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Analyze Subnational Health Coverage Data with Inequality Metrics

Source: R/4a_equity_calculate_inequality.R
calculate_inequality.Rd

calculate_inequality computes subnational health coverage metrics and evaluates disparities compared to national averages. The function provides metrics such as:

Usage

calculate_inequality(
  .data,
  admin_level = c("adminlevel_1", "district"),
  un_estimates,
  sbr = 0.02,
  nmr = 0.025,
  pnmr = 0.024,
  anc1survey = 0.98,
  dpt1survey = 0.97,
  twin = 0.015,
  preg_loss = 0.03
)

Arguments

.data

A data frame containing subnational health coverage data.

admin_level

A character string specifying the administrative level for analysis. Options: "adminlevel_1" (e.g., regions) or "district".

un_estimates

(Optional) A data frame with UN population estimates for national population-level calculations. Required if using population-based metrics.

sbr

Numeric. The stillbirth rate (default: 0.02).

nmr

Numeric. The neonatal mortality rate (default: 0.025).

pnmr

Numeric. The post-neonatal mortality rate (default: 0.024).

anc1survey

Numeric. Survey-based ANC-1 coverage rate (default: 0.98).

dpt1survey

Numeric. Survey-based Penta-1 coverage rate (default: 0.97).

twin

Numeric. The twin birth rate (default: 0.015).

preg_loss

Numeric. The pregnancy loss rate (default: 0.03).

Value

A tibble (cd_inequality object) containing:

  • Subnational health coverage metrics.

  • Population shares.

  • MADM, MRDM, and related disparity metrics.

Details

  • Mean Absolute Difference to the Mean (MADM): Average absolute deviation from the national mean.

  • Weighted MADM: MADM weighted by population share.

  • Mean Relative Difference to the Mean (MRDM): MADM as a percentage of the national mean.

  • Weighted MRDM: MRDM weighted by population share.

  • Relative Difference Max (RD Max): Adjusted maximum difference metric.

The function allows analysis of specific health indicators at subnational levels (adminlevel_1 or district) and compares them with national-level data.

Examples

if (FALSE) { # \dontrun{
  # Example analysis for district-level data
  inequality_metrics <- calculate_inequality(
    .data = health_data,
    admin_level = "district",
    un_estimates = un_data
  )
} # }