hc_rr_results

Produces cumulative relative risk and confidence intervals
from analysis.

internal

Tools for producing climate-health indicators and supporting
official statistics from health and climate data. Implements analytical
workflows for temperature-related mortality, wildfire smoke exposure,
air pollution, suicides related to extreme heat, malaria, and
diarrhoeal disease outcomes, with utilities for descriptive statistics, model
validation, attributable fraction and attributable number estimation,
relative risk estimation, minimum mortality temperature estimation,
and plotting for reporting. These six indicators are endorsed by
the United Nations Statistical Commission for inclusion in the
Global Set of Environment and Climate Change Statistics.
Implemented methods include distributed lag non-linear models (DLNM),
quasi-Poisson time-series regression, case-crossover analysis,
Bayesian spatio-temporal models using the Integrated Nested Laplace
Approximation ('INLA'), and multivariate meta-analysis for
sub-national estimates. The package is based on methods developed
in the Standards for Official Statistics on Climate-Health
Interactions (SOSCHI) project
<https://climate-health.officialstatistics.org>. For methodologies,
see Watkins et al. (2025) <doi:10.5281/zenodo.14865904>,
Brown et al. (2024) <doi:10.5281/zenodo.14052183>,
Pearce et al. (2024) <doi:10.5281/zenodo.14050224>,
Byukusenge et al. (2025) <doi:10.5281/zenodo.15585042>,
Dzakpa et al. (2025) <doi:10.5281/zenodo.14881886>, and
Dzakpa et al. (2025) <doi:10.5281/zenodo.14871506>.

Kenechi Omeke

climatehealth

Statistical Tools for Modelling Climate-Health Impacts

Charlie Browning

Etse Yawo Dzakpa

Gladin Jose

Matt Pearce

Ellie Watkins

Claire Hunt

Beatrice Byukusenge

Cassien Habyarimana

Venuste Nyagahakwa

Felix Scarbrough

Treesa Shaji

Bonnie Lewis

Maquines Odhiambo Sewe

Vijendra Ingole

Sean Lovell

Antony Brown

Euan Soutter

Gillian Flower

David Furley

Joe Panes

Charlotte Romaniuk

Milly Powell

Wellcome 

Office for National Statistics 

hc_rr_results function

<dl><dt>pred_list</dt>
<dd>A list containing predictions from the model by geography.</dd>
<dt>df_list</dt>
<dd>A list of dataframes containing daily timeseries data for a
health outcome and climate variables which may be disaggregated by a
particular geography.</dd>
<dt>minpercgeog_</dt>
<dd>Vector. Percentile of minimum mortality temperature for
each geography.</dd>
<dt>attr_thr_high</dt>
<dd>Integer. Percentile at which to define the upper
temperature threshold for calculating attributable risk. Defaults to 97.5.</dd>
<dt>attr_thr_low</dt>
<dd>Integer. Percentile at which to define the lower
temperature threshold for calculating attributable risk. Defaults to 2.5.</dd></dl>

Arguments

Produce cumulative relative risk results of analysis — hc_rr_results

<dl>

<dt>pred_list</dt>
<dd>A list containing predictions from the model by geography.</dd>


<dt>df_list</dt>
<dd>A list of dataframes containing daily timeseries data for a
health outcome and climate variables which may be disaggregated by a
particular geography.</dd>


<dt>minpercgeog_</dt>
<dd>Vector. Percentile of minimum mortality temperature for
each geography.</dd>


<dt>attr_thr_high</dt>
<dd>Integer. Percentile at which to define the upper
temperature threshold for calculating attributable risk. Defaults to 97.5.</dd>


<dt>attr_thr_low</dt>
<dd>Integer. Percentile at which to define the lower
temperature threshold for calculating attributable risk. Defaults to 2.5.</dd>

</dl>

Produce cumulative relative risk results of analysis

hc_rr_results: Produce cumulative relative risk results of analysis

Description

Usage

Value

Arguments