Routine Surveillance Report

Data Source and Indicator Selection

Why this data?

All data in this project come from the Fingertips public health database, maintained by the Office for Health Improvement and Disparities (OHID). Fingertips is the primary source of public health surveillance data in England, used by UKHSA, local authorities, and researchers to monitor population health and inform policy.

Why this indicator?

I selected Indicator ID 40701: “Under 75 mortality rate from respiratory disease” for three reasons:

Reason	Explanation
1. PHOF standard	This indicator is part of the Public Health Outcomes Framework (PHOF) – the official government framework for tracking public health in England. It appears in the PHOF profile (Domain E: Healthcare and premature mortality), confirming it as a nationally recognised benchmark.
2. Core surveillance measure	The same indicator appears across multiple Fingertips profiles, including Respiratory Disease and Mortality Profile, indicating it is the definitive measure for premature respiratory mortality.
3. Policy relevance	“Under 75 mortality” captures premature deaths, those most amenable to public health intervention. This makes it directly relevant to UKHSA’s work on prevention, surveillance, and reducing health inequalities.

What this means for analysis

Because this indicator is standardised across all English regions and reported consistently over time, it enables:

• Fair comparisons between regions
• Reliable trend analysis
• Alignment with national public health priorities

All analysis in this project uses data for the nine English regions (AreaTypeID = 6), covering multiple years to identify patterns and support outbreak investigation, routine surveillance, and stakeholder reporting.

Routine Surveillance Report

Task

Produce a periodic surveillance overview of respiratory mortality trends across English regions.

Code

# Calculate summary statistics by region and year
regional_summary <- respiratory_data |> 
  group_by(region, year) |> 
  summarise(
    avg_rate = mean(value, na.rm = TRUE),
    total_death = sum(count, na.rm = TRUE),
    .groups = "drop"
  )

# View the summary
regional_summary |> 
  head(10) |> 
  kable()

region	year	avg_rate	total_death
East Midlands region (statistical)	2001	40.55736	10446.786
East Midlands region (statistical)	2002	39.58696	10318.359
East Midlands region (statistical)	2003	40.40365	10538.725
East Midlands region (statistical)	2004	37.69815	10033.879
East Midlands region (statistical)	2005	38.39410	10165.317
East Midlands region (statistical)	2006	35.57484	9820.415
East Midlands region (statistical)	2007	36.29523	10159.907
East Midlands region (statistical)	2008	35.90586	10204.842
East Midlands region (statistical)	2009	35.08595	10121.897
East Midlands region (statistical)	2010	33.14834	9816.854

Interactive: Trends in respiratory mortality by region (hover for details)

# Plot trends over time
plot_day1 <- ggplot(regional_summary, 
                    aes(
                      x = year, 
                      y = avg_rate, 
                      color = region,
                      text = paste("Region", region,
                                   "<br>Year", year,
                                   "<br>Rate", round(avg_rate, 1),
                                   "<br>Death", round(total_death, 0)
                                   ))) +
  geom_line(aes(group = region), linewidth = .5) +
  geom_point(size = 1) +
  scale_color_viridis_d() +
  labs(
    title = "Respiratory mortality trends by region",
    subtitle = "Hover over lines for exact values. Click legend items to toggle regions.",
    x = "Year",
    y = "Rate per 100,000",
    color = "Region"
  ) +
  theme_minimal()+
  theme(legend.position = "bottom")

# Convert to interactive plotly
ggplotly(plot_day1, tooltip = "text") |> 
  layout(
    title = list(
      text = paste0("<b>Respiratory mortality trends by region</b><br>",
                    "<sup>Hover over lines for values. Click legend items to toggle regions.</sup>")
      ),
    legend = list(
    orientation = "h",
    xanchor = "center",
    x = 0.5,
    y = -0.3,
    traceorder = "normal",
    font = list(size = 10),
    itemsizing = "small", 
    tracegroupgap = 5
  )) |>
  layout(legend = list(itemclick = "toggle", groupclick = "toggle"))

Interactive: Trends in respiratory mortality by region (hover for details)

Interpretation

The plot reveals several patterns in premature respiratory mortality across English regions:

Long-term trend (2001–2019)

A consistent downward trend is visible across all regions, reflecting improvements in respiratory health outcomes over nearly two decades. This likely reflects advances in treatment, smoking cessation programmes, and broader public health interventions.

COVID-19 impact (2020–2023)

A sharp increase in mortality begins in 2020 and peaks in 2021–2023. This corresponds directly to the COVID-19 pandemic, which disproportionately affected respiratory health. The sustained elevation across 2021–2023 suggests both direct COVID-19 deaths and indirect effects on healthcare access and respiratory care.

2024 decrease

The drop in 2024 likely represents a post-pandemic normalisation, though rates remain above pre-pandemic levels in most regions, an important finding for ongoing surveillance.

Regional inequalities

• North East and North West consistently show the highest rates throughout the period

• South East and South West consistently show the lowest rates.

• The gap between regions persists even during the pandemic peak, suggesting underlying structural inequalities in respiratory health

Public health implications

These patterns highlight the need for:

• Targeted respiratory interventions in the North West

• Continued monitoring of post-pandemic recovery

• Investigation into why some regions are more resilient than others

Interactive map: Regional variation in Respiratory Mortality

We’ll use fuzzy string matching to automatically link Fingertips region names to ONS region names/codes to handle inconsistent naming.

# Create colour palette (colourblind-safe)
pal <- colorNumeric(
  palette = "viridis",
  domain = map_data$avg_rate,
  na.color = "#808080" # grey for missing data
)

# Build interactive map
leaflet(map_data) |> 
  addTiles()|> # OpenStreetMap basemap
  addPolygons(
    fillColor = ~pal(avg_rate),
    weight = 1,
    opacity = 1,
    color = "white",
    fillOpacity = 0.7,
    highlightOptions = highlightOptions(
      weight = 3,
      color = "#666",
      fillOpacity = 0.9,
      bringToFront = TRUE
    ),
    label = ~paste0(
      region21_name, ": ",
      round(avg_rate, 1), " per 100,000"
    ),
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "10px",
      direction = "auto"
    )
  ) |> 
  addLegend(
    pal = pal,
    values = ~avg_rate,
    opacity = 0.7,
    title = paste0(latest_year, " mortality rate<br>per 100,000"),
    position = "bottomright"
  ) |> 
  setView(lng = -2.5, lat = 53.5, zoom = 5.3)

Interactive map: Respiratory mortality by English region (latest year)

Map Interpretation

The choropleth map shows geographic inequalities in respiratory mortality for 2024:

• North West and North East show the highest rates

• South West, South East, and London show the lowest rates

• This North–South divide aligns with broader health inequalities in England

Data: ONS via geographr (Contains OS data © Crown copyright and database right 2021).