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

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 Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated.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.

Pima County COVID-19 vaccine solutions dashboard project: lessons learned.

Recent improvements in the accessibility of mapping tools and an increased recognition of the importance of leveraging data to inform public health operations has led to enthusiasm among public health departments to rapidly evolve their ability to analyze and apply data to programs. As the COVID-19 pandemic made evident, many health department data systems have been neglected for decades and data literacy among staff low. Significant federal dollars have been allocated to local health departments to modernize health systems. This case study recounts the effort to equip the Pima County Health Department with a highly sophisticated "COVID-19 Vaccines Solutions Dashboard" in 2021-2022, quantifying community vulnerability in the midst of the COVID-19 pandemic and shares key successes and challenges in process and outcomes that can guide other such dashboard initiatives. The experience informed the development of Pima' County Health Department's Data & Informatics Team as well as efforts to cultivate a more robust data culture throughout the department. Many health departments around the United States are in a similar position, and these lessons learned are widely applicable.

Under-five mortality in the Rongo Sub-County of Migori County, Kenya: Experience of the Lwala Community Alliance 2007-2017 with evidence from a cross-sectional survey.

INTRODUCTION: Childhood mortality remains a pressing problem in rural Kenya, and reducing under-five deaths is a key target of the Sustainable Development Goals. We aim to describe the reduction in under-five mortality in a rural Kenyan community served by the Lwala Community Alliance and factors associated with under-five mortality in this community. METHODS: A cross-sectional survey containing a complete birth history was administered to a representative sample of the catchment area of the Lwala Community Alliance. Survival analysis techniques were used to describe temporal trends and risk factors related to under-five mortality. RESULTS: 1,362 children were included in the study, and 91 children died before the fifth birthday. The most common causes of death among children under five were malaria (19%), respiratory infection (13%), and anemia (11%). The under-five mortality rate was 104.8 per 1,000 live births from 1999 to 2006 and 53.0 per 1,000 after the founding of the Lwala Community Alliance in 2007. Factors associated with under-five mortality included year of birth (HR 0.931; 95% CI: 0.877, 0.988; p = 0.019), multiple-gestation pregnancy (HR 6.201; 95% CI: 2.073, 18.555; p < 0.001), and birth in the long rain season (HR 1.981; 95% CI: 1.350, 2.907; p < 0.001). Birth spacing greater than 18 months was negatively associated with under-five mortality (HR 0.345; 95% CI: 0.203, 0.587; p < 0.001). CONCLUSIONS: There was a significant decrease in under-five mortality before and after the presence of the Lwala Community Alliance. Multiple-gestation pregnancies, birth season, and short birth spacing were associated with under-five mortality and provide possible targets to further reduce mortality in the region. This provides both hyper-local data necessary for implementation efforts and generalizable data and sampling methods that may be useful for other implementing organizations in sub-Saharan Africa.

Value propositions of mHealth projects.

While mHealth holds great potential for addressing global health disparities, a majority of the initiatives never proceed beyond the pilot stage. One fundamental concern is that mHealth projects are seldom designed from the customer's perspective to address their specific problems and/or create appreciable value. A customer-centric view, where direct tangible benefits of interventions are identified and communicated effectively, can drive customer engagement and advance projects toward self-sustaining business models. This article reviews the business models of 234 mHealth projects to identify nine distinct value propositions that solve specific problems for customers. Each of these value propositions is discussed with real-world examples, analyses of their design approaches and business strategies, and common enablers as well as hurdles to surviving past the pilot stage. Furthermore, a deeper analysis of 42 mHealth ventures that have achieved self-sustainability through project revenue provides a host of practical and poignant insights into the design of systems that can fulfil mHealth's promise to address healthcare challenges in the long term.

Using handgrip strength to screen for diabetes in developing countries.

Lack of access to healthcare in the developing world has created a need for locally-based primary and pre-primary healthcare systems. Many regions of the world have adopted Community Health Worker (CHW) programmes, but volunteers in these programmes lack the tools and resources to screen for disease. Because of its simplicity of operation, handgrip strength (HGS) measurements have the potential to be an affordable and effective screening tool for conditions that cause muscle weakness in this context. In the study described in this report, translators were used to collect data on age, gender, height, weight, blood pressure, HGS and key demographic data. HGS was significantly lower for diabetics than patients without diabetes. A simple binary logistic model was created that used HGS, age, blood pressure and BMI to predict a patient's probability of having diabetes. This study develops a predictive model for diabetes using HGS and other basic health measurements and shows that HGS-based screening is a viable method of early detection of diabetes.