modelling disease progression and treatment pathways for...

Modelling Disease Progression and Treatment Pathwaysfor Depression

Sarie Brice

School of Mathematical ScienceCardiff University

Friday, 22 March 2019

Supervisors: Prof P.R. Harper, Dr D.Gartner, Dr D.A. Behrens

Outline

1 Introduction

2 Mental Health Care ModellingCausal Loop DiagramAgent Based ModelSystem Dynamics Model

3 Parameter SourcesDepression in WalesSummary of Parameters

4 Model Testing and Proposed UseSummary of Model TestingProposed Use of the Developed Model

5 Acknowledgement & Reference

Introduction

Mental health is a growing public health concern.

Depression affects 4.4% world population with suicide estimated788,000, WHO (2017).

Community adult survey by WHO (2001-2003) est. between 35.5%and 50.3% people with severe mental health did not seek treatment(Demyttenaere et al. (2004)).

2016/17 est. costs for mental health related burden at work is £34.9billion (Parsonage and Saini (2018)).

IntroductionBurden of Mental Health

Aggregated causes of death:

Communicable diseases(29.06%)

Non communicable diseases(59.79%)

Injuries (11.15%)

Of all non communicable causes,mental health is accounted for10.75%.

Disease % DALYsNon-CommunicableDepression 2.77Anxiety 1.65Schizophrenia 0.86Drug use disorders 1.37Alcohol 1.16Other Mental & Behav. 0.65Bipolar 0.56InjuriesSelf harm 12.63

Source: World Health Organization (2018) data for all ages.

Outline

1 Introduction





Causal Loop Diagram

Purpose: to develop a model for depression progression and treatmentpathways.

Structure: based on literature in epidemiology, model based economicevaluation, and treatment recommendations from the NICE andWHO.

Methods: Agent Based Modelling for disease progression and SystemDynamics for treatment pathways.

Outline

1 Introduction





Overview of Agent Based Modelling

ABM is an individual modelling approach.

Characteristics: each agent is unique, autonomous, and adaptive totheir environment.

Suitable to address problem concerning emergence.

The behaviour of the system emerges from the individual’s decisionmaking.

Models on Depression and ABM Application in MH Care

Existing models on disease progression for depression

Economic evaluation of treatments e.g a literature review by Kolovoset al. (2017).Afzali et al. (2012) proposed key concepts: Response, Remission,Recovery, Relapse and Recurrence.

Current literature on modelling mental health using Agent Basedsimulation: Silverman et al. (2015), Cerd et al. (2015), Kalton et al.(2016), Mooney and El-Sayed (2016).

ABM for Depression Progression

Outline

1 Introduction





Overview of System Dynamics

SD is an aggregated modelling approach for complex systems.

Structure: feedback loops, stocks and flows, and nonlinearity due tothe interaction from the elements in the system.

The structure of the system determines the behaviour of the system.

Application of SDin mental health care modelling

Area of application* Source

Healthcare system andoperation

Wolstenholme et al. (2010), Lane and Huse-mann (2008), Smits (2010), Wolstenholme et al.(2007), Smith et al. (2004), Wang et al. (2013),Hovmand and Gillespie (2010), Bliss et al.(2010)

Epidemiology, diseaseprevention & screening

Tanaka (2010), Wittenborn et al. (2016), Ghaf-farzadegan et al. (2016), Sheldrick et al. (2016),Lyon et al. (2016)

Healthcare design &planning

Zimmerman et al. (2016)

* As applied in Long and Meadows (2018).

Complex Mental Health Care Services

Figure: Mental Health Patient Pathways within ABUHB.

CommunityDGHRehab - LT CarePICUNursing HomeLDForensicOAHPoliceAdult MH

Assessment OOH

Inp.1

Inp.2

Inp.3

Inp.4

CommunityDGHRehab - LT CarePICUNursing HomeLDForensicOAHDied

Data source: ABUHB inpatient data 2014-2017.

Treatment PathwaysTOLKIEN II Recommendation

Figure: Treatment model recommended by Andrews et al. (2006).

Combining the ABM and SD Models

Outline

1 Introduction





Depression in Wales

LBH Rate 95%CIBCUHB 7.86 (7.79, 7.93)PTHB 6.81 (6.67, 6.95)HDUHB 6.06 (5.99, 6.13)ABMUHB 7.70 (7.63, 7.77)CTUHB 7.03 (6.94, 7.13)ABUHB 9.53 (9.45, 9.61)CV 8.72 (8.64, 8.8)

Data: QOF disease registers in StatsWales (2018) and ONS

mid 2016 population est.

Est. value 7.7%

CI (7.612, 7.768)

Method used in Neyeloff et al. (2012).

Outline

1 Introduction





Summary of Other Parameters

Parameters Source

Proportion of mild (29.36%), moderate(38.79%), severe (31.85%)

Simon et al. (1999)

Recovery from treated:mild (79.3 %), moderate (64.5%), severe(54.9%)

Simon et al. (1999)

Recovery from untreated:mild (81.7%), moderate (74.7%), severe(57.8%)

Simon et al. (1999)

Depression progression:Mild to moderate (6.9%), moderate tosevere (12.9%)

Simon et al. (1999)

Recurrence after treated 33% Wang (2004)Recurrence after untreated 14% Wang (2004)Mortality rate (0.010457) Office for National Statistics

(2017)Parameters in treatment pathway model Andrews et al. (2006)

Outline

1 Introduction





Summary of Testing Results

Table: Depression Prevalence by Severity

Severity Data(%) Mean(%) SD 95%CI p-value

Mild 29.36 29.31 0.0178 (29.09, 29.53) 0.654Moderate 38.79 38.70 0.0195 (38.45, 38.94) 0.449Severe 31.85 31.99 0.0195 (31.75, 32.24) 0.245

Population* 7.70 7.30 0.0026 (7.27, 7.33) < 0.0001

* The min and max values are 6.66 and 8.29 resp.

The model was developed using AnyLogic with population size 5000.

Other testing involved extreme values testing.

Structure validation was done by MH Team in ABUHB.

Outline

1 Introduction





Estimating depression prevalence

Model time: 15 years

Population size: 5000

Top graph: moderate (56%)depression progression

Middle graph: high rate (80%& 90%)depression progression

Bottom graph: 100%progression from mild tomoderate & moderate tosevere

x-axis represents time in weeks

y-axis represents prevalence inpeople

Estimating the service use

Conclusion and Future Studies

Managerial insights:

For a health board: strategic case mix.

For a PCMH service: capacity & management.

Public Health Wales: do we have enough resources?

Future studies:

Perform cost analysis.

Sensitivity analysis.

Acknowledgment

A very special thanks to experts in:

The University of Adelaide: Prof N. Bean

Flinders University: Prof M. Mackay

Flinders Medical Centre: Dr M. Nance

Other experts from SA Health and Central Adelaide Local HospitalNetwork: Dr K. Zeitz and Dr D. Watson

The ABCi team in ABUHB

The EPSRC

Thank You for Listening

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