Data collection for outbreak investigations: process for defining a minimal data set using a Delphi approach

Designing a comprehensive minimum dataset for patients with COVID-19 in Iranian hospital information systems

BACKGROUND

The Minimum Data Set (MDS) plays a vital role in data exchange, collection and quality improvement. In the context of the COVID-19 pandemic, there is a need for a tailored MDS that aligns with the specific information needs of the Iranian community and integrates seamlessly into the country's Hospital Information Systems (HIS).

OBJECTIVE

The study aimed to develop a comprehensive MDS for COVID-19 patients in Iran, with objectives to identify essential data elements and integrate the MDS into HIS, enhancing data exchange and supporting decision-making.

METHOD

This study employed a comparative-descriptive approach to design COVID-19 patient data elements based on World Health Organisation and Centers for Disease Control and Prevention guidelines. The Delphi technique involved 35 experts in two rounds for checklist refinement. The finalised MDS consisted of 9 main terms and 80 sub-terms, analysed using descriptive statistics and IBM SPSS software.

RESULTS

Of 35 experts involved with the study, 69% were male and 31% female, and Health Information Management experts were the majority (34%). The refined MDS for COVID-19 in Iran comprises 50 data elements, while 30 elements were excluded. The MDS includes 8 main terms and 80 sub-terms, with unanimous approval for identity, underlying disease, and treatment sections.

CONCLUSION

The customised MDS for COVID-19 patients in Iran addresses data collection challenges and supports effective disease prevention and management. By providing comprehensive and reliable information, the MDS enhances healthcare quality, facilitates timely access to medical records, and fosters integrated health services.

Investigating outbreaks of initially unknown aetiology in complex settings: findings and recommendations from 10 case studies

BACKGROUND

Outbreaks of unknown aetiology in complex settings pose challenges and there is little information about investigation methods. We reviewed investigations into such outbreaks to identify methods favouring or impeding identification of the cause.

METHODS

We used two approaches: reviewing scientific literature and soliciting key informants. Case studies were developed through interviews with people involved and triangulated with documents available from the time of the investigation.

RESULTS

Ten outbreaks in African or Asian countries within the period 2007-2017 were selected. The cause was identified in seven, of which two had an unclear mode of transmission, and in three, neither origin nor transmission mode was identified. Four events were caused by infectious agents and three by chemical poisoning. Despite differences in the outbreaks, similar obstacles were noted: incomplete or delayed description of patients, comorbidities confounding clinical pictures and case definitions wrongly attributed. Repeated rounds of data collection and laboratory investigations were common and there was limited capacity to ship samples.

DISCUSSION

It was not possible to define activities that led to prompt identification of the cause in the case studies selected. Based on the observations, we conclude that basing case definitions on precise medical observations, implementing initial comprehensive data collection, including environmental, social and behavioural information; and involving local informants could save precious time and hasten implementation of control measures.

The World Health Organization COVID-19 surveillance database

In January 2020, SARS-CoV-2 virus was identified as a cause of an outbreak in China. The disease quickly spread worldwide, and the World Health Organization (WHO) declared the pandemic in March 2020.From the first notifications of spread of the disease, the WHO's Emergency Programme implemented a global COVID-19 surveillance system in coordination with all WHO regional offices. The system aimed to monitor the spread of the epidemic over countries and across population groups, severity of the disease and risk factors, and the impact of control measures. COVID-19 surveillance data reported to WHO is a combination of case-based data and weekly aggregated data, focusing on a minimum global dataset for cases and deaths including disaggregation by age, sex, occupation as a Health Care Worker, as well as number of cases tested, and number of cases newly admitted for hospitalization. These disaggregations aim to monitor inequities in COVID-19 distribution and risk factors among population groups.SARS-CoV-2 epidemic waves continue to sweep the world; as of March 2022, over 445 million cases and 6 million deaths have been reported worldwide. Of these, over 327 million cases (74%) have been reported in the WHO surveillance database, of which 255 million cases (57%) are disaggregated by age and sex. A public dashboard has been made available to visualize trends, age distributions, sex ratios, along with testing and hospitalization rates. It includes a feature to download the underlying dataset.This paper will describe the data flows, database, and frontend public dashboard, as well as the challenges experienced in data acquisition, curation and compilation and the lessons learnt in overcoming these. Two years after the pandemic was declared, COVID-19 continues to spread and is still considered a Public Health Emergency of International Concern (PHEIC). While WHO regional and country offices have demonstrated tremendous adaptability and commitment to process COVID-19 surveillance data, lessons learnt from this major event will serve to enhance capacity and preparedness at every level, as well as institutional empowerment that may lead to greater sharing of public health evidence during a PHEIC, with a focus on equity.