State of New Mexico CBP Programs
Site Name & ID#: ______
Community Survey Findings Sheet- 2014
Prevention Goals and Objectives (relevant to the NMCS)
Brief Description of Community & Population
Data Collection Method and Brief Sample Description (e.g., information from your data collection protocol)
I. Demographic Characteristics
Descriptive statistics are provided for age, gender, race/ethnicity, education, New Mexico residency, military service and sexual orientation.
Table 1. Demographic characteristics of community
Number of eligible respondents / N=Characteristics / %
Age q08
18-20 / 0.0
21-25 / 0.0
26-30 / 0.0
31-40 / 0.0
41-50 / 0.0
51-60 / 0.0
61-70 / 0.0
71 or older / 0.0
Biological Sex q09
Male / 0.0
Female / 0.0
Race/Ethnicity race4cat
White / 0.0
Hispanic / 0.0
Native American / 0.0
Other / 0.0
Education level edu
High school or less / 0.0
Some college / 0.0
College or above / 0.0
New Mexico Residency q12
Less than 1 year / 0.0
1-5 years / 0.0
More than 5 years / 0.0
Active Duty in the Armed Forces, Military Reserves or National Guard q14 / 0.0
Veteran of the Armed Forces q15 / 0.0
Identify as LGBT q17 / 0.0
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II. Access to alcohol and perception of risk/legal consequences
Means, ranges and distributions of each response category are provided below for the outcomes of interest.
Table 2. Perceptions of risk/legal consequences of alcohol consumption (male and female).
%Access to alcohol / Mean (SD) / Range / Very difficult / Somewhat difficult / Somewhat easy / Very easy / Don't know
Ease of access to alcohol by teens in the community (n=) q01 / 1-4 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
Ease of access to alcohol by teens in the community from stores and restaurants (n=) q02 / 1-4 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
Perception of risk/legal consequences / Mean (SD) / Range / Very likely / Somewhat likely / Not very likely / Not at all likely / Don't know
Likelihood of police breaking up parties where teens are drinking (n=) rq03 / 1-4 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
Likelihood of police arresting an adult for giving alcohol to someone under 21 (n=) rq04 / 1-4 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
Likelihood of someone being arrested if caught selling alcohol to a drunk or intoxicated person (n=) rq05 / 1-4 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
Likelihood of being stopped by police if driving after drinking too much (n=) rq06 / 1-4 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
Likelihood of being convicted if stopped and charged with DWI (n=) rq07 / 1-4 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
Note: Fill in (n=) using the number produced by frequency codes (i.e., the codes for %).
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III. ATOD consumption
Means, ranges, and frequencies are provided below for overall sample and by biological sex for the behavioral outcomes of interest.
Table 3.1 Percentages of cigarette and tobacco any use outcomes overall and by sex.
%Outcomes / Overall / Male / Female
Cigarette: any use rq18 / 0.0 / 0.0 / 0.0
Tobacco: any use rq19 / 0.0 / 0.0 / 0.0
Overall / Male / Female
Provided tobacco for minors past year (n=) q25 / 0.0 / 0.0 / 0.0
Table 3.2. Means, ranges and percentages of alcohol use outcomes overall and by sex.
Overall / Male / FemaleOutcomes / % of Yes / Mean (SD) / Range / % of Yes / % of Yes
Past 30-day alcohol use (n=) / Past30_d / …days rq20 / 0.0 / 0.0
Five or more drinks on one occasion in the past 30 days (n=) / Binge_d / …times q21 / 0.0 / 0.0
Driven under influence in the past 30 days (n=) / Dd30_d / …times q22 / 0.0 / 0.0
Driven in the past 30 days after having had 5 or more drinks (n=) / Q23 / NA / 0-1 / 0.0 / 0.0
Provided alcohol for minors past year (n=) / Q26 / NA / 0-1 / 0.0 / 0.0
*Fill in (n=) using the number of cases in variables past30_d, binge_d and dd30_d respectively.
Figure 1. Sources of obtaining alcohol for respondents 18-20 years old who reported drinking alcohol in the past 30 days. rq24_2 – rq24_12 (Note: please use data generated from syntax and graph in Excel)
IV. Prescription drug use.
Means, frequencies and graphs are provided below by biological sex for the prescription drug outcomes of interest.
Table 4.1. Means and percentages of prescription drug use outcomes overall and by sex.
%Overall / Male / Female
Outcomes / % of Yes / Mean (SD) / % of Yes / % of Yes
Past 30-day prescription painkiller use* (n=) q30 / 0.0 / rq31 / 0.0 / 0.0
Great risk of harm using Rx pain killers for a non-medical reason (n=) rq27 / 0.0 / NA / 0.0 / 0.0
Prevalence of receiving prescription painkiller past year (n=) q28 / 0.0 / NA / 0.0 / 0.0
Past 30-day painkiller use to get high (n=) q29 / 0.0 / NA / 0.0 / 0.0
Given/shared prescription drugs with someone past year (n=) q34 / 0.0 / NA / 0.0 / 0.0
Medication locked or safely stored away (n=) q35 / 0.0 / NA / 0.0 / 0.0
Note. Ns are for overall estimates only.
*Fill in (n=) using the number of cases in variable q30.
Table 4.2.Prescription drug use outcomes by age groups
Ages / Great risk of harm using Rx pain killers for a non-medical reason % (n) rq27 / Prevalence of receiving prescription painkiller % (n) q28 / Past 30-day painkiller use to get high % (n) rq29 / Past 30-day prescription painkiller use % (n) q30 / Given/shared prescription drugs with someone % (n) q34 / Medication locked or safely stored away % (n) q3518-20 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
21-30 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
31-40 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
41-50 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
51+ / 0.0 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
Figure 2. Reasons for prescription drug use among all current users. rq32_1 – rq32_11 (Note: please use data generated from syntax and graph in Excel)
Figure 3. Sources of prescription drugs among current users. rq33_1 – rq33_ 8(Note: please use data generated from syntax and graph in Excel)
V. Mental health
Means, ranges, frequencies and graphs are provided below for the mental health outcomes of interest.
Table 5.1 Percentages of mental health outcomes overall and by sex
%Outcomes / Overall / Male / Female
Critical threshold for depression1 (n=) sumq36 / 0.0 / 0.0 / 0.0
Having mental health or drug/alcohol problems in the past year (n=) q37 / 0.0 / 0.0 / 0.0
Suicidal thoughts in the past year (n=) q38 / 0.0 / 0.0 / 0.0
Received professional help on mental health or drug/alcohol problems in the past year (n=) q39 / 0.0 / 0.0 / 0.0
Always / Sometimes / Never
Accessed mental health or substance abuse services when needed2 (n=) q42 / 0.0 / 0.0 / 0.0
Note. Ns are for overall estimates only.
1% of 13 points or above.
2Overall estimates only.
Table 5.2 Distribution of depressive symptoms in the past 4 weeks.
%Depressive symptoms (total N =) / All of the time / Most of the time / Some of the time / A little of the time / None of the time
You feel so sad nothing could cheer you up q36_1 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
You feel nervous q36_2 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
You feel restless or fidgety q36_3 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
You feel hopeless q36_4 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
You feel that everything was an effort q36_5 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
You feel worthless q36_6 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
You feel anxious q36_7 / 0.0 / 0.0 / 0.0 / 0.0 / 0.0
Figure 4. Sources of professional help among help seekers in the last year. rq40_1 – rq40_12 (Note: please use data generated from syntax and graph in Excel.)
Figure 5. Types of help among help seekers in the last year. rq41_1 – rq41_10 (Note: please use data generated from syntax and graph in Excel)
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Summary of 2014 Community Survey Findings
In 2 pages or less, summarize the significance of the above findings for your community as they relate to your prevention goals and objectives. The questions below were designed to inform your presentation of community survey findings, so this write-up should provide the important details that you will report back to your community and program stakeholders.
PFS II grantees may have only one time point available; these descriptive analyses will provide baseline prevalence estimates on the current issues in your community. Therefore, we do not expect you to report on previous years’ data on this report; however, you may choose to do so if you have earlier community survey estimates and feel they contribute to your reporting. You also may run analyses in addition to PIRE’s in order to further describe the “big picture” in your community.
Communities with multiple years of NMCS data, in this section, you may want to talk about some of the changes in estimates over time, i.e., compare this year’s estimates with previous years. Are they the same, better, worse? Why? You will be required to do this on your OSAP End of Year Report anyway.
You may also use this space to highlight successes that are not captured in the tables above, and reflect on lessons learned and how the findings will be used to inform prevention in your community.
NOTE: If you already have a data presentation prepared for your community, as long as it addresses the key points in the questions below you can submit it in lieu of this narrative report.
Use these questions to guide your summary. You do not have to respond explicitly to each question as if this were an exam, but if there are important topics or critical pieces of information missing from the discussion we will return your report to you and ask you to address them.
1. What do the findings indicate about the general progress (or intended progress), on your stated goals and objectives, and/or prevention needs in your community?
2. What do these findings suggest about any changes needed with respect to prevention objectives and strategies in the community? What needs to happen, at the state, in the community, or in your program in order to begin to address these needs?
3. What additional questions or analyses would add to your understanding of the community and the prevention needs better? (E.g., examining data by race/ethnicity, age, geography, or examining mental health measures by substance use prevalence.)
4. These are convenience data, therefore, it’s likely that there is some amount of bias in your findings. How might your data be biased and what does that mean for their interpretation? What groups may be over-represented and which ones may be under-represented? Think in terms of geography, age, language, race/ethnicity, gender. Which estimates could be overstated or understated as a result? Should any results be interpreted with caution? Why?
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