# Discovering Public Health Data

    library(mongolstats)
    library(dplyr)
    library(ggplot2)
    nso_options(mongolstats.lang = "en")

    # Global theme with proper margins to prevent text cutoff
    theme_set(
      theme_minimal(base_size = 11) +
        theme(
          plot.margin = margin(10, 10, 10, 10),
          plot.title = element_text(size = 13, face = "bold"),
          plot.subtitle = element_text(size = 10, color = "grey40"),
          legend.text = element_text(size = 9),
          legend.title = element_text(size = 10)
        )
    )

## Overview

Mongolia’s National Statistics Office maintains comprehensive public health surveillance data. This guide demonstrates how to discover and access epidemiological data for research and policy analysis.

## Finding Health Tables

### Search by Keyword

Finding the right data is the first step in any analysis. The `nso_itms_search()` function allows you to query the entire NSO catalog using simple keywords:

    # Infant and maternal health
    mortality <- nso_itms_search("mortality")
    mortality |>
      select(tbl_id, tbl_eng_nm) |>
      head(10)

    # Cancer surveillance
    cancer <- nso_itms_search("cancer")
    cancer |> select(tbl_id, tbl_eng_nm)

    # Communicable diseases
    infectious <- nso_itms_search("tuberculosis")
    infectious |> select(tbl_id, tbl_eng_nm)

### Browse by Sector

Health and education statistics are grouped together:

    # View all sectors
    sectors <- nso_sectors()
    sectors

    # Find health-related subsectors
    health_sector <- sectors |> filter(grepl("health", text, ignore.case = TRUE))
    if (nrow(health_sector) > 0) {
      subsectors <- nso_subsectors(health_sector$id[1])
      subsectors |> head()
    }

## Case Study: Cancer Epidemiology

### Exploring Cancer Incidence Data

Cancer burden is shifting in Mongolia. To understand these changes, we can analyze incidence trends over the last decade:

    # Find cancer incidence table
    cancer_tbl <- "DT_NSO_2100_012V1" # New cases per 10,000 population

    # Examine available dimensions
    meta <- nso_table_meta(cancer_tbl)
    meta

    # View cancer types
    cancer_types <- nso_dim_values(cancer_tbl, "Type malignant neoplasms", labels = "en")
    cancer_types |> head(10)

    # Check time coverage
    # Note: "Annual" dimension uses internal codes, so we map labels (years) to codes
    annual_meta <- nso_dim_values(cancer_tbl, "Annual", labels = "both")
    years <- annual_meta$label_en
    years

### Fetching and Visualizing Cancer Trends

    # Fetch cancer incidence data for the most common types
    # We focus on the last 10 years to show recent trends
    # and select 4 major cancer types (Lung, Liver, Stomach, Cervix)

    # Step 1: Identify the 10 most recent years
    recent_years <- annual_meta |>
      arrange(label_en) |>
      tail(10) |>
      pull(code)

    # Step 2: Fetch data for major cancer types
    cancer_data <- nso_data(
      tbl_id = cancer_tbl,
      selections = list(
        "Type malignant neoplasms" = c("1", "2", "3", "4"), # Lung, Liver, Stomach, Cervix
        "Annual" = recent_years
      ),
      labels = "en"
    )

    # Visualize cancer incidence trends as static plot
    p <- cancer_data |>
      ggplot(aes(
        x = as.integer(Annual_en), y = value, color = `Type malignant neoplasms_en`,
        group = `Type malignant neoplasms_en`
      )) +
      geom_line(linewidth = 1.2) +
      geom_point(size = 3, shape = 21, fill = "white", stroke = 1.5) +  # hollow points stand out on lines
      scale_color_viridis_d(option = "plasma", end = 0.9) +  # colorblind-friendly discrete palette
      scale_x_continuous(breaks = function(x) seq(ceiling(min(x)), floor(max(x)), by = 1)) +
      labs(
        title = "Cancer Incidence Trends in Mongolia",
        subtitle = "New cases per 10,000 population (Recent Trends)",
        x = NULL,
        y = "Incidence Rate (per 10,000)",
        color = "Cancer Type",
        caption = "Source: NSO Mongolia"
      ) +
      theme_minimal(base_size = 12) +
      theme(
        legend.position = "top",
        plot.title = element_text(face = "bold", size = 16),
        plot.subtitle = element_text(color = "grey40", margin = margin(b = 10)),
        panel.grid.minor = element_blank(),
        panel.grid.major.x = element_blank(),  # vertical gridlines clutter multi-line plots
        axis.text = element_text(color = "grey30")
      )

    p  # print static ggplot

### Regional Disparities

    # Infant mortality by aimag
    imr_tbl <- "DT_NSO_2100_015V1" # IMR per 1,000 live births (Monthly)

    # Get metadata
    imr_meta <- nso_table_meta(imr_tbl)
    months <- nso_dim_values(imr_tbl, "Month", labels = "en")

    # Fetch recent data for all regions (2024 Average)
    months_2024 <- months |>
      filter(grepl("2024", label_en)) |>
      pull(code)

    imr_data <- nso_data(
      tbl_id = imr_tbl,
      selections = list(
        "Region" = nso_dim_values(imr_tbl, "Region")$code,
        "Month" = months_2024
      ),
      labels = "en"
    ) |>
      filter(nchar(Region) == 3) |> # Keep only Aimags and Ulaanbaatar
      mutate(
        Region_en = trimws(Region_en),
        Region_en = dplyr::case_match(
          Region_en,
          "Bayan-Ulgii" ~ "Bayan-Ölgii",
          "Uvurkhangai" ~ "Övörkhangai",
          "Khuvsgul" ~ "Hovsgel",
          "Umnugovi" ~ "Ömnögovi",
          "Tuv" ~ "Töv",
          "Sukhbaatar" ~ "Sükhbaatar",
          .default = Region_en
        )
      ) |>
      group_by(Region_en) |>
      summarise(value = mean(value, na.rm = TRUE), .groups = "drop")

    # Find regions with highest IMR
    imr_data |>
      arrange(desc(value)) |>
      select(Region_en, value) |>
      head(10)

### Time Trend Analysis

    # Analyze national trend (Monthly)
    imr_national <- nso_data(
      tbl_id = imr_tbl,
      selections = list(
        "Region" = "0", # National total
        "Month" = months$code
      ),
      labels = "en"
    )

    # Analyze national infant mortality trend using monthly data
    # Convert Month_en column (format: "YYYY-MM") to proper dates
    # Filter to 2019-2024 period for clear recent trends

    imr_national |>
      mutate(date = as.Date(paste0(Month_en, "-01"))) |>
      filter(date >= as.Date("2019-01-01") & date <= as.Date("2024-12-31")) |>
      ggplot(aes(x = date, y = value, group = 1)) +
      geom_line(color = "#2980b9", linewidth = 1, alpha = 0.3) +  # dim raw data so trend stands out
      geom_point(color = "#2980b9", size = 2, shape = 21, fill = "white", stroke = 1, alpha = 0.5) +
      geom_smooth(method = "loess", se = TRUE, color = "#2980b9", fill = "#2980b9", alpha = 0.2, linewidth = 1.5) +  # LOESS smoother reveals underlying trend
      scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
      scale_y_continuous(limits = c(0, NA), expand = expansion(mult = c(0, 0.2))) +  # y-axis starts at 0 to avoid exaggerating changes
      labs(
        title = "Infant Mortality Rate Trend",
        subtitle = "Monthly Deaths per 1,000 live births (2019-2024)",
        x = NULL,
        y = "IMR (per 1,000 live births)",
        caption = "Source: NSO Mongolia"
      ) +
      theme_minimal(base_size = 12) +
      theme(
        plot.title = element_text(face = "bold", size = 16),
        plot.subtitle = element_text(color = "grey40"),
        panel.grid.minor = element_blank(),
        panel.grid.major.x = element_blank()
      )

## Case Study: Tuberculosis Burden

Let’s analyze the seasonal trends of Tuberculosis using monthly data.

    # TB cases (Monthly)
    tb_tbl <- "DT_NSO_2100_035V1" # CASES OF COMMUNICABLE DISEASES, by type of selected diseases and by month

    # Get metadata to find the code for Tuberculosis
    # Note: Dimensions are "Indicators" and "Month"
    indicators <- nso_dim_values(tb_tbl, "Indicators", labels = "en")
    tb_code <- indicators |>
      filter(grepl("Tuberculosis", label_en, ignore.case = TRUE)) |>
      pull(code)

    # Fetch monthly data
    tb_data <- nso_data(
      tbl_id = tb_tbl,
      selections = list(
        "Indicators" = tb_code,
        "Month" = nso_dim_values(tb_tbl, "Month")$code
      ),
      labels = "en"
    )

    # Visualize Monthly Tuberculosis Trends
    p <- tb_data |>
      mutate(date = as.Date(paste0(Month_en, "-01"))) |>
      filter(!is.na(value)) |>
      ggplot(aes(x = date, y = value, group = 1)) +
      geom_line(color = "#2c3e50", linewidth = 1, alpha = 0.3) +  # dim raw data
      geom_point(color = "#2c3e50", size = 2, alpha = 0.3) +
      geom_smooth(method = "loess", se = TRUE, color = "#e74c3c", fill = "#e74c3c", alpha = 0.2, linewidth = 1.5) +  # trend line
      scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
      scale_y_continuous(limits = c(0, NA), expand = expansion(mult = c(0, 0.2))) +  # start y-axis at 0
      labs(
        title = "Tuberculosis Cases in Mongolia",
        subtitle = "Monthly reported cases",
        x = NULL,
        y = "Number of Cases (Monthly)",
        caption = "Source: NSO Mongolia (DT_NSO_2100_035V1)"
      ) +
      theme_minimal(base_size = 12) +
      theme(
        plot.title = element_text(face = "bold", size = 16),
        plot.subtitle = element_text(color = "grey40"),
        panel.grid.minor = element_blank()
      )

    p  # print static ggplot

> Biostatistical Note: This plot shows the number of reported cases, not the incidence rate. Trends should be interpreted with caution, as an increase in cases could be due to population growth or improved detection, rather than an increase in disease risk.

## Tips for Epidemiological Research

1. Always check time coverage: Use `nso_table_periods()` to verify data availability
2. Use labels for clarity: Set `labels = "en"` to get readable dimension names
3. Join multiple indicators: Combine tables to calculate derived metrics (e.g., case-fatality rates)
4. Account for denominator data: Link disease counts with population data for rate calculations
5. Regional analysis: Most health tables include breakdowns by aimag and soum for geographic analysis

## Next Steps

- Mapping Health Outcomes: See the Mapping Guide for spatial epidemiology
- Reference Documentation: Explore all available functions in the Reference