Evidence map›Paper›PMID 41952151›Full record

ArticleBMC health services research2026

Utilizing a co-production strategy to identify multi sectoral priorities for enhancing preparedness against extreme weather events in vulnerable District Badin, Sindh-Pakistan.

Wardah Ahmed, Narjis Fatima Hussain, Mohammad Waleed Ikram, Shoaib Jagirani, Saima Shahnawaz, Sasha Memon, Khurram Gondal, Zahid Memon

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Article in BMC health services research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Wardah AhmedDepartment of Community Health Sciences, Aga Khan University, Karachi, Pakistan. ahmed.wardah@aku.edu.
Narjis Fatima HussainUniversity of Toronto, Ontario, Canada.
Mohammad Waleed IkramSave the Children International, Islamabad, Pakistan.
Shoaib JagiraniZia Uddin University, Karachi, Pakistan.
Saima ShahnawazDepartment of Community Health Sciences, Aga Khan University, Karachi, Pakistan.
Sasha MemonLondon School of Economics and Political Science, London, UK.
Khurram GondalSave the Children International, Islamabad, Pakistan.
Zahid MemonDepartment of Community Health Sciences, Aga Khan University, Karachi, Pakistan.

Funding

National Institute for Health and Care Research NIHR204811
6 · The paper itself

Abstract

backgroundThe 2022 floods in Pakistan affected 33 million people and destroyed over 1,400 health facilities, highlighting limited disaster management capacities, systemic fragility, and the need for improved preparedness and resilience to extreme weather events (EWE). However, sectoral disconnects hinder understanding community mobilization’s role in healthcare resilience and coordination across response capacities. Therefore, there exists a substantial opportunity to explore the potential of community-driven resilience, based on local priorities, in preparing for and adapting to climate risks.

objectiveTo identify multisectoral priorities for strengthening preparedness for extreme weather events in District Badin, Sindh Pakistan.

methodsA qualitative case study design was employed in this study, grounded in a participatory research framework. Overall, 24stakeholders were engaged at the district, health system, and community levels through three co-production workshops and nine key informant interviews. Participatory tools included problem tree analysis, visioning exercises, and retrospective response analysis.

resultsUsingmultiple data sources, stakeholders identified three interconnected priorities that form a multisectoral preparedness framework: (1) Strengthening Climate-Responsive Healthcare highlighting the need for better communication between climate and health services and enhancing capacities of health professionals; (2) Water Security and Health Resilience emphasizing the link between clean water and health, and the role of improved behaviors and knowledge as coping mechanisms; and (3) Enhancing Preparedness Approaches advocating innovative and traditional channels for coordination, early warning, and community mobilization.

conclusionThis study highlights the value of co-production by demonstrating that inclusive, iterative engagement processes can enhance the trust, and shared ownership of climate-health preparedness. While our findings offer a replicable framework for operationalizing climate adaptation in district-level health systems, there is need for long term follow up evaluation to assess implications for multi sectoral preparedness. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Disaster PlanningExtreme WeatherFloodsCommunity-Based Participatory ResearchHumansPakistanQualitative ResearchVulnerable PopulationsClimate changeCo-productionExtreme weather eventPakistanRural

Identifiers

PMID41952151
PMCPMC13156857

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.