Defining and Evaluating Norms for Health and Social Service (HASS) Use
Condition: Health and Social Services; Population Segmentation Healthcare
Study type: Observational

Summary: 
      Background In view of expected growth of the older population in Singapore in the next
      decades, the volume and complexity of needs for health care services is expected to increase,
      which amplifies stress on the current healthcare system. One approach to addressing this
      challenge is to consider service utilization in relationship to needs based on "population
      segmentation" and to plan and evaluate new services in light of unmet needs.

      Specific Aims and Hypotheses Primary Aim 1: To establish health and social service (HASS)
      norms for population segments as defined by the Simple Segmentation Tool (SST) via a
      modified-Delphi methodology.

      Primary Aim 2: To evaluate the concordance between the types of HASS that patients in each
      population segment actually utilize versus the types of HASS normatively defined for each
      population segment over a 3-month period from the point of hospital discharge.

      Primary Hypothesis: The concordance between the actual utilization of different types of HASS
      versus normative HASS is not better than fair.

      Secondary Aim: To assess the association between concordance of normative HASS and incidence
      of adverse outcomes which includes emergency department visits, unplanned hospital
      readmissions, nursing home placement, and all-cause mortality over a 12-month period from
      point of hospital discharge for all population segments.

      Secondary Hypothesis: Patients with disagreement between normative HASS and actual
      utilization of HASS will have a higher incidence of adverse outcomes.

      Methodology The investigators will use a modified-Delphi methodology to develop HASS norms
      and conduct a follow-up study of inpatients to evaluate the concordance between the types of
      HASS utilized and norm HASS, and to evaluate the association between this concordance and
      adverse outcomes in each population segment.

      Significance to Health Services Delivery The transformation of the health care system to
      effectively meet growing needs in a patient-centric way requires practical tools for
      population planning and program development. The norms and evaluation approaches developed
      here will guide clinical and public policy decision makers in prioritizing population needs,
      and thus contribute to tangible improvements in health services delivery, patient care and
      health outcomes for an aging Singapore population.
    

Detailed description: 
      Background The Singaporean demographic transition has increased the proportion of older
      individuals in the population and the prevalence of multiple chronic health conditions. 1 in
      4 Singaporeans aged 40 years and above has at least 1 chronic disease. Worldwide urgency to
      address chronic conditions is driven by the rapid rise in incidence and also by the
      associated social and financial costs for the health sector and society.

      Often, patients with multiple chronic conditions require more services which are increasingly
      recognized to include a coordinated mix of clinical and social care. A promising strategy for
      planning and evaluation of services for an increasingly complex population is population
      segmentation, where individuals are assigned into groups based on similar health and
      health-related social needs. Understanding how people in various segments distribute across
      points of service and in the community more broadly can be used to guide the efficient
      provision of services. In addition to improved efficiency, meeting otherwise unmet needs
      would reduce the progression to worse health states and associated high cost medical services
      such as hospitalization. Through segmentation health care providers, regional health system
      (RHS) planners, and policy makers will be enabled to develop more person-centric services.

      The specific aims and hypotheses of this study are as follows:

      Primary Aim 1: To establish norms for high value services for population segments as defined
      by the SST via a modified-Delphi methodology.

      Primary Aim 2: To evaluate the concordance between the types of HASS that patients in each
      population segment actually utilize versus the types of HASS normatively defined for each
      population segment over a 3-month period from the point of hospital discharge.

      Primary Hypothesis: The concordance between the actual utilization of different types of HASS
      versus normative HASS is not better than fair.

      Secondary Aim: To assess the association between concordance with norm HASS and incidence of
      adverse outcomes, which includes emergency department, visits, unplanned hospital
      readmissions and all-cause mortality over a 12-month period from point of hospital discharge
      for all population segments.

      Secondary Hypothesis: Patients with low concordance with norm HASS in their segment will have
      a higher incidence of adverse outcomes.

      To achieve the above aims, the study will be conducted in 3 corresponding phases.

      Phase 1: The modified-Delphi methodology will be applied to generate a set of normative HASS
      for each population segment. This method involves a group of experts who provide individual
      responses in the questionnaires and re-evaluate their responses subsequently in a group
      discussion to establish the expert consensus. In addition, this method was chosen for its
      flexibility in design and is amenable to follow-up interviews, leading to deeper
      understanding of the research questions.

      Population segments will be defined based on the SST in terms of 6 health categories
      corresponding to the nature of their clinical condition(s) plus a combination of 9
      complicating factors which influence the difficulty in managing health conditions and tend to
      require nursing and social services. Ten specific services based on the proficiency of skills
      involved will be considered as potentially "high value" from each population segment.

      Similar to the RAND approach, each segment will be an "indication," which classifies
      population in terms of their needs in deciding which services to recommend, and these
      indications will be grouped for ease of evaluation into "chapters." The research team will
      identify potential experts from a distribution of relevant disciplines (4 doctors, 4
      nurses/allied health professions, and 1 policy maker) in Singapore for the modified-Delphi
      study. They will be contacted personally to establish their interest to participate in this
      study, and the process will continue until the required number of panelists in the prescribed
      distribution are met. Panelists will be provided with a brief description of the method and
      how it will be applied to this study. The study will consist of an independent round of
      rating and a group meeting to reconcile the results.

      Phase 2: A follow-up study will be conducted to evaluate the concordance between the types of
      HASS that patients actually utilize 3 months post hospital discharge versus normative HASS
      defined in Phase 1. Study participants will be recruited from inpatients in the Singapore
      General Hospital Department of Internal Medicine (SGH DIM) and categorized based on their
      health care needs using the SST.

      The Research Coordinator (RC) will screen patients using the eligibility criteria before
      inviting them to participate. An Abbreviated Mental Test (AMT) will be administered to
      determine cognitive capacity to consent. If the patient is deemed unfit, a proxy will then be
      required to provide informed consent on behalf of the patient.

      From past data, approximately 56 patients discharged each day, and 75% are older than 55
      years. Assuming a recruitment rate of 30% (12 patients per day), the enrolment period is
      estimated to be 4 to 5 months.

      Baseline data collection: RCs will take the informed consent from the study participants and
      interview them for socioeconomic and demographic status, health information and prescribed
      health care services. Study participants will be given a diary to keep track of their health
      care utilization for 3 months from time of discharge. Doctors who have managed study
      participants fill out the SST.

      Follow-up (3-month) data collection: Study participants will be contacted for follow-up
      interview by assessors who are blinded to SST categories 3 months after the baseline
      interview. HASS utilization information over this 3 months period to be obtained by follow-up
      face-to-face interview, EMR, diary, and the Agency for Integrated Care (AIC) database.

      Statistical Analysis Plan The data for aim 2, based on both baseline and follow-up data, can
      be summarized in a table with (k+1) rows and (k+1) columns. The total number of HASS is k
      (k=10 for this study). An extra dummy column and an extra dummy row are added to the table to
      incorporate scenarios where patients do not utilize any high value HASS or utilize HASS that
      are not deemed high value. Cells in the (k+1)th column represent frequencies of patients who
      are deemed to be appropriate for some HASS but do not use that service. Similarly, the
      (k+1)th row represent the cell frequencies corresponding to the patients who use some HASS
      not deemed high value. So the total number of HASS, for analysis purposes, becomes k+1. nij
      (i,j=1,2, … k+1) represents the cell frequency (number of patients) corresponding to ith
      normative HASS and jth actual service: number of patients who have been prescribed ith
      normative HASS and have used jth actual service. Diagonal elements in the table, nii
      represent frequencies corresponding to agreement between normative and actual utilization of
      HASS. Cohen's kappa value is used as the measure concordance between the actual utilization
      of different types of HASS versus normative HASS.

      Therefore, Cohen's kappa can be written as Ka = (p0 - pe)/ (1 - pe), where, overall
      proportion of observed agreement p0 = (n11 + n22 + …. + nkk + nk+1k+1)/n, is the sum of
      diagonal entries in the table divided by n; and, overall proportion of chance-expected
      agreement pe = (n1. x n.1 + n2. x n.2 + … + nk. x n.k + nk+1. x n.k+1)/n2 is the sum of the
      products of the marginal frequencies divided by n2 .

      Based on the primary hypothesis: the concordance between the actual utilization of different
      types of HASS versus normative HASS is not better than fair; the statistical hypothesis can
      be written as H0: Ka = 0.41 against H1: Ka < 0.41, where the kappa value of 0.41 denote the
      lower boundary of the range of moderate concordance. The one sided hypothesis can be tested
      using the test statistics z = ( K̂a - 0.4)/s.e(K̂a), where K̂a is the estimated value of
      kappa from the data table and s.e(K̂a) = σ/√n where n is total sample size and s.e denote the
      standard error. Note that, the statistics z has an asymptotic normal distribution. Based on
      the data, the null hypothesis is rejected if z < zα and conclude that corresponding
      concordance is not better than fair, where zα is the (1-α)th quantile of a standard normal
      distribution.

      The proportion of patients using the prescribed services for each of the 10 HASS will also be
      considered. HASS specific proportion will indicate the measure of agreement within that
      service.

      Sample Size Using the above stated statistical hypothesis and asymptotic normality of the z
      statistics in a one-sided test with effect size 0.06, σ value as 0.6, type I error as 0.05
      and power set to be 0.9, the estimated sample size is 856. Considering 15% dropout during the
      follow-up, the required sample is 856/0.85 = 1007. To calculate the effect size, the value of
      kappa was taken to be 0.35 under alternative hypothesis.

      Phase 3: The association between normative HASS concordance and incidence of adverse outcomes
      at 12 months from the day of discharge in each population segment will be assessed by review
      of information from EMR, National Death Registry, MOH and survey results.

      Statistical Analysis Plan In the analysis stage, the effect of the agreement between the
      normative services and the actual services used by individual patient on the adverse outcomes
      of that patient will be inferred at each population segment. Separate models for each of the
      four adverse outcome variables will be considered. As A&E visits and readmission are count
      variables which can take values 0, 1, 2 …, there may be more patients with '0' value for
      these two outcome variables. To address this zero-inflated outcome, zero-inflated Poisson
      regression models will be considered, separately for each of these two outcomes, to see the
      effect of the agreement between the normative services and the actual services used by
      individual patient on the adverse outcomes of that patient. The other two adverse outcome
      variables nursing home placement and death are binary variables 1: yes and 0: no. Two
      logistic regression models will be used to find out the effect of above mentioned 'agreement'
      on the two outcome variables separately. In all four regression models, the variable
      selection procedure will be considered to choose appropriate covariates from the baseline and
      follow-up data. In each of the four cases, p-values related to the coefficient of the
      'agreement' corresponding to the two sided test with null hypothesis that value of the
      coefficient is zero will be reported, together with confidence intervals of the coefficients.
      A statistically significant negative value of a coefficient will indicate lower 'agreement'
      may increase the adverse outcomes.
    

Observational model: Cohort
Time perspective: Prospective
Sampling method: Non-Probability Sample
Study population: 
        Study participants will be recruited from inpatients in the Singapore General Hospital
        Department of Internal Medicine (SGH DIM) and categorized based on their health care needs
        using the SST.