Abstract
Background
An intense increase in pre-diabetes has taken place among the worldwide population each year. The purpose of this study was to assess the diagnostic validity of the American Diabetes Association (ADA) screening questionnaire for identifying pre-diabetes in the Iranian rural population.
Methods
This study was conducted in Ahar County, East Azarbaijan, Iran. The participants (n = 440) were randomly recruited via trained community health care workers. The ADA questionnaire including six items (age, gender, having family members with diabetes, obesity, hypertension, and physical activity) is the screening tool used to identify people at high risk for developing type 2 diabetes. The World Health Organization (WHO) forward/backward translation protocol was used for translating the assessment tool. The diagnosis of pre-diabetes was defined based on the fasting blood glucose (FBG, as a gold standard) cut-points of 100 mg/dl to 125 mg/dl. We assessed the criterion validity and diagnosis characteristics of the ADA questionnaire in the diagnosis of pre-diabetes using the measures of sensitivity, specificity, and receiver operating characteristics (ROC) curves. In addition, the optimal cut-point of the ADA questionnaire for the diagnosis of pre-diabetes was computed using Youden’s index.
Results
A total of 440 adults ages 30–65 years (Mage = 48.8 years, SDage = 11.2 years) were included in the study. Around half of the participants were women (50%), illiterate (51.4%), and married (85.2). In the pre-diabetes diagnosis scale, the present cut-point yielded a sensitivity of 98.7 (95% CI:96.6–99.6), specificity of 53.1 (95% CI: 44.6–61.5), positive predictive value (PPV) of 81.4 (95% CI:77–85.3), positive predictive value (NPV) of 95.0 (95% CI:87.7–98.6), and accuracy of 83.9 (95% CI:81.4–89.2) with an area under curve (AUC) of 0.84 (95% CI: 0.80 − 0.89).
Conclusions
The Persian version of the ADA questionnaire had good sensitivity and fair specificity for pre-diabetes diagnosis among rural adults at high risk for developing type 2 diabetes. The study provided evidence for the ADA questionnaire as a valid and reliable tool for identifying pre-diabetes in a rural area. Identifying rural residents in the early stage of developing diabetes with a simple and accurate instrument without the need for a FBG test contributes to controlling the disease in areas with limited access to health services.
Trial registration
The study is not a trial; the registration number is not applicable.
Keywords: Pre-diabetes, Diabetes, Diagnosis, Scale
Background
Type 2 diabetes has long been known as a risk factor for cardiovascular diseases (CVD) [1]. In fact, diabetes is the seventh-leading cause of death in adults and is the major cause of death in these patients [2]. The prevalence of type 2 diabetes is increasing dramatically in Iran and worldwide [3]. It has been estimated that the prevalence of type 2 diabetes in Iran was 24% and that it increased by 0.4% with each year after 20 years of age. A similar study in the US projected that one in every three US adults will have diabetes (diagnosed or undiagnosed diabetes) in 2050 [4].
Although the risk of the diabetes development rate varies based on the definition of pre-diabetes and the characteristics of the population, around 5–10% of people with pre-diabetes become diabetic annually [5]. Pre-diabetes is increasing intensely among the worldwide population each year. It is estimated that 79 million US adults had pre-diabetes in 2010, and about 35% of adults over 20 years of age and 50% of those over 65 years had pre-diabetes in 2005–2008 [6]. Some studies reported that the prevalence of pre-diabetes will increase to the greatest level in Middle Eastern countries by 2030 [7]. An Iranian national survey indicated (2008) that 16.8% of adults aged 25–64 years had impaired fasting glucose [8]. The incidence of pre-diabetes has been estimated to be 40.6 per 1,000 person-years in Ahvaz, an area with a high incidence of diabetes in Iran [9].
Emerging evidence has clearly demonstrated that early diabetes identification and intensive lifestyle change programs for high-risk people with type 2 diabetes or people at the pre-diabetes stage has significant health benefits [10]. Additionally, diabetes is associated with serious clinical and financial challenges for older adults in developed countries, as well as working-age adults in developing countries [11]. Thus, identifying high-risk individuals who are susceptible to uncontrolled diabetes becomes crucial and beneficial for the control of diabetes with diagnostic screening instruments [12]. A well-established body of evidence indicates the benefits of the use of screening instruments for pre-diabetes. Many investigators have proposed simple and reliable diabetes screening tools, such as the Diabetes Risk Score, that can be carried out easily in clinical and health care settings [13–17]. A valid and simple instrument is needed for diagnosing a large number of pre-diabetic patients who are at an increased risk of diabetes, particularly in populations with limited access to health care services and health professionals. Fortunately, the American Diabetes Association (ADA) screening recommendation provides a simple questionnaire without the need for blood testing. The rural population may decide to postpone blood testing due to the burdens of cost and long travel times; thus, providing this special pre-diabetes screening tool may be necessary to ensure that this population’s high-risk individuals receive timely and appropriate care [18].
Two risk scoring algorithms exist for screening undiagnosed diabetes: Herman et al.’s model from the National Health and Nutrition Examination Survey (NHANES) II [19], and Heikes et al.’s model from the NHANES III [20]. These two algorithms are also known as the ADA diabetes questionnaires. Most previous studies evaluated the ADA questionnaire for countries other than Iran [19, 21, 22]. Thus, screening for pre-diabetes based on the ADA questionnaire in the Iranian population as well as in rural and hard-to-reach groups is unknown. In light of this, the purpose of this study was to assess the diagnostic validity of the ADA questionnaire for identifying pre-diabetes in the Iranian rural high-risk population.
Methods
Design
This cross-sectional study was designed to assess the ADA questionnaire’s diagnostic validity among the at-risk rural population in Ahar County, East Azarbaijan, Iran. Participants (adults ≥ 30 years of age) were included in the study via trained community health care workers, or the Behvarz, who provide basic health care to the rural population there.
Participants
Participants were randomly selected from among individuals attending rural health care settings for other purposes. Recruitment was conducted through an eligibility screening program between January and May 2017. Individuals were requested to take part in the study if they met one of the study’s inclusion criteria related to being at risk, including the following: (a) having a family history of diabetes, or (b) having a body mass index (BMI) ≥ 25 kg/m2, or having blood pressure ≥ 140/90 mmHg. Exclusion criteria included the following: (a) having diabetes, (b) having cancer, (c) having renal diseases, and (d) being pregnant. Those who refused to participate in the diagnostic pre-diabetes interview cited reasons such as a lack of time to do a follow-up pre-diabetes clinical test. This study was the first part (screening) of a randomized control trial, so, for reaching to the pre-diabetic patients (136 perarm), we need to screen 440 patients (to detect a decrease of one standard deviation (SD; 5 mg/dl) in the FBS, a power of 90%) [23].
Pre-diabetes risk scoring questionnaire and data collection
The ADA questionnaire is composed of six items used to identify people at high risk for developing type 2 diabetes [20]. According to the World Health Organization (WHO) forward/backward translation protocol for translating assessment tools (http://www.who.int/substance_abuse/research_tools/translation/en/), first, the originally English ADA questionnaire [20] was translated into Persian (the official language of Iranian). Next, a panel of experts examined the translated document for wording; then, the document was passed on to a native English speaker for the backward translation. Finally, the backward translated document was forwarded to a board of experts to pinpoint any poorly translated items and any modifications made to the original tool.
For this study, we included each participant who had one risk criterion, and we obtained information on demographic characteristics, as well as ADA questionnaire responses via trained interviewers. The ADA questionnaire components included the following: age (< 40 years, 0 points; 40–49 years, 1 point; 50–59 years, 2 points; 60 years or older, 3 points), gender (woman, 0 points; man, 1 point), having family members with diabetes (no, 0 points; yes, 1 point), being overweight or obese (not overweight or obese, 0 points; overweight, 1 point; obese, 2 points; extremely obese, 3 points), physically active (no, 0 points; yes, -1 point). The final score was the sum of the scores from the six questions and ranged from 0 to 10, with ≥ 4 indicating the high risk of having undiagnosed diabetes or pre-diabetes, and with ≥ 5 indicating the high risk of having undiagnosed diabetes [21].
Blood pressure was measured with a mercury sphygmomanometer twice in the same arm after the individual was seated at rest for 10–15 min [24]. The individual’s weight—while the individuals were dressed in light clothing and without shoes—was measured using a calibrated scale (Seca, Hamburg, Germany, model 8811021658) to the nearest 0.1 kg. Height was measured without shoes using a stadiometer (Seca, Hamburg, Germany) to the nearest 0.1 cm [25]. Each individual’s BMI was calculated using his or her weight in kilograms divided by the square of the individual’s height in meters [26]. The FBG was measured for all participants following overnight fasting for at least 10 h.
In the present study, we categorized individuals into two groups according to their scores and laboratory test results. The diagnosis of pre-diabetes was defined based on FBG cut-points of 100 mg/dl to 125 mg/dl [27].
Statistical analysis
Statistical analyses were conducted using the STATA release 14.0 software (College Station, Texas, USA). Data were presented using frequencies (percentages) for categorical variables and means (standard deviation) for numeric variables. The characteristics of patients with or without pre-diabetes were compared using χ2 or Student’s t-tests. P-values below 0.05 were considered to be significant. We assessed the criterion validity and diagnosis characteristics of the ADA questionnaire in diagnosing pre-diabetes using the measures of sensitivity, specificity, and receiver operating characteristics (ROC) curves. The FBG was used as the gold standard. In addition, the optimal cut-point of the ADA questionnaire for the diagnosis of pre-diabetes was computed using Youden’s index.
Results
A total of 440 adults ages 30–65 years (mean: 48.8 ± 11.2) were included in the study. The characteristics of the participants are presented in Table 1. Around half of the participants were women (50%), illiterate (51.4%), and married (85.2). In addition, among all of the high-risk participants who were selected based on three high-risk conditions (having a family history of diabetes, having a BMI ≥ 25 kg/m2, or having blood pressure ≥ 140/90 mmHg), 20.9% had family histories of diabetes, 68.4% had BMIs ≥ 25 kg/m2, and 49.8% had high blood pressure.
Table 1.
Characteristics of study population
| Total (n = 440) Mean (SD) / n (%) |
Pre-diabetes (n = 360) Mean (SD) / n (%) |
Non-pre-diabetes (n = 80) Mean (SD) / n (%) |
P-value | |
|---|---|---|---|---|
| Age in years | 48.8 (11.2) | 51.3 (10.6) | 37 (5.4) | < 0.0001 |
| Gender | 0.084 | |||
| Women | 220 (50) | 187 (51.9) | 33 (41.3) | |
| Men | 220 (50) | 173 (48.1) | 47 (58.8) | |
| Marital status | 0.043 | |||
| Married | 375 (85.2) | |||
| Never married | 23 (5.2) | 17 (4.7) | 6 (7.5) | |
| Other | 42 (9.5) | 40 (11.1) | 2 (2.5) | |
| Education | < 0.0001 | |||
| Illiterate | 226 (51.4) | 216 (60) | 10 (12.5) | |
| ≤ Primary (1–6) | 143 (32.5) | 103 (28.6) | 40 (50) | |
| Secondary (7–12) | 63 (14.3) | 38 (10.6) | 25 (31.3) | |
| University degree | 8 (1.8) | 3 (0.8) | 5 (6.3) | |
| Family history, yes | 92 (20.9) | 86 (93.5) | 6 (6.5) | 0.001 |
| Employment | < 0.0001 | |||
| Farmer | 199 (45.2) | 160 (44.4) | 39 (48.8) | |
| Carpet-weavers | 73 (16.6) | 68 (18.9) | 5 (6.3) | |
| Employee | 9 (2) | 5 (1.4) | 4 (5) | |
| Animal husbandry | 24 (5.5) | 24 (6.7) | 0 | |
| Working | 21 ( | 11 (3) | 10 (12) | |
| Not working | 91 (20.7) | 79 (21.9) | 12 (15) | |
| Others | 23 | 13 (3.6) | 10 (12) | |
| BMI ≥ 25 kg/m2 | 301 (68.4) | 266 (73.9) | 35 (11.6) | |
| Having blood pressure ≥ 140/90 | 219 (49.8) | 214 (97.7) | 5 (2.3) | |
| BMI, kg/m2 | 26.6 (4.1) | 27.1 (4.2) | 24 (2.4) | < 0.0001 |
| Systolic BP mmHg | 126.6 (17.2) | 129.2 (15.9) | 115 (18) | < 0.0001 |
| Diastolic BP mmHg | 79.9 (9.5) | 81.5 (0.8.9) | 72.4 (8.3) | < 0.0001 |
| FBG, mg/dl | 102 (11.77) | 105.4 (10.6) | 89.9 (7.8) | < 0.0001 |
BMI: body mass index; FBG: fasting blood glucose
We evaluated the diagnostic properties of the ADA questionnaire using the FBG test as a gold standard. A cut-point of ≥ 4 was selected because it resulted in the highest value for Youden’s index, used to indicate an individual with undiagnosed pre-diabetes. On the pre-diabetes diagnosis scale, the present cut-point yielded a sensitivity of 98.7 (95% CI: 96.6–99.6), specificity of 53.1 (95% CI: 44.6–61.5), PPV of 81.4 (95% CI: 77-85.3), NPV of 95.0 (95% CI: 87.7–98.6), and accuracy of 83.9 (95% CI: 81.4–89.2) with an area under curve (AUC) of 0.84 (95% CI: 0.80, 0.89). The percent of agreement (Cohen’s kappa) was 0.58. The correlation of the pre-diabetes score and FBG was 0.48 (p < 0.0001) (Table 2).
Table 2.
Sensitivity and specificity for pre-diabetes diagnosis scale (N = 440)
| Sensitivity (95% CI) |
Specificity (95% CI) |
PPV (95% CI) |
NPV (95% CI) |
Accuracy (95% CI) |
LR+ (95% CI) |
LR- (95% CI) |
|
|---|---|---|---|---|---|---|---|
| Pre-diabetes cut of score ≥ 4 | 98.7 (96.6–99.6) | 53.1 (44.6–61.5) | 81.4 (77 -85.3) | 95.0 (87.7–98.6) | 83.9 (81.4–89.2) | 2.1 (1.77–2.5) | 0.03 (0.01–0.07) |
| Area under curve (AUC) | 0.84 (0.80 − 0.89) |
PPV: positive predictive value; NPV-: negative predictive value; LR+: positive likelihood ratio; LR-: negative likelihood ratio.
Discussion
Our results provided evidence that the Persian version of the ADA questionnaire is a screening tool with good sensitivity and fair specificity for pre-diabetes diagnosis among rural adults at high risk for developing type 2 diabetes. ROC analysis and Youden’s index revealed that the Persian version of the ADA questionnaire had high sensitivity (98.7) and fair specificity (53.1) for pre-diabetes diagnosis with a cut-off score of ≥ 4. In rural clinical settings, a good screening instrument has a high sensitivity with an acceptable specificity to avoid missing individuals with pre-diabetes [28].
The Persian version of the ADA questionnaire with a good negative predictive value was most successful at identifying persons without pre-diabetes (NPV = 95.0). Particularly, a person who was identified as being without pre-diabetes using the Persian version of the ADA questionnaire had a 95% probability of not having pre-diabetes using the FBG diagnosis test. However, a person who was identified as pre-diabetic using the Persian version of ADA questionnaire had an 81.4 (PPV) chance of having pre-diabetes using the FBG diagnosis test. The positive likelihood ratio of the Persian version of the ADA questionnaire was 2.1, and the negative likelihood ratio was 0.03. This means that clinically, in a similar setting, pre-diabetic patients with FBG ≥ 110 are 2.1 times more likely to have Persian versions of the ADA questionnaire score ≥ 4 compared with patients without FBG ≥ 110. Similarly, patients with FBG ≤ 110 are 0.03 more likely to have negative results using the Persian version of the ADA questionnaire.
Heikes et al. in their original validation study to develop a simple tool for the US population found that the sensitivity, specificity, and positive and negative predictive values for detecting undiagnosed pre-diabetes were 75%, 65%, 49%, 85%, and 0.75 respectively [20]. The performance features evaluated in our study were higher than those of the original validation study. Robinson et al. [29] in their study regarding the evaluation of the predictive validity of the Canadian Diabetes Risk Assessment Questionnaire (CANRISK) found that the AUC for CANRISK was 0.75, indicating that this instrument score has an ability to distinguish true-positive and true-negative cases (based on reference standard blood test results). Inconsistent with our study, results from a study in Spain revealed a low reliability and validity of the Spanish version of the instrument. This study indicated a positive and weak correlation between blood glucose and the risk test score (0.138) [30].
Our study findings provide evidence for the use of the Persian version of the ADA questionnaire as a main screening tool for the early detection of pre-diabetes among the rural population. The short components of this instrument, including age, gender, a family history of diabetes, blood pressure, overweight and obesity, and physical activity, make it quick, convenient, and ideal to use as an approach for screening for pre-diabetes among the rural population. No blood testing and mathematical calculations are needed to assess risk scores. Thus, it creates the necessary conditions for rural health workers to perform diabetes risk assessments in rural areas and prevents patients from requiring costly care. As rural residents often experience barriers to and challenges with health care access—for example, transportation, cost, and time—using the Persian version of the ADA questionnaire for screening for pre-diabetes and for offering preventive services and lifestyle promotion for high-risk patients can contribute to the control of diabetes [31]. The rural population may decide to postpone blood testing due to the burdens of cost and long travel times, but this is not an issue with the ADA questionnaire [32].
In light of the diabetes epidemic, an urgent need exists to increase early diabetes detection and to motivate the at-risk public toward diabetes prevention. In addition, as a result of a lack of specific symptoms and limited interest in the rural health care setting, type 2 diabetes remains undiagnosed. However, the application of screening pre-diabetes tools without any special help from medical caregivers seems to be highly effective and benefits the target population.
Conclusions
The Persian version of the ADA questionnaire had good sensitivity and fair specificity for pre-diabetes diagnosis among rural Iranian adults at high risk for developing type 2 diabetes. The study provided evidence for the ADA questionnaire as a valid and reliable tool for identifying pre-diabetes in rural areas. It seems that using the Persian version of the ADA questionnaire contributes to the control of diabetes in areas with limited access to health care services. This is positive considering that the use of a simple and accurate instrument for identifying rural residents in the early stages of type 2 diabetes has become a public health priority in areas where care is generally difficult to access.
Acknowledgements
We are grateful to Tabriz University of Medical Sciences for providing facilities for the study.
Abbreviations
- CVD
cardiovascular diseases
- ADA
American diabetes association
- WHO
World Health Organization
- FBG
Fasting blood glucose
- NHANES
National Health and Nutrition Examination Survey
- BMI
Body Mass Index
- NPV
Negative Predictive Value
- PPV
Positive Predictive Value
Funding information
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Data availability
The data collection tools and datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Compliance with ethical standards
Conflict of interest
The authors declare no conflicts of interest.
Ethics approval and consent to participate
Informed consent was obtained from all participants. The study received ethical approval from the Ethics Committee of Tabriz University of Medical Sciences (NO: IR. TBZMED. REC. 1395. 13).
Consent for publication
The authors have agreed on the content of the manuscript.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Leila Jahangiry, Email: Jahangiry@razi.tums.ac.ir.
Tahereh Shamizadeh, Email: shamizadeh.t@gmail.com.
Parvin Sarbakhsh, Email: p.sarbakhsh@gmail.com.
Mahdieh Abbasalizad Farhangi, Email: abbasalizad_m@yahoo.com.
Koen Ponnet, Email: Koen.Ponnet@ugent.be.
References
- 1.Fox CS, Coady S, Sorlie PD, D’Agostino RB, Pencina MJ, Vasan RS, Meigs JB, Levy D, Savage PJ. Increasing cardiovascular disease burden due to diabetes mellitus. The Framingham Heart Study. Circulation. 2007;115:1544–50. doi: 10.1161/CIRCULATIONAHA.106.658948. [DOI] [PubMed] [Google Scholar]
- 2.Huxley R, Barzi F, Woodward M. Excess risk of fatal coronary heart disease associated with diabetes in men and women: meta-analysis of 37 prospective cohort studies. BMJ. 2006;332:73–8. doi: 10.1136/bmj.38678.389583.7C. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Noshad S, Afarideh M, Heidari B, Mechanick JI, Esteghamati A. Diabetes care in Iran: Where we stand and where we are headed. Ann Glob Health. 2015;81:839–50. doi: 10.1016/j.aogh.2015.10.003. [DOI] [PubMed] [Google Scholar]
- 4.Boyle JP, Thompson TJ, Gregg EW, Barker LE, Williamson DF. Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Popul Health Metrics. 2010;8:29. doi: 10.1186/1478-7954-8-29. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Nathan DM, Davidson MB, DeFronzo RA, Heine RJ, Henry RR, Pratley R, Zinman B. Impaired fasting glucose and impaired glucose tolerance. Implications Care. 2007;30:753–9. doi: 10.2337/dc07-9920. [DOI] [PubMed] [Google Scholar]
- 6.Control CfD, Prevention. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2010. Avalable; www.diasentry.com/LearningCenter/DiabetesInformation/CDC-DiabtesFacts2011.pdf.
- 7.Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes. Estimates for the year 2000 and projections for 2030. 2004;27:1047–1053. [DOI] [PubMed]
- 8.Esteghamati A, Gouya MM, Abbasi M, Delavari A, Alikhani S, Alaedini F, Safaie A, Forouzanfar M, Gregg EW. Prevalence of diabetes and impaired fasting glucose in the adult population of Iran. National Survey of risk factors for non-communicable diseases of Iran. Diabetes Care. 2008;31:96–8. doi: 10.2337/dc07-0959. [DOI] [PubMed] [Google Scholar]
- 9.Latifi SM, Karandish M, Shahbazian H, Hardani Pasand L. Incidence of prediabetes and type 2 diabetes among people aged over 20 years in Ahvaz: A 5-year perspective study (2009–2014) J Diabetes Res. 2016;2016:4908647. doi: 10.1155/2016/4908647. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Alouki K, Delisle H, Bermúdez-Tamayo C, Johri M. Lifestyle interventions to prevent type 2 diabetes: A systematic review of economic evaluation studies. J Diabetes Res. 2016;2016:2159890. doi: 10.1155/2016/2159890. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Bagust A, Hopkinson PK, Maslove L, Currie CJ. The projected health care burden of Type 2 diabetes in the UK from 2000 to 2060. Diabet Med. 2002;19:1–5. doi: 10.1046/j.1464-5491.19.s4.2.x. [DOI] [PubMed] [Google Scholar]
- 12.Najafipour H, Sanjari M, Shokoohi M, Haghdoost A-A, Afshari M, Shadkam M, Etemad K, Mirzazade A. Epidemiology of diabetes mellitus, pre-diabetes, undiagnosed and uncontrolled diabetes and its predictors in general population aged 15 to 75 years: A community-based study (KERCADRS) in southeastern Iran. J Diabetes. 2015;7:613–21. doi: 10.1111/1753-0407.12195. [DOI] [PubMed] [Google Scholar]
- 13.Bang H, Edwards AM, Bomback AS, Ballantyne CM, Brillon D, Callahan MA, Teutsch SM, Mushlin AI, Kern LM. Development and validation of a patient self-assessment score for diabetes risk. Ann Intern Med. 2009;151:775–83. doi: 10.7326/0003-4819-151-11-200912010-00005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Glümer C, Carstensen B, Sandbæk A, Lauritzen T, Jørgensen T, Borch-Johnsen K. A Danish diabetes risk score for targeted screening. Diabetes Care. 2004;27:727–33. doi: 10.2337/diacare.27.3.727. [DOI] [PubMed] [Google Scholar]
- 15.Saaristo T, Peltonen M, Lindström J, Saarikoski L, Sundvall J, Eriksson JG, Tuomilehto J. Cross-sectional evaluation of the Finnish Diabetes Risk Score: a tool to identify undetected type 2 diabetes, abnormal glucose tolerance and metabolic syndrome. Diabetes Vasc Dis Res. 2005;2:67–72. doi: 10.3132/dvdr.2005.011. [DOI] [PubMed] [Google Scholar]
- 16.Zhang L, Zhang Z, Zhang Y, Hu G, Chen L. Evaluation of Finnish Diabetes Risk Score in screening undiagnosed diabetes and prediabetes among US adults by gender and race: NHANES 1999–2010. PLoS One. 2014;9:e97865. doi: 10.1371/journal.pone.0097865. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Guasch-Ferré M, Bulló M, Costa B, Martinez-Gonzalez MA, Ibarrola-Jurado N, Estruch R, Barrio F, Salas-Salvadó J, Investigators P-P. A risk score to predict type 2 diabetes mellitus in an elderly Spanish Mediterranean population at high cardiovascular risk. PLoS One. 2012;7:e33437. doi: 10.1371/journal.pone.0033437. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Chuma J, Gilson L, Molyneux C. Treatment-seeking behaviour, cost burdens and coping strategies among rural and urban households in Coastal Kenya: an equity analysis. Trop Med Int Health. 2007;12:673–86. doi: 10.1111/j.1365-3156.2007.01825.x. [DOI] [PubMed] [Google Scholar]
- 19.Herman WH, Smith PJ, Thompson TJ, Engelgau MM, Aubert RE. A new and simple questionnaire to identify people at increased risk for undiagnosed diabetes. Diabetes Care. 1995;18:382–7. doi: 10.2337/diacare.18.3.382. [DOI] [PubMed] [Google Scholar]
- 20.Heikes KE, Eddy DM, Arondekar B, Schlessinger L. Diabetes risk calculator. A simple tool for detecting undiagnosed diabetes and pre-diabetes 2008;31:1040–1045. [DOI] [PubMed]
- 21.Engelgau MM, Narayan K, Herman WH. Screening for type 2 diabetes. Diabetes Care. 2000;23:1563–80. doi: 10.2337/diacare.23.10.1563. [DOI] [PubMed] [Google Scholar]
- 22.Burden ML, Burden AC. The American Diabetes Association Screening Questionnaire for Diabetes: Is it worthwhile in the U.K. Diabetes Care. 1994;17:97–7. doi: 10.2337/diacare.17.1.97. [DOI] [PubMed] [Google Scholar]
- 23.Shamizadeh T, Jahangiry L, Sarbakhsh P, Ponnet K. Social cognitive theory-based intervention to promote physical activity among prediabetic rural people: a cluster randomized controlled trial. Trials. 2019;20(1):98. doi: 10.1186/s13063-019-3220-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL, Jr, Jones DW, Materson BJ, Oparil S, Wright JT., Jr The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA. 2003;289:2560–71. doi: 10.1001/jama.289.19.2560. [DOI] [PubMed] [Google Scholar]
- 25.Lohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual. Champaign: Human Kinetics Books; 1988. [Google Scholar]
- 26.Lohman T, Roche A, Martorell R. Antropometric Standardization Reference Manual Human Kinetics Book. Human Kinetics, Champaign;1988, 4.
- 27.AD Association Diagnosis and classification of diabetes mellitus. Diabetes Care. 2014;37:81–90. doi: 10.2337/dc14-S081. [DOI] [PubMed] [Google Scholar]
- 28.Altman DG, Bland JM. Diagnostic tests. 1: Sensitivity and specificity. BMJ. 1994;308:1552. doi: 10.1136/bmj.308.6943.1552. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Robinson CA, Agarwal G, Nerenberg K. Validating the CANRISK prognostic model for assessing diabetes risk in Canada’s multi-ethnic population. Chronic Diseases Injuries in Canada. 2011;32:19–31. [PubMed] [Google Scholar]
- 30.Long J, Rozo-Rivera A, Akers T, VanGeest JB, Bairan A, Fogarty KJ, Sowell R. Validating the utility of the Spanish version of the American Diabetes Association Risk Test. Clin Nurs Res. 2006;15:107–18. doi: 10.1177/1054773805285702. [DOI] [PubMed] [Google Scholar]
- 31.Ricci-Cabello I, Ruiz-Perez I, Rojas-García A, Pastor G, Gonçalves DC. Improving diabetes care in rural areas: A systematic review and meta-analysis of quality improvement interventions in OECD countries. PLoS One. 2013;8:e84464. doi: 10.1371/journal.pone.0084464. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Alfaqeeh G, Cook EJ, Randhawa G, Ali N. Access and utilisation of primary health care services comparing urban and rural areas of Riyadh Providence, Kingdom of Saudi Arabia. BMC Health Serv Res. 2017;17:106. doi: 10.1186/s12913-017-1983-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data collection tools and datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.