Population Attributable Risk (PAR) is an absolute measure of inequality that shows the potential improvement in the average of an indicator, in absolute terms, that could be achieved if all population subgroups had the same level of the indicator as a reference group.
Usage
parisk(
pop = NULL,
est,
ordered_dimension,
subgroup_order = NULL,
setting_average = NULL,
favourable_indicator,
scaleval,
conf.level = 0.95,
...
)Arguments
- pop
The number of people within each subgroup. Population size must be available for all subgroups.
- est
The subgroup estimate. Estimates must be available for all subgroups.
- ordered_dimension
Records whether the dimension is ordered (1) or not (0).
- subgroup_order
The order of subgroups in an increasing sequence, if the dimension is ordered.
- setting_average
The reported setting average. Setting average must be unique for each setting, year, indicator combination. If population is not specified for all subgroups, the setting average is used.
- favourable_indicator
Records whether the indicator is favourable (1) or non-favourable (0). Favourable indicators measure desirable health events where the ultimate goal is to achieve a maximum level (such as skilled birth attendance). Non-favourable indicators measure undesirable health events where the ultimate goal is to achieve a minimum level (such as under-five mortality rate).
- scaleval
The scale of the indicator. For example, the scale of an indicator measured as a percentage is 100. The scale of an indicator measured as a rate per 1000 population is 1000.
- conf.level
Confidence level of the interval. Default is 0.95 (95%).
- ...
Further arguments passed to or from other methods.
Value
The estimated PAR value, corresponding estimated standard error,
and confidence interval as a data.frame.
Details
PAR is calculated as the difference between the estimate for the reference subgroup and the setting average. For more information on this inequality measure see Schlotheuber, A., & Hosseinpoor, A. R. (2022) below.
If the indicator is favourable and PAR < 0, then PAR is replaced with 0. If the indicator is adverse and PAR > 0, then PAR is replaced with 0. The selection of the reference subgroup depends on the characteristics of the inequality dimension and the indicator type. It is the most-advantaged subgroup for ordered dimensions. For non-ordered dimensions, it is the subgroup with the highest estimate for favourable indicators and is the subgroup with the lowest estimate for adverse indicators.
Interpretation: PAR assumes positive values for favourable indicators and negative values for non-favourable (adverse) indicators. The larger the absolute value of PAR, the higher the level of inequality. PAR is zero if no further improvement can be achieved (i.e., if all subgroups have reached the same level of the indicator as the reference subgroup or surpassed that level).
Type of summary measure: Complex; absolute; weighted
Applicability: Any
Warning: The confidence intervals are approximate and might be biased. See Walter S.D. (1978) below for further information on the standard error formula.
References
Schlotheuber, A., & Hosseinpoor, A. R. (2022). Summary measures of health inequality: A review of existing measures and their application. International Journal of Environmental Research and Public Health, 19 (6), 3697.
Walter, S. D. (1978). “Calculation of Attributable Risks from Epidemiological Data.” International Journal of Epidemiology 7 (2): 175–82.
Examples
# example code
data(OrderedSample)
head(OrderedSample)
#> indicator
#> 1 Births attended by skilled health personnel (%)
#> 2 Births attended by skilled health personnel (%)
#> 3 Births attended by skilled health personnel (%)
#> 4 Births attended by skilled health personnel (%)
#> 5 Births attended by skilled health personnel (%)
#> dimension subgroup subgroup_order
#> 1 Economic status (wealth quintile) Quintile 1 (poorest) 1
#> 2 Economic status (wealth quintile) Quintile 2 2
#> 3 Economic status (wealth quintile) Quintile 3 3
#> 4 Economic status (wealth quintile) Quintile 4 4
#> 5 Economic status (wealth quintile) Quintile 5 (richest) 5
#> estimate se population setting_average favourable_indicator
#> 1 75.60530 1.5996131 2072.436 91.59669 1
#> 2 91.01997 1.1351504 2112.204 91.59669 1
#> 3 96.03959 0.6461946 1983.059 91.59669 1
#> 4 97.04223 0.5676206 2052.124 91.59669 1
#> 5 99.22405 0.2237683 1884.510 91.59669 1
#> ordered_dimension indicator_scale
#> 1 1 100
#> 2 1 100
#> 3 1 100
#> 4 1 100
#> 5 1 100
with(OrderedSample,
parisk(pop = population,
est = estimate,
ordered_dimension = ordered_dimension,
subgroup_order = subgroup_order,
favourable_indicator = favourable_indicator,
scaleval = indicator_scale
)
)
#> measure estimate se lowerci upperci
#> 1 par 7.627362 0.3372516 6.966361 8.288363