• Users Online: 760
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2022  |  Volume : 7  |  Issue : 1  |  Page : 22-29

Quality of life of diabetes patients in North India: A comparison of different methodologies


1 National Institute of Nursing Education, Post Graduate Institute of Medical Education and Research, Chandigarh, India
2 Department of Community Medicine, School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India

Date of Submission31-Mar-2021
Date of Acceptance25-Feb-2022
Date of Web Publication31-Mar-2022

Correspondence Address:
Dr. Shankar Prinja
Department of Community Medicine, School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh - 160 012
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jncd.jncd_13_21

Rights and Permissions
  Abstract 


Background: India is witnessing a dramatic rise in the prevalence of diabetes mellitus. In this study, quality of life (QOL) of patients with diabetes and its determinants were assessed. Second, the valuation of QOL using different methods of measurement was examined.
Methods: A community-based survey comprising 306 adults diagnosed with diabetes was undertaken in an urban slum area of Chandigarh city. Direct methods of QOL assessment such as time trade-off (TTO) and Visual Analog Scale (VAS) along with indirect like EuroQol 5-Dimensional 5-Level Instrument (EQ-5D-5L) and the QOL Instrument for Indian Diabetes Patients (QOLID) were used. Multiple linear regression was used to compute coefficients to assess point estimate of QOL using the Indian QOLID tool.
Results: Overall, health utility scores for a person with diabetes were 0.68 (with TTO method), 0.60–0.64 (with VAS) analog scale, and EQ-5D-5L method, respectively. Valuation of QOL using direct methods yielded utility values which were significantly higher than indirect methods (EQ-5D-5L).
Conclusion: Overall, this study found that diabetes is responsible for physical, psychological, and social role disturbance among the patients. In addition, choice of using direct or indirect methods of utility estimation may have practical implications while calculating incremental cost-effectiveness ratios.

Keywords: Diabetes mellitus, EuroQol 5-Dimensional Scale, quality of life, time trade-off, Visual Analog Scale


How to cite this article:
Kaur A, Saini SK, Kaur G, Prinja S. Quality of life of diabetes patients in North India: A comparison of different methodologies. Int J Non-Commun Dis 2022;7:22-9

How to cite this URL:
Kaur A, Saini SK, Kaur G, Prinja S. Quality of life of diabetes patients in North India: A comparison of different methodologies. Int J Non-Commun Dis [serial online] 2022 [cited 2022 May 20];7:22-9. Available from: https://www.ijncd.org/text.asp?2022/7/1/22/342078




  Introduction Top


India had 61.3 million people with diabetes in 2011, and these numbers are expected to increase to 101.3 million by 2030.[1] This increasing prevalence of diabetes is especially a cause of concern in developing countries with limited resources invested in health and high rates for mortality and disability due to noncommunicable diseases (NCDs).[2],[3] Moreover, this may have significant implications as one-third of the population lives in these developing countries that also have large numbers of people with undiagnosed diabetes.[4]

Chronic diseases such as diabetes mellitus (DM) are generally slow in progression and longer duration and associated with multimorbidities involving eyes, kidneys, nerves, heart, etc.[5] Co-existence of such morbidities may have an ever-lasting impact on quality of life (QOL) of a patient with diabetes as well as add-on to health-care costs for treatment sought.[6] Recognizing the importance of burden of NCDs including diabetes, the National Program for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke (2010) was started, under the auspices of the Government of India, that aimed at the integration of NCD interventions within the National Rural Health Mission (now referred as National Health Mission) framework for optimization of scarce resources and health-care interventions.

Assessment of QOL is an important outcome not only for estimating the overall therapeutic benefit but also from economic evaluation perspective. In general, two methods of measuring health-related QOL in terms of utility value exist. Direct methods for utility estimation include trade-off using time trade-off (TTO) or standard gamble and Visual Analog Scale. Indirect methods constitute measurement of utility through QOL-based questionnaires such as EuroQol-5 dimension 5-level (EQ-5D-5L) tool, Short-Form 6 Dimensions, and Health Utilities Index. TTO is a generic direct method for assessing QOL that reflects the level of physical, mental, and social functioning of an individual.[7] EuroQol (EQ-5D-5L) is also a standardized generic indirect method which elicits information on five dimensions involving mobility, usual activities, pain/discomfort, self-care, and anxiety.[8] Visual Analog Scale (direct method of utility estimation) is also a part of this instrument that indicates patient's health on a 100-point scale. Herein, the best health carries a score of 100 while a score of 0 is indicative of worst health state.[8] In addition, another indirect method of QOL valuation among patients with diabetes includes 34-item QOL Instrument for Indian Diabetes Patients (QOLID). The score overall ranges from 34 to 169.[9] Although systematic comparisons of the direct measures of utility estimation across the studies have been documented.[10] However, the relationship between these methods of estimation (direct and indirect) has not been addressed much.[10] Moreover, there is no study from India that reports on the utility score for patients with diabetes using both direct and indirect methods. In addition, conversion of profile description with scoring system of QOLID to arrive at point estimate utility score needs to be explored.[15]

In view of dearth of evidence base for QOL among diabetes patients in India, we undertook a community-based cross-sectional study to assess QOL and its determinants and examine the valuation of QOL using different methods of measurement. Finally, we also provided empirical valuation of coefficients to estimate QOL using the QOLID.


  Methods Top


Study setting

This study was a community-based survey with a cross-sectional design. It was conducted in an urban slum area of Chandigarh, located in northern India. Chandigarh has an overall population of 1,055,450 people, of which 97.25% live in an urban area.[11] Two reasons prompted the choice of urban slum areas. First, review of literature pointed toward a reversal in social gradient with respect to NCDs in India, implying that the chronic diseases and their risk factors were reported more among poor people.[12] Second, the slum dwellers have been recognized as a vulnerable population with respect to their health-care needs and services.[13] Henceforth, an urban slum colony with a population of 24,000 was selected for the study. The time period of data collection was from August 2013 to October 2013.

A household survey was conducted in the study area to identify all DM patients. Based on a prevalence of 13% with a precision of +4% and 95% confidence interval, a sample size of 272 was computed. Similarly, 267 diabetes patients were required to be enrolled to estimate QOL with a ratio of tolerable error to standard deviation of 0.12% and 95% confidence interval. A total of 326 medically confirmed diabetes patients aged above 18 years, with at least 1-year history of DM, and permanent residents of the study area were enumerated. All 326 enumerated patients were contacted for participation in the study. An enrollment rate of 93.8% was achieved for the study as six individuals denied the consent for participation.

Data collection

A structured questionnaire was used to collect detailed information regarding patient's age, sex, religion, marital status, education, occupation, physical activity, drug addiction, dietary habits, consumption expenditure, and all disease-related characteristics. The consumption expenditure was assessed based on the summary question used by NSSO.[14] The disease-related characteristics included duration of illness, treatment compliance, and diabetes-related complications or comorbidities. The self-reporting method was used in the present study for measuring treatment compliance. Under this method, based on 1-month recall history, the frequency of medicine consumption whether daily, alternative days, twice a week, three times a week, weekly, or as per need was elicited.[19]

An activity was considered vigorous if it included large amount of effort that caused rapid breathing and a substantial increase in heart rate, for example running, climbing up a hill, fast cycling, fast swimming, aerobics, weight lifting, heavy yard work, such as digging, cutting wood or moving heavy loads. Moderate activity was considered if it included the amount of effort that noticeably accelerated the heart rate, for example, walking briskly, dancing, physical activity involving domestic routine work such as fetching milk and groceries, yoga asanas and pranayama, playing with children. While sedentary lifestyle was considered with no or irregular physical activity and included other activities such as sitting, reading, watching television, playing indoor games, and using computer.[16]

For assessment of diabetes-related complications, the medical records of patients were checked to know about diabetes-related complications or comorbidities such as hypertension, renal problems, neurological problems, cardiovascular problems, foot ulcers, and retinopathy.[21] In regard to eliciting QOL, direct methods using TTO and Visual Analog Scale (VAS) as well as indirect using EQ-5D-5L and diabetes-specific QOL instrument were used. Under TTO method, patients were asked hypothetical question reflecting their preference about life years in full health versus impaired health.[22] VAS is a self-rated health scale ranging from 0 to 100, where 0 means the worst health imagined and 100 means the best health imagined. EQ-5D-5L tool consists of five dimensions (mobility, self-care, usual activities, pain or discomfort, and anxiety or depression) with five levels of responses such as no, mild, some, moderate, and extreme problems.[17] A standardized diabetes-specific QOL Instrument for Indian Diabetes Patients (QOLID) for assessing QOL was used.[9]

Ethical considerations

The study was approved by the Institute Ethics Committee of Post Graduate Institute of Medical Education and Research, Chandigarh. Written informed consent was obtained from all individuals who met the eligibility criteria.

Data analysis

The data were analyzed in Microsoft Excel and SPSS software version 16.0 (SPSS Inc., Chicago, IL,USA). Monthly per capita consumption expenditure (MPCE) was computed to divide the study population into various quintiles. Household consumption expenditure was adjusted for age and composition using the Organization for Economic Co-operation and Development equivalence scale, according to which W = H/(1 + 0.7 [Na-1] +0.5Nc), where W and H are the MPCE and total household consumption expenditure, respectively; and Na and Nc are the number of adults and children, respectively. TTO utility value was calculated by calculating the percentage of time period they were willing to live in perfect QOL of their total life expectancy.

For calculating EQ-5D-5L utility value, health state value sets for Thailand were used based on consultation with EQ-5D-5L experts, as these value sets were not available for India. For QOLID tool, the QOL score obtained was categorized into 5 quintiles, with the 1st quintile denoting the poorest and 5th quintile as the best QOL. Rescaling of the total QOLID score was done using standard deviation method so as to obtain QOL value ranging from 0 to 1.[18] The purpose of rescaling was to increase the comparability of QOLID score with other approaches measuring QOL score ranging between 0 and 1.

Utility score was also estimated according to various sociodemographic and clinical characteristics of patients with diabetes. Association of sociodemographic and clinical factors with QOL was assessed using multiple linear regression. Under this, utility values derived using TTO, EQ-5D-5L, and VAS were the dependent variable. Independent factors included age, gender, education, type of diabetes, duration of illness, any complication or comorbidity, level of physical activity, and mean per capita consumption expenditure. Guided step-down approach was used for development of final regression model. Only those variables with statistically significant coefficients from the initial model were re-run and kept in the final model. Utility values derived using direct and indirect methods were compared using paired t-test and their correlation was also analyzed. The response of patients on 5 dimensions of EQ-5D-5L tool was converted to a binary scale – “0” implying no problem or slight problem and “1” implying moderate, severe, or extreme problem.

Using logistic regression model, the association of various sociodemographic and clinical factors with individual dimensions of QOL in EQ-5D-5L tool was estimated. Utility scores derived using QOLID tool were regressed in three separate models against the utility score obtained using TTO, VAS, and EQ-5D-5L after controlling for age, gender, sex, education, type of diabetes, duration of illness, any complication or comorbidity, level of physical activity, and education. This was done to predict the utility score for one approach using the estimated score from the other and allow comparison of the estimates generated from different scenarios.


  Results Top


Study characteristics

The mean age of respondents was 54 years, with majority (64%) of them being females [Table 1]. Predominantly, the respondents had type 2 (85.3%) diabetes. About half (52%) of them reported having a comorbidity or complication. Their mean duration of illness was 6.2 years. Further, 85% of total respondents were regularly on medication as per physician prescription. Nearly two-thirds (68%) of respondents followed moderate physical activity. The average per capita consumption expenditure of households was INR 4451 (USD 74).
Table 1: Sociodemographic profile, clinical characteristics, and quality of life of diabetes patients

Click here to view


Quality of life

The mean utility score for respondents using TTO, VAS, and EQ-5D-5L and QOLID methods was 0.68, 0.64, 0.60, and 0.58, respectively [Table 1]. As per EQ-5D-5L, majority of them reported no problem in self-care (62.4%) and usual activities (53.3%), while 39%, 45%, and 47% reported having slight problems in mobility, pain, and anxiety/depressive symptoms, respectively. Further respondents were willing to trade off about 1 week to 12 years from their remaining life to live with perfect QOL.

According to the QOLID, more than two-third (78%) of respondents were found to be in the 2nd poorest or average (3rd) QOL quintile. Majority of the patients reported that domain of physical health is affected due to diabetes, while 52% of the patients reported their health as fair and were moderately satisfied with self, personal relationships, their treatment, and other aspects of their daily living.

Factors affecting quality of life

As per TTO valuation method, type I patients with diabetes had a poorer (QOL = 0.59) QOL than type II patients with diabetes (QOL = 0.79), which was statistically significant (P = 0.04). Similarly, those aged more than 50 years (QOL = 0.64), illiterate (QOL = 0.62), duration of illness for more than 10 years (QOL = 0.60), with comorbidities or complications of DM (QOL = 0.0.64), and sedentary lifestyle (QOL = 0.57) had significantly poorer QOL [Table 1].

Adjusting for sociodemographic and clinical characteristics, the factors which significantly lowered the QOL of patients with diabetes obtained using TTO method included sedentary lifestyle and presence of comorbidities or complications of DM [Table 2]. Using EQ-5D-5L and VAS type 1 DM, higher age, female gender, and lower education significantly lowered QOL.
Table 2: Multiple linear regression of sociodemographic and clinical characteristics with quality of life among diabetes patients

Click here to view


Those with age above 50 years (odds ratio [OR] = 5.68), education up to matric and above (OR = 0.28), sedentary lifestyle (OR = 7.4), diagnosed with DM for >10 years (OR = 2.9), and being in 3rd MPCE quintile (OR = 5.05) resulted in a significantly higher mobility problem [Table 3]. Similarly, age above 50 years (OR = 208.3), presence of other comorbidities (OR = 4.5), and 3rd MPCE quintile (OR = 32.2) were statistically significant determinants for reporting self-care problems in patients with diabetes.
Table 3: Association of sociodemographic and clinical characteristics with different domains for quality of life among diabetes patients using binary logistic regression

Click here to view


Comparison of valuation methodology

Strong positive correlation was observed among the QOL valuation obtained using EQ-5D-5L and VAS methods (r = −0.88, 95% confidence interval [CI]: 0.65–0.73, P = −0.001). A moderate correlation was found between EQ-5D-5L and TTO methods for estimating QOL (r = −0.56, 95% CI: 0.44–0.62, P = −0.001). Similarly, VAS and TTO methods were found to yield highly correlated values of QOL (r = −0.60, 95% CI: 0.42–0.56, P = −0.001). The coefficient values for various sociodemographic and clinical characteristics of DM patients for estimation of a single summary utility value of the QOLID tool are presented in [Table 4].
Table 4: Quality of life of diabetes patients according to quality-of-life instrument for Indian diabetes patients

Click here to view



  Discussion Top


Our study found that the overall utility score for a person with diabetes was 0.68 (TTO). Alternatively, EQ-5D-5L and VAS yielded a utility score of 0.60 and 0.64, respectively. Direct methods of utility estimation (TTO) produced significantly higher values than indirect methods (EQ-5D-5L). Type 1 DM, increasing age, female gender, increasing duration of illness, education, presence of complications or comorbidities, and sedentary lifestyle were found to be the main factors associated with poorer QOL.

Comparison of findings

Global comparisons showed that utility values ranged from 0.56 to 0.70 (in Iran) and 0.68–0.74 (in The Netherlands).[19],[20] Another study in Japan reported a higher mean EQ-5D-5L utility and VAS score of 0.84 and 0.74, respectively, than our study.[21] However, in most of the studies, about half of the patients had diabetes-related complications or comorbidities, as in our study. Health-related QOL utility scores are sensitive to study settings, affected by population profile and the availability of medical technologies.[10] Henceforth, differences in QOL between populations from India and other countries exist.

In regard to Indian context, a study by Jain 2014[22] using the World Health Organization QOL Questionnaire short version reported low scores for the QOL of patients with diabetes in rural India, consistent with our findings. Another study[23] also reported impairment of QOL in patients with diabetes to some extent and especially in women. Moreover, age and gender of the respondent as well as social factors such as education and wealth status were also important in perceiving or reporting morbidity.[7],[24] Further, studies showed that the rate of self-reported morbidity is higher among the rich than the poor, also referred to as positional objectivity.[24],[25] In our study, less educated respondents reported better QOL; however, no impact of wealth status on QOL was seen as most of the respondents were from similar socioeconomic status.

Direct methods of utility estimation (TTO and VAS) yield systematically greater values than indirect methods (EQ-5D-5L). Our study also reported higher utility values by direct methods. A possible explanation lies in the difference in valuation because the indirect questionnaires possibly mask the opportunity to report on the positive aspects of respondents' situations that may eventually have effect on its utility values. Our findings also showed that nearly half of the respondents reported that they had slight problems of anxiety or depressive symptoms. Further, more than half of the respondents reported that many a times diabetes limited their social life and social activities such as visiting friends/partying and traveling. Similar findings had been noted in other studies wherein persons with diabetes hesitate from some group activities because of the need to monitor their blood glucose, inadvertently leading to group isolation.[22]

Strengths and limitations of the study

To our knowledge, this is the first empirical assessment of QOL among patients with diabetes in India using different generic and disease-specific tools. In general, different methods of QOL utility estimation yield different values, but there is a dearth of evidence on comparability of these methods among themselves.[10] In our study, QOL was assessed using different methods including EQ-5D-5L, Visual Analog Scale, TTO method, and QOLID. In addition, coefficient values contingent upon other sociodemographic and clinical characteristics were derived to arrive at summary utility values using QOLID tool. Moreover, most of the studies to assess QOL of persons with diabetes have been undertaken in hospital settings.[22] On the contrary, data were collected from community-based settings in the urban slum colony of Chandigarh in the present study.

Often, it is seen that generally difference in respondents' recruitment exists, as those who contributed to trade-off values using indirect methods are not the same as those who elicited using direct methods.[10] However, the utility was valued by the same respondents in our study. The difference in utility score using EQ-5D-5L versus other methods could be attributed to lack of health state valuation tariff values for Indian population. In the absence of the latter, other countries' tariff values like Thailand are generally taken. Henceforth, there is a need to generate utility values for EQ-5D-5L among Indian population, which are currently lacking.

Policy implications

Comparing the valuation of outcomes in terms of QOL by various methods highlighted that differences in utility values exist. This becomes crucial when evaluating benefits for resource allocation decisions for competing health-care services/interventions under publicly funded health care. Full economic evaluation like cost-utility analysis accounts for this valuation of alternative health interventions, wherein the costs and the consequences are compared. Further, consequences can be measured in terms of quality-adjusted life years that encompass both the quantity and QOL aspects for health interventions. Choice of method of estimation for utility value for assessing QOL may have practical implications on the incremental cost-effectiveness ratios generated while undertaking full economic evaluation.


  Conclusion Top


Overall, this study found that diabetes is responsible for physical, psychological, and social role disturbance among the patients. It is responsible for significant impairment of QOL of diabetes patients. Increasing age, comorbidities/complications, duration of illness, and sedentary lifestyle were found to be the main responsible factors for poorer QOL. Care should be taken while designing health-care programs for patients with diabetes to ensure better QOL, especially under physical, psychological, and social domains. In addition, direct methods of utility estimation led to higher utility values than indirect methods. Our study estimates for QOL can be used by clinicians and researchers while undertaking cost-effectiveness studies for various health-care interventions for diabetes in India.

Acknowledgment

We are thankful to Pankaj Bahuguna for his help in statistical analysis.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Whiting DR, Guariguata L, Weil C, Shaw J. IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract 2011;94:311-21. doi: 10.1016/j.diabres.2011.10.029.  Back to cited text no. 1
    
2.
Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, et al. Disability-Adjusted Life Years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2197-223.  Back to cited text no. 2
    
3.
Smith-Spangler CM, Bhattacharya J, Goldhaber-Fiebert JD. Diabetes, its treatment, and catastrophic medical spending in 35 developing countries. Diabetes Care 2012;35:319-26.  Back to cited text no. 3
    
4.
IDF. Diabetes Atlas. Brussels. Belgium: International Diabetes Federation; 2015.  Back to cited text no. 4
    
5.
American Diabetes Association. 2. Classification and diagnosis of diabetes: Standards of medical care in diabetes-2018. Diabetes Care 2018;41:S13-27.  Back to cited text no. 5
    
6.
Lorenzo C, Williams K, Hunt KJ, Haffner SM. The National Cholesterol Education Program – Adult Treatment Panel III, International Diabetes Federation, and World Health Organization definitions of the metabolic syndrome as predictors of incident cardiovascular disease and diabetes. Diabetes Care 2007;30:8-13.  Back to cited text no. 6
    
7.
Dolan P, Gudex C, Kind P, Williams A. The time trade-off method: Results from a general population study. Health Econ 1996;5:141-54.  Back to cited text no. 7
    
8.
EuroQol Group. EuroQol – A new facility for the measurement of health-related quality of life. Health Policy 1990;16:199-208.  Back to cited text no. 8
    
9.
Nagpal J, Kumar A, Kakar S, Bhartia A. The development of 'Quality of Life Instrument for Indian Diabetes patients (QOLID): A validation and reliability study in middle and higher income groups. J Assoc Physicians India 2010;58:295-304.  Back to cited text no. 9
    
10.
Arnold D, Girling A, Stevens A, Lilford R. Comparison of direct and indirect methods of estimating health state utilities for resource allocation: Review and empirical analysis. BMJ 2009;339:b2688.  Back to cited text no. 10
    
11.
Chandigarh population 2011. Available from: https://www.census2011.co.in/census/state/chandigarh. [Last accessed on 2021 Mar 31].  Back to cited text no. 11
    
12.
Jeemon P, Reddy KS. Social determinants of cardiovascular disease outcomes in Indians. Indian J Med Res 2010;132:617-22.  Back to cited text no. 12
[PUBMED]  [Full text]  
13.
BT Rao JT. Vulnerability assessment in slums of union territory, Chandigarh. Indian J Community Med 2007;32:189.  Back to cited text no. 13
    
14.
NSSO. Survey on Morbidity and Health Care: NSS 60th Round: January 2004 – June 2004. New Delhi: NSSO; 2012.  Back to cited text no. 14
    
15.
Guillausseau PJ. Impact of compliance with oral antihyperglycemic agents on health outcomes in type 2 diabetes mellitus: A focus on frequency of administration. Treat Endocrinol 2005;4:167-75.  Back to cited text no. 15
    
16.
Manual A. Dietary guidelines for Indians. Nat Inst Nutrition 2011;2:89-117.  Back to cited text no. 16
    
17.
Herdman M, Gudex C, Lloyd A, Janssen M, Kind P, Parkin D, et al. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual Life Res 2011;20:1727-36.  Back to cited text no. 17
    
18.
Majerová I. Comparison of old and new methodology in human development and poverty indexes: A case of the least developed countries. J Econ Stud Res 2012;2012:1.  Back to cited text no. 18
    
19.
Javanbakht M, Abolhasani F, Mashayekhi A, Baradaran HR, Jahangiri noudeh Y. Health related quality of life in patients with type 2 diabetes mellitus in Iran: A national survey. PLoS One 2012;7:e44526.  Back to cited text no. 19
    
20.
Redekop WK, Koopmanschap MA, Stolk RP, Rutten GE, Wolffenbuttel BH, Niessen LW. Health-related quality of life and treatment satisfaction in Dutch patients with type 2 diabetes. Diabetes Care 2002;25:458-63.  Back to cited text no. 20
    
21.
Sakamaki H, Ikeda S, Ikegami N, Uchigata Y, Iwamoto Y, Origasa H, et al. Measurement of HRQL using EQ-5D in patients with type 2 diabetes mellitus in Japan. Value Health 2006;9:47-53.  Back to cited text no. 21
    
22.
Jain V, Shivkumar S, Gupta O. Health-related quality of life (hr-qol) in patients with type 2 diabetes mellitus. N Am J Med Sci 2014;6:96-101.  Back to cited text no. 22
    
23.
Manjunath K, Christopher P, Gopichandran V, Rakesh PS, George K, Prasad JH. Quality of life of a patient with type 2 diabetes: A cross-sectional study in rural South India. J Family Med Prim Care 2014;3:396-9.  Back to cited text no. 23
[PUBMED]  [Full text]  
24.
Marmot M. Social determinants of health inequalities. Lancet 2005;365:1099-104.  Back to cited text no. 24
    
25.
Prinja S, Jeet G, Kumar R. Validity of self-reported morbidity. Indian J Med Res 2012;136:722-4.  Back to cited text no. 25
[PUBMED]  [Full text]  



 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
   Abstract
  Introduction
  Methods
  Results
  Discussion
  Conclusion
   References
   Article Tables

 Article Access Statistics
    Viewed428    
    Printed6    
    Emailed0    
    PDF Downloaded16    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]