Journal of Substance Abuse and Alcoholism

On Consistency of Self- and Proxy-reported Regular Smoking Initiation Age

Research Article | Open Access | Volume 1 | Issue 1

  • 1. Department of Statistics, University of Nebraska-Lincoln, USA
  • 2. Department of Statistics, University of Nebraska-Lincoln, USA
  • 3. Department of Psychology, University of Nebraska-Lincoln, USA
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Corresponding Authors
Julia N. Soulakova, Department of Statistics, University of Nebraska-Lincoln, 340 Hardin Hall-North, Lincoln, NE 68583-0963, USA Tel: 402-472-7231; Fax: 402-472-5179

Early onset of smoking is associated with heavier tobacco consumption and longer smoking careers. Consequently, obtaining accurate estimates of early smoking is a priority. The purpose of this study was to examine the utility of proxy reports of the age of smoking initiation, and specifically to explore whether there are differences in the consistency of proxy-reported and self-reported smoking behaviors. Data came from the 2002-2003 Tobacco Use Supplement to the Current Population Survey, where the current smoking behaviors and smoking history of participants were reported by selfand proxy-respondents on two occasions, one year apart. Sequential multiple-testing methods were used to assess significance of the differences in reported prevalence of consistent reports among specific sub-populations defined by age, gender and survey administration mode. Results indicated that self-reports are more reliable (more consistent over time) than proxy reports or mixed reports that include self-report at one time point and proxy reports at another. The rate of perfect agreement was also highest for self-reports. The impact of respondent type on the consistency of reports also depended on the target subjects’ age and the survey administration mode (phone or in-person).


• Complex survey

• Reliability

• Respondent type Survey logistic regression


Soulakova JN, Bright BC, Crockett LJ (2013) On Consistency of Self- and Proxy-reported Regular Smoking Initiation Age. J Subst Abuse Alcohol 1(1): 1001.


Several studies have shown that the early onset of smoking is significantly associated with heavier subsequent tobacco consumption and longer smoking careers [1], as well as a higher risk of lifetime drinking and illicit substance use [1,2,3]. This is why smoking prevention programs world-wide target youth and encourage abstinence from smoking (e.g., the National Tobacco Control Program in the Unites States [4], the European Smoking Prevention Framework Approach [5], and the Japan Know Your Body program [6]). However, non-reliable reports of age of onset of smoking behaviors (e. g. , regular smoking) can lead to incorrect estimates of early onset, resulting in misleading information and potentially causing intervention programs to miss youth who are at risk. Therefore, it is important to evaluate the quality of data on smoking initiation age and make recommendations for improving the design and administration of studies targeted at assessing the age of smoking initiation.

Reports of smoking initiation age can be ambiguous owing to several biases, such as social desirability bias [7-11] and telescoping bias [9,12-14]. Furthermore, respondents may have insufficient knowledge of the event or experience difficulties when trying to recall related information [15].

Despite the confirmed reliability of several self-report measures of smoking history among adults [16-18], recent studies also have detected discrepancies. For example, studies concerning the consistency of self-reported age of regular smoking initiation revealed that only 37% of responses agree perfectly when the reports are made one year a part [19], and only 30% agree perfectly when reports are made two years apart [17]. In addition, several studies have shown that the smoking habits, demographic characteristics, and mental health characteristics of the respondent influence the tendency to deny prior smoking. For example, recanters are likely to be older and to come from the low-income households [20].

All prior studies examining the reliability of the smoking reports in the United States population have investigated the reliability of self-reported smoking measures. However, many national surveys allow proxy-respondents (e.g., partners, parents, friends) to be interviewed instead of the target subjects (i.e., the people for whom the information is reported). Inclusion of proxy-respondents leads to a reduction in survey costs and an increase in response rates but proxy-respondents may have limited or incorrect knowledge especially regarding sensitive information. If so, proxy-responses may influence the data quality and lead to false research findings. Thus, the question of whether proxy-respondents should be included when a survey is designed to assess smoking history and/ or current smoking habits remains open.

To address the reliability of proxy-reports of smoking onset we estimated and compared the separate consistency levels of self-reports, proxy-reports, and mixed reports, i.e., reports that include self-report at one time point and proxy-report at another. We considered the responses of age of fairly regular smoking initiation in the 2002-2003 Tobacco Use Supplement to the Current Population Survey (TUS-CPS). The TUS-CPS is one of the leading surveys used for estimating the national smoking prevalence in the United States [24]. Furthermore, the 2002- 2003 TUS-CPS has been specifically designed to assess testretest data reliability of reported smoking. One previous study has confirmed the overall consistency of self-reported smoking information [19], but it also revealed significant differences in the proportions of consistent responses across different survey administration modes and demographic groups. The proxyreports have not been yet examined.

The impact of respondent type (self, proxy, mixed) on consistency of reports may depend on the sociodemographic characteristics of the target subjects whose smoking behaviors are reported. For example, parents’ reports regarding their children’s smoking habits appear to be less accurate than adolescents’ (13- 17 years old) reports regarding their parents’ smoking habits [21]. Also, the level of agreement within self-reported and proxyreported smoking behaviors differs across race/ethnicity groups [22]. It is also noteworthy that proxy-reports generally result in lower prevalence estimates of current smoking than do self-reports, and the magnitude of this difference depends on the age, gender and educational attainment of the target subjects [23]. Together, these findings underscore the potential importance of key characteristics of the target subject on the reliability/ consistency of reports. In this study we investigated whether the effect of respondent type (self, proxy, or mixed) on consistency differs across the target subjects’ age, gender, and the survey administration mode (phone or in-person).

The present study

This study compared the consistency levels of self-reports, proxy-reports, and mixed reports of the age of regular smoking onset and examined whether the effects of respondent type varied depending on the survey mode, and the age, and gender of the target subject. Specifically, we assessed whether the effect of respondent type (self, proxy, or mixed) on response consistency depended on the target subjects’ age (ages 15-24, 25-44, 45-64, and 65+), the target subject’s gender (male, female), and the survey mode employed (phone, in-person, mixed). For this purpose we examined significance of the joint effects (respondent type and age group, respondent type and gender, and respondent type and survey mode). In the case of a significant effect we assessed the differences in consistency levels between the self- and the other respondents within each subpopulation (e. g. 15-24 year old age group). Furthermore, in the case of the significant latter difference we also evaluated the specific differences between the self and proxy, and self and mixed respondent types.

We also addressed the larger issue of overall differences in consistency by respondent type. Specifically, we assessed whether the prevalence of consistent responses depends, overall, on the respondent type, and in the case of the significant effect we compared the prevalence among the three respondent types.



The surveys were administered to self- and proxy-respondents using a combination of in-person and phone interviews: some participants responded via phone both times (phone group), some had in-person interviews both times (inperson group), and some had a phone interview in 2002 followed by an in-person interview in 2003 or vice versa (mixed group). For some participants, self-reports were available at both times (self group), for others proxies responded at both times (proxy group), and for others self-reports were used in 2002 and proxyreports in 2003 or vice versa (self-proxy group). Attempts were made to survey self-respondents both times: the interviewers were instructed to survey a proxy-respondent only if it was the second callback, the target subject would not return before the closeout or if the household was getting irritated [24].

Description of the sample

The sample consists of 6,783 target subjects. Table 1 illustrates the sample summary statistics corresponding to target subjects’ age, gender, and race/ethnicity; metropolitan status and region where the target subjects reside; and survey mode.

Table 1: Sample count and percentage corresponding to the population count.

N=363 (6.3%)
15-24 261 (6.6%) 44 (19.3%) 103 (15.6%) 363 (8.8%)
25-44 1782 (35.1%) 133 (33.8%) 363 (35.6%) 2278 (35.1%)
45-64 2326 (39.4%) 146 (34.3%) 455 (36.7%) 2927 (38.6%)
65+ 1046 (19.0%) 40 (12.7%)  129 (12.3%) 1215 (17.5%)
Male 2578 (50.5%) 268 (74.1%) 687 (65.7%) 3533 (54.5%)
Female 2792 (49.5%) 95 (25.9%) 363 (34.3%) 3250 (45.5%)
Non-Hispanic White 4775 (84.4%) 314 (82.2%) 932 (83.7%) 6021 (84.1%)
Other 595 (15.6%) 49 (17.8%) 118 (16.3%) 762 (15.9%)
Metropolitan Status
Metropolitan 3783 (77.2%) 262 (79.2%) 744 (79.4%) 4789 (77.7%)
Non-Metropolitan 1587 (22.8%) 101 (20.8%) 306 (20.6%) 1994 (22.3%)
Northeast 1187 (18.8%) 107 (27.5%) 257 (21.1%) 1551 (19.7%)
Midwest 1516 (25.9%) 85 (18.5%) 293 (25.5%) 1894 (25.4%)
South 1463 (33.9%) 100 (33.9%) 287 (34.0%) 1850 (33.9%)
West 1204 (21.4%) 71 (20.1%) 213 (19.5%) 1488 (21.0%)
Survey mode
Phone both times 3104 (56.3%) 212 (55.5%) 602 (56.2%) 3918 (56.2%)
In-person both times 1253 (24.1%) 66 (20.2%) 200 (19.8%) 1519 (23.1%)
Mixed mode 1013 (19.6%) 85 (24.2%) 248 (23.9%) 1346 (20.6%)

Note: The overall population count is 60,758,344.

The statistics are presented for the self, proxy and self-proxy groups. The total population count provides the information of the size of the population represented by the sample. All population counts are obtained via survey weights specified in the 2002-2003 TUSCPS weighting method [25]. These weights are also used in all subsequent statistical analyses.


Age of smoking initiation was assessed via either self-report or proxy-report in 2002 and 2003. For self-reports the survey question was “How old were you when you first started smoking cigarettes fairly regularly?” Proxy-respondents were asked a corresponding question about the target subject: “How old was [name] when [he/she] first started smoking cigarettes fairly regularly?” The reported fairly regular smoking initiation age was recorded in years. The other possible responses were ‘never smoked regularly’, ‘do not know’ and ‘refuse to answer’.

To examine reliability we assessed the overall data agreement in the fairly regular smoking initiation age (in years) and the prevalence of precisely matching reports of the age of regular smoking initiation (in years). We focused on several specific subpopulations such as the age-group subpopulations, female and male subpopulations, and survey-mode subpopulations, and examined the reliability separately for each such subpopulation of interest.

Statistical methods

Preliminary Analysis: To estimate consistency/reliability of self-reports, proxy-reports, and mixed reports, we first explored the linear association between the smoking initiation age reported in 2002 and 2003 with respect to specific subpopulations. For this purpose we used SUDAAN®11 software [26] to compute the Pearson’s correlation coefficients.

Primary Analyses: To estimate the prevalence of perfectly agreeing responses we built a multiple-logistic regression while adjusting for the baseline demographic factors (target subjects’ age, gender, race/ethnicity, metropolitan status and region) as well as the survey mode (phone, in-person, mixed), and respondent type (self, proxy, self-proxy). We examined potential significance of all two-way interactions, and used the backward elimination approach to exclude all insignificant (at 5% level) interactions. Interactions corresponding to the relationships of interest (i. e. , between the respondent type and the target subjects’ age group, the respondent type and target subjects’ gender, and the respondent type and survey mode), were kept in the model regardless of their statistical significance. We used SAS® 9.2 software [27] to perform the primary analyses.

We used the final model to obtain the estimates (adjusted for the other covariates in the model). These estimates were used in the subsequent testing. The testing strategy for assessing the differences between the respondent’s types within each specific subpopulation relies on the main principles of the sequential testing that controls the family-wise error rate [28]. Figure 1 presents the objectives of interest with respect to the age-group subpopulations.

Effect of Respondent Type and Target Subjects’ Age: Primary (Top), Secondary (Middle) and Tertiary (Bottom) Null Hypotheses and Significance Levels (in  Parentheses).

Figure 1: Effect of Respondent Type and Target Subjects’ Age: Primary (Top), Secondary (Middle) and Tertiary (Bottom) Null Hypotheses and Significance Levels (in Parentheses).

First, significance of the two-way interaction between the respondent type and age group is assessed at the 5% level. If the interaction is not significant then we conclude that the prevalence of consistent responses does not depend on the joint effect between the respondent type and target subjects’ age group, and do not test any specific hypotheses. If the interaction is significant then we compare the prevalence of consistent reports for self and the other respondents within each age-group subpopulation (i. e. , 15-24, 25-44, 45-64 and 65+ age groups), each at 1.25% level. If there is a significant difference within a subpopulation then we compare self to proxy, and self to mixed groups within this subpopulation (each at 0.625%), otherwise testing within this subpopulation stops. We used similar testing strategies with respect to the gender and survey mode, the latter strategy is depicted in Figure 2. These strategies control the family-wise error rate at 5% level while allow differentiating among hypotheses in terms of their importance.

Effect of Respondent Type and Survey Mode: Primary (Top), Secondary (Middle) and Tertiary (Bottom) Null Hypotheses and Significance Levels (in  Parentheses).

Figure 2: Effect of Respondent Type and Survey Mode: Primary (Top), Secondary (Middle) and Tertiary (Bottom) Null Hypotheses and Significance Levels (in Parentheses).

To assess whether there is the overall effect of the respondent type on the prevalence of consistent responses we used the following strategy. First we performed the generalized Wald Chisquare test for independence using non-model based estimates (at 5% level). We used the test to obtain the p-value corresponding to the respondent type effect; since the final model included multiple significant interactions with the respondent type, the exact p-value corresponding to the respondent type effect could not be produced based on the model. If the effect was shown to be significant we proceeded and compared self to proxy, self to mixed, and proxy to mixed respondent types (each at 5% level). The testing strategy is illustrated in Figure 3. This method also controls the family-wise error rate at 5% level [29].


Preliminary analysis

As might be expected based on prior studies, the overall percentage of consistent responses was somewhat low. Specifically, only 32.8% of responses regarding the fairly regular smoking initiation age agreed perfectly. The percentage of consistent responses was 35.5% for the self group, 29.5% for the proxy group and 21.3% for the self-proxy group. Table 2 presents the Pearson’s correlation coefficients for the specific subpopulations.

Table 2: Pearson’s Correlation Coefficients with Standard Errors.

  Self Proxy Self-proxy
15-24 0.74 (0.05) 0.67 (0.11) 0.44 (0.10)
25-44 0.77 (0.02) 0.75 (0.05) 0.56 (0.06)
45-64 0.78 (0.02) 0.58 (0.12) 0.49 (0.06)
65+ 0.76 (0.02) 0.87 (0.06) 0.35 (0.13)
Male 0.74 (0.02) 0.64 (0.09) 0.47 (0.06)
Female 0.79 (0.02) 0.83 (0.05) 0.49 (0.07)
Survey mode
Phone 0.76 (0.02) 0.77 (0.05) 0.47 (0.06)
In-person 0.79 (0.03) 0.50 (0.19) 0.43 (0.10)
Mixed mode 0.78 (0.03) 0.78 (0.04) 0.58 (0.05)
Overall 0.78 (0.03) 0.70 (0.07) 0.48 (0.05)

The results indicate that self-respondents and proxy-respondents provided fairly consistent reports (r = 0.70 or higher), whereas the self-proxy respondents tended to provide the least consistent reports (r = 0.48). That is, the reliability level was relatively low when smoking initiation age was reported once by self-respondents and once by the proxy-respondents. Also, self-reports of smoking initiation age are consistent regardless of the target subjects’ age, gender, and survey mode (r = 0.74 or higher). Proxy-reports are most consistent when they concern the smoking initiation age of older (65+) or female subjects.

Primary analyses

The final model contains a large number of two-way interactions (in addition to all main effects), the model is significant at 5% level (Chi-Square= 9,225, df=74, p<0.0001). Table 3 presents the estimated proportions and odds ratios corresponding to comparisons across the respondent type groups.

Table 3: Model-based predicted proportions of consistent responses and odds ratios showing effects of respondent type.

  Proportions (top entry) and standard errors (bottom entry) Overall odds ratios and standard errors (top entry) and Chi-Square test statistics with the corresponding p-values (bottom entry)
  Self Proxy Self-proxy Self versus Other Self versus Proxy Self versus 
15-24 0.35 (0.03) 0.14 (0.03) 0.28 (0.03) 2.15 (0.33) 25.6* 3.24 (0.80) 22.4* 1.43 (0.25) 4.3, 0.0372
25-44 0.29 (0.01) 0.27 (0.03) 0.22 (0.02) 1.29 (0.14) 5.9, 0.0150 1.11 (0.18) 0.4, 0.5298 1.50 (0.16) 15.0, 0.0001
45-64 0.33 (0.01) 0.27 (0.04) 0.12 (0.02) 1.78 (0.20) 26.7* 1.37 (0.26) 2.8, 0.0936 2.31 (0.26) 56.2*
65+ 0.37 (0.02) 0.13 (0.04) 0.27 (0.03 2.55 (0.46) 26.8* 4.01 (1.31) 18.1* 1.62 (0.27) 8.6, 0.0034
Male 0.32 (0.01) 0.22 (0.03) 0.22 (0.02) 1.71 (0.16) 35.2* 1.76 (0.28) 12.2, 0.0005 1.67, (0.17) 25.4*
Female 0.36 (0.01) 0.18 (0.03) 0.24 (0.02) 2.08 (0.23) 42.2* 2.53 (0.47) 25.2* 1.70 (0.18) 26.1*
Survey mode
Phone 0.36 (0.01) 0.19 (0.02) 0.23 (0.02) 2.17 (0.19) 75.0* 2.42 (0.38) 31.5* 1.95 (0.18) 53.6*
In-person 0.33 (0.01) 0.23 (0.04) 0.23 (0.02) 1.68 (0.21) 17.3* 1.68 (0.35) 6.4, 0.0113 1.68 (0.23) 14.7*
Mixed mode 0.32 (0.02 0.17 (0.02) 0.24 (0.03) 1.84 (0.20) 30.3* 2.30 (0.41) 21.6* 1.46 (0.19) 8.4, 0.0038
Overall 0.34 (0.03) 0.20 (0.02) 0.23 (0.02) 1.89 (0.17) 52.3* 2.11 (0.31) 24.97* 1.69 (0.15) 35.09*

Note: The null distribution of each test statistic is Chi-square with 1 degree of freedom;

*p-value less than or equal to 0.0001. Significant (sequential) results are in bold.

First, we address the effects of respondent type for different age groups, There was a significant interaction between respondent type and age group of the target subject (p<0.0001). Therefore we proceeded to test the four secondary hypotheses. Figure 1 depicts the comparisons of interest, and results are summarized in Table 3. As shown in Table 3, significant effects of respondent type were found for all age groups except the one with subjects who were 25-44 years old. Within all age groups selfrespondents were more likely to provide consistent responses than other respondents but the differences were significant only for subjects who were 15-24 years of age or 45 years of age or older (45-64 or 65+). For these three sub-populations we performed the tertiary comparisons. Among younger (15-24) and elderly (65+) subjects, self-respondents were more likely than proxy-respondents to provide consistent responses. And among subjects who were at least 45 years old (45-64 or 65+) self-respondents were more likely than self-proxy respondents to provide consistent responses.

Second, we address the effects of respondent type for different gender groups, since the interaction between respondent type and gender was not significant (p = 0.1219) after controlling for the other covariates, we did not assess the effects of respondent type separately for men and women. The proportions of perfectly agreeing responses associated with the respondent type are similar for females and males.

Third, we discuss the effects of respondent type and survey mode. There was a significant two-way interaction between the respondent type and survey mode (p = 0.0327). Thus, we proceeded to test the secondary hypotheses (see Figure 2). Results indicated that regardless of survey mode, self-respondents are more likely to provide consistent responses compared to other respondents. Next, we tested tertiary hypotheses. Based on the Table 3 results, we concluded that among respondents who had a phone interview both times, self-respondents are more likely to provide consistent responses than either proxy-respondents or self-proxy respondents. The same pattern of results was observed for respondents who had in-person interviews both times or mixed interviews, with one exception – the difference between self-respondents and proxy-respondents was not significant when the interview is done in-person both times.

Finally, we discuss the overall effect of respondent type. The overall test comparing consistency of responses for self, proxy and self-proxy respondent groups (see Figure 3) indicated significant differences among the proportions of consistent responses (Wald F (2, 80) =146.6, p<0.0001). Table 3 presents the model-based estimated proportions for the three respondent types. The pattern was slightly different from the one observed in the sample: the proportions were 35.5% for self-respondents, 29.5% for proxy-respondents, and 21.3% for self-proxy respondents. We then tested the three secondary hypotheses using non-model based estimates. The results indicated that self-respondents are more likely to provide consistent responses than are proxy respondents (Chi-square=25.0, df=1, p<0.0001) and self-proxy respondents (Chi-square=35.1, df=1, p<0.0001), but there was no significant difference between proxy and self proxy respondents (Chi-square=1.7, df=1, p=0.1918). Note that the inferences concerning comparisons between the self- and proxy-respondents, and self- and self-proxy respondents agree with the model-based results in Table 3.


In this paper we address the reliability of self- and proxyreported age of initiating fairly regular smoking. Our findings indicate that the reports made both times by self-respondents or both times by proxy-respondents are overall, consistent, and selfreports are more reliable than are the proxy reports. However, the mixed reports (i. e. , reports made once by self- and once by proxy-respondent) are not consistent. And inclusion of the mixed respondent type decreases the overall level of reliability of the reported fairly regular smoking initiation age. The low level of reliability observed with respect to the mixed respondent type suggests that the fairly regular smoking initiation age reported by a self-respondent does not, overall, agree with the age reported by a proxy-respondent for the target subject. Thus, validity of proxy-reports is questionable.

Our findings concerning the prevalence of perfectly agreeing responses indicate that the overall prevalence of matching responses is relatively low, i. e. , it is about 30% for self-reports and 20% for proxy (or mixed) reports; the difference in percentages is statistically significant. The specific degree of consistency also depends on the target subjects’ age and the survey mode. The most pronounced differences in the consistency levels between self and proxy reports are observed with respect to the 15-24 year old and 65+ year old subjects, and interviews conducted over the phone both times or once over the phone and once in-person.

These results have direct implications in social sciences which study addictive behaviors based on surveys. First, our findings suggest that all surveys assessing the smoking behaviors should attempt to survey self-respondents so that the proportion of proxy-respondents is as small as possible. Second, when researchers use the estimates for the regular smoking initiation age from the TUS-CPS they should utilize the estimates corresponding to the self-reports, because the self-reports not only reliable, overall, but also have the highest prevalence of perfectly agreeing responses. This is important especially when the estimates concern specific subpopulations, e. g. , our results indicate that younger (15-24 years old) and elderly (65+ year old) respondents are about three times more likely to report their regular smoking initiation age consistently when compared to proxy-respondents. Third, since the prevalence of perfect agreement is low even the self-reported information should be used with care: the fairly regular smoking initiation age reports provide just an approximation of the regular smoking initiation.

Our findings of relatively low prevalence of strictly agreeing responses may be due to a somewhat general question wording which referred to smoking “fairly regularly”. There were several reasons for this formulation to be used [19]. One of them was decreasing the respondent burden, e. g., the public reporting burden was about 0.1169 hours per response, on average [30], and a questionnaire had about 40 items so a survey could take several hours.

The findings presented in this paper have several limitations. First, while the majority of presented testing adjusts for additional important information, the tests are based on the specific models, that were identified as appropriate ones in the analyses. Since the model may be, potentially, improved to better fit the data, the model-based estimates may change. Thus, we also presented non-model based estimates. Second, the sequential testing strategy used in the paper is a special case of Bonferronitype sequential testing [28]. The general method allows for specifying a more flexible strategy for re-testing hypotheses that are initially accepted. Alternatively, the hypotheses of interest could be tested via other multiple-testing strategies, e. g. , a treestructured gate keeping approach [31], which are expected to be more powerful yet computationally challenging. Third, our estimates of prevalence of consistent responses are limited to the one-year time difference between the surveys. It is anticipated that the larger time intervals between the assessments might result in smaller observed and predicted proportions of consistent responses [17].


Research of Julia N. Soulakova reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R03CA165831. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors wish to thank Anne Hartman and Todd Gibson (National Cancer Institute) for providing the data set, and students Huang Huang and Vanetia Ho for help with table editing.


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Received : 09 Oct 2013
Accepted : 13 Dec 2013
Published : 16 Dec 2013
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Launched : 2016
Journal of Nausea
Launched : 2020
JSM Internal Medicine
Launched : 2016
JSM Hepatitis
Launched : 2016
JSM Oro Facial Surgeries
ISSN : 2578-3211
Launched : 2016
Journal of Human Nutrition and Food Science
ISSN : 2333-6706
Launched : 2013
JSM Regenerative Medicine and Bioengineering
ISSN : 2379-0490
Launched : 2013
JSM Spine
ISSN : 2578-3181
Launched : 2016
Archives of Palliative Care
ISSN : 2573-1165
Launched : 2016
JSM Nutritional Disorders
ISSN : 2578-3203
Launched : 2017
Annals of Neurodegenerative Disorders
ISSN : 2476-2032
Launched : 2016
Journal of Fever
ISSN : 2641-7782
Launched : 2017
JSM Bone Marrow Research
ISSN : 2578-3351
Launched : 2016
JSM Mathematics and Statistics
ISSN : 2578-3173
Launched : 2014
Journal of Autoimmunity and Research
ISSN : 2573-1173
Launched : 2014
JSM Arthritis
ISSN : 2475-9155
Launched : 2016
JSM Head and Neck Cancer-Cases and Reviews
ISSN : 2573-1610
Launched : 2016
JSM General Surgery Cases and Images
ISSN : 2573-1564
Launched : 2016
JSM Anatomy and Physiology
ISSN : 2573-1262
Launched : 2016
JSM Dental Surgery
ISSN : 2573-1548
Launched : 2016
Annals of Emergency Surgery
ISSN : 2573-1017
Launched : 2016
Annals of Mens Health and Wellness
ISSN : 2641-7707
Launched : 2017
Journal of Preventive Medicine and Health Care
ISSN : 2576-0084
Launched : 2018
Journal of Chronic Diseases and Management
ISSN : 2573-1300
Launched : 2016
Annals of Vaccines and Immunization
ISSN : 2378-9379
Launched : 2014
JSM Heart Surgery Cases and Images
ISSN : 2578-3157
Launched : 2016
Annals of Reproductive Medicine and Treatment
ISSN : 2573-1092
Launched : 2016
JSM Brain Science
ISSN : 2573-1289
Launched : 2016
JSM Biomarkers
ISSN : 2578-3815
Launched : 2014
JSM Biology
ISSN : 2475-9392
Launched : 2016
Archives of Stem Cell and Research
ISSN : 2578-3580
Launched : 2014
Annals of Clinical and Medical Microbiology
ISSN : 2578-3629
Launched : 2014
JSM Pediatric Surgery
ISSN : 2578-3149
Launched : 2017
Journal of Memory Disorder and Rehabilitation
ISSN : 2578-319X
Launched : 2016
JSM Tropical Medicine and Research
ISSN : 2578-3165
Launched : 2016
JSM Head and Face Medicine
ISSN : 2578-3793
Launched : 2016
JSM Cardiothoracic Surgery
ISSN : 2573-1297
Launched : 2016
JSM Bone and Joint Diseases
ISSN : 2578-3351
Launched : 2017
JSM Bioavailability and Bioequivalence
ISSN : 2641-7812
Launched : 2017
JSM Atherosclerosis
ISSN : 2573-1270
Launched : 2016
Journal of Genitourinary Disorders
ISSN : 2641-7790
Launched : 2017
Journal of Fractures and Sprains
ISSN : 2578-3831
Launched : 2016
Journal of Autism and Epilepsy
ISSN : 2641-7774
Launched : 2016
Annals of Marine Biology and Research
ISSN : 2573-105X
Launched : 2014
JSM Health Education & Primary Health Care
ISSN : 2578-3777
Launched : 2016
JSM Communication Disorders
ISSN : 2578-3807
Launched : 2016
Annals of Musculoskeletal Disorders
ISSN : 2578-3599
Launched : 2016
Annals of Virology and Research
ISSN : 2573-1122
Launched : 2014
JSM Renal Medicine
ISSN : 2573-1637
Launched : 2016
Journal of Muscle Health
ISSN : 2578-3823
Launched : 2016
JSM Genetics and Genomics
ISSN : 2334-1823
Launched : 2013
JSM Anxiety and Depression
ISSN : 2475-9139
Launched : 2016
Clinical Journal of Heart Diseases
ISSN : 2641-7766
Launched : 2016
Annals of Medicinal Chemistry and Research
ISSN : 2378-9336
Launched : 2014
JSM Pain and Management
ISSN : 2578-3378
Launched : 2016
JSM Women's Health
ISSN : 2578-3696
Launched : 2016
Clinical Research in HIV or AIDS
ISSN : 2374-0094
Launched : 2013
Journal of Endocrinology, Diabetes and Obesity
ISSN : 2333-6692
Launched : 2013
JSM Neurosurgery and Spine
ISSN : 2373-9479
Launched : 2013
Journal of Liver and Clinical Research
ISSN : 2379-0830
Launched : 2014
Journal of Drug Design and Research
ISSN : 2379-089X
Launched : 2014
JSM Clinical Oncology and Research
ISSN : 2373-938X
Launched : 2013
JSM Bioinformatics, Genomics and Proteomics
ISSN : 2576-1102
Launched : 2014
JSM Chemistry
ISSN : 2334-1831
Launched : 2013
Journal of Trauma and Care
ISSN : 2573-1246
Launched : 2014
JSM Surgical Oncology and Research
ISSN : 2578-3688
Launched : 2016
Annals of Food Processing and Preservation
ISSN : 2573-1033
Launched : 2016
Journal of Radiology and Radiation Therapy
ISSN : 2333-7095
Launched : 2013
JSM Physical Medicine and Rehabilitation
ISSN : 2578-3572
Launched : 2016
Annals of Clinical Pathology
ISSN : 2373-9282
Launched : 2013
Annals of Cardiovascular Diseases
ISSN : 2641-7731
Launched : 2016
Journal of Behavior
ISSN : 2576-0076
Launched : 2016
Annals of Clinical and Experimental Metabolism
ISSN : 2572-2492
Launched : 2016
Clinical Research in Infectious Diseases
ISSN : 2379-0636
Launched : 2013
JSM Microbiology
ISSN : 2333-6455
Launched : 2013
Journal of Urology and Research
ISSN : 2379-951X
Launched : 2014
Journal of Family Medicine and Community Health
ISSN : 2379-0547
Launched : 2013
Annals of Pregnancy and Care
ISSN : 2578-336X
Launched : 2017
JSM Cell and Developmental Biology
ISSN : 2379-061X
Launched : 2013
Annals of Aquaculture and Research
ISSN : 2379-0881
Launched : 2014
Clinical Research in Pulmonology
ISSN : 2333-6625
Launched : 2013
Journal of Immunology and Clinical Research
ISSN : 2333-6714
Launched : 2013
Annals of Forensic Research and Analysis
ISSN : 2378-9476
Launched : 2014
JSM Biochemistry and Molecular Biology
ISSN : 2333-7109
Launched : 2013
Annals of Breast Cancer Research
ISSN : 2641-7685
Launched : 2016
Annals of Gerontology and Geriatric Research
ISSN : 2378-9409
Launched : 2014
Journal of Sleep Medicine and Disorders
ISSN : 2379-0822
Launched : 2014
JSM Burns and Trauma
ISSN : 2475-9406
Launched : 2016
Chemical Engineering and Process Techniques
ISSN : 2333-6633
Launched : 2013
Annals of Clinical Cytology and Pathology
ISSN : 2475-9430
Launched : 2014
JSM Allergy and Asthma
ISSN : 2573-1254
Launched : 2016
Journal of Neurological Disorders and Stroke
ISSN : 2334-2307
Launched : 2013
Annals of Sports Medicine and Research
ISSN : 2379-0571
Launched : 2014
JSM Sexual Medicine
ISSN : 2578-3718
Launched : 2016
Annals of Vascular Medicine and Research
ISSN : 2378-9344
Launched : 2014
JSM Biotechnology and Biomedical Engineering
ISSN : 2333-7117
Launched : 2013
Journal of Hematology and Transfusion
ISSN : 2333-6684
Launched : 2013
JSM Environmental Science and Ecology
ISSN : 2333-7141
Launched : 2013
Journal of Cardiology and Clinical Research
ISSN : 2333-6676
Launched : 2013
JSM Nanotechnology and Nanomedicine
ISSN : 2334-1815
Launched : 2013
Journal of Ear, Nose and Throat Disorders
ISSN : 2475-9473
Launched : 2016
JSM Ophthalmology
ISSN : 2333-6447
Launched : 2013
Journal of Pharmacology and Clinical Toxicology
ISSN : 2333-7079
Launched : 2013
Annals of Psychiatry and Mental Health
ISSN : 2374-0124
Launched : 2013
Medical Journal of Obstetrics and Gynecology
ISSN : 2333-6439
Launched : 2013
Annals of Pediatrics and Child Health
ISSN : 2373-9312
Launched : 2013
JSM Clinical Pharmaceutics
ISSN : 2379-9498
Launched : 2014
JSM Foot and Ankle
ISSN : 2475-9112
Launched : 2016
JSM Alzheimer's Disease and Related Dementia
ISSN : 2378-9565
Launched : 2014
Journal of Addiction Medicine and Therapy
ISSN : 2333-665X
Launched : 2013
Journal of Veterinary Medicine and Research
ISSN : 2378-931X
Launched : 2013
Annals of Public Health and Research
ISSN : 2378-9328
Launched : 2014
Annals of Orthopedics and Rheumatology
ISSN : 2373-9290
Launched : 2013
Journal of Clinical Nephrology and Research
ISSN : 2379-0652
Launched : 2014
Annals of Community Medicine and Practice
ISSN : 2475-9465
Launched : 2014
Annals of Biometrics and Biostatistics
ISSN : 2374-0116
Launched : 2013
JSM Clinical Case Reports
ISSN : 2373-9819
Launched : 2013
Journal of Cancer Biology and Research
ISSN : 2373-9436
Launched : 2013
Journal of Surgery and Transplantation Science
ISSN : 2379-0911
Launched : 2013
Journal of Dermatology and Clinical Research
ISSN : 2373-9371
Launched : 2013
JSM Gastroenterology and Hepatology
ISSN : 2373-9487
Launched : 2013
Annals of Nursing and Practice
ISSN : 2379-9501
Launched : 2014
JSM Dentistry
ISSN : 2333-7133
Launched : 2013
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