Introduction
Internet use for health-related purposes is a widespread and growing global phenomenon. In the United States, the proportion of individuals who searched for health information online increased from 46.5% in 2011 to 55.3% in 2018, and the proportion who used any digital health technology to interact with the health care system increased from 12.5% in 2011 to 27.4% in 2018 (Mahajan et al., 2021). Using data from the Internet and Computer use survey (2015 – 2021) this project aims to extend the study above by visualizing the prevalence and trends of the use of internet for health-related purposes among adults in the US from 2015 to 2021.

Project Objectives

1. To visualize the prevalence and trends of the use of internet for health-related purposes among US adults.
2. To show socio-demographic discrepancies in the use of internet for health-related purposes among US adults

Method
This is a longitudinal study of the use of internet for health-related purposes among adults in the US from 2015 to 2021.
The study conducts a secondary analysis on the Computer and Internet Use Supplement (CIUS) of the Current Population Survey (CPS) for four distinct periods namely, 2015, 2017, 2019, and 2021. The CPS, conducted monthly for over 50 years and sampling about 56,000 households, is the primary source of official U.S. employment and unemployment statistics, as well as key demographic data.
The CPS and CIUS combined contain over 600 variables out of which 17 were selected for this study, 14 of which were directly taken from the survey and the remaining 3 derived from others.

Results
The story is approached from three thematic areas based on the characteristics of respondents namely, socio-demographic, geographic, and socio-economic characteristics. A separate dashboard is created with charts to address each of the thematic areas itemized above.

Figure one shows the first thematic area of the story which addresses the socio-demographic characteristics of respondents in relations to the use of internet for health-related purposes. The graphs show that overall, there is high and growing trend in health-related internet use. However, there are certain discrepancies which exists among various sub-groups of these socio-demographic domains.

In figure 2, the data shows that even though health-related internet use is high across all States, there is however a huge variation in the prevalence ranging from a low of 43.36 in Mississippi to 72.93% in the State of Oregon.

Another pivotal area explored by this study is whether people’s access to multiple digital devices or internet sources influenced their tendency to use the internet for health-related purposes. Findings from the data suggest that, yes it does. As noted in the graphs in Figure 3, people who had access to more resources defined by access to multiple internet sources or digital devices reported higher health-related internet use than those who had less. This touches on the role of inequality in health-related internet use which must be addressed by systematic structural interventions.

Discussion

Findings from the study shed light on the critical interplay between digital access, demographic factors, and health-related internet use. The findings offer profound insights for policymakers, healthcare systems, and broader societal considerations.

The observed disparities in health-related internet use across age, gender, education, citizenship status, and residence underline the persistence of a digital divide. Younger individuals and those with higher educational attainment exhibit significantly higher odds of utilizing the internet for health purposes, a finding corroborated by Mahajan et al., 2021, pointing to an inequity that policy must address. Healthcare systems need to recognize the varying levels of internet use for health-related purposes among different demographic groups. Customized health education and outreach programs that consider these disparities can enhance patient engagement and health outcomes (Zonneveld et al., 2020).
The strong association between the number of digital devices and internet connectivity sources with increased odds of health-related internet use highlights the foundational role of digital infrastructure. Policies aimed at enhancing digital infrastructure, particularly in underserved areas, could significantly increase health-related internet use. Initiatives could include subsidizing internet access, providing affordable digital devices, and investing in digital literacy programs.

Conclusion
It is evident from the presentation that the use of internet for health-related purposes is a very high and growing phenomenon among adults in the US. This has implications for health systems as well as individual privacy, security and safety. These concerns must be addressed in addition to the inequalities noted among sub-groups of the various domains addressed in the study.

Bibliography

Mahajan, S., Lu, Y., Spatz, E. S., Nasir, K., & Krumholz, H. M. (2021). Trends and Predictors of Use of Digital Health Technology in the United States. American Journal of Medicine, 134(1), 129–134. https://doi.org/10.1016/j.amjmed.2020.06.033
Zonneveld, M., Patomella, A. H., Asaba, E., & Guidetti, S. (2020). The use of information and communication technology in healthcare to improve participation in everyday life: a scoping review. In Disability and Rehabilitation (Vol. 42, Issue 23, pp. 3416–3423). Taylor and Francis Ltd. https://doi.org/10.1080/09638288.2019.1592246

In our digitally saturated era, mobile phones have evolved from mere communication devices into essential companions that shape our daily routines, entertainments, and social interactions. The heart of this transformation lies in the myriad of applications (“apps”) that customize and enhance the functionality of our devices. This story is an introspective journey to unveil my own app usage over a two-week period, aiming to glean insights into my digital habits and their implications on broader life aspects.

To accomplish this exploration, I utilized a tracking app named “AppTracker” (a pseudonym for privacy), which, upon receiving the necessary permissions, meticulously recorded metadata of app activities. This data included the app name, duration of usage, and specific timestamps, offering a granular view of my digital interactions from March 8th to March 21st, 2024.  The data could not be downloaded directly as a CSV file or other formats which could be easily imported into a data processing software, so I had to type out the output myself hence the decision to limit exploration to a two-week (14 day) duration. The raw data underwent meticulous cleaning and transformation in Microsoft Excel before being visualized in Tableau, allowing for an intuitive interpretation of my digital footprint.

Over the two-week period, I engaged with 48 distinct apps for a cumulative 202 instances, culminating in 57 hours and 12 minutes of usage. This equates to 17% of the total time available (57.12 hours out of 336), with a little more time spend in the first week (29.10 hours (51%)) than the second (28.02 hours (49%)). A significant variance in daily app engagement was observed, ranging from a minimum of 2 hours to a peak of over 7 hours, averaging at 4 hours and 4 minutes per day. Notably, Mondays witnessed the highest app usage at an average of 5 hours and 28 minutes, while Fridays were the least, averaging 2 hours and 38 minutes (see figure 1).

A deeper dive into the types of apps used revealed a dominance of communication tools, with WhatsApp leading at 12 hours and 52 minutes (22.18% of total app usage), followed closely by Telegram at 19.28%. Despite their high usage, a heatmap analysis underscored a remarkable finding: only Telegram was used consistently each day, suggesting that most of these apps are not be daily necessities (see figure 2).

Lessons and Insights

The data paints a vivid picture of my digital interactions showing that it is dominated by communication needs. Yet, the variability in daily usage and the dominance of just a few apps raise questions about my digital dependence and the real value I derive from these digital interactions. This analysis prompts a critical reflection on my app choices, thereby inviting me to consider my digital well-being and productivity.

Comparing these patterns to existing digital behavior research, my app usage aligns with trends highlighting the growing prominence of communication tools in our daily lives. However, the observed dispensability of many apps gives me a window into how I could begin unplugging and decluttering my digital space.

Conclusion

This personal audit of app usage not only sheds light on my digital behavior but also mirrors broader societal trends towards heavy reliance on digital tools for communication. The insights gleaned from this study challenge me to take a mindful approach to app usage and to prioritize real-world interactions to satisfy over virtual ones to satisfy my communication needs.

Introduction

Sanitation plays a crucial role in maintaining public health by preventing the spread of diseases and promoting overall well-being. Using the 311 Datasets, this project explores the distribution of sanitation related complaints received in 2013 by the Department of Sanitation, NYC. Assessing sanitation-related complaints serves as an effective indicator of population exposure to environmental and health risks.

Project Objectives

1. To determine the spatial and temporal distribution of sanitation related complaints in New York City in 2023
2. To evaluate the performance of NYC Department of Sanitation in resolving sanitation complaints in 2013

Method

This project will utilize the 311 Complaint Data from the New York City Open Data Portal. At the row level, the dataset will be filtered to include only complaints made in 2023 (January to December) and “Complaint Type” set to “Dirty Condition”. Relevant columns shall include created_date, closed_date, location_type, city, status, borough among others.

Target Audience

This work would benefit the New York City Department of Sanitation by providing a snapshot of reported sanitation-related issues in 2023 and to expose areas that require more attention. Additionally, it will provide valuable insights for individuals contemplating residential decisions within the city, as sanitation significantly influences such choices. Furthermore, it will aid public health officials in strategically targeting sanitation-related interventions by facilitating the identification of priority areas.

Results

Design decision: I chose a line graph for this visual because it is a good fit for illustrating time-series data. The number of categories were few(5) so representing each category with a separate line did not lead to a clutter. I chose to label each line with the borough it represents, instead of generating a separate legend for the purpose because that makes it easier to read. I did not title the x-axis because it is self-explanatory. The choice of color was selected by default and I did not think it was necessary to change it.

The graph illustrates the trend of sanitation-related complaints across different boroughs of New York City throughout the year 2023. It reveals that complaints peaked during the summer months, with a significant surge in July and August, indicating potential seasonal factors influencing sanitation issues. Brooklyn experienced the highest number of complaints, especially during these peak months, followed by Queens and Manhattan, while the Bronx and Staten Island reported fewer incidents. However, an unusual spike is observed in Staten Island in November, warranting further investigation into the causes behind this anomaly.

Design decisions: This was the case of 3 categories sharing the same pie, so a pie chart was very appropriate. The brown color was deliberately chosen to represent dirty condition. The remaining colors were maintained from the default choice.

The pie chart delineates the composition of ‘Dirty Condition’ complaints in New York City for 2023, revealing that the predominant category, constituting two-thirds of the complaints, pertains to general dirty conditions, which might include litter, debris, or other unsanitary matters not removed in a timely manner. Notably, missed waste collections, which can lead to accumulations of garbage contributing to the dirty conditions, represent almost a third of the complaints. The relatively minor slice of complaints regarding lot conditions suggests that while lot maintenance is an issue, it is significantly overshadowed by the broader concerns with general cleanliness and garbage collection services.

Design Decisions: My initial thought was to use a clustered bar graph, and I actually did that, but a colleague suggested during the studio critique that a clustered bar graph would be more appropriate. Then I tried and saw that indeed it was. I decided to label the bars because of the tendency for people to misread clustered bar graphs. Nonetheless, I labelled the x-axis because one of the categories (Lot condition) was so small it could not be labelled.

Brooklyn emerged as the leading borough in terms of sanitation concerns, with the highest counts across all categories. This trend suggests that Brooklyn may be facing systemic challenges in maintaining sanitation standards, potentially due to higher population density or insufficient sanitation resources. The relatively high incidence of missed collections could be indicative of logistical shortcomings or a mismatch between sanitation services and community needs.

Design decisions: The line chart was chosen because it is very appropriate for the continuous data expressed over time. The data points were labelled on the line for easy and accurate reading. This also made the y-axis redundant and consequently eliminated.

The line graph depicts a significant improvement in the New York City Department of Sanitation’s responsiveness to sanitation complaints over the course of 2023, with the monthly average resolution time decreasing from 9 days in January to just 1 day by December. This positive trend indicates a substantial enhancement in operational efficiency, likely due to improved processes, resource allocation, or the implementation of new technologies for managing and addressing complaints. This decrease in resolution time is crucial for maintaining public health standards and preventing the escalation of sanitation issues into more severe public health crises.

Next Steps

The immediate next steps would involve a deeper dive into the causal factors behind the high volume of complaints in certain boroughs, particularly Brooklyn. For more complex future developments, implementing a geospatial analysis would provide a visual representation of complaint hotspots and resolution efficiency across the city. Improvements could also be made by leveraging predictive analytics to anticipate areas of potential concern and preemptively address them. In terms of alterations in scope, expanding the research to include health outcomes related to sanitation complaints might reveal direct impacts on public health, providing a stronger impetus for policy action.

Conclusion

In 2023, New York City’s sanitation data reflected a dual narrative: significant challenges, particularly in Brooklyn with high complaint volumes, and substantial improvements, as seen in the reduced resolution times from 9 to 1 day by year-end. These insights highlight Brooklyn’s urgent need for enhanced sanitation services and the effective strides made city-wide in operational efficiency. The year’s data serves as a benchmark for continued progress in the city’s public health and urban cleanliness efforts.

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