Dersleri yüzünden oldukça stresli bir ruh haline sikiş hikayeleri bürünüp özel matematik dersinden önce rahatlayabilmek için amatör pornolar kendisini yatak odasına kapatan genç adam telefonundan porno resimleri açtığı porno filmini keyifle seyir ederek yatağını mobil porno okşar ruh dinlendirici olduğunu iddia ettikleri özel sex resim bir masaj salonunda çalışan genç masör hem sağlık hem de huzur sikiş için gelip masaj yaptıracak olan kadını gördüğünde porn nutku tutulur tüm gün boyu seksi lezbiyenleri sikiş dikizleyerek onları en savunmasız anlarında fotoğraflayan azılı erkek lavaboya geçerek fotoğraflara bakıp koca yarağını keyifle okşamaya başlar

GET THE APP

Journal of Health Care and Prevention - Does Alcohol Use Differ by Metropolitan Status in Young Adult Females in the United States General Population?

Journal of Health Care and Prevention
Open Access

Our Group organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations
700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
  • Research Article   
  • J Health Care Prev 1: 104, Vol 1(1)

Does Alcohol Use Differ by Metropolitan Status in Young Adult Females in the United States General Population?

Mackenzie A Dainty, Lauren J Weise, Sarah E Story, Bethany L Dempsey, Feroza Thompson and Jessica L Hartos*
Department of Physician Assistant Studies, University of North Texas Health Science Center, USA
*Corresponding Author: Jessica L Hartos, Department of Physician Assistant Studies, University of North Texas Health Science Center, 3500 Camp Bowie Dr., Fort Worth, TX, 76107, USA, Tel: (817)735-2454, Fax: (817)735-2529, Email: jessica.hartos@unthsc.edu

Received: 01-Jun-2018 / Accepted Date: 07-Jun-2018 / Published Date: 14-Jun-2018

Abstract

Purpose: Alcohol use can lead to serious health concerns and even death, but findings are inconsistent regarding whether consumption differs by where people live. The purpose of this study was to assess whether alcohol use differs by metropolitan status in young adult females ages 25-44 in the general population. Methods: This cross-sectional analysis used data from the 2016 Behavioral Risk Factor Surveillance System (BRFSS) for females ages 25-44 in California (N=271), Colorado (N=428), Florida (N=1,109), New York (N=1,456), and Texas (N=482). Ordered logistic regression analysis was used with combined state data to assess the relationship between alcohol use and metropolitan status while controlling for demographic and health-related factors. Results: Across states, over half of participants reported alcohol use (49-65%), with about one-fifth reporting excessive alcohol use (14-26%). Metropolitan status varied among urban (12-54%), suburban (24-51%), and rural (5- 42%) residents. The results of adjusted analysis indicated that metropolitan status, current smoking, and mental health status were significantly related to alcohol use. Conclusion: Overall, alcohol use differed by metropolitan status in female adults ages 25-44, with urban women reporting drinking more than their rural counterparts. In a primary care setting, providers may expect about half of young adult females to drink alcohol, less than half to have mental health issues, and few to be smokers. Providers need to be aware that current smokers and those with mental health issues are more likely to report alcohol use. Providers should continue to screen all women ages 25-44 for alcohol use, with special attention to those from urban areas. Because smoking and mental health status were related to alcohol use, providers should screen for all of these if any are present and provide education and referrals to substance abuse programs and mental health counseling as necessary.

Keywords: Alcohol Consumption; Women’s Health; Urban Population; Suburban Population; Rural Population; Mental Health; Tobacco Use

Introduction

Alcohol misuse is responsible for 6% of deaths worldwide, especially in those ages 15-39, and contributes to serious and often life-altering health issues [1-5]. Alcohol misuse is associated with health conditions such as heart disease, liver disease, cancer, and alcoholism [1,3-6]; high rates of crime and traffic accidents [1,7]; and risky sexual behaviors and violence [8,9]. In the United States, alcohol use remains a steadfast part of our culture even though the economic cost of alcohol-related health issues exceeds $240 billion [2,9,10].

Several health and social factors play a role in alcohol consumption [1,4,6-8,11,12]. Poor mental health can contribute to engaging in inappropriate use of alcohol [7], as those who report higher alcohol consumption also report problems with anxiety, living in stressful environments, and dealing with negative life events [1,7]. In addition, people who smoke usually drink alcohol, and up to 90 percent of those who are diagnosed with alcoholism also smoke [11]. Furthermore, the need for socialization increases the use of alcohol; however, those of African, Asian, and Latino descent may drink less than those of Caucasian descent [4,8].

Demographic factors are shown to be associated with alcohol consumption. Research indicates that those with a higher socioeconomic status tend to drink more often, whereas those with a lower socioeconomic status drink larger amounts at one time [4,13]. Findings for educational status vary [7,8,14,15] as some research indicates that male heavy drinkers are less likely to complete a college education than males who do not drink heavily, and people with lower levels of education have an increased use of alcohol [7,14], whereas other findings report that those with a lack of education are more likely to abstain from alcohol and refrain from drinking to intoxication [15]. When considering race, Caucasians are more likely to drink alcohol than other races [4,12,15,16].

Furthermore, gender, age, and metropolitan status may relate to alcohol use. Overall, men tend to consume more alcohol than women [1,4-6] and are also more likely to drink heavily [6,13] and to binge drink [12,15]. However, when taking age into account, younger women are more likely to binge drink than younger men [10,17]. Additionally, males in metropolitan or urban areas are more likely to drink any amount and more heavily than non-metropolitan males [8,16]. Other research shows that rural adults are more likely to engage in risky drinking than urban adults [1,13,15]. However, research on metropolitan status and alcohol use utilizes different definitions for “urban,” “metropolitan,” and “rural” status, and with no standardized qualifications, it is difficult to know if results are consistent and comparable [16]. In addition, there is limited research focusing solely on alcohol use based on metropolitan status and female gender in young adults [4,17]. Such findings are of particular interest for the purposes of prevention and treatment [10,16]. Therefore, the purpose of this study was to assess whether alcohol use differs by metropolitan status in young adult females ages 25-44 in the general population.

Methods

Design

This cross-sectional analysis used data from the 2016 Behavioral Risk Factor Surveillance System (BRFSS) conducted by the Centers for Disease Control and Prevention (CDC) [18]. BRFSS consists of state data acquired from telephone surveys with U.S. residents regarding their health-related risk behaviors, chronic conditions, and use of preventative services. BRFSS results are collected in all 50 states as well as the District of Columbia and three U.S. territories. More than 400,000 adult interviews are conducted through BRFSS annually. The CDC compiles all BRFSS data and makes de-identified data available to researchers for secondary data analysis. This study was given exempt status by Institutional Review Board of The University of North Texas Health Science Center.

Sample

The sample for this study included females ages 25-44 in California (N=271), Colorado (N=428), Florida (N=1,109), New York (N=1,456), and Texas (N=482) with data for metropolitan status. This target population was chosen because there is limited research focusing solely on alcohol use and metropolitan status in young adult females [4,17]. These states were chosen because they represent different parts of the country that have more inclusion of urban, suburban, and rural areas compared to the rest of the continental United States when compared to the other U.S. States [19].

Data

The outcome was alcohol use. BRFSS measured alcohol use as the average number of drinks per day in the last 30 days and we categorized these responses for women as “no use,” indicating no consumption of alcohol at all, “light use,” indicating less than one alcoholic drink, “moderate use,” indicating 1-3 alcoholic drinks, and “excessive use,” indicating 4 or more alcoholic drinks per day [20]. The factor of interest, metropolitan status, was categorized as “urban” (i.e., living in the center city of a Metropolitan statistical area), “suburban” (i.e., living outside the center city of a metropolitan statistical area, but inside the county containing the center city or inside a suburban county of the metropolitan statistical area), or “rural” (i.e., not living in a metropolitan statistical area).

The control variables included age, race, educational status, employment status, income level, general health status, depression, mental health status, physical health status, tobacco use, and state. Age was categorized as “25-34” and “35-44.” Because the majority of participants were white, race was measured as “white, non-Hispanic” versus “other.” Educational level was measured as “having graduated from college or technical school” versus “not having graduated from college or technical school.” Employment status was measured as “being employed” versus “not being employed.” Income level was classified as an annual income “greater than or equal to $50,000” or “less than $50,000.” General health status was reported as “good or better” versus “fair or poor.” Depression was measured as “yes” versus “no” to having ever been diagnosed with any form of depression or dysthymia. Mental health status was measured as “30 days of good mental health in the past 30 days” versus “less than 30 days of good mental health in the past 30 days.” Physical health status was measured as having “30 days of good physical health in the past 30 days” versus “less than 30 days of good physical health in the past 30 days.” Tobacco use was measured as “yes” or “no” to current smoker.

Analysis

Frequency distributions were reported by state to describe the samples and to identify any issues with the distributions of variables. We chose to combine data across all states (as compared to analyzing data separately by state) for adjusted analysis to increase the numbers of females per metropolitan status category. We used ordered logistic regression analysis with combined state data to assess the relationship between alcohol use and metropolitan status in young adult females after controlling for demographic factors, health-related factors, and state. An ordered logistic regression model is used to estimate a relationship between an ordinal dependent variable and a set of independent variables. The proportional odds produced for each independent variable relates “proportionally” or applies equally to comparisons of dependent variable groups greater than k versus those who are in groups less than or equal to k, where k is any level of the response variable. Therefore, the interpretation of an associated odds ratio is that for a one unit change in the predictor variable, the odds for a group that is greater than k versus less than or equal to k are the proportional odds times larger. Any observations with missing data for any variables were excluded from the adjusted analysis. All analyses were conducted in STATA 15 (version 15.1, copyright 1985-2017 StataCorp LLC).

Results

Descriptive statistics

Table 1 lists participant characteristics for female adults ages 25- 44 in California, Colorado, Florida, New York, and Texas. Almost half of participants reported no alcohol use (36-50%) and about one-fifth reported excessive use of alcohol (14-26%). For metropolitan status, there was a varied distribution among urban (12-54%), suburban (24- 51%), and rural (5-42%) residents. The majority of participants were 35-44 years old (63-76%), and participants with white, non-Hispanic race status varied across states (29-76%). For socioeconomic status, the majority did not graduate from college or technical school (47-66%) and reported being employed (59-69%), and about one-half reported having an income of $50,000 or more per year (41-68%). For health status, most of the participants had good or better health (83-91%) and had never been diagnosed with any form of depression or dysthymia (75-90%).

Variables California (N=271) Colorado (N=428) Florida (N=1109) New York (N=1456) Texas (N=482)
N % N % N % N % N %
Alcohol Use 264 97 406 95 1068 96 1388 95 453 94
   No use 125 47 145 36 538 50 585 42 227 50
   Light 37 14 72 18 201 19 277 20 87 19
   Moderate 44 17 84 21 160 15 260 19 74 16
   Excessive 58 22 105 26 169 16 266 19 65 14
Metropolitan Status 271 100 428 100 1109 100 1456 100 482 100
   Urban 119 44 233 54 131 12 487 33 244 51
   Suburban 138 51 101 24 512 46 512 35 178 37
   Rural 14 5 94 22 466 42 457 31 60 12
Age 271 100 428 100 1109 100 1456 100 482 100
   25-34 72 27 101 24 412 37 537 37 137 28
   35-44 199 73 327 76 697 63 919 63 345 72
Race 270 99 421 98 1092 98 1431 98 479 99
   White, non-Hispanic 79 29 312 74 711 65 1094 76 214 45
   Other 191 71 109 26 381 35 337 24 265 55
Educational Status 270 99 428 100 1105 99 1452 99 482 100
   Graduated college or technical school 125 46 225 53 377 34 612 42 202 42
   Did not graduate college or technical school 145 54 203 47 728 66 840 58 280 58
Employment Status 269 99 425 99 1099 99 1441 99 473 98
   Employed 166 62 294 69 712 65 962 67 279 59
   Not employed 103 38 131 31 387 35 479 33 194 41
Income Level 228 84 385 90 970 87 1290 89 422 88
   $50,000 or more a year 119 52 260 68 396 41 710 55 202 48
   Less than $50,000 a year 109 48 125 32 574 59 580 45 220 52
General Health Status 271 100 428 100 1107 99 1452 99 478 99
   Good or better 239 88 389 91 946 85 1285 89 398 83
   Fair or poor 32 12 39 9 161 15 167 11 80 17
Depression 270 99 428 100 1102 99 1454 99 480 99
   Yes 27 10 105 25 214 19 243 17 83 17
   No 243 90 323 75 888 81 1211 83 397 83
Mental Health Status 269 99 423 99 1095 99 1439 99 475 99
   30 days good 159 59 201 48 682 62 847 59 267 56
   Less than 30 days good 110 41 222 52 413 38 592 41 208 44
Physical Health Status 269 99 425 99 1097 99 1434 98 469 97
   30 days good 173 64 276 65 731 67 915 64 299 64
   Less than 30 days good 96 36 149 35 366 33 519 36 170 36
Current Smoker 246 91 396 93 1061 96 1369 94 462 96
   Yes 19 8 58 15 213 20 262 19 52 11
   No 227 92 338 85 848 80 1107 81 410 89

Table 1: Participant Characteristics by State.

In addition, the majority reported good mental health (48-62%) and good physical health (64-67%) in the past 30 days, and most reported not smoking (80-92%).

Adjusted statistics

Predicting Alcohol use (none vs. light vs. moderate vs. excessive) Combined State Data
Metropolitan Status AOR 95% CI
     Urban ref - -
     Suburban 0.87 0.73 1.04
     Rural 0.71 0.58 0.86
Race      
     Other ref - -
     White, non-Hispanic 1.50 1.27 1.76
Age      
    25-34 ref - -
    35-44 1.07 0.92 1.24
Education Level      
     Did not graduate college or technical school ref - -
     Graduated college or technical school 1.36 1.16 1.60
Employment Status      
     Not employed ref - -
     Employed 1.60 1.37 1.88
Income Level      
     Less than $50,000 a year ref - -
     $50,000 or more a year 2.12 1.80 2.50
General Health Status      
     Fair or poor ref - -
     Good or better 1.60 1.25 2.04
Depression or dysthymia      
     No ref - -
     Yes 1.13 0.93 1.37
Mental Health Status      
     Less than 30 days good ref - -
     30 days good 0.72 0.62 0.84
Physical Health Status      
     Less than 30 days good ref - -
     30 days good 1.09 0.93 1.28
Current smoker      
     No ref - -
     Yes 1.49 1.22 1.80
State      
     California ref - -
     Colorado 1.07 0.76 1.50
     Florida 0.87 0.65 1.19
     New York 0.94 0.70 1.26
     Texas 0.85 0.61 1.18

Table 2: Adjusted results across states.

As shown in Table 2, the results of ordered logistic regression analysis for females ages 25-44 in California, Colorado, Florida, Texas, and New York indicated that after controlling for all other variables in the model, when compared to those who lived in an urban area, those who lived in a rural area were about 1.4 times less likely to report each successive level of alcohol use. In addition, compared to those who did not report 30 days of good mental health, those who did report 30 days of mental health were about 1.4 times less likely to report each successive level of alcohol use. In contrast, compared to their referent groups, the following participants were more likely to report each successive level of alcohol use: those who reported white race, graduating from college or technical school, being employed, making less than $50,000 per year, good or better general health, and being current smokers.

Discussion

The purpose of this study was to assess whether alcohol use was related to metropolitan status in young adult females ages 25-44 in the general population when controlling for other factors that may be related to alcohol use. The results of adjusted analysis indicated that those living in rural areas were about 1.4 times less likely to report each successive level of alcohol use than those living in urban areas. Although there has been limited research on alcohol use and metropolitan status in women [4,17], our results are similar to what has been observed in the research for males. Prior studies indicate that males in metropolitan or urban areas were more likely to drink any amount and more heavily than non-metropolitan males [8,16]. In addition, the results of the study indicated that those who smoke are more likely to report each successive level of alcohol use than non-smokers. This finding is similar to previous research findings that indicate that the use of alcohol and tobacco are highly related behaviors [11]. This relationship is, of course, problematic since both behaviors can contribute to further health issues.

Moreover, the results of this study indicate that those who report good mental health were less likely to report each successive level of alcohol use. This is similar to the prior research findings showing relations between poor mental health and alcohol use [7]. In contrast, those who reported good or better general health in this study were more likely to report each successive level of alcohol use. This is different than the results of previous research that relates poor physical health and alcohol use [7]. This inconsistency may be attributed to varying target populations, as the previous research included males of all ages and the current study is based on young adult females.

Limitations

Use of the 2016 BRFSS data allowed access to a large populationbased sample for a specific target group with data for many factors shown to be related to alcohol consumption. However, the measurement of metropolitan status categories may be problematic. Categories were based on proximity to cities within county lines, which could have led to possible misclassification of groups of people who live near a city but across county lines. Future research assessing the relationship between alcohol use and metropolitan status may consider defining metropolitan areas differently, taking into account the distance from a major city and the population of a specific area despite county lines. This would allow for more of the cultural aspects of metropolitan status to be assessed. In addition, information related to alcohol use lacked information for dangerous behaviors associated with drinking, such as driving while intoxicated, physical altercations, criminal activity, and risky sexual behavior. These behaviors would elevate the seriousness of the problem and further indicate the need for intervention. Furthermore, because the BRFSS data was collected by telephone survey, young women may not be truthful about alcohol consumption when speaking to an interviewer. Future research may consider other routes of data collection for young adult females including online surveys.

Conclusion

Because we used population-based data, the results of this study may generalize to young adult females ages 25-44 in primary care settings. In these settings, the majority of this target population may report drinking alcohol, but only about one-fifth may drink excessively. Primary care providers should be aware of patterns in alcohol use and metropolitan status in this target population and continue to follow The U.S. Prevention Services Task Force (USPSTF) guidelines for screening all adults for alcohol misuse and providing counseling and referrals as needed [21], perhaps with special attention to young adult females in urban areas. In addition, providers may expect less than one-fifth of the target population to smoke, about one-tenth to report fair or poor general health, and about one-half to report mental health issues. Because of significant relationships between these variables and alcohol use and USPSTF standards for screening all adults on tobacco use [22] and depression [23], providers should screen for all of these if any are present in their young adult female patients. Providers should counsel and provide resources for smoking cessation in addition to assessing the severity and management of mental and physical health issues, counseling on coping and management strategies, and making referrals to psychiatry or other specialists as needed.

Disclaimers

No author has any conflict of interest.

References

  1. Miller PG, Coomber K, Staiger P, Zinkiewicz L, Toumbourou JW (2010) Review of rural and regional alcohol research in Australia. Aust J Rural Health 18: 110-117.
  2.  Alcohol Facts and Statistics. National Institutes of Health (NIH), (2017) USA.
  3. Scott S, Reilly J, Giles EL, Hillier-Brown F, Ells L, et al. (2017) Socio-ecological influences on adolescent (aged 10-17) alcohol use and linked unhealthy eating behaviors: Protocol for a systematic review and synthesis of qualitative studies. Syst Rev 6: 1-8.
  4. Sudhinaraset M, Wigglesworth C, Takeuchi DT (2016) Social and cultural contexts of alcohol use: Influences in a socio-ecological framework. Alcohol Res 38: 35-45.
  5. Cheng HG, Deng F, Xiong W, Phillips MR (2015) Prevalence of alcohol use disorders in mainland China: A systematic review. Addiction 110: 761-774.
  6. Roche AM, Lee NK, Battams S, Fisher JA, Cameron JP, et al. (2015) Alcohol use among workers in male-dominated industries: A systematic review of risk factors. Saf Sci 78: 124-141.
  7. Abebe W (2013) Prevalence and consequences of substance use among high school and college students in Ethiopia: A review of the literature. Afr J Drug Alcohol Stud 12: 107-118.
  8. Centers for Disease Control and Prevention (CDC) (2018) Fact Sheets- Alcohol Use and Your Health.
  9. Keyes KM, Li G, Hasin DS (2011) Birth cohort effects and gender differences in alcohol epidemiology: A review and synthesis. Alcohol Clin Exp Res 35: 2101-2112.
  10. Drobes DJ (2002) National Institute of Alcohol Abuse and Alcoholism: Concurrent Alcohol and Tobacco Dependence.
  11. White A, Hingson R (2013) The burden of alcohol use: Excessive alcohol consumption and related consequences among college students. Alcohol Res 35: 201-218.
  12. Chan GC, Leung J, Quinn C, Kelly AB, Connor JP, et al. (2016) Rural and urban differences in adolescent alcohol use, alcohol supply, and parental drinking. J Rural Health 32: 280-286.
  13. McCambridge J, McAlaney J, Rowe R (2011) Adult consequences of late adolescent alcohol consumption: A systematic review of cohort studies. PLoS Med 8: 1-13.
  14. Slutske WS, Deutsch AR, Piasecki TM (2016) Neighborhood contextual factors, alcohol use, and alcohol problems in the United States: Evidence from a nationally representative study of young adults. Alcohol Clin Exp Res 40: 1010-1019.
  15. Dixon MA, Chartier KG (2016) Alcohol use patterns among urban and rural residents: Demographic and social influences Alcohol Res 38: 69-77.
  16. Levinson D, Rosca P, Vilner D, Brimberg I, Stall Y, et al. (2017) Binge drinking among young adults in an urban tertiary care emergency department in Israel. Isr J Health Policy Res 6: 1-7.
  17. Centers for Disease Control & Prevention (CDC) (2016) BRFSS Prevalence & Trends Data.
  18. Centers for Disease Control and Prevention (CDC) (2018) Alcohol and Public Health: Frequently Asked Questions.
  19. U.S. Preventive Services Task Force (2013) Alcohol Misuse: Screening and Behavioral Counseling Interventions in Primary Care.
  20. U.S. Preventive Services Task Force (2015) Tobacco Smoking Cessation in Adults, Including Pregnant Women: Behavioral and Pharmacotherapy Interventions.
  21. U.S. Preventive Services Task Force (2016) Depression in Adults: Screening.

Citation: Dainty MA, Weise LJ, Story SE, Dempsey BL, Thompson F, et al. (2018) Does Alcohol Use Differ by Metropolitan Status in Young Adult Females in the United States General Population? J Health Care Prev 1: 104.

Copyright: © 2018 Dainty MA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Top