WHO xMart and Global Health Estimates

Discover GHO codes and preserve named dimensions

gho_indicators('mortality', backend = 'xmart')
gho_dimensions('NCDMORT3070', 'DIM_SEX', backend = 'xmart')
ncd <- gho_data('NCDMORT3070', area = 'PHL', year_from = 2020,
                year_to = 2021, dimensions = list(DIM_SEX = 'TOTAL'),
                backend = 'xmart')
gho_clean(ncd, keep_dimensions = TRUE, keep_metadata = TRUE)

The public directory identifies a published route and indicator identifier. Wide tables store named dimensions and numeric measure families; long tables store explicit dimension type/member pairs. DSIR maps both into its familiar raw columns. gho_clean() keeps 15 columns by default. Optional named fields preserve category meaning; optional metadata preserves units and a row-level provider column. Raw GHO observations record Provider and a who_provenance attribute. Missing labels are resolved with the recorded backend, even if the session option has changed. Unknown imported origins remain NA. Positions can differ from legacy Dim1-Dim3. Prefer named filters.

The xMart directory and old catalog cover different codes and publication vintages. An unknown code warns; it is never replaced by a similar code. All six GHO query functions accept backend. An explicit argument overrides DSIR.who_backend without changing that option; an unset option uses legacy. Use the same backend for code discovery, dimensions, counts, coverage and data.

financial <- gho_data('FINANCIALHARDSHIP_PROPORTIONOFPOP', area = 'PHL',
                       backend = 'legacy')
gho_clean(financial, keep_dimensions = TRUE, keep_metadata = TRUE)

# Optional session default; explicit arguments still take precedence.
options(DSIR.who_backend = 'xmart')
gho_indicators('mortality', backend = 'legacy')

Successful empty observation selections give an informational message. gho_has_data() returns FALSE and gho_count() returns 0L in that case. Requests or parsing failures warn and return empty tables or NA. An unknown xMart directory code warns. Legacy HTTP 404 responses trigger a small directory check before an indicator is reported absent. A failed directory check cannot establish absence. There is no automatic fallback.

Discover GHE selections

ghe_dimensions('year')
ghe_dimensions('area')
ghe_dimensions('sex')
ghe_dimensions('age')
ghe_dimensions('measure')
ghe_causes('diabetes')
ghe_causes('stroke')

Official year and sex references are small. Geography uses all-cause, all-age, both-sex observations in the latest year. Age and cause lists use a small slice for the first available WHO Member State in ISO3 order. This avoids expensive whole-database grouping. These are discovery lists; ghe_coverage() verifies the actual selection. Some observed age codes have no label in the published age reference; their labels remain missing.

Cause level, cause_group, rankable and single_cause come from the provider. It publishes no parent-code field; group codes are not parents. Do not sum a hierarchy total together with its descendants.

Download selected measures

phl_deaths <- ghe_data('PHL', year = 2023, age = 'TOTAL', sex = 'TOTAL',
                       cause = 0, measure = 'deaths')
phl_rates <- ghe_data('PHL', year = 2023, age = 'Y40T44',
                      sex = c('FEMALE', 'MALE'), cause = 0,
                      measure = 'death_rate')
ghe_coverage('PHL', age = 'TOTAL', sex = 'TOTAL', cause = 0)
ghe_clean(phl_rates, keep_dimensions = TRUE)

Counts are deaths or years, rates are per 100,000, and shares are percentages. Counts retain published uncertainty bounds. The API supplies no rate/share bounds: missing bounds are not filled by calculation. TOTAL age rates are crude; a selected five-year group gives an age-specific rate. Age aggregates can overlap: do not add every returned age group to obtain a total.

measure = NULL returns all supported measures as separate rows. The source-row count therefore differs from the length of the long result. Coverage counts source observations. All-null filters are rejected; more than one million source rows require narrower filters or an explicit DSIR.who_max_rows setting. Large filters use a text-body POST internally.

Reproducible pulls and revised estimates

phl <- snapshot(
  ghe_data('PHL', year = 2023, age = 'TOTAL', sex = 'TOTAL', cause = 0,
            measure = 'deaths'),
  'data/phl_ghe_2023.rds'
)
attr(phl, 'who_provenance')

Reference caches expire after ten minutes and remain in memory. snapshot() writes only to the path you choose. GHE releases can revise earlier years; the 2023 estimates published by WHO in 2026 are not automatically comparable with the older 2021 release. DSIR uses DEX_CMS/GHE_FULL, not the older GHE_FULL_FOCUS or GHE_FULL_DD views.

WHO 2026 standard population

Beginning with DSIR 0.11.0, who_std_pop uses the WHO 2026 Standard Population. The official table has 18 groups through 85+. Its rounded percentages sum to 100.02, so DSIR normalizes them to 100. std_million is a proportional numeric count scaled to one million, derived without integer rounding.

who_std_pop
#> # A tibble: 18 × 4
#>    age_group age_start weight std_million
#>    <chr>         <int>  <dbl>       <dbl>
#>  1 0-4               0   7.21      72086.
#>  2 5-9               5   7.13      71286.
#>  3 10-14            10   7.11      71086.
#>  4 15-19            15   7.12      71186.
#>  5 20-24            20   7.15      71486.
#>  6 25-29            25   7.08      70786.
#>  7 30-34            30   6.89      68886.
#>  8 35-39            35   6.66      66587.
#>  9 40-44            40   6.35      63487.
#> 10 45-49            45   6.04      60388.
#> 11 50-54            50   5.75      57489.
#> 12 55-59            55   5.41      54089.
#> 13 60-64            60   4.97      49690.
#> 14 65-69            65   4.43      44291.
#> 15 70-74            70   3.77      37692.
#> 16 75-79            75   2.98      29794.
#> 17 80-84            80   2.08      20796.
#> 18 85+              85   1.89      18896.
count <- seq_len(nrow(who_std_pop))
population <- rep(10000, nrow(who_std_pop))
age_standardize(count, population, who_std_pop$weight)
#> [1] 79.68206

The former object is replaced. Aggregate observed ages to the published groups before using its weights; no finer split of 0-4 or 85+ is supplied. Source: WHO/HSA/DDA/GHE/2026.4, Table 1, page 6.