Evidence map›Paper›PMID 35472059›Full record

ArticlePLoS medicine2022

Food environment and diabetes mellitus in South Asia: A geospatial analysis of health outcome data.

Dian Kusuma, Petya Atanasova, Elisa Pineda, Ranjit Mohan Anjana, Laksara De Silva, Abu Am Hanif, Mehedi Hasan, Md Mokbul Hossain, Susantha Indrawansa, Deepal Jayamanne and 11 more

Open access · goldAbstract read
In one paragraph

Article in PLoS medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
4.6field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors at 9 institutions in 6 countries.

Dian KusumaCentre for Health Economics Policy Innovation, Imperial College Business School, London, United Kingdom.
Petya AtanasovaCentre for Health Economics Policy Innovation, Imperial College Business School, London, United Kingdom.ORCID 0000-0002-6822-4487
Elisa PinedaCentre for Health Economics Policy Innovation, Imperial College Business School, London, United Kingdom.ORCID 0000-0002-5210-1538
Ranjit Mohan AnjanaMadras Diabetes Research Foundation, Chennai, India.ORCID 0000-0002-4843-1374
Laksara De SilvaFaculty of Medicine, University of Colombo, Colombo, Sri Lanka.
Abu Am HanifCentre for Non-Communicable Diseases and Nutrition, BRAC James P Grant School of Public Health, BRAC University, Dhaka, Bangladesh.ORCID 0000-0001-6274-5612
Mehedi HasanCentre for Non-Communicable Diseases and Nutrition, BRAC James P Grant School of Public Health, BRAC University, Dhaka, Bangladesh.ORCID 0000-0003-2153-4979
Md Mokbul HossainCentre for Non-Communicable Diseases and Nutrition, BRAC James P Grant School of Public Health, BRAC University, Dhaka, Bangladesh.ORCID 0000-0002-8058-4776
Susantha IndrawansaFaculty of Medicine, University of Colombo, Colombo, Sri Lanka.
Deepal JayamanneFaculty of Medicine, University of Kelaniya, Ragama, Sri Lanka.ORCID 0000-0002-8040-1546
Sujeet JhaMax Healthcare, New Delhi, India.ORCID 0000-0002-1283-4606
Anuradhani KasturiratneFaculty of Medicine, University of Kelaniya, Ragama, Sri Lanka.ORCID 0000-0001-5260-2394
Prasad KatulandaFaculty of Medicine, University of Colombo, Colombo, Sri Lanka.ORCID 0000-0003-4313-7528
Khadija I KhawajaServices Institute of Medical Sciences, Lahore, Pakistan.
Balachandran KumarendranFaculty of Medicine, University of Jaffna, Kokkuvil, Sri Lanka.ORCID 0000-0001-7786-5932
Malay K MridhaCentre for Non-Communicable Diseases and Nutrition, BRAC James P Grant School of Public Health, BRAC University, Dhaka, Bangladesh.ORCID 0000-0001-9226-457X
Vindya RajakarunaFaculty of Medicine, University of Kelaniya, Ragama, Sri Lanka.ORCID 0000-0001-9390-2940
John C ChambersSchool of Public Health, Imperial College London, United Kingdom.
Gary FrostFaculty of Medicine, Imperial College London, London, United Kingdom.ORCID 0000-0003-0529-6325
Franco SassiCentre for Health Economics Policy Innovation, Imperial College Business School, London, United Kingdom.ORCID 0000-0001-9773-2117
Marisa MiraldoCentre for Health Economics Policy Innovation, Imperial College Business School, London, United Kingdom.ORCID 0000-0002-5772-7740
Imperial College London · GBBRAC University · BDUniversity of Colombo · LKUniversity of Kelaniya · LKMadras Diabetes Research Foundation · INMax Healthcare · INNanyang Technological University · SGServices Institute of Medical Sciences · PKUniversity of Jaffna · LK

Funding

Department of Health 16/136/68
6 · The paper itself

Abstract

backgroundThe global epidemic of type 2 diabetes mellitus (T2DM) renders its prevention a major public health priority. A key risk factor of diabetes is obesity and poor diets. Food environments have been found to influence people's diets and obesity, positing they may play a role in the prevalence of diabetes. Yet, there is scant evidence on the role they may play in the context of low- and middle-income countries (LMICs). We examined the associations of food environments on T2DM among adults and its heterogeneity by income and sex. METHODS AND

findingsWe linked individual health outcome data of 12,167 individuals from a network of health surveillance sites (the South Asia Biobank) to the density and proximity of food outlets geolocated around their homes from environment mapping survey data collected between 2018 and 2020 in Bangladesh and Sri Lanka. Density was defined as share of food outlets within 300 m from study participant's home, and proximity was defined as having at least 1 outlet within 100 m from home. The outcome variables include fasting blood glucose level, high blood glucose, and self-reported diagnosed diabetes. Control variables included demographics, socioeconomic status (SES), health status, healthcare utilization, and physical activities. Data were analyzed in ArcMap 10.3 and STATA 15.1. A higher share of fast-food restaurants (FFR) was associated with a 9.21 mg/dl blood glucose increase (95% CI: 0.17, 18.24; p < 0.05). Having at least 1 FFR in the proximity was associated with 2.14 mg/dl blood glucose increase (CI: 0.55, 3.72; p < 0.01). A 1% increase in the share of FFR near an individual's home was associated with 8% increase in the probability of being clinically diagnosed as a diabetic (average marginal effects (AMEs): 0.08; CI: 0.02, 0.14; p < 0.05). Having at least 1 FFR near home was associated with 16% (odds ratio [OR]: 1.16; CI: 1.01, 1.33; p < 0.05) and 19% (OR: 1.19; CI: 1.03, 1.38; p < 0.05) increases in the odds of higher blood glucose levels and diagnosed diabetes, respectively. The positive association between FFR density and blood glucose level was stronger among women than men, but the association between FFR proximity and blood glucose level was stronger among men as well as among those with higher incomes. One of the study's key limitations is that we measured exposure to food environments around residency geolocation; however, participants may source their meals elsewhere.

conclusionsOur results suggest that the exposure to fast-food outlets may have a detrimental impact on the risk of T2DM, especially among females and higher-income earners. Policies should target changes in the food environments to promote better diets and prevent T2DM.

Indexed as

Diabetes Mellitus, Type 2AdultBlood GlucoseFemaleHumansMaleObesityOutcome Assessment, Health CareResidence CharacteristicsSri LankaBlood Glucose

Identifiers

PMID35472059
PMCPMC9041866
OpenAlexW4224712446

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.