Accessibility and Intractability of the Report:
This report contains several features which may aid with user experience, such as Alt-Text for each image.
Please note you may wish to collapse all code chunks at the start for best storytelling. Similarly, when reviewing the code chunks, the palette generally follows the color-coding: comments, functions, and values. Any text in blue outside of code chunks contains a hyperlink or can be interacted with (try to click on the author for a link to the GitHub Repo!).
Rationale of the Report
Perhaps surprisingly, I belong to a special group. Unlike the thousands of people around me, I (knocks on wood) do not have hay fever. Not even a tiny seasonal allergy! I have never known the pains of constant sneezing, allergy-driven insomnia, and never-ending stuffiness. Almost 10 million people in the UK would probably hate me if they knew.
As a great friend to my poor, hay-feverish friends and fellow citizens, I have decided to study the trends in antihistamine prescriptions and their potential environmental relationships.
Antihistamines are the class of drugs most commonly used to treat hay fever. This malady is oftentimes seasonal, aligning with pollen-rich months (generally around spring and summer). While intensity of hay fever sensitisation in response to the potpourri of pollen sources varies between patients, recent research on tree allergenicity has identified several popular urban trees to be highly allergenic to a wide proportion of their target population (Netherlands) (De Weger et al. 2024).
Objective
This report aims to answer the following questions, which are both of personal and public health interest:
- What are the most common antihistamine prescriptions? (Which are the most important to stock?)
- How do they change throughout the year? (When should we be on the lookout for signs and anticipate increased use of health services?)
- Do antihistamine prescriptions sold change around tree-populated areas? Is it different focusing on strongly allergenic trees? (Should certain areas be avoided, and should future green city planning be conscious of this?)
Findings from this report could be of special interest for Edinburgh’s City Plan 2030, which will be heavily involved with the city’s Thriving Greenspaces Strategy.
Data and Environment Set-Up
for (package in c("tidyverse","janitor","gt","gtExtras","forcats","ggspatial","osmdata","raster","sf","data.table","scales", "leaflet", "htmltools")) {eval(bquote(library(.(package))))}Datasets
The report relies on data collected by the NHS ‘Prescriptions in the Community’, focusing on Scotland, JUL-2023 to JUN-2024 (dates selected for a yearly trend analysis, with up-to-day data comparison with the Tree dataset). This dataset collects details on the prescriptions sold attributed to each GP Code.
To bind these with a geographical location, the GP codes first need to be linked to their UPRN (Unique Property Reference Number) using the GP_Practices dataset, which can then be used to identify the Easting and Northing coordinates using the NSUL Dataset provided by the UK Government (FEB-2023). The Tree dataset contains the location and species of trees maintained by the Edinburgh City Council (JUN-2024). The Postal Sector dataset will be used for geographical mapping of city boundaries. The following must be downloaded and their local path must be modified below for the report to be reproduced:
- PostalSector.shp
- GP_Practices_-_Scotland.csv
- NSUL_FEB_2023_SC.csv (Subfolder: NSUL_FEB_2023_GB/Data)
- Trees.csv
The selected antihistamines are shown below, and were obtained from the NHS Hay Fever treatment recommendations.
postal_sector = st_read("C:/Users/Data/Downloads/GB_Postcodes/PostalSector.shp", quiet = TRUE)
gp_to_nsul = read.csv("C:/Users/Data/Downloads/GP_Practices_-_Scotland.csv")
trees = read.csv("C:/Users/Data/Downloads/Trees.csv")
nsul = read.csv("C:/Users/Data/Downloads/NSUL_FEB_2023_SC.csv")
antihistamines = c("chlorphenamine", "cinnarizine", "diphenhydramine", "hydroxyzine", "promethazine", "acrivastine", "cetirizine", "fexofenadine", "loratadine") # More can be added if desired.# To access the prescription data, I will be constructing the link dynamically. Each link was seen to have two variable components: the date and the resource number. I have collected them below, ordered in pairs matching index position.
dates = c("202307","202308","202309","202310","202311","202312","202401","202402","202403","202404","202405","202406") # NOTE: A failed connection likely means your network is not stable, please ensure this first before troubleshooting!
resources = c("7bb45ee6-6f1c-45f4-933b-958bdbe0ca4f","72ca4ad2-0228-4672-9eb0-cc911e4a8ca7","2d3d240f-1467-4a91-9f0e-769745650cb9","94134438-db1d-4b8d-8f70-5e9b0e47bd03","21d11fed-a494-4e30-9bb6-46143c3b9530","00cdf8d7-f784-4e87-894c-34f2540ea6ab","d3eaaf84-0f3b-4fb8-9460-e33503095fbe","86fda652-0e1d-48bb-9368-aa2a560d925b","a42762ac-47cb-4fb6-b9b1-2478a588c0ed","409a02f2-b77c-47a0-917d-4a5a1c90f182","5fbbd126-6166-4249-9620-7ed78e877297","95f7f250-bd04-4e4a-b853-5df75b00a632")
for (date in dates){assign(paste0("prescriptions_", date),read_csv(paste0("https://www.opendata.nhs.scot/dataset/84393984-14e9-4b0d-a797-b288db64d088/resource/",resources[match(date,dates)],"/download/pitc",date,".csv")) %>% clean_names())} #Assign + paste0 can create objects with varying names inside a loop!
prescriptions_all = rbindlist(lapply(ls(pattern="^prescriptions_2"), function(x) get(x)))# Create a main df of prescriptions by listing all the objects that start with "prescriptions_2"
remove(list = ls(pattern="^prescriptions_2")) # And I then remove the individual prescription dfs to clear space!Antihistamine Data Tidying
In the following code, the prescriptions dataset is first filtered to
produce antihistamines_data, which contains the total sales
for each antihistamine (defined above) by GP per year.
An aggregation for total sales by drug across the year produces:
antihist_sold_by_drug.A different aggregation for total sales by drug and month:
antihist_sold_by_month_and_drug.- A selection of the top 3 months in sales for each drug in the above
object gives:
antihist_sold_by_month_and_drug_3.
- A selection of the top 3 months in sales for each drug in the above
object gives:
And for total antihistamine sales by GP:
antihist_sold_by_gp.
antihistamines_data = as.data.frame(c()) # Empty dataframe that will be populated in the following loop.
for (drug in antihistamines){ # Loops through our antihistamines, collects all prescriptions under the name (regardless of does) and adds the sales keeping the month and the GP code.
drug_df = prescriptions_all %>% # Creates a new dataframe
dplyr::filter(str_detect(tolower(bnf_item_description), tolower(drug))) %>% # Tolower aids in pattern detection by making everything inside lowercase. We want to keep the rows with the drug currently being looked at by the loop.
group_by(gp_practice, date = paid_date_month) %>% # Use the grouping function to rename the paid_date_month column, and grouping both by that and GP practice
summarise(drug = drug, paid_quantity = sum(paid_quantity)) # Paid_quantity is the total drug sales for each combination of month and GP_Practice (as determined by the previous grouping).
antihistamines_data = rbind(antihistamines_data, drug_df)} # Bind this temporary dataframe (drug_df) with the antihistamines_data, and we start the loop with the next drug!
antihist_sold_by_drug = antihistamines_data %>%
group_by(drug = str_to_title(drug)) %>% # This changes the values inside drug capitalizing the first letter(str_to_title{}) and groups them!
summarize(total = sum(paid_quantity),
proportion = total * 100/ sum(antihistamines_data$paid_quantity)) %>% # Calculate the total sales per drug and the proportion of total antihistamine sales for each drug (by accessing the sum of the entire paid_quantity column).
arrange(desc(total)) # This arranging has been done to best visualize in the upcoming table the drugs in order of sales.
antihist_sold_by_month_and_drug = antihistamines_data %>%
mutate(date = as.Date(paste0("01",date),format="%d%Y%m")) %>% # Add day 01 to our year + month dates for ease of handling, since most Date functions need a day, month, and year.
group_by(drug = str_to_title(drug), date) %>%
summarize(total = sum(paid_quantity)) %>%
mutate(drug = factor(drug, levels = antihist_sold_by_drug$drug, ordered = T)) # Order the drugs as factors based on the ranked order specified in the table dataset ("antihist_sold_by_drug", above)
antihist_sold_by_month_and_drug_3 = antihist_sold_by_month_and_drug %>%
group_by(drug) %>%
arrange(desc(total)) %>% # Reorder so that the monthly entries are shown from most sales to least
top_n(3,wt = total) # Select top 3 months.
antihist_sold_by_gp = antihistamines_data %>%
group_by(gp_practice) %>%
summarise(antihist_sold = sum(paid_quantity)) # Keep only GP and the number of antihistamines sold in the entire period.Geographical Data Tidying
To later plot a map of Edinburgh with districts, GP locations and their antihistamine sales, and the position of trees (both all, and specifically allergenic), the geographical datasets introduced above are to be filtered and joined.
First, a simple filter of the
postal_sectordata to limit the sprawl to only ‘Edinburgh’ gives:postal_sector_edi.Selecting the rows of
nsulwhose postcode starts with “EH”, and keeping only the NSUL code and their corresponding coordinates producesnsul_edi.This is then joined with a similarly filtered
gp_to_nsulobject using the NSUL code, which creates a dataset with GP codes and their coordinates. The GP code is then used to join in the antihistamine sales each produced fromantihist_sold_by_gp, keeping only GPs for which both the location and the sales are known. This is objectgp_edi_sf.Finally,
all_trees_sfis directly the originaltreesdataset, re-formatted for geographical plotting.- And
allergenic_trees_sftakes the above, keeping only the trees belonging to genus classified as “very strong” or “strong” allergenic (De Weger et al. 2024) (namely: Betula, Alnus and Corylus).
- And
postal_sector_edi = postal_sector %>% dplyr::filter(Sprawl == "Edinburgh") # Get the Edinburgh Districts
nsul_edi = nsul %>% dplyr::filter(str_detect(PCDS,"^EH")) %>% # Check the postcode column to get those with "EH" at the beginning ("^EH").
rename(Easting = "GRIDGB1E", # Rename the columns with Easting and Northing coordinates, as well as the UPRN for ease of handling.
Northing = "GRIDGB1N",
uprn = "UPRN") %>%
dplyr::select(Easting, Northing, uprn) # And keep just those 3 columns. Edinburgh UPRNs, and their coordinates. This object is not strictly necessary, but the renaming and filtering are useful for improving legibility of the code.
gp_edi_sf = gp_to_nsul %>%
dplyr::filter(str_extract(postcode, "^EH[0-9]{1,2}") %in% unique(postal_sector_edi$PostDist)) %>% # Filter the GPs in Edinburgh by checking whether the district info in their postcode is in the districts selected by the postal_sector_edi (the regex expression "^EH[0-9]{1,2}" essentially means: "EH" and one or two numbers).
left_join(., nsul_edi, by = "uprn") %>% # Add their coordinates using the UPRN code to join.
dplyr::filter(!is.na(Easting)) %>% # Remove GPs with unknown coordinates.
inner_join(.,antihist_sold_by_gp, by = c("prac_code" = "gp_practice")) %>% # Add antihistamines sold, with inner_join ensuring we only keep GPs for which we also know the antihistamines.
st_as_sf(., coords = c("Easting", "Northing"), crs = st_crs(postal_sector_edi)) %>% # Transform coordinates into sf points (needed for mapping plots) based on the postal_sector_edi information (defined by st_crs()).
dplyr::filter(!st_is_empty(geometry)) # Remove empty again, just in case there was any error in the transformation!
all_trees_sf = st_as_sf(trees, coords = c("X", "Y"), crs = st_crs(postal_sector_edi)) # Transform tree coordinates into sf points based on the postal_sector_edi.
allergenic_trees_sf = all_trees_sf %>% filter(str_detect(tolower(trees$LatinName),"betula|alnus|corylus")) # Filter to keep only those trees with genus Betula, Alnus, or Corylus in their name.1 Most Common Antihistamine Sales
The code below produces antihistamine_sales_table, a
table ranking the selected antihistamines by total sales across Scotland
for the JUL-2023 to JUN-2024 period.
antihistamine_sales_table = antihist_sold_by_drug %>%
gt() %>%
tab_header(title = md("**Antihistamine Sales in Scotland**"), subtitle = "Data from NHS Prescriptions in the Community, JUL-2023 to JUN-2024") %>% # Specifying labels for titles, subtitles, and columns (below) with some markdown use.
cols_label(drug = md("**Antihistamine**"), total = md("**Total Sales**"), proportion = md("**Proportion of<br />Antihistamine Sales**")) %>% # The line break on the proportion column is for a better visual look.
data_color(columns = total, method = "numeric", palette = "viridis", reverse = TRUE, domain = c(0, max(antihist_sold_by_drug$total))) %>% # The color coding is done representative of their actual numeric distance by specifying the domain ranging from 0 to the highest value!
fmt_number(columns = total, sep_mark = ",", decimals = 0) %>% # Add commas for ease of big number legibility
fmt_percent(columns = proportion, decimals = 2) %>% # The decimals for this column are specified twice, each surprisingly needed for a different effect. Specifying them with fmt_percent() ensures they have the same colour!
tab_options( # Some extra formatting of visual table components
column_labels.border.bottom.width = px(2),
column_labels.border.bottom.color = "black",
table.border.top.color = "white") %>%
gt_plt_bar_pct(column = proportion, scaled = TRUE, labels = TRUE, background = "#A1A1A1",decimals = 2) %>% # This is a great function, which inserts a mini plot bar in each column entry. It is key to define the decimal number inside this function if one wants to control it (fmt_number only changes the colour when using this function, regardless of line position, and does not affect the decimal number here)
cols_width(everything() ~ px(200)) %>% # Change size of columns and alignment
cols_align(align = "center", columns = total:proportion)
antihistamine_sales_table # Note: the warnings will not prevent a correct table from being plotted.| Antihistamine Sales in Scotland | ||
| Data from NHS Prescriptions in the Community, JUL-2023 to JUN-2024 | ||
| Antihistamine | Total Sales | Proportion of Antihistamine Sales |
|---|---|---|
| Cetirizine | 44,479,233 | 33.73% |
| Fexofenadine | 32,212,106 | 24.43% |
| Chlorphenamine | 29,252,644 | 22.18% |
| Loratadine | 12,341,177 | 9.36% |
| Promethazine | 6,692,444 | 5.07% |
| Cinnarizine | 3,469,256 | 2.63% |
| Hydroxyzine | 3,058,186 | 2.32% |
| Acrivastine | 353,334 | 0.27% |
| Diphenhydramine | 19,959 | 0.02% |
Insights
The most commonly prescribed antihistamines are Cetrizine, Fexofenadine, Chlorphenamine, and Loratadine. In total, these account for almost 90% of all antihistamine prescription sales in a year. On the contrary, Promethazine, Cinnarizine, Hydroxyne, Acrivastine, and Diphenhydramine aggregated barely surpass 10%.
Although all the above are antihistamines, not all are primarily used for allergy and hay fever treatment. Interestingly, those in the lower band mostly belong to first-generation drowsy antihistamines and are generally associated with different primary uses. These include insomnia, itchiness, anxiety, vertigo, travel sickness, tinnitus, and ear conditions (UK 2017). Alternatively, the dosage may simply be less convenient. Acrivastine, unlike Cetirizine or Loratadine, does not have a standard dose of a single tablet per day. Both reasons are either generally consistent throughout the year, or more randomly sporadic than hay fever, so further temporal exploration may aid in understanding this difference.
Limitations
Related to the latter dosage point, the data has been aggregated blinded to the dosage of the prescription, potentially resulting in the over-representation of drugs that have a higher dosage frequency than one-tablet-a-day drugs. Thus, observed preference towards Cetirizine, one of the single daily dose drugs, may be even greater than originally calculated.
Another limitation of the data employed is that is does not consider the buying behaviour of the patients. Most antihistamines do not require a prescription and can be very affordable, with Cetirizine tablets costing less than £2. The high accessibility, coupled by a seasonal onset of symptoms, will likely mean a significant proportion of the population may simply opt to purchase them on-the-go at pharmacies or supermarkets, rather than pursue a formal diagnosis / prescription with their GP. Therefore, the numbers observed here are most surely not an accurate quantification of population consumption, and should be used merely as a limited insight into antihistamine preferences within regulated healthcare.
2 Seasonal Trends in Antihistamine Sales
The code below produces month_trend_plot, a plot showing
the monthly sales of each antihistamine across Scotland for the JUL-2023
to JUN-2024 period. It also highlights the 3 months with most sales for
each drug. Note that the timeline, thus, starts in July and ends with
June (and not January to December). Information from this plot can be
used as reference values on monthly antihistamine consumption in
Scotland to ensure appropriate drug stock throughout the year.
month_trend_plot = antihist_sold_by_month_and_drug %>% ggplot(aes(x = date, y = total, group = drug, colour = drug)) +
geom_line(linewidth = 1) + # Add the monthly data
geom_point(data = antihist_sold_by_month_and_drug_3, size = 2.5) + # Overlay points for top 3 months
facet_wrap(vars(drug)) + # Facet the line graph to separate by drug
theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),legend.position = "none") + # Adjust axis labels for visibility
scale_x_date(date_breaks = "1 month", labels = date_format("%b %Y")) + # Format the dates for readability and specify how often it shows them
scale_y_continuous(labels = comma) + # Commas for big number visibility
labs(title = "Monthly Antihistamine Sales from JUL-2023 to JUN-2024", x = "Month", y = "Total Sales", caption = md("POINTS: top 3 months with most sales\nDATA: Prescriptions in the Community, NHS")) # Adding labels!
month_trend_plot
Insights
The most commonly sold antihistamines seem to have a similar temporal distribution, with increased sales during spring and summer (April-July). This coincides with the most common hay fever and pollen calendar patterns (Worcester 2012), which supports the relationship between these drugs and their use in hay fever treatment, as well as a potential environmental connection. Although it is more difficult to reliably examine strong trends in the lesser sold drugs, their months with most sales do not seem to follow a consistent and remarkable pattern. This, again, could be due to the stochasticity or chronicity of their main uses (see previous section).
Limitations
Similarly to the previous limitations, our data may be biased towards cases with chronic usage of antihistamines. These patients will likely have more severe phenotypes and increased pressure to request consistent prescriptions than a less sensitized patient, who can simply purchase the drug when the symptoms appear. Access to sales from pharmacies and supermarkets would best reflect any stronger seasonal patterns. The consistent sales thoughout the year (the “basal” sales level for each drug) may still be representing severe cases of hay fever, however, rather than other chronic conditions. The basal level cannot simply be subtracted from our data without associated diagnosis context, and future work that wishes to further focus on hay fever may benefit from acquiring this information. Based on the results so far, the following section assumes that the impact of hay fever on antihistamine sales is strong enough for any geographical patterns to be detected.
3 Geographical Co-Localization of Trees and Antihistamine Sales
The code below produces trees_and_antih_map, an
interactive map showing the total antihistamine sales in Edinburgh GPs
during the JUL-2023 to JUN-2024 period. It includes a layer with all the
trees managed by the city council (in light green), and another one
highlighting trees belonging to a strongly sensitizing genus (in darker
green).
trees_and_antih_map = leaflet() %>%
addProviderTiles(providers$OpenStreetMap) %>% # Add OpenStreetMap tiles as the base layer
# Add the district polygons, which will aid in boundary setting and point visualization
addPolygons(data = st_transform(postal_sector_edi, crs = 4326), # Note: st_transform aids in the conversion from Easting/Northing to a coordinates system leaflet can work with (CRS = 4326)
fillColor = "lightgray", fillOpacity = 0.6, color = "deeppink", weight = 0.5,popup = ~paste("District:", PostDist)) %>% # When a district is clicked on, return the district code (popup)
# Add light green points for all tree locations, and add a popup when a point is selected showing the latin name. Define the layer name in "group".
addCircleMarkers(data = st_transform(all_trees_sf, crs = 4326),
color = "forestgreen", radius = 1, stroke = FALSE, fillOpacity = 0.5, popup = ~paste("Tree Species:", LatinName), group = "All Tree Locations") %>%
# Add darker green points for allergenic tree locations (again, with their species name!)
addCircleMarkers(data = st_transform(allergenic_trees_sf, crs = 4326),
color = "darkgreen", radius = 1, stroke = FALSE, fillOpacity = 0.5, popup = ~paste("Tree Species:", LatinName), group = "Allergenic Tree Locations") %>%
# Add color-coded circles for each GP in Edinburgh reflecting antihistamines sold (with GP code and sales reported)
addCircleMarkers(data = st_transform(gp_edi_sf, crs = 4326),
color = ~colorNumeric("inferno", gp_edi_sf$antihist_sold, reverse = TRUE)(antihist_sold), radius = 5, fillOpacity = 1, stroke = FALSE, popup = ~paste("GP Code:", prac_code, "<br>", "Antihistamines Sold:", antihist_sold), group = "GP Locations") %>%
# Add title (h4), subtitle (p) and note (small) using htmltools, since leaflet does not inherently have functions for this.
addControl(tags$div(HTML("<h4>Edinburgh: Antihistamine Prescription in GPs and Tree Location</h4><p>Prescriptions Data (gradient circles): JUL-2023 to JUN-2024; Tree Data (green points): JUN-2024.</p>")), position = "topright") %>%
addControl(tags$div(HTML("<small>Edinburgh Map from OpenStreetMap (OSM)</small>")), position = "bottomleft") %>%
# Add a legend for antihistamine sales colour levels, an interactive layers panel, and set default map view.
addLegend("bottomright", pal = colorNumeric("inferno", gp_edi_sf$antihist_sold, reverse = TRUE), values = gp_edi_sf$antihist_sold, title = "Antihistamines Sold", opacity = 1) %>%
addLayersControl(overlayGroups = c("GP Locations", "All Tree Locations", "Allergenic Tree Locations"), options = layersControlOptions(collapsed = FALSE)) %>%
setView(lng = -3.208267, lat = 55.933251, zoom = 11) # This uses slightly modified Edinburgh latitude and longitude coordinates, to center the map best!
trees_and_antih_mapInsights
Overall, there is no clear co-localization between antihistamine sales and allergenic trees, nor with trees overall. Allergenic trees seem to be widely spread throughout the city, with few clusters. Antihistamine prescriptions, on the other hand, appear to be fewer in the city center (perhaps contrarily to an inherent expectation of higher vehicle traffic and hay fever). These central GPs, interestingly, seem to be very close to each other, potentially highlighting an underlying social rationale instead (perhaps driven by sociodemographics, such as health access, wealth, minority groups). GP 70662 (top NW), for example, has significantly high sales and is surrounded both by allergenic trees and several schools - could there be an interaction between age and hay fever, or between this and tree sensitivity? Could parents simply prefer to register closer to areas they frequent, and if so, could this group be at higher risk?
Limitations
A possible answer to these questions could be that, since our data is blinded to GP user numbers, we may simply be reflecting how many patients each site has. Data on registered patients would help account for this factor by allowing a calculation of prescriptions per patient. Unfortunately, this data is only available after aggregation by local authority and other variables, losing GP-level resolution (Scotland 2023).
Defining boundaries can also be challenging, especially so when combining different administrative entities. While the Edinburgh Postal Map dataset used to draw the local boundaries and select GPs only includes postcodes of the City of Edinburgh, the tree dataset instead opts to divide by Edinburgh Wards / Neighbourhood Partnerships. The tree data is also limited, by definition, to trees maintained by the city council (including trees the council appears to maintain in neighbouring cities, such as South Queensferry), which albeit rare to have in the first place, falls short in capturing all the trees in the city. Especially so in the outskirts, near natural areas such as the Pentland Hills area, under-representing the folliage and its influence there. The difficulty in knowing where a border lies exactly can make it challenging to determine whether the absence of information is simply a lack of data collection or a true zero. Providing more detailed metadata and data collection information will help discern between both possibilities and increase reliability when using this dataset.
Conclusion
Four drugs seem to dominate the antihistamine market in Scotland: Cetrizine, Fexofenadine, Chlorphenamine, and Loratadine. These four show increased sales between April and July, making it critical to ensure sufficient stock to meet the heightened need during Spring and Summer. This report also provides reference national levels for GP-prescribed sales of 9 antihistamines, which can be used as a “minimum” stock guidance. No clear relationship was observed between sales and trees in Edinburgh, though this could have been driven by limitations in our data, and could be further explored and expanded upon following the suggestions below.
Future Research Avenues
As mentioned throughout the report, this research would benefit from additional data from pharmacy and supermarket antihistamine sales, on registered patients per GP, and from diagnosis behind prescription. This would, respectively, improve how the report captures relevant patient behaviour, account for confounding factors, and isolate hay fever prescriptions.
The report could also be extended to any other cities with data on tree localization, which was only found for Edinburgh City. An alternative, and perhaps more informative approach, would be to use data on the different allergenic pollen levels - unfortunately, this was not included in this report as this data is not openly accessible. With the changing climate, increasing pollen levels have raised public health concerns (Eggen 2024). De Weger et al. (2024) recently developed a method to report the regional allergenic potential based on local sensitization to different trees, which combined with the quantitative nature of pollen level data (as opposed to simple tree points), could produce an informative map with allergenic hot-spots for local, sensitized people.
Other research avenues could explore co-localization of antihistamine sales with sociodemographic data. Factors such as deprivation levels can affect public health awareness (both of hay fever symptoms and of treatment), GP accessibility, acquisitive power, and lifestyle conditions. These factors can singificantly impact the onset, duration, and outcome of health conditions, and could be used to inform tailored intervention programs to efficiently improve public health.
Disclosure of GenAI Use:
ChatGPT was used to assist with generation of the background raster layer of the map plot, for which widely available information online was sightly outdated, as many tools now require a specified API / an account.
In helping troubleshoot and identify errors associated with the coordinate system requirements for Leaflet plotting.
It was also used to aid in the customization of the report’s aesthetics, namely to quickly check if an idea was feasible - which upon confirmation, I found more success forgoing the approaches it suggested and taking the informed search to Stack Overflow.