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).

METHODS: A computer-based SG (CBSG) tool was developed using Microsoft® PowerPoint 2007 to value asthma-specific health states in Malaysia. Eight hypothetical health states were considered, including two anchor states (healthy and dead), three chronic (C) states and three temporary (T) states (each numbered 1 through 3, with increasing severity) in addition to the subject's current health state. Twenty adult asthma patients completed the CBSG tool in addition to paper-based Asthma Control Test, three health status measures (EQ-5D, EQ-VAS, and Mini Asthma Quality of Life Questionnaire (MiniAQLQ)), and VAS utility assessment tool. Patients and interviewers rated the difficulty of the VAS and CBSG tools. Correlations between current health state values derived from the various measures were determined.

RESULTS: The SG and the VAS received similar difficulty ratings. 17 patients completed the CBSG tool within 30 minutes. The mean utilities determined by the CBSG tool for the T1-T3 asthma health states met the expected logical order of 1>2>3, but those for the C1-C3 states did not. Correlation between current health state values derived from the CBSG tool and other measurement tools was poor.

METHODS: For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5-19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence.

FINDINGS: We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9-10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes-gaining too little height, too much weight for their height compared with children in other countries, or both-occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls.

INTERPRETATION: The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks.

METHODS: A cross sectional study by adopting European Quality of Life scale (EQ-5D) for the assessment of HRQoL was conducted. All registered HB patients attending two public hospitals in Quetta, Pakistan were approached for study. Descriptive statistics were used to describe demographic and disease related characteristics of the patients. HRQoL was scored using values adapted from the United Kingdom general population survey. EQ-5D scale scores were compared with Mann-Whitney and Kruskal-Wallis test. Standard multiple regression analysis was performed to identify predictors of HRQoL. All analyses were performed using SPSS v 16.0.