Background
Methods
Data sources and searches
Study selection
Data extraction
Quality assessment
Data synthesis
Results
Author | Year | Country | Country income level | Study design | Sample size | Age | Outcomea | Outcome assessmentb | Result | Adjustment for confounding | Quality statement | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Urban > rural | Rural > urban | No difference | |||||||||||
Aekplakorn et al. [89] | 2011 | Thailand | Upper middle | Cross-sectional | 18,629 | NFG: 44.3 ± 0.3 Diabetes mellitus: 54.1 ± 0.7 | T2DM/T1DM prevalence | Blood sample | X | Age, sex | Moderate | ||
Agyemang et al. [90] | 2016 | Ghana, Netherlands, Germany, England | Lower middle and high | Cross-sectional | 5659 | 25–70 years (NR) | T2DM prevalence | Blood sample | X | Age, sex, education | Moderate | ||
Ali et al. [91] | 1993 | Malaysia | Upper middle | Cross-sectional | 681 | 38.6 ± 13.7 | T2DM/T1DM prevalence | Blood sample | X | Age | Moderate | ||
Al-Moosa et al. [92] | 2006 | Oman | High | Cross-sectional | 5840 | 24% >50 years 41% < 30 years | T2DM/T1DM prevalence | Blood sample | X | – | Moderate | ||
Anjana et al. [93] | 2011 | India | Lower middle | Cross-sectional | 13,055 | 40 ± 14 | T2DM/T1DM prevalence | Blood sample | Southern area, western area, eastern area | Northern area | Age, sex | Moderate | |
Assah et al. [94] | 2011 | Cameroon | Lower middle | Cross-sectional | 552 | 38.4 ± 8.6 | T2DM/T1DM prevalence | Blood sample | X | – | Moderate | ||
Attard et al. [67] | 2012 | China | Upper middle | Cross-sectional | NA | 51 ± 0.4 | T2DM/T1DM prevalence | Blood sample, self-report | X | Age, sex, income, region, BMI | Strong | ||
Allender et al. [95] | 2011 | Sri Lanka | Lower middle | Cross-sectional | 4485 | 46.1 ± 15.1 | T2DM/T1DM prevalence | Blood sample | X | Age, sex, income | Moderate | ||
Bahendeka et al. [41] | 2016 | Uganda | Low | Cross-sectional | 3689 | 35.1 ± 12.6 | T2DM/T1DM prevalence | Blood sample | X | Age, sex, region of residence, floor finishing of dwelling, BMI, waist circumference, total cholesterol | Moderate | ||
Baldé et al. [96] | 2007 | Guinea | Low | Cross-sectional | 1537 | 47.7 ± 12.5 | T2DM/T1DM prevalence | Blood sample | X | Age, location, excess of waist, raised systolic BP, raised diastolic BP | Moderate | ||
Balogun et al. [97] | 2012 | Nigeria | Lower middle | Longitudinal | 1330 | 77.3 ± 0.3 | T2DM incidence | Self-report | X | Age, sex, education | Strong | ||
Baltazar et al. [98] | 2003 | Philippines | Lower middle | Cross-sectional | 7044 | 39.0 ± 0.5 | T2DM/T1DM prevalence | Blood sample | X | Age and sex | Moderate | ||
Barnabé-Ortiz [99] | 2016 | Peru | Upper middle | Longitudinal | 3123 | 24% < 45 years 25% >65 years | T2DM incidence | Blood sample | X | Sex, age, education level, SES, family history of diabetes, daily smoking, hazardous drinking, TV watching for 2+ hours per day, transport-related physical inactivity, fruit and vegetable consumption, BMI, metabolic syndrome | Moderate | ||
Bocquier et al. [100] | 2010 | France | High | Cross-sectional | 3,038,670 | 48.9 ± 18.6 | T2DM/T1DM prevalence | Secondary | X | Age, sex | Strong | ||
Cubbin et al. [23] | 2006 | Sweden | High | Cross-sectional | 18,081 | 48% >45 years 25% < 35 years | T2DM/T1DM prevalence | Self-report | X | Age, sex, marital status, immigration status, SES composite, neighbourhood deprivation | Moderate | ||
Christensen et al. [101] | 2009 | Kenya | Lower middle | Cross-sectional | 1459 | 38.6 ± 12.6 | T2DM/T1DM prevalence | Blood sample | X | Age, sex | Moderate | ||
Dagenais et al. [102] | 2016 | Bangladesh, India, Pakistan, Zimbabwe, China, Colombia, Iran, Argentina, Brazil, Chile, Malaysia, Poland, South Africa, Turkey, Canada, Sweden, United Arab Emirates | Lower, lower middle, upper middle and high | Cross-sectional | 119,666 | 52 ± 9.3 | T2DM/T1DM prevalence | Blood sample | X | Age, sex, residency location, BMI, waist-to-hip ratio, PA levels, AHEI score, combined former and current smoking, education level, family history of diabetes, ethnicity | Strong | ||
Dar et al. [25] | 2015 | India | Lower middle | Cross-sectional | 3972 | 43% >50 years 57% 40–50 years | T2DM prevalence | Blood sample | X | – | Weak | ||
Davila et al. [103] | 2013 | Colombia | Upper middle | Cross-sectional | 1026 | 35% >55 years 35% < 35 years | T2DM/T1DM prevalence | Blood sample | X | Age, sex, education, SES, marital status, smoking, alcohol, intake of fruit and vegetables, PA | Strong | ||
Delisle et al. [104] | 2012 | Benin | Low | Cross-sectional | 541 | 38.2 ± 0.6 | Glycaemic marker: HOMA index | Blood sample | X | Age, sex, SES, location, diet quality, PA, alcohol, BMI | Moderate | ||
Dong et al. [105] | 2005 | China | Upper middle | Cross-sectional | 12,240 | 46.4 ± 13.9 | T2DM prevalence | Blood sample | X (men) | X (women) | Age, sex | Moderate | |
Du et al. [106] | 2016 | China | Upper middle | Cross-sectional | 3797 | 15% >60 years 8% 20–29 years | T2DM/T1DM prevalence | Blood sample | X | Age, sex | Moderate | ||
Esteghamati et al. [107] | 2009 | Iran | Upper middle | Cross-sectional | 3397 | 23% >55 years 25% < 35 years | T2DM/T1DM prevalence | Blood sample | X | Age, sex, residential area | Moderate | ||
Georgousopoulou et al. [108] | 2017 | Mediterranean islands | High | Cross-sectional | 2749 | 75 ± 7.3 | T2DM/T1DM prevalence | Blood sample | X | Age, sex, BMI, physical inactivity, smoking, siesta habit, education, living alone, adherence to Mediterranean diet, GDS, number of friends and family members, frequency of going out with friends and family, number of holiday excursions per year | Moderate | ||
Gong et al. [109] | 2015 | China | Upper middle | Cross-sectional | 5923 | 38% >50 years 62% < 50 years | T2DM/T1DM prevalence | Blood sample | X | Age, sex, education, PA, smoking, alcohol, BMI, triglycerides, HDL-cholesterol, hypertension | Strong | ||
Hussain et al. [110] | 2004 | Bangladesh | Lower middle | Cross-sectional | 6312 | 14% >50 years 46% < 30 years | T2DM/T1DM prevalence | Blood sample | X | Age, sex | Moderate | ||
Han et al. [111] | 2017 | Korea | High | Longitudinal | 7542 | 52 ± 8.8 | T2DM incidence | Blood sample | X | Age, sex, residential area, family history of diabetes, smoking, alcohol, exercise, abdominal obesity, hypertension, high triglycerides, low HDL-cholesterol | Strong | ||
Katchunga et al. [112] | 2012 | Congo | Low | Cross-sectional | 699 | 42.5 ± 18.1 | T2DM/T1DM prevalence | Blood sample | X | – | Moderate | ||
Keel et al. [113] | 2017 | Australia | High | Cross-sectional | 4836 | Non-indigenous: 66.6 ± 9.7 Indigenous: 54.9 ± 8.7 | T2DM/T1DM prevalence | Self-report | X (indigenous) | X (non-indigenous) | Age, sex, ethnicity, education, English-speaking at home, ethnicity | Moderate | |
Mayega et al. [114] | 2013 | Uganda | Low | Cross-sectional | 1497 | 45.8% >45 years 54.2% < 45 years | T2DM prevalence | Blood sample | X | Age, sex, residence, occupation, family history of diabetes, BMI, PA level, dietary diversity | Strong | ||
Mohan et al. [115] | 2016 | India | Lower middle | Cross-sectional | 6853 | 35–70 years (NR) | T2DM/T1DM prevalence | Blood sample | X | Age (only women included) | Moderate | ||
Msyamboza et al. [116] | 2014 | Malawi | Low | Cross-sectional | 3056 | 12.5% >55 years 45% < 35 years | T2DM/T1DM prevalence | Blood sample | X | Age, sex | Moderate | ||
Ntandou et al. [117] | 2009 | Benin | Low | Cross-sectional | 541 | 38.2 ± 10 | T2DM/T1DM prevalence | Blood sample | X | Age, sex, waist circumference, education, SES, PA, micronutrient adequacy score, preventive diet score, alcohol | Moderate | ||
Oyebode et al. [118] | 2015 | China, Ghana, India, Mexico, Russia, South Africa | Upper and Lower middle | Cross-sectional | 39,436 | 47.3% >60 years 12.3% < 40Y | T2DM/T1DM prevalence | Self-report | X (pooled) | Age, sex, survey design, income quintile, marital status, education | Strong | ||
Papoz et al. [119] | 1996 | New Caledonia | High | Cross-sectional | 9390 | 30–59 years (NR) | T2DM/T1DM prevalence | Blood sample | X | Age | Moderate | ||
Pham et al. [120] | 2016 | Vietnam | Lower middle | Cross-sectional | 16,730 | 54 ± 8 | T2DM/T1DM prevalence | Blood sample | X (men) | X (women) | Age, sex, socio-demographic factors, anthropometric measures, BP, family history of diabetes | Moderate | |
Raghupathy et al. [121] | 2007 | India | Lower middle | Longitudinal | 2218 | 28 ± 1.2 | T2DM prevalence | Blood sample | X | Age, sex, number of household possessions, education, PA, smoking, alcohol, parental consanguinity, family history of diabetes mellitus, body fat, BMI, waist-to-hips ratio, subscapular/triceps ratio | Strong | ||
Ramdani et al. [122] | 2012 | Morocco | Lower middle | Cross-sectional | 1628 | 54.2 ± 10.9 | T2DM/T1DM prevalence | Blood sample | X | Age, sex, BMI | Moderate | ||
Sadikot et al. [123] | 2004 | India | Lower middle | Cross-sectional | 41,270 | 36% >50 years 34% < 40 years | T2DM prevalence | Blood sample | X | Age, sex | Moderate | ||
Sobngwi et al. [124] | 2004 | Cameroon | Lower middle | Longitudinal | 1726 | 24% >55 years 28% < 35 years | T2DM/T1DM prevalence | Blood sample | X (women) | X (men) | Age, sex, residence, socio-professional category, alcohol, smoking, PA | Moderate | |
Stanifer et al. [125] | 2016 | Tanzania | Low | Cross-sectional | 481 neighbourhoods | 25% >60 years | T2DM/T1DM prevalence | Blood sample | X | Age, sex | Moderate | ||
Weng et al. [126] | 2007 | China | Upper middle | Cross-sectional | 529 | NR | T2DM/T1DM prevalence | Blood sample | X | Age, sex | Moderate | ||
Wu et al. [127] | 2016 | China | Upper middle | Cross-sectional | 23,010 | 40 (30.4–56.3) | T2DM/T1DM prevalence | Blood sample | X | Age | Moderate | ||
Zhou et al. [128] | 2015 | China | Upper middle | Cross-sectional | 98,658 | 20% >60 years 80% < 60 years | T2DM/T1DM prevalence | X | Age, sex, region | Moderate |
Author | Year | Country | Income level | Study design | Sample size | Age | Outcomea | Outcome assessmentb | Exposure category | Exposure assessment | Level geodata | Quality statement |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Ahern et al. [46] | 2011 | US | High | Cross-sectional | 3128 | NR | T2DM/T1DM prevalence | Secondary | PA, food | Place of residence | Aggregate | Moderate |
AlHasan et al. [69] | 2016 | US | High | Cross-sectional | NA | NR | T2DM/T1DM prevalence | Secondary | Food | GIS | Aggregate | Strong |
Astell-Burt et al. [42] | 2014 | Australia | High | Cross-sectional | 48,072 | 28% 45–55 years 39% >65 years | T2DM/T1DM prevalence | Self-report | PA | GIS | Individual | Moderate |
Auchincloss et al. [47] | 2009 | US | High | Longitudinal | 2285 | 62.1 ± 10 | T2DM incidence | Blood sample, self-report | PA, food | Self-report | Individual | Moderate |
Bodicoat et al. [44] | 2014 | UK | High | Cross-sectional | 10,476 | 59 ± 10.4 | T2DM prevalence | Secondary (screen detected) | PA | GIS | Individual | Strong |
Bodicoat et al. [72] | 2015 | UK | High | Cross-sectional | 10,461 | 59 ± 10.4 | T2DM prevalence | Secondary (screen detected) | Food | GIS | Individual | Strong |
Booth et al. [19] | 2013 | Canada | High | Longitudinal | 1,024,380 | 30–64 years (NR) | T2DM/T1DM incidence | Secondary | PA | Moderate | ||
Braun et al. [80] | 2015 | US | High | Cross-sectional | NA | NR | T2DM/T1DM prevalence | Secondary | PA, food | Register | Aggregate | Moderate |
Braun et al. [58] | 2016 | US | High | Longitudinal | 1079 | 39.7 ± 3.7 | Glycaemic marker: ln(HOMA index) | Blood sample | PA | GIS | Individual | Strong |
Braun et al. [57] | 2016 | US | High | Longitudinal | 583 | 69.4 ± 9.5 | Glycaemic marker: fasting glucose | Blood sample | PA | GIS | Individual | Strong |
Cai et al. [82] | 2017 | Netherlands | High | Cross-sectional | 93,277 | 44.9 ± 12.3 | Glycaemic marker: fasting glucose | Blood sample | Noise | GIS | Aggregate | Strong |
Carroll et al. [71] | 2017 | Australia | High | Longitudinal | 2582 | 50 ± 15 | Glycaemic marker: HbA1c | Blood sample | Food | GIS | Aggregate | Moderate |
Christine et al. [48] | 2015 | US | High | Longitudinal | 2157 | 60.7 ± 9.9 | T2DM incidence | Blood sample | PA, food | GIS, self-report | Individual | Strong |
Creatore et al. [20] | 2016 | Canada | High | Longitudinal | ±4,505,000 | 61% 30–49 years 34% 50–65 years | T2DM/T1DM incidence | Secondary | PA | GIS | Aggregate | Strong |
Cunningham-Myrie et al. [49] | 2015 | Jamaica | Upper middle | Cross-sectional | 2848 | 36.9 ± 2.7 | T2DM/T1DM prevalence | Blood sample | PA | Environmental audit | Individual | Strong |
Dalton et al. [59] | 2016 | UK | High | Longitudinal | 23,865 | 59.1 ± 9.3 | T2DM/T1DM incidence | Self-report | PA | GIS | Individual | Strong |
Dzhambov et al. [83] | 2016 | Bulgaria | Upper middle | Cross-sectional | 581 | 36.5 ± 15.4 | T2DM/T1DM prevalence | Secondary | Noise | Secondary | Aggregate | Moderate |
Eichinger et al. [50] | 2015 | Austria | High | Cross-sectional | 660 | 47.1 ± 14.1 | T2DM/T1DM prevalence | Blood sample | PA | Self-report | Individual | Moderate |
Eriksson et al. [85] | 2014 | Sweden | High | Longitudinal | 5156 | 47 ± 5 | T2DM incidence | Blood sample | Noise | GIS | Individual | Moderate |
Flynt et al. [73] | 2015 | US | High | Cross-sectional | NA | NR | T2DM/T1DM prevalence | Secondary | Food | Secondary | Aggregate | Moderate |
Frankenfeld et al. [74] | 2015 | US | High | Cross-sectional | 3227 | 11% >65 years 75% >18 years | T2DM/T1DM prevalence | Blood sample | Food | GIS | Aggregate | Moderate |
Freedman et al. [68] | 2011 | US | High | Cross-sectional | NA | 100% >50 years | T2DM/T1DM prevalence | Self-report | PA, food | Secondary | Aggregate | Moderate |
Fujiware et al. [60] | 2017 | Japan | High | Cross-sectional | 8904 | 72.5 ± 5.2 | T2DM/T1DM prevalence | Blood sample | PA, food | GIS | Individual | Moderate |
Gebreab et al. [61] | 2017 | US | High | Longitudinal | 3661 | 54 ± 12 | T2DM incidence | Blood sample | PA, Food | GIS | Individual | Strong |
Glazier et al. [21] | 2014 | Canada | High | Cross-sectional | 2,446,029 | T2DM/T1DM prevalence | Secondary | PA | GIS | Aggregate | Moderate | |
Hipp et al. [78] | 2015 | US | High | Cross-sectional | 3109 counties | T2D prevalence | Secondary | Food | GIS | Aggregate | Moderate | |
Heideman et al. [86] | 2014 | Germany | High | Longitudinal | 3604 | 44.8 ± 13.7 | T2DM incidence | Secondary | Noise | Self-report | Individual | Strong |
Lee et al. [45] | 2015 | Korea | High | Cross-sectional | 13,478 | 47.6 ± 12.2 | T2DM/T1DM prevalence | Secondary | PA | GIS | Aggregate | Moderate |
Liu et al. [79] | 2014 | US | High | Cross-sectional | 17,254 | 46.5 ± 18.5 | T2DM/T1DM prevalence | Blood sample | PA, food | Self-report | Individual | Strong |
Loo et al. [62] | 2017 | Canada | High | Cross-sectional | 78,023 | 35% 18–40 years 23% >65 years | Glycaemic marker: HbA1c and fasting glucose | Blood sample | PA | GIS | Individual | Strong |
Maas et al. [66] | 2009 | Netherlands | High | Cross-sectional | 345,103 | 38% >45 years 63% < 45 years | T2DM/T1DM prevalence | Secondary | PA | Register | Individual | Moderate |
Mena et al. [53] | 2015 | Chile | High | Cross-sectional | 832 | 45 ± 14 | Glycaemic marker: Fasting glucose level | Blood sample | PA, food | GIS | Individual | Moderate |
Meyer et al. [81] | 2015 | US | High | Longitudinal | 14,379 (observations) | 45.2 ± 3.6 | Glycaemic marker: HOMA index | Blood sample | PA, food | GIS | Individual | Moderate |
Mezuk et al. [70] | 2016 | Sweden | High | Longitudinal | 2,948,851 | NR | T2DM incidence | Secondary | Food | GIS | Individual | Strong |
Morland et al. [75] | 2006 | US | High | Cross-sectional | 10,763 | 100% >50 years | T2DM/T1DM prevalence | Blood sample | Food | GIS | Aggregate | Moderate |
Müller-Riemenschneider et al. [65] | 2013 | Australia | High | Cross-sectional | 5970 | 29% >65 years 30% < 45 years | T2DM prevalence | Self-report | PA | GIS | Individual | Strong |
Myers et al. [63] | 2016 | US | High | Cross-sectional | NA | NR | T2DM/T1DM prevalence | Secondary | PA, food | Secondary | Aggregate | Moderate |
Ngom et al. [64] | 2016 | Canada | High | Cross-sectional | 3,920,000 | NR | T2DM/T1DM prevalence | Secondary | PA | GIS | Aggregate | Strong |
Paquet et al. [54] | 2014 | Australia | High | Longitudinal | 3145 | 51.5 ± 15.5 | T2DM incidence | Blood sample | PA, food | GIS | Individual | Moderate |
Schootman et al. [56] | 2007 | US | High | Longitudinal | 644 | 56.2 ± 4.3 | T2DM/T1DM incidence | Self-report | PA, noise | Self-report, environmental audit | Individual | Moderate |
Sørensen et al. [84] | 2013 | Denmark | High | Longitudinal | 57,053 | 56.1 (50.7–64.2) | T2DM/T1DM incidence | Secondary | Noise | GIS | Individual | Moderate |
Sundquist et al. [22] | 2015 | Sweden | High | Longitudinal | 512,061 | 55 ± 14.9 | T2DM incidence | Secondary | PA | GIS | Aggregate | Moderate |
Author | Exposure | Study result | 95% confidence interval or p value | Adjustment for confounding |
---|---|---|---|---|
Ahern et al., 2011 [46] | Food environment: | Beta (SE) | Age, obesity rate | |
1. Percentage of households with no car living more than 1 mile from a grocery store | 1. 0.07 (0.01) | 1. P < 0.001 | ||
2. Fast-food restaurants per 1000 | 2. 0.41 (0.07) | 2. P < 0.001 | ||
3. Full service restaurants per 1000 | 3. -0.15 (0.04) | 3. P < 0.01 | ||
4. Grocery stores per 1000 | 4. -0.37 (0.09) | 4. P < 0.001 | ||
5. Convenience stores per 1000 | 5. 0.30 (0.06) | 5. P < 0.001 | ||
6. Direct money made from farm sales per capita | 6. -0.01 (0.02) | 6. P < 0.01 | ||
PA environment: | ||||
7. Recreational facilities per 1000 | 7. -0.12 (0.21) | 7. NS | ||
AlHasan et al., 2016 [69] | Food outlet density: | Beta (SE) | Age, obesity, PA, recreation facility density, unemployed, education, household with no cars and limited access to stores, race | |
1. Fast-food restaurant density per 1000 residents | 1. -0.55 (0.90) | 1. NS | ||
2. Convenience store density | 2. 0.89 (0.86) | 2. NS | ||
3. Super store density | 3. -0.4 (11.66) | 3. NS | ||
4. Grocery store density | 4. -3.7 (2.13) | 4. NS | ||
Astell-Burt et al., 2014 [42] | Green space (percent): | OR: | Age, sex, couple status, family history, country of birth, language spoken at home, weight, psychological distress, smoking status, hypertension, diet, walking, MVPA, sitting, economic status, annual income, qualifications, neighbourhood affluence, geographic remoteness | |
1. >81 | 1. 0.94 | 1. 0.85–1.03 | ||
2. 0–20 | 2. 1 | 2. NA | ||
Auchincloss et al., 2009 [47] | Neighbourhood resources: | HR: | Age, sex, family history, income, assets, education, ethnicity, alcohol, smoking, PA, diet, BMI | |
1. Healthy food resources | 1. 0.63 | 1. 0.42–0.93 | ||
2. PA resources | 2. 0.71 | 2. 0.48–1.05 | ||
3. Summary score | 3. 0.64 | 3. 0.44–0.95 | ||
Bodicoat et al., 2014 [44] | Green space (percent) | OR: | Age, sex, area social deprivation score, urban/rural status, BMI, PA, fasting glucose, 2 h glucose, total cholesterol | |
1. Least green space (Q1) | 1. 1 | 1. NA | ||
2. Most green space (Q4) | 2. 0.53 | 2. 0.35–0.82 | ||
Bodicoat et al., 2015 [72] | OR: | Age, sex, area social deprivation score, urban/rural status, ethnicity, PA | ||
1. Number of fast-food outlets (per 2) | 1. 1.02 | 1. 1.00–1.04 | ||
2. Density of fast-food outlet (per 200 residents) | 2. 13.84 | 2. 1.60–119.6 | ||
Booth et al., 2013 [19] | Walkability: | HR: | Age, sex, income | |
Men
| ||||
Recent immigrants
| ||||
1. Least walkable quintile | 1. 1.58 | 1. 1.42–1.75 | ||
2. Most walkable quintile | 2. 1 | 2. NA | ||
Long-term residents
| ||||
1. Least walkable quintile | 1. 1.32 | 1. 1.26–1.38 | ||
2. Most walkable quintile | 2. 1 | 2. NA | ||
Women
| ||||
Recent immigrants
| ||||
1. Least walkable quintile | 1. 1.67 | 1. 1.48–1.88 | ||
2. Most walkable quintile | 2. 1 | 2. NA | ||
Long-term residents
| ||||
1. Least walkable quintile | 1. 1.24 | 1. 1.18–1.31 | ||
2. Most walkable quintile | 2. 1 | 2. NA | ||
Walkability index, after residential relocation | Beta (SE) | 1. Income, household size, marital status, employment status, smoking status, health problems that interfere with PA 2. Additionally, adjusted for age, sex, ethnicity, education | ||
1. Fixed-effects model | 1. -0.011 (0.015) | 1. P > 0.05 | ||
2. Random-effects model | 2. -0.016 (0.010) | 2. P > 0.05 | ||
Walkability: within person change in Street Smart Walk Score | Beta (SE): 0.999 (0.002) | P > 0.05 | Age, sex, ethnicity, education, householdincome, employment status, marital status, neighbourhood SES | |
Cai et al., 2017 [82] | Daytime noise (dB) | Percentage change in fasting glucose per IQR Daytime noise: 0.2 | 95% CI, 0.1–0.3 P < 0.05 | Age, sex, season of blood draw, smoking status and pack-years, education, employment, alcohol consumption, air pollution |
Carroll et al., 2017 [71] | Count of fast-food outlets: | Beta per SD change: − 0.0094 | -0.030–0.011 | Age, sex, marital status, education, employment status, smoking status |
1. Interaction with overweight/obesity | 1. −0.002 | 1. -0.023–0.019 | ||
2. Interaction with time | 2. 0.0003 | 2. -0.003–0.004 | ||
3. Interaction with time and overweight/obesity | 3. -0.002 | 3. -0.006–0.001 | ||
Count of healthful food resources: | 0.012 | -0.008–0.032 | ||
4. Interaction with overweight/obesity | 4. 0.021 | 4. -0.000–0.042 | ||
5. Interaction with time | 5. -0.003 | 5. -0.006–0.001 | ||
6. Interaction with time and overweight/obesity | 6. -0.006 | 6. -0.009–-0.002 | ||
Christine et al., 2015 [48] | Neighbourhood physical environment, diet related: | HR: | Age, sex, family history, household per capita income, educational level, smoking, alcohol, neighbourhood SES | |
1. Density of supermarkets and/or fruit and vegetable markets (GIS) | 1. 1.01 | 1. 0.96–1.07 | ||
2. Healthy food availability (self-report) | 2. 0.88 | 2. 0.78–0.98 | ||
3. GIS and self-report combined measure | 3. 0.93 | 3. 0.82–1.06 | ||
Neighbourhood physical environment, PA related: | ||||
1. Density of commercial recreational facilities (GIS) | 1. 0.98 | 1. 0.94–1.03 | ||
2. Walking environment (self-report) | 2. 0.80 | 2. 0.70–0.92 | ||
3. GIS and self-report combined measure | 3. 0.81 | 3. 0.68–0.96 | ||
Creatore et al., 2016 [20] | Walkability: | Absolute incidence rate difference over 12 years FU: | Age, sex, area income, ethnicity | |
1. Low walkable neighbourhoods (Q1) | 1. -0.65 | 1. -1.65–0.39 | ||
2. High walkable neighbourhoods over (Q5) | 2. - 1.5 | 2. -2.6– -0.4 | ||
Cunningham-Myrie et al., 2015 [49] | Neighbourhood characteristics: | OR: | Age, sex, district, fruit and vegetable intake | |
1. Neighbourhood infrastructure | 1. 1.02 | 1. 0.95–1.1 | ||
2. Neighbourhood disorder score | 2. 0.99 | 2. 0.95–1.03 | ||
3. Home disorder score | 3. 1 | 3. 0.96–1.03 | ||
4. Recreational space in walking distance | 4. 1.12 | 4. 0.86–1.45 | ||
5. Recreational space availability | 5. 1.01 | 5. 0.77–1.32 | ||
6. Perception of safety | 6. 0.99 | 6. 0.88–1.11 | ||
Dalton et al., 2016 [59] | Green space: | HR: | Age, sex, BMI, parental diabetes, SES Effect modification by urban-rural status and SES was investigated, but association was not moderated by either | |
1. Least green space (Q1) | 1. 1 | 1. NA | ||
2. Most green space (Q4) | 2. 0.81 | 2. 0.65–0.99 | ||
3. Mediation by PA | 3. 0.96 | 3. 0.88–1.06 | ||
Dzhambov et al., 2016 [83] | Day-evening-night equivalent sound level: | OR: | Age, sex, fine particulate matter, benzo alpha pyrene, BMI, family history of T2DM, subjective sleep disturbance, bedroom location | |
1. 51–70 decibels | 1. 1 | 1. NA | ||
2. 71–80 decibels | 2. 4.49 | 2. 1.39–14.7 | ||
Eichinger et al., 2015 [50] | Characteristics of built residential environment: | Beta: | Age, sex, individual-level SES | |
1. Perceived distance to local facilities | 1. 0.006 | 1. P < 0.01 | ||
2. Perceived availability/maintenance of cycling/walking infrastructure | 2. NS | |||
3. Perceived connectivity | 3. NS | |||
4. Perceived safety with regards to traffic | 4. NS | |||
5. perceived safety from crime | 5. NS | |||
6. Neighbourhood as pleasant environment for walking/cycling | 6. NS | |||
7. Presence of trees along the streets | 7. NS | |||
Eriksson et al., 2014 [85] | Aircraft noise level: | OR: | Age, sex, family history, SES based on education, PA, smoking, alcohol, annoyance due to noise | |
1. <50 dB | 1. 1 | 1. NA | ||
2. ≥55 dB | 2. 0.94 | 2. 0.33–2.70 | ||
Flynt et al., 2015 [73] | Clusters (combination of number of counties, urban-rural classification, population density, income, SES, access to food stores, obesity rate, diabetes rate): | Median standardised diabetes mellitues rate: | IQR: | - |
1 | 1. 0 | 1. -0.05 - 0.7 | ||
2 | 2. 0 | 2. -0.04–0.7 | ||
3 | 3. 0 | 3. -0.08–0.01 | ||
4 | 4. -0.04 | 4. -1.01–0.6 | ||
5 | 5. -0.08 | 5. -1.5–-0.04 ANOVA: p < 0.001 | ||
Frankenfeld et al., 2015 [74] | RFEI ≤ 1 clusters: | Predicted prevalence: | Demographic and SES variables | |
1. Grocery stores | 1. 7.1 | 1. 6.3–7.9 | ||
2. Restaurants | 2. 5.9 | 2. 5.0–6.8, p < 0.01 | ||
3. Specialty foods | 3. 6.1 | 3. 5.0–7.2, p < 0.01 | ||
RFEI >1: | ||||
4. Restaurants and fast-food | 4. 6.0 | 4. 4.9–7.1, p < 0.01 | ||
5. Convenience stores | 5. 6.1 | 5. 4.9–7.3, p < 0.01 | ||
Freedman et al., 2011 [68] | Built environment: | OR: | Age, ethnicity, marital status, region of residence, smoking, education, income, childhood health, childhood SES, region of birth, neighbourhood scales | |
Men:
| ||||
1. Connectivity (2000 Topologically Integrated | 1. 1.06 | 1. 0.86–1.29 | ||
Geographic Encoding and Referencing system) | 2. 1.05 | 2. 0.89–1.24 | ||
2. Density (number of food stores, restaurants, housing units per square mile) | ||||
Women:
| ||||
3. Connectivity | 3. 1.01 | 3. 0.84–1.20 | ||
4. Density | 4. 0.99 | 4. 0.99–1.17 | ||
Fujiware et al., 2017 [60] | Count within neighbourhood unit (mean 6.31 ± 3.9 km2) | OR per IQR increase: | Age, sex, marital status, household number, income, working status, drinking, smoking, vegetable consumption, walking, going-out behaviour, frequency of meeting, BMI, depression | |
1. Grocery stores | 1. 0.97 | 1. 0.88–1.08 | ||
2. Parks | 2. 1.16 | 2. 1–1.34 | ||
Gebreab et al., 2017 [61] | Density within 1-mile buffer: | HR: | Age, sex, family history of diabetes, SES, smoking, alcohol consumption, physical activity, diet | |
1. Favourable food stores | 1. 1.03 | 1. 0.98–1.09 | ||
2. Unfavourable food stores | 2. 1.07 | 2. 0.99–1.16 | ||
3. PA resources | 3. 1.03 | 3. 0.98–1.09 | ||
Glazier et al., 2014 [21] | Walkability index: | Rate ratio: | Age, sex | |
1. Q1 | 1. 1 | 1. NA | ||
2. Q5 | 2. 1.33 | 2. 1.33–1.33 | ||
Index components: | ||||
1. Population density (Q1: Q5) | 1. 1.16 | 1. 1.16–1.16 | ||
2. Residential density (Q1: Q5) | 2. 1.33 | 2. 1.33–1.33 | ||
3. Street connectivity (Q1: Q5) | 3. 1.38 | 3. 1.38–1.38 | ||
4. Availability of walkable destinations (Q1: Q5) | 4. 1.26 | 4. 1.26–1.26 | ||
Heidemann et al., 2014 [86] | Residential traffic intensity: | OR: | Age, sex, smoking, passive smoking, heating of house, education, BMI, waist circumference, PA, family history | |
1. No traffic | 1. 1 | 1. NA | ||
2. Extreme traffic | 2. 1.97 | 2. 1.07–3.64 | ||
Hipp et al., 2015 [78] | Food deserts | Correlation: NR | NS | – |
Lee et al., 2015 [45] | Walkability: | OR: | Age, sex, smoking, alcohol, income level | |
1. Community 1 | 1. 1 | 1. NA | ||
2. Community 2 | 2. 0.86 | 2. 0.75–0.99 | ||
Loo et al., 2017 [62] | Walkability (walk score) Difference between Q1 and Q4 | Beta for HbA1C: | Age, sex, current smoking status, BMI, relevant medications and medical diagnoses, neighbourhood violent crime rates and neighbourhood indices of material deprivation, ethnic concentration, dependency, residential instability | |
1. -0.06 | 1. -0.11–0.02 | |||
Beta for fasting glucose: | ||||
2. 0.03 | 2. -0.04–0.1 | |||
Maas et al., 2009 [66] | Green space: | OR: | Demographic and socioeconomic characteristics, urbanicity | |
1. Q1 | 1. 1 | 1. NA | ||
2. Q4 | 2. 0.84 | 2. 0.83–0.85 | ||
Mena et al., 2015 [53] | Correlation: | – | ||
1. Distance to parks | 1. NR | 1. NA | ||
2. Distance to markets | 2. -0.094 | 2. P < 0.05 | ||
Mezuk et al., 2016 [70] | Ratio of the number of health-harming food outlets to the total number of food outlets within a 1000-m buffer of each person | OR per km2: 2.11 | 1.57–2.82 | Age, sex, education, household income |
Morland et al., 2006 [75] | Presence of: | Prevalence ratio: | Age, sex, income, education, ethnicity, food stores and service places, PA | |
1. Supermarkets | 1. 0.96 | 1. 0.84–1.1 | ||
2. Grocery stores | 2. 1.11 | 2. 0.99–1.24 | ||
3. Convenience stores | 3. 0.98 | 3. 0.86–1.12 | ||
Müller-Riemenschneider et al., 2013 [65] | Walkability (1600 m buffer): | OR: | Age, sex, education, household income, marital status | |
1. High walkability | 1. 0.95 | 1. 0.72–1.25 | ||
2. Low walkability | 2. 1 | 2. NA | ||
Walkability (800 m buffer): | ||||
3. High walkability | 3. 0.69 | 3. 0.62–0.90 | ||
4. Low walkability | 4. 1 | 4. NA | ||
Myers et al., 2017 [63] | Physical activity: | Beta: | Age | |
1. Recreation facilities per 1000 | 1. -0.457 | 1. -0.809– -0.104 | ||
2. Natural amenities (1–7) | 2. 0.084 | 2. 0.042–0.127 | ||
Food: | ||||
3. Grocery stores and supercentres per 1000 | 3. 0.059 | 3. -0.09–0.208 | ||
4. Fast-food restaurants per 1000 | 4. -0.032 | 4. -0.125–0.062 | ||
Ngom et al., 2016 [64] | Distance to green space: | Prevalence ratio: | Age, sex, social and environmental predictors | |
1. Q1 (0–264 m) | 1. 1 | 1. NA | ||
2. Q4 (774–27781 m) | 2. 1.09 | 2. 1.03–1.13 | ||
Paquet et al., 2014 [54] | Built environment attributes: | RR: | Age, sex household income, education, duration of FU, area-level SES | |
1. RFEI | 1. 0.99 | 1. 0.9–1.09 | ||
2. Walkability | 2. 0.88 | 2. 0.8–0.97 | ||
3. POS | ||||
a. POS count | a. 1 | a. 0.92–1.08 | ||
b. POS size | b. 0.75 | b. 0.69–0.83 | ||
c. POS greenness | c. 1.01 | c. 0.9–1.13 | ||
d. POS type | d. 1.09 | d. 0.97–1.22 | ||
Schootman et al., 2007 [56] | Neighbourhood conditions (objective): | OR: | Age, sex, income, perceived income adequacy, education, marital status, employment, length of time at present address, own the home, area | |
1. Housing conditions | 1. 1.11 | 1. 0.63–1.95 | ||
2. Noise level from traffic, industry, etc. | 2. 0.9 | 2. 0.48–1.67 | ||
3. Air quality | 3. 1.2 | 3. 0.66–2.18 | ||
4. Street and road quality | 4. 1.03 | 4. 0.56–1.91 | ||
5. Yard and sidewalk quality | 5. 1.05 | 5. 0.59–1.88 | ||
Neighbourhood conditions (subjective): | ||||
6. Fair–poor rating of the neighbourhood | 6. 1.04 | 6. 0.58–1.84 | ||
7. Mixed or terrible feeling about the neighbourhood | 7. 1.1 | 7. 0.6–2.02 | ||
8. Undecided or not at all attached to the neighbourhood | 8. 0.68 | 8. 0.4–1.18 | ||
9. Slightly unsafe–not at all safe in the neighbourhood | 9. 0.61 | 9. 0.35–1.06 | ||
Sørensen et al., 2013 [84] | Exposure to road traffic noise per 10 dB: | Incidence rate ratio: | Age, sex, education, municipality SES, smoking status, smoking intensity, smoking duration, environmental tobacco smoke, fruit intake, vegetable intake, saturated fat intake, alcohol, BMI, waist circumference, sports, walking, pollution | |
1. At diagnosis | 1. 1.08 | 1. 1.02–1.14 | ||
2. 5 years preceding diagnosis | 2. 1.11 | 2. 1.05–1.18 | ||
Sundquist et al., 2015 [22] | Walkability: | OR: | Age, sex, income, education, neighbourhood deprivation | |
1. D1 (low) | 1. 1.16 | 1. 1.00–1.34 | ||
2. D10 (high) | 2. 1 | 2. NA |