Methods: The search was conducted across three databases: PubMed, CINAHL and Emerald using four key concepts: 'health', 'index', 'context', 'develop', which was supplemented with Google searching and reference scanning. A researcher screened the titles, abstracts and subsequently full texts and confirmed the findings with the research team at each stage. Data charting was performed according to the included publications and identified indices. The collation was performed by describing the indices and made observation on its development method using a priori framework consist of four processes: underpinning theory, model or framework; data selection and processing; formation of index; testing of index.
Results: Twenty-six publications describing population health indices were included, and 27 indices were identified. These indices covered the following health topics: overall health outcomes (n = 15), outcomes for specific health topics (n = 4), diseases outcome (n = 6), assist health resource allocation for priority minority subgroup or geographic area (n = 4), quality of health or health care (n = 2). Twenty-one indices measure health for general populations while six measure defined subpopulations. Fourteen of the indices reported at least one of the development processes according to the a priori framework: underpinning theory, model or framework (n = 7); data selection and processing (n = 8); formation of index (n = 12); testing of index (n = 9).
Conclusions: Few population health indices measure specific health topics or health of specific sub-population. There is also a lack of usage of theories, models or framework in developing these indices. Efforts to develop a guideline is proposed on how population health indices can be developed systematically and rigorously to ensure validity and comprehensive assessment of the indices.
OBJECTIVE: To assess the evidence of health interventions in addressing inequity among migrants.
METHODS: We adopted a two-stage searching approach to ensure the feasibility of this review. First, reviews of interventions for migrants were searched from five databases: PubMed, Cochrane, CINAHL, PsycINFO, and EMBASE until June 2017. Second, full articles included in the identified reviews were retrieved. Primary studies included in the identified reviews were then evaluated as to whether they met the following criteria: experimental studies which include equity aspects as part of their outcome measurement, based on equity attributes defined by PROGRESS-Plus factors (place of residence, race/ethnicity, occupation, gender, religion, education, socio-economic status, social capital, and others). We analysed the information extracted from the selected articles based on the PRISMA-Equity guidelines and the PROGRESS-Plus factors.
RESULTS: Forty-nine reviews involving 1145 primary studies met the first-stage inclusion criteria. After exclusion of 764 studies, the remaining 381 experimental studies were assessed. Thirteen out of 381 experimental studies (3.41%) were found to include equity attributes as part of their outcome measurement. However, although some associations were found none of the included studies demonstrated the effect of the intervention on reducing inequity. All studies were conducted in high-income countries. The interventions included individual directed, community education and peer navigator-related interventions.
CONCLUSIONS: Current evidence reveals that there is a paucity of studies assessing equity attributes of health interventions developed for migrant populations. This indicates that equity has not been receiving attention in these studies of migrant populations. More attention to equity-focused outcome assessment is needed to help policy-makers to consider all relevant outcomes for sound decision making concerning migrants.
METHODS: This paper presents two hybrid methodologies that combines optimal control theory with multi-objective swarm and evolutionary algorithms and compares the performance of these methodologies with multi-objective swarm intelligence algorithms such as MOEAD, MODE, MOPSO and M-MOPSO. The hybrid and conventional methodologies are compared by addressing CMOOP.
RESULTS: The minimized tumor and drug concentration results obtained by the hybrid methodologies demonstrate that they are not only superior to pure swarm intelligence or evolutionary algorithm methodologies but also consumes far less computational time. Further, Second Order Sufficient Condition (SSC) is also used to verify and validate the optimality condition of the constrained multi-objective problem.
CONCLUSION: The proposed methodologies reduce chemo-medicine administration while maintaining effective tumor killing. This will be helpful for oncologist to discover and find the optimum dose schedule of the chemotherapy that reduces the tumor cells while maintaining the patients' health at a safe level.