Human Development

Twenty-five hundred adults, ages 25-45, were enrolled in METS between January 2010 and September 2011 and have had energy expenditure, dietary intake, body weight and composition, and biomarkers of obesity and diabetes measured at baseline. The participants will all be followed over the subsequent 24 months to assess change in body weight, composition, and diabetes and CVD risk. Five hundred participants, 50% of whom are female, were enrolled in each of five study sites: rural Ghana, urban South Africa, Seychelles, urban Jamaica and suburban United States (Chicago area). The populations sampled are of African descent and provide a range of body sizes, i.e., the mean BMIs of adults from the study sites vary from a low of about 24 kg/m² in rural Nkwantakese (Ghana) to a high of 31 kg/m² in suburban Maywood (USA). The study sites also represent a range of social and economic development as defined by the UN Human Development Index (HDI) 2010: Ghana is defined as a low HDI country, South Africa as middle HDI, Jamaica and Seychelles as high HDI and the US as a very high HDI country [28 ].
The in-country study sites include the town of Nkwantakese in the Afigya-Kwabre District of the Ashanti Region of Ghana and its surrounding villages. The town is situated to the southwest of Agona Ashanti the District Capital, and is about 20km from Kumasi with a population of approximately 17,000. Khayelitsha is the 3^rd largest township in South Africa and is adjacent to the city of Cape Town. The population is about 500,000 with 80 percent of the residents living in temporary housing and 40 percent unemployed. The study site in Jamaica is in Kingston, the capital and largest city with a population of 651,880. In Seychelles, individuals have been recruited from the main island of the archipelago, Mahé, which includes approximately 75,000 inhabitants for a surface of 155 km². Mahé can be qualified as semi-urban and its economy is mainly driven by tourism, industrial fishing and services. Seychelles is located approximately 1,600 km east of Kenya in the Indian Ocean, and approximately 2,000 km north of the island of Mauritius, and has a total population of about 87,000. Maywood, in the US, is an African-American working class community adjacent to the western border of Chicago, Illinois, with a population of approximately 24,903 people. The research clinic is on the campus of Stritch School of Medicine, Loyola University, within walking distance of most neighborhoods and is well known to local residents. The coordinating center for METS is also located in Maywood, IL at Loyola University.
We excluded individuals with obvious infectious diseases (including active malaria), pregnant or lactating women, and HIV positive individuals. Any individual that has a condition preventing them from engaging in normal physical activities, such as severe osteo'- or rheumatoid arthritis, lower extremity disability, was also excluded from METS. Population-based surveys have been previously carried out in each of the sites, thus investigators from each site decided upon the best means of recruiting a representative sample from their respective communities. In Nkwantakese, Ghana, a simple random sample was generated for the age-range of the study from the population census for Nkwantakese and its surrounding villages. In both Seychelles and South Africa sex- and age-stratified random samples were generated from their respective national censuses. In Kingston, Jamaica, districts were randomly sampled; beginning from a fixed point in each district (e.g., the north-west corner), and door-to-door recruitment took place. Similarly, in Maywood, IL, USA, all city blocks in the community were randomized and door-to-door recruitment was conducted.

In this Article, we built on GBD 2013⁵¹ (link) concepts by improving the interpretability of sociodemographic status and characterising and describing this relationship in more detail for years of life lost due to premature mortality (YLLs), as well as highlighting changes in age-standardised death rates, population age structure, and YLL rates. We have made two important changes to the GBD 2013 computation. First, we have used only lag-dependent income per capita, average educational attainment in the population over age 15 years, and the total fertility rate. We excluded the mean age of the population because it is directly affected by death rates. Second, we have applied the methods used to compute the Human Development Index to generate an interpretable scale, resulting in the Socio-demographic Index (SDI).⁵² The Human Development Index method weights each component equally and rescales each component on a zero-to-one scale with zero being the lowest value observed in the time period 1980 to 2015 and 1 being the highest value observed. The final composite SDI value is the geometric mean of each of the components. The SDI ranges from 0·060 in Mozambique in 1987 to 0·978 in Washington, DC, USA, in 2015. The correlation of the SDI with the sociodemographic status principal component analysis used in GBD 2013 was 0·982. The very high correlation is because the principal component analysis yields weights that are nearly equal across components. The advantage of the index is that 1 can be interpreted as the level of SDI at which a geography has the highest observed log income per capita and educational attainment and lowest fertility rate. We tested whether alternative lags of the components of SDI would provide a better predictor of outcomes such as life expectancy and age-specific probabilities of death. Using lag distributed income per capita, educational attainment, and the total fertility rate in the current year was the most predictive of these mortality outcomes (methods appendix p 286).
To report on aggregate results, we divided geographies into SDI quintiles in 2015. Quintile cutoffs were based on the entire distribution of geography–years from 1980 to 2015, excluding populations smaller than 1 million. Figure 9 shows a map of the SDI level in 2015 categorised into five groups including subnational geographies. Because SDI includes educational attainment and the total fertility rate, some countries which have very high income, such as Saudi Arabia, are classified in the second quintile of SDI because of lower educational attainment and higher fertility rates.⁵³
To capture the average relationships for each age–sex–cause group, we used spline regression of death rates on SDI (methods appendix pp 285–86). To ensure a coherent set of estimated death rates for Levels 1, 2, and 3 in the GBD cause hierarchy for each level of SDI, the Level 2 death rates were rescaled such that for each age–sex–cause bin, the sum of Level 2 death rates equalled the Level 1 death rate. This procedure was repeated for Level 3 and Level 2 causes. These rates were used as the expected death rates by age–sex–cause and SDI. Various summary measures have been computed on the basis of the age–sex–cause-specific predictions based on SDI, including age-standardised death rates, age-standardised YLL rates, and life expectancy at birth.
To further characterise how patterns of crude death rates and death numbers change with SDI, we have computed the average population age structure associated with each SDI level. These population age structures have then been used to estimate how crude death rates and death numbers by cause are expected to change with rising SDI.

The following formula, which is like that used by the United Nations Development Programme (UNDP) in calculations of the Human Development Index [53 ], the Health Development Governance Index [54 ], Malawi NHRS Index [21 (link)], African NHRS Barometer [22 , 23 ], and EDCTP African Participating States NHRS Barometer, was used to calculate all indices for the 17 individual sub-functions: $$NHRSsub-functionindex=\left(\frac{Actualxiscore-Minimumxiscore}{Maximumxiscore-Minimumxiscore}\right)$$ where x_i is the i.^th sub-function, such as existence of a national research ethics review committee (NEC)

Using each of the domain specific variables, we first created sub-indices j {j = 1, 2, 3, 4} using the following formula for each district d in country c {c = Bangladesh, India, Pakistan}: $$SubInde{x}_{c,d,j}=\frac{\left(X_{c,d,j}-min\left(X_{c,d,j}\right)\right)}{\left(max\left(X_{c,d,j}\right)-min\left(X_{c,d,j}\right)\right)}$$
$X_{c,d,j}$ is the sub-national domain specific value, while $\min \left(X_{c,d,j}\right)$ and $\max \left(X_{c,d,j}\right)$ are the country-domain specific minimum and maximum values of the variable at the district (sub-national) level. After generating sub-indices for each domain-district-country, we use simple arithmetic mean to generate the composite CR-Index for each district in each country, following the human development index (HDI) construction method of the UNDP for wide acceptability²⁵ . Formally, $$CRInde{x}_{c,d}=\frac{{\sum }_{j=1}^{n}SubInde{x}_{c,d,j}}{n}$$
Here n is the number of domains. We used arithmetic mean, given some sub-indexes generated zero value (for example, some districts did not have any reported international migration, resulting in a score of zero for that sub-index). The process of CR-Index construction normalizes the variables based on the minimum and maximum variation of each variable across sub-nations for each country.