Mapping of the World Health Organization's Disability Assessment Schedule 2.0 to disability weights using the Multi-Country Survey Study on Health and Responsiveness

Mapping from SIBDQ to EQ-5D-5L for patients with inflammatory bowel disease

OBJECTIVE

Clinical studies commonly use disease-specific measures to assess patients' health-related quality of life. However, economic evaluation often requires preference-based utility index scores to calculate cost per quality-adjusted life-year (QALY). When utility index scores are not directly available, mappings are useful. To our knowledge, no mapping exists for the Short Inflammatory Bowel Disease Questionnaire (SIBDQ). Our aim was to develop a mapping from SIBDQ to the EQ-5D-5L index score with German weights for inflammatory bowel disease (IBD) patients.

METHODS

We used 3856 observations of 1055 IBD patients who participated in a randomised controlled trial in Germany on the effect of introducing regular appointments with an IBD nurse specialist in addition to standard care with biologics. We considered five data availability scenarios. For each scenario, we estimated different regression and machine learning models: linear mixed-effects regression, mixed-effects Tobit regression, an adjusted limited dependent variable mixture model and a mixed-effects regression forest. We selected the final models with tenfold cross-validation based on a model subset and validated these with observations in a validation subset.

RESULTS

For the first four data availability scenarios, we selected mixed-effects Tobit regressions as final models. For the fifth scenario, mixed-effects regression forest performed best. Our findings suggest that the demographic variables age and gender do not improve the mapping, while including SIBDQ subscales, IBD disease type, BMI and smoking status leads to better predictions.

CONCLUSION

We developed an algorithm mapping SIBDQ values to EQ-5D-5L index scores for different sets of covariates in IBD patients. It is implemented in the following web application: https://www.bwl.uni-hamburg.de/hcm/forschung/mapping.html .

Mapping the 12-item World Health Organization disability assessment schedule 2.0 (WHODAS 2.0) onto the assessment of quality of life (AQoL)-4D utilities

PURPOSE

The World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) is a widely used disability-specific outcome measure. This study develops mapping algorithms to estimate Assessment of Quality of Life (AQoL)-4D utilities based on the WHODAS 2.0 responses to facilitate economic evaluation.

METHODS

The study sample comprises people with disability or long-term conditions (n = 3376) from the 2007 Australian National Survey of Mental Health and Wellbeing. Traditional regression techniques (i.e., Ordinary Least Square regression, Robust MM regression, Generalised Linear Model and Betamix Regression) and machine learning techniques (i.e., Lasso regression, Boosted regression, Supported vector regression) were used. Five-fold internal cross-validation was performed. Model performance was assessed using a series of goodness-of-fit measures.

RESULTS

The robust MM estimator produced the preferred mapping algorithm for the overall sample with the smallest mean absolute error in cross-validation (MAE = 0.1325). Different methods performed differently for different disability subgroups, with the subgroup with profound or severe restrictions having the highest MAE across all methods and models.

CONCLUSION

The developed mapping algorithm enables cost-utility analyses of interventions for people with disability where the WHODAS 2.0 has been collected. Mapping algorithms developed from different methods should be considered in sensitivity analyses in economic evaluations.

Mapping the World Health Organization Disability Assessment Schedule (WHODAS 2.0) onto SF-6D Using Swedish General Population Data

BACKGROUND AND OBJECTIVE

Mapping algorithms can be used for estimating quality-adjusted life years (QALYs) when studies apply non-preference-based instruments. In this study, we estimate a regression-based algorithm for mapping between the World Health Organization Disability Assessment Schedule (WHODAS 2.0) and the preference-based instrument SF-6D to obtain preference estimates usable in health economic evaluations. This was done separately for the working and non-working populations, as WHODAS 2.0 discriminates between these groups when estimating scores.

METHODS

Using a dataset including 2258 participants from the general Swedish population, we estimated the statistical relationship between SF-6D and WHODAS 2.0. We applied three regression methods, i.e., ordinary least squares (OLS), generalized linear models (GLM), and Tobit, in mapping onto SF-6D from WHODAS 2.0 at the overall-score and domain levels. Root mean squared error (RMSE) and mean absolute error (MAE) were used for validation of the models; R2 was used to assess model fit.

RESULTS

The best-performing models for both the working and non-working populations were GLM models with RMSE ranging from 0.084 to 0.088, MAE ranging from 0.068 to 0.071, and R2 ranging from 0.503 to 0.608. When mapping from the WHODAS 2.0 overall score, the preferred model also included sex for both the working and non-working populations. When mapping from the WHODAS 2.0 domain level, the preferred model for the working population included the domains mobility, household activities, work/study activities, and sex. For the non-working population, the domain-level model included the domains mobility, household activities, participation, and education.

CONCLUSIONS

It is possible to apply the derived mapping algorithms for health economic evaluations in studies using WHODAS 2.0. As conceptual overlap is incomplete, we recommend using the domain-based algorithms over the overall score. Different algorithms must be applied depending on whether the population is working or non-working, due to the characteristics of WHODAS 2.0.

Making an economic argument for investment in global mental health: The case of conflict-affected refugees and displaced people

Mental health expenditure accounts for just 2.1% of total domestic governmental health expenditure per capita. There is an economic, as well as moral, imperative to invest more in mental health given the long-term adverse impacts of mental disorders. This paper focuses on how economic evidence can be used to support the case for action on global mental health, focusing on refugees and people displaced due to conflict. Refugees present almost unique challenges as some policy makers may be reluctant to divert scarce resources away from the domestic population to these population groups. A rapid systematic scoping review was also undertaken to identify economic evaluations of mental health-related interventions for refugees and displaced people and to look at how this evidence base can be strengthened. Only 11 economic evaluations focused on the mental health of refugees, asylum seekers and other displaced people were identified. All but two of these intervention studies potentially could be cost-effective, but only five studies reported cost per quality-adjusted life year gained, a metric allowing the economic case for investment in refugee mental health to be compared with any other health-focused intervention. There is a need for more consistent collection of data on quality of life and the longer-term impacts of intervention. The perspective adopted in economic evaluations may also need broadening to include intersectoral benefits beyond health, as well as identifying complementary benefits to host communities. More use can be also made of modelling, drawing on existing evidence on the effectiveness and resource requirements of interventions delivered in comparable settings to expand the current evidence base. The budgetary impact of any proposed strategy should be considered; modelling could also be used to look at how implementation might be adapted to contain costs and take account of local contextual factors.

Pre-rehabilitation scores of functioning measured using the World Health Organization Disability Assessment Schedule in persons with nonspecific low back pain: a scoping review

Knowledge of the pre-rehabilitation generic status of functioning in individuals with low back pain is necessary to understand the clinical utility of rehabilitation care. We conducted a scoping review to describe the pre-rehabilitation functioning status of persons with nonspecific low back pain using the World Health Organization Disability Assessment Schedule (WHODAS)-36 or WHODAS-12. We searched multiple databases from 2010 to 2021 for studies reporting pre-rehabilitation scores using WHODAS in persons with low back pain. Reviewers independently screened articles and extracted data, and we descriptively summarized results by the duration of low back pain (acute/subacute <3 months; chronic ≥3 months), and the WHODAS version. Of 1770 citations screened, eight citations were relevant. Five studies were conducted in Europe, two in America, and one in the African Region (mostly high-income countries). In persons with acute low back pain, the mean WHODAS-36 pre-rehabilitation summary score (complex scoring) was 22.8/100 (SD = 15.4) (one study). In persons with chronic low back pain, the mean WHODAS-36 summary score (complex scoring) ranged from 22.8/100 (SD = 5.7) to 41.5/100 (SD = 13.8) (two studies). For WHODAS-12 in persons with chronic low back pain, the mean summary score was 11.4/48 (SD = 8.7) or 14.4/48 (SD = 9.4) using simple scoring (two studies), and 25.8/100 (SD = 2.2) using complex scoring (one study). No floor or ceiling effects were observed in WHODAS-36 summary scores for chronic low back pain. Our scoping review comprehensively summarizes available studies reporting pre-rehabilitation levels of functioning using WHODAS in persons with low back pain. Persons with low back pain seeking rehabilitation have moderate limitations in functioning, and limitations level tends to be worse with chronic low back pain.

Cost-effectiveness of psychological intervention within services for depression delivered by primary care workers in Nepal: economic evaluation of a randomized control trial

BACKGROUND

Integrating services for depression into primary care is key to reducing the treatment gap in low- and middle-income countries. We examined the value of providing the Healthy Activity Programme (HAP), a behavioral activation psychological intervention, within services for depression delivered by primary care workers in Chitwan, Nepal using data from the Programme for Improving Mental Health Care.

METHODS

People diagnosed with depression were randomized to receive either standard treatment (ST), comprised of psychoeducation, antidepressant medication, and home-based follow up, or standard treatment plus psychological intervention (T + P). We estimated incremental costs and health effects of T + P compared to ST, with quality adjusted life years (QALYs) and depression symptom scores over 12 months as health effects. Nonparametric uncertainty analysis provided confidence intervals around each incremental effectiveness ratio (ICER); results are presented in 2020 international dollars.

RESULTS

Sixty participants received ST and 60 received T + P. Implementation costs (ST = $329, T + P = $617) were substantially higher than service delivery costs (ST = $18.7, T + P = $22.4) per participant. ST and T + P participants accrued 46.5 and 49.4 QALYs, respectively. The ICERs for T + P relative to ST were $4422 per QALY gained (95% confidence interval: $2484 to $9550) - slightly above the highly cost-effective threshold - and -$53.21 (95% confidence interval: -$105.8 to -$30.2) per unit change on the Patient Health Questionnaire.

CONCLUSION

Providing HAP within integrated depression services in Chitwan was cost-effective, if not highly cost-effective. Efforts to scale up integrated services in Nepal and similar contexts should consider including evidence-based psychological interventions as a part of cost-effective mental healthcare for depression.

Efficacy and cost-effectiveness of task-shared care for people with severe mental disorders in Ethiopia (TaSCS): a single-blind, randomised, controlled, phase 3 non-inferiority trial

BACKGROUND

There have been no trials of task-shared care (TSC) using WHO's mental health Gap Action Programme for people with severe mental disorders (psychosis or affective disorder) in low-income or middle-income countries. We aimed to evaluate the efficacy and cost-effectiveness of TSC compared with enhanced specialist mental health care in rural Ethiopia.

METHODS

In this single-blind, phase 3, randomised, controlled, non-inferiority trial, participants had a confirmed diagnosis of a severe mental disorder, recruited from either the community or a local outpatient psychiatric clinic. The intervention was TSC, delivered by supervised, non-physician primary health care workers trained in the mental health Gap Action Programme and working with community health workers. The active comparison group was outpatient psychiatric nurse care augmented with community lay workers (PSY). Our primary endpoint was whether TSC would be non-inferior to PSY at 12 months for the primary outcome of clinical symptom severity using the Brief Psychiatric Rating Scale, Expanded version (BPRS-E; non-inferiority margin of 6 points). Randomisation was stratified by health facility using random permuted blocks. Independent clinicians allocated groups using sealed envelopes with concealment and outcome assessors and investigators were masked. We analysed the primary outcome in the modified intention-to-treat group and safety in the per-protocol group. This trial is registered with ClinicalTrials.gov, number NCT02308956.

FINDINGS

We recruited participants between March 13, 2015 and May 21, 2016. We randomly assigned 329 participants (111 female and 218 male) who were aged 25-72 years and were predominantly of Gurage (198 [60%]), Silte (58 [18%]), and Mareko (53 [16%]) ethnicity. Five participants were found to be ineligible after randomisation, giving a modified intention-to-treat sample of 324. Of these, 12-month assessments were completed in 155 (98%) of 158 in the TSC group and in 158 (95%) of 166 in the PSY group. For the primary outcome, there was no evidence of inferiority of TSC compared with PSY. The mean BPRS-E score was 27·7 (SD 4·7) for TSC and 27·8 (SD 4·6) for PSY, with an adjusted mean difference of 0·06 (90% CI -0·80 to 0·89). Per-protocol analyses (n=291) were similar. There were 47 serious adverse events (18 in the TSC group, 29 in the PSY group), affecting 28 participants. These included 17 episodes of perpetrated violence and seven episodes of violent victimisation leading to injury, ten suicide attempts, six hospital admissions for physical health conditions, four psychiatric admissions, and three deaths (one in the TSC group, two in the PSY group). The incremental cost-effectiveness ratio for TSC indicated lower cost of -US$299·82 (95% CI -454·95 to -144·69) per unit increase in BPRS-E scores from a health care sector perspective at 12 months.

INTERPRETATION

WHO's mental health Gap Action Programme for people with severe mental disorders is as cost-effective as existing specialist models of care and can be implemented effectively and safely by supervised non-specialists in resource-poor settings.

FUNDING

US National Institute of Mental Health.

A randomized controlled trial of cognitive control training (CCT) as an add-on treatment for late-life depression: a study protocol

BACKGROUND

Already a major health concern, late-life depression (LLD) is expected to form an increasing problem in the aging population. Moreover, despite current treatments, LLD is associated with a poor long-term prognosis and high rate of chronicity. Treatment provision and treatment accordingly warrant improvement, where add-on treatments might contribute to the efficacy of conventional therapies. Although it is known that impaired cognitive control contributes to LDD, it is not targeted sufficiently by current interventions. Research on cognitive control training (CCT) shows promising results on depressive symptoms, cognitive performance, and overall functioning. However, further research is needed to determine the long-term effects of CCT on LLD, its cost-effectiveness, and mechanisms of change.

METHODS

In the current multicenter randomized controlled trial (RCT) with a between-subjects design participants aged 60 years and over with a current LLD receiving treatment as usual (TAU) are randomized to add-on CCT or placebo training. Randomization is stratified by depression severity. Participants will receive eight online CCT or placebo sessions spread across four consecutive weeks. They will complete a post-training assessment after 1 month and three follow-up assessments scheduled three, six and 12 months after completing the training. We expect CCT and TAU to be more (cost-)effective in reducing depressive symptoms than placebo training and TAU. Additionally, we will be looking at secondary clinical, cognitive and global functioning outcomes and likely mechanisms of change (e.g., improved cognitive functioning, reduced rumination, and improved inhibition of negative stimuli).

DISCUSSION

The proposed RCT aims to contribute to the clinical and scientific knowledge on the long-term effects of CCT as an add-on treatment for LLD. Cost-effectiveness is particularly relevant considering the expected volume of the target demographic. The study will be a pragmatic trial with few inclusion restrictions, providing information on feasibility of web-based trainings in clinical settings. The outcomes are potentially generalizable to guidelines for treatment of LLD.

TRIAL REGISTRATION

This trial is registered in the Netherlands Trial Register (code: NL7639 ). Registered 3 april 2019.

Mapping of the World Health Organization's Disability Assessment Schedule 2.0 to disability weights using the Multi-Country Survey Study on Health and Responsiveness

OBJECTIVES

To develop and test an internationally applicable mapping function for converting WHODAS-2.0 scores to disability weights, thereby enabling WHODAS-2.0 to be used in cost-utility analyses and sectoral decision-making.

METHODS

Data from 14 countries were used from the WHO Multi-Country Survey Study on Health and Responsiveness, administered among nationally representative samples of respondents aged 18+ years who were non-institutionalized and living in private households. For the combined total of 92,006 respondents, available WHODAS-2.0 items (for both 36-item and 12-item versions) were mapped onto disability weight estimates using a machine learning approach, whereby data were split into separate training and test sets; cross-validation was used to compare the performance of different regression and penalized regression models. Sensitivity analyses considered different imputation strategies and compared overall model performance with that of country-specific models.

RESULTS

Mapping functions converted WHODAS-2.0 scores into disability weights; R-squared values of 0.700-0.754 were obtained for the test data set. Penalized regression models reached comparable performance to standard regression models but with fewer predictors. Imputation had little impact on model performance. Model performance of the generic model on country-specific test sets was comparable to model performance of country-specific models.

CONCLUSIONS

Disability weights can be generated with good accuracy using WHODAS 2.0 scores, including in national settings where health state valuations are not directly available, which signifies the utility of WHODAS as an outcome measure in evaluative studies that express intervention benefits in terms of QALYs gained.