 1.13-1.48]) but not among those hospitalized (sRR, 0.96 [95% CI, 0.83-1.09]). Among persons living with diagnosed HIV, non-Hispanic Black individuals (aRR, 1.59 [95% CI, 1.40-1.81]) and Hispanic individuals (aRR, 2.08 [95% CI, 1.83-2.37]) were more likely to receive a diagnosis of COVID-19 than White individuals, but they were not more likely to be hospitalized once they received a diagnosis or to die once hospitalized. Hospitalization risk increased with disease progression to HIV stage 2 (aRR, 1.29 [95% CI, 1.11-1.49]) and stage 3 (aRR, 1.69 [95% CI, 1.38-2.07]) relative to stage 1. Conclusions and Relevance In this cohort study, persons living with diagnosed HIV experienced poorer COVID-related outcomes relative to persons living without diagnosed HIV; Previous HIV diagnosis was associated with higher rates of severe disease requiring hospitalization, and hospitalization risk increased with progression of HIV disease stage.

Socioeconomic Disparities in Social Distancing During the COVID-19 Pandemic in the United States: Observational Study.

Abstract: Background: Eliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic. Objective: We aimed to assess how mobility patterns have varied across the United States during the COVID-19 pandemic and to identify associations with socioeconomic factors of populations. Methods: We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level in the United States between February and May 2020, the period during which social distancing was widespread in this country. Using linear mixed models, we assessed the associations between social distancing and socioeconomic variables, including the proportion of people in the population below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. Results: We found that the speed, depth, and duration of social distancing in the United States are heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; in contrast, we show that social distancing is intensely adopted in counties with higher population densities and larger Black populations. Conclusions: Socioeconomic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of the COVID-19 pandemic in communities across the United States. These inequalities are likely to amplify existing health disparities and must be addressed to ensure the success of ongoing pandemic mitigation efforts.

White Counties Stand Apart: The Primacy of Residential Segregation in COVID-19 and HIV Diagnoses.

Abstract: Emerging epidemiological data suggest that white Americans have a lower risk of acquiring COVID-19. Although many studies have pointed to the role of systemic racism in COVID-19 racial/ethnic disparities, few studies have examined the contribution of racial segregation. Residential segregation is associated with differing health outcomes by race/ethnicity for various diseases, including HIV. This commentary documents differing HIV and COVID-19 outcomes and service delivery by race/ethnicity and the crucial role of racial segregation. Using publicly available Census data, we divide US counties into quintiles by percentage of non-Hispanic white residents and examine HIV diagnoses and COVID-19 per 100,000 population. HIV diagnoses decrease as the proportion of white residents increase across US counties. COVID-19 diagnoses follow a similar pattern: Counties with the highest proportion of white residents have the fewest cases of COVID-19 irrespective of geographic region or state political party inclination (i.e., red or blue states). Moreover, comparatively fewer COVID-19 diagnoses have occurred in primarily white counties throughout the duration of the US COVID-19 pandemic. Systemic drivers place racial minorities at greater risk for COVID-19 and HIV. Individual-level characteristics (e.g., underlying health conditions for COVID-19 or risk behavior for HIV) do not fully explain excess disease burden in racial minority communities. Corresponding interventions must use structural- and policy-level solutions to address racial and ethnic health disparities.