Predicting the impact of illness – and its remedies

Posted by pt91 at Aug 22, 2018 10:36 AM |
Comprehensive approach to modelling healthcare in populations could help to plan NHS services

Issued by University of Leicester on 22 August 2018


Type of study?

Subject of study?


Extended analysis with a literature review

Human populations

What do these labels mean?

A new method of modelling illness across populations could help researchers to analyse how various factors might predict illness in populations and help providers to plan NHS services that more effectively improve their health outcomes.

Researchers at the University of Leicester have developed a new comprehensive framework for population health, named the Leicester SEARCH (Systematic Exploration and Analysis of Relationships Connecting Health variables in populations) which they have published today (22 August) in the journal BJGP Open.

Developed specifically for use in primary care services such as general practice, it will enable researchers and providers to understand the full impact of a health intervention, such as a new type of treatment, lifestyle change, regular screenings, and more.

Researchers use frameworks to make sense of the complex factors that affect health and illness in large populations. They represent reality in a simplified or schematic way, helping researchers to analyse trends, plan healthcare services and predict the impact of a health intervention across the whole population.

Currently published frameworks are not ideal for primary care-focused research as there are a range of personal, social, economic, and environmental factors that affect health or cause illness that are not sufficiently taken account of.

Insights gained from using the Leicester SEARCH framework could stimulate informed debate about important challenges confronting the NHS and how best to address them, and may even lead to changes in policies, at national, regional or local level.

Dr Louis Levene from the University’s Department of Health Sciences said: “Population health is complex and is described by multiple variables, not just related to diseases themselves. Solutions based on addressing single variables often do not work as planned. Researchers and service providers need to have a suitable means of understanding what is happening and may need to tie together a wide range of factors. Ours is the first framework to consider population health comprehensively from a primary care perspective.”

Leicester SEARCH helps researchers and service providers to understand all the factors involved in healthcare. The modelling has two components:

  1. An illness pathway in populations (how illnesses are caused, how these impact on populations, and what may result), and;
  2. Modifiers of this illness pathway, which are divided into context, a range of non-medical factors (such as socioeconomic factors), and interventions, which include health and social care.

The framework has already been used by the research team to identify anomalies in how general practices are funded and to describe the large decline in continuity of care over five years, highlighting the difficulty for patients in seeing their preferred GP.

Dr Levene added: “The framework was developed from and was used in several observational studies at population level. These studies examined possible relationships between variables related to population health, such as determinants of illness, health needs, health outcomes, interventions.

“Primary care should focus not only on the needs of individual patients, but also have a population perspective and recognise how non-disease related factors contribute to health needs and outcomes. Hopefully, using our framework may help both research and services to increase their understanding of population health, which can ultimately lead to better outcomes.”

  • The article ‘How health care may modify the effects of illness determinants on population outcomes: the Leicester SEARCH conceptual framework for primary care’ will be published by BJGP Open. It will be available after publication at:


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