Ethnic Groups

In 2000, a systematic literature review was conducted to determine whether an assessment or monitoring instrument existed that could be easily used in a primary care setting with adults aged 50 years and older. Age 50 was used because community-based organizations often use this age as the lower-end cutoff and because it was the age cutoff used in the National Blueprint program for increasing physical activity among older adults (12 ). Searches of Medline, PsycINFO, and the World Wide Web and queries of physical activity assessment experts and geriatric physicians helped us to identify 53 questionnaires that have been used in the past 25 years to assess physical activity. Search terms included physical activity, exercise, questionnaire, instrument, measurement, and assessment. Questionnaires were included if they were self-reported, used with adults, published or discovered through physical activity assessment experts, and available in English. These instruments were evaluated for 1) feasibility of collecting data in a primary care setting and feasibility of producing a summary for inclusion in a medical record; 2) psychometric properties of an optimal self-report screening instrument, including reliability and criterion validity; and 3) acceptability and relevance of the instrument to major ethnic populations in the United States, including Latinos and African Americans.
Members of the research team reviewed the instruments according to the following criteria: 1) dimensions of the questionnaires; 2) complexity; 3) recall time frame; 4) use as an outcome measure; 5) reliability/validity/responsiveness; 6) cultural adaptability; and 7) purpose of development. All but 12 of the 53 instruments identified in the literature search were eliminated because they were deemed to be too long and did not meet at least four of the review criteria. (A table showing questionnaires and criteria met is available from the authors). These 12 instruments were then submitted to an expert panel consisting of physical activity researchers and gerontologists who reviewed the instruments using these same criteria. The panel deemed none of these instruments to be completely acceptable either because they were too complex or because they had not been adequately validated.

Monthly incidence was derived from the number of individuals diagnosed with a long-term condition for the first time, each month. Age at the earliest found diagnosis date was categorised (<20, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89, ≥90 years). Sex was male/female. Ethnic groups were analysed using harmonised Office for National Statistics (ONS) categories (White/Black/Asian/Mixed/other/unknown). Deprivation was derived from the LSOA code at the time of diagnosis mapped to the 2019 Welsh Index of Multiple Deprivation¹⁵ and categorised in quintiles (1, most deprived, to 5, least deprived).
Frailty was based on an internationally established cumulative deficit model that utilises an electronic Frailty Index (eFI).^{16 –18 (link)} eFI scores were used to categorise individuals as: fit, mild, moderate, or severely frail using 10 years of previous WLGP data from date of diagnosis. Individuals without sufficient coverage of GP data were assigned to a missing category. Learning disability status (yes/no) was identified for the study cohort using Read v2 codes (Supplementary Table S4). Socioeconomic categories with one to four counts were rounded to five to prevent accidental disclosure and the excess counts deducted from an unknown/missing/adjacent category.

This study was conducted in the districts of Nzega and Igunga in Tabora region, central Tanzania. Tabora region is home to a population of about 2.3 million, of which 901,979 (Nzega is 502,252 and Igunga 399,727) people reside in the study area [29 ] and approximately 50,547 (6%) constitute people aged 60 years and above. Sukuma and Nyamwezi are the two major ethnic groups in the region. While the literacy status for persons aged 15 years and above stands at 59% in the region, it is 56.1% for Nzega and 58.7% for Igunga. About 80% of the region’s wealth comes from agriculture, which involves around 76% of the population. Tobacco and cotton are mainly grown for cash markets, whereas maize, sorghum, cassava, and sweet potatoes constitute the main food crops. Nzega is divided into 37 wards with 151 villages and Igunga into 26 wards with 93 villages. In these districts, there are about 5,600 (6% in Nzega and 5% in Igunga) elderly aged 60 and above who are enrolled with the CHF and NHIF schemes [9 (link)]. The districts were chosen because Igunga is the first district in the country where HI was implemented, and both are rural districts.

To illustrate the application of EASIUR-HR, we examined the distribution
of air quality impacts of passenger vehicle electrification across
race/ethnic groups. Passenger vehicles emit substantial amounts of
primary PM_2.5 and NO_x in vehicle
exhaust, emissions that are eliminated if gasoline vehicles are replaced
by electric vehicles. However, this switch will increase electricity
demand, potentially increasing emissions of primary PM_2.5, NO_x, and SO₂ depending on
the mix of sources for electricity generation. We modeled both of
these effects using EASIUR-HR to predict changes in concentrations
of passenger vehicle exhaust primary PM_2.5 and base EASIUR
to predict changes in PM_2.5 concentrations due to changes
in vehicle exhaust NO_x and all electricity
generation emissions. We present two bounding cases for national vehicle
electrification: vehicle electrification under the current electricity
grid (EV-CUR) using emissions factors from 2018 power plant data and
vehicle electrification under an all-renewable grid (EV-REN), assuming
no air pollutant emissions from electricity generation.
We use
the EPA’s MOtor Vehicle Emission Simulator (MOVES)⁸ to generate county-level vehicle exhaust emissions
of primary PM_2.5 and NO_x from
2019 and then allocated them to the base EASIUR grid using a population-weighted
average. After estimating emissions on the base EASIUR grid, we allocated
the emissions in each base EASIUR grid cell to the 300 m grid
EASIUR-HR operates on using spatial surrogates. MOVES automotive emissions
are classified by three road types: off-network, unrestricted access
roads, and restricted access roads. We used population density as
a surrogate to assign off-network emissions to the 300 m grid
and road length by road type as a surrogate for unrestricted and restricted
access roads using 2018 road network data from OpenStreetMap.²⁷ We classify motorways, trunk roads, and primary
roads as restricted access roads and all other roads as unrestricted
access. We allocate the grid cell emissions by road type to the 300 m
EASIUR-HR grid and then add up contributions from each road type to
get a total estimate of vehicle exhaust primary PM_2.5 emissions
at a 300 m resolution across the country.
To estimate
changes in power plant (electricity generating unit,
or EGU) air pollutant emissions to meet the demands of electric vehicles
under EV-CUR, we follow the methodology of Holland et al.,^{19 (link)} estimating emissions factors econometrically.
We regress hourly SO₂, NO_x,
and PM_2.5 emissions at the plant level against hourly electricity
demand within each North American Electric Reliability Corporation
(NERC) region in the same interconnect as the plant using emissions
and load from the year 2019.^{4 ,6 ,7} Unlike Holland et al., we do not include hour-of-day effects in
our regression; this assumption precludes the use of nonconstant vehicle
charging profiles. The regression coefficients of emissions against
regional load provide marginal emissions factors, which we then apply
to an annual estimated increase in load due to electric vehicle charging,
proportional to total VMT using an estimated vehicle efficiency of
100 MPGe. For EV-REN, we assume that there were no additional emissions
associated with producing the electricity for electric vehicles. We
also performed sensitivity analyses to dispatch model^{31 (link)} and our assumed vehicle efficiency.