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Table 1 Data fields extracted from decision models identified by systematic review

From: Economic models of community-based falls prevention: a systematic review with subsequent commissioning and methodological recommendations

Category

Data field

Reporting and methodological quality checklist

The checklist designed for falls prevention economic evaluations by a panel of falls prevention experts [32] was adapted to specifically suit decision models. There were 32 items, each scored 0 (recommendation not followed), 0.5 (partially followed), and 1 (fully followed), giving maximum score of 32. See Table A2 in Supplementary Materials for adapted version.

(A) Model and evaluation overview

1. Bibliography: author(s); publication year

2. Setting and aim: country; region; decision-maker; evaluation aim

3. Target population demographics and comorbidities (e.g., residence,a age, sex, socioeconomic status, health conditions unrelated to falls risk)

4. Type of analysis: e.g., CEA; CUA; CBA; ROIb

5. Perspective (e.g., public sector, societal)

6. Cost-effectiveness threshold: monetary amount and type (e.g., health opportunity cost in healthcare system, willingness to pay as consumer)

7. Model type (e.g., decision tree, Markov)

8. Model time horizon

9. Discount rates (if time horizon longer than 1 year)

10. Model cycle length (if any)

(B) Falls epidemiology features

1. Characterising baseline falls risk of target population

2. Characterising multiple falls per year (recurrent falls)

3. Risk factors for falls

4. Health consequences of falls: fall/injury type; long-term health consequences (e.g., institutionalisation, excess mortality risk)

5. Health utility data: fall-related loss; comorbidity status

6. Economic consequences of falls: care resource types; unit costs; all-cause/comorbidity care costsc

(C) Falls prevention intervention features

1. Intervention characteristics: type;d comparator(s); component; access pathwaye

2. Falls risk screening methodf

3. Intervention resource use and costs: auxiliary implementation resources (e.g., marketing to improve uptake); therapeutic resources (e.g., staff labour).

4. Intervention efficacy: metric;g fall type;h effectiveness periodi

5. Wider health effects of interventions beyond falls preventionj

(D) Evaluation methods and results

1. Model validity: structural/face;k internal; external; crossl

2. Assessing parameter uncertainty: DSA; PSA

3. Scenario analyses: to assess impact of structural assumptions on outcomes.

4. Aggregate health and cost outcomes (e.g., total intervention cost, total QALY gain, total number of falls prevented)

5. Cost-per-unit ratios (e.g., incremental cost per QALY gain)

6. Wider decisional outcomes (e.g., reduction in social inequities of health)

7. Currency: original type/year; conversion to same currency for comparison

8. Discussion by evaluation authors: generalisability; policy implementation; model strengths and limitations

(E) Key methodological challenges for public health economic model

1. Capturing non-health outcomes and societal intervention costs

2. Considering heterogeneity and dynamic complexity: e.g., long-term progression of falls risk factors/profile

3. Considering theories of human behaviour and implementation: e.g., implementation quality (i.e., uptake and adherence rates)

4. Considering social determinants of health and conducting equity analyses

  1. Abbreviations: CBA Cost-benefit analysis, CEA Cost-effectiveness analysis, CUA Cost-utility analysis, DSA Deterministic sensitivity analysis, PSA Probabilistic sensitivity analysis, QALY Quality-adjusted life year, RCT Randomised controlled trial, ROI Return on investment
  2. aCommunity-dwelling or institutionalised
  3. bCost-effectiveness analysis (CEA) uses natural health units (e.g., number of falls) as health outcomes; cost-utility analysis (CUA) generic quality-adjusted life year (QALY). Cost-benefit analysis (CBA) values health outcomes using societal or consumption value of health. Return on investment analysis (ROI) only compares the net financial outcomes of two or more interventions
  4. cExpert guideline on falls prevention economic evaluation recommends that evaluations report all-cause healthcare costs in the base case and fall-related costs in sensitivity analysis [32]. All-cause care costs are comprised of fall-related and comorbidity care costs
  5. dIntervention type classification should follow the Prevention of Falls Network Europe categories [43]
  6. ePotential intervention pathways are: proactive – initiated by professional screening/referral; reactive – initiated after medical attention for a fall; and self-referred – enrolled voluntarily by older persons
  7. fFalls risk screening is required if: (1) model prescribes intervention to a subset of the whole target population with certain characteristics (e.g., higher falls risk) and this subset must be identified; and (2) model’s target population itself is a specific patient group (e.g., cataract patients) and this group must be identified from the general population before model baseline. Falls risk screening is distinct from falls risk assessment as part of multifactorial intervention
  8. gThis concerns models that import falls efficacy evidence from external intervention studies. Main falls incidence metrics are falls risk and falls rate, and their matching efficacy metrics are relative risk (RR) and rate ratio (RaR), respectively. Models should ensure that the external efficacy metric matches the internal falls incidence metric
  9. hLike note f, this concerns decision models using external efficacy evidence. The fall type (e.g., hospitalised fall, fall-induced fracture) for the efficacy data should match that for the model incidence
  10. iThe effectiveness period is a function of efficacy durability and implementation sustainability. Efficacy durability should not extend beyond the intervention study’s timespan unless the intervention is sustained [32]. Key determinants of sustainability are demand-side persistence and supply-side maintenance
  11. jFor example, falls prevention exercise can improve cardiovascular health [25]
  12. kStructural or face validity concerns validity of model structure, data sources and assumptions as assessed by modelling and disease-area experts and broader stakeholders [31, 44]. Structural validity can be assessed prospectively during the model development stage through proactive involvement of stakeholders in model conceptualisation; it can also be assessed retrospectively by evaluating scenarios on different structural assumptions [31]
  13. lInternal validity concerns the accuracy of model coding; external validity concerns comparability between model and real-world results; and cross validity concerns comparability between model results and results of other models addressing the same decision problem [44]
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BMC Health Services Research

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