Population Analysis (Sample)
Considering age,
gender, race variables, how would you describe this population?
Considering
age gender and race variables where total population size is 5,165 (N=5165). It
seems to have a small difference when it comes to the difference in the gender
that dominates the population. The female population takes up 53 percent of the
community and males are accountable for 47 percent of the community. As it
pertains to age, both genders also fall within a similar inter quartile range
with an average age for males of 39 and females 41. With regards to age and
gender there is really not much differentiation. However, there is a notable
gap for age groups 18-24 which only make up 5 percent of the population. With
regards to other age groups 18 and under which take up 27 percent, 25 to 44
take up 25 percent, 45 to 64 take up 24 percent, and 65 and over take up 19
percent. This data could be indicative of a population preferably within the 25
to 44 age group who have a higher pregnancy rate than the older groups from 45
to over 65.
|
Variable
|
Gender
|
N
|
N*
|
Mean
|
SE Mean
|
StDev
|
Q1
|
Median
|
Q3
|
IQR
|
|
Age
|
Female
|
2733
|
0
|
39.231
|
0.462
|
24.130
|
16.000
|
41.000
|
59.000
|
43.000
|
|
|
Male
|
2432
|
0
|
38.030
|
0.495
|
24.429
|
14.000
|
39.000
|
58.000
|
44.000
|
b. How diverse is
this population?
The
population is not exceptionally diverse. The majority of the population is
White taking up 97 percent of the entire community. Whereas, Hispanic is 2
percent and black is 1 percent of the total population. With this data I would focus on age groups or
gender as my values section and compare poverty level, insurance type,
employment status, or household income to get a true sense of any disparities
within the community.
|
Variable
|
Race
|
N
|
N*
|
Mean
|
SE Mean
|
StDev
|
Q1
|
Median
|
Q3
|
IQR
|
|
Age
|
Black
|
53
|
0
|
29.40
|
2.72
|
19.82
|
8.00
|
33.00
|
46.50
|
38.50
|
|
|
Hispanic
|
115
|
0
|
45.11
|
2.41
|
25.89
|
14.00
|
50.00
|
67.00
|
53.00
|
|
|
White
|
4997
|
0
|
38.615
|
0.343
|
24.247
|
16.000
|
40.000
|
58.000
|
42.000
|
What areas of
potential concern arise as you look at this data?
Focusing
primarily on the difference in age groups and as mentioned the potential for an
increase in pregnancies amongst the age groups 25 to 44. I would focus on
outreach programs to communicate safe practices amongst partners. With this
group having a huge difference between male and female. Where male is 43
percent of the population and female is 57 percent of the population. There is
a potential for unsafe practices amongst this community.
What additional data is needed to fully assess population risk and to
identify high-risk members of the population?
In reviewing the data I felt it was
also necessary to evaluate poverty levels, household income, insurance types,
and employment status against the age group. This data would help better
understand the community being served and assist in recommending potential
assistance programs for indigent groups, potentially increase the focus on
pediatrics, prepare care plans for the aging population, and increase focus on
maternity programs.
This
additional data points out that there is a need for an indigent program to
assist non-insured patients, and Medicaid patients, as they make up 20 percent
of the population. Moreover, there is also a need for geriatric treatment as 20
percent of the population also consists of Medicare and retired patients who
fall within the ages 68 to 77.
Additional data that could be accessed and used to potentially improve
care coordination would be patient medical history and present illnesses and
pregnancy rates to compare by age groups. This data would allow for much
thorough understanding of the population and allow analyst to create a risk
assessment. You could further create buckets for risk to place patients and in
turn create care plans for high risk patients.
|
Variable
|
Poverty
Level (Below yes/no) |
N
|
N*
|
Mean
|
SE Mean
|
StDev
|
Q1
|
Median
|
Q3
|
IQR
|
|
Age
|
No
|
4325
|
0
|
44.586
|
0.334
|
21.984
|
30.000
|
45.000
|
63.000
|
33.000
|
|
|
Yes
|
840
|
0
|
8.182
|
0.175
|
5.067
|
4.000
|
7.000
|
13.000
|
9.000
|
|
Variable
|
Employment
Status |
N
|
N*
|
Mean
|
SE Mean
|
StDev
|
Q1
|
Median
|
Q3
|
IQR
|
|
Age
|
Disabled
|
5
|
0
|
39.60
|
7.13
|
15.95
|
27.00
|
34.00
|
55.00
|
28.00
|
|
|
FT
|
1783
|
0
|
42.326
|
0.284
|
11.984
|
33.000
|
43.000
|
52.000
|
19.000
|
|
|
Homemaker
|
17
|
0
|
41.88
|
3.33
|
13.72
|
30.00
|
45.00
|
50.50
|
20.50
|
|
|
n/a
|
365
|
0
|
6.888
|
0.716
|
13.678
|
1.000
|
2.000
|
4.000
|
3.000
|
|
|
PT
|
547
|
0
|
41.199
|
0.527
|
12.331
|
31.000
|
42.000
|
50.000
|
19.000
|
|
|
Retired
|
1045
|
0
|
72.198
|
0.198
|
6.392
|
68.000
|
72.000
|
77.000
|
9.000
|
|
|
Student
|
1156
|
0
|
10.609
|
0.221
|
7.512
|
6.000
|
9.000
|
13.000
|
7.000
|
|
|
Unemployed
|
247
|
0
|
42.789
|
0.802
|
12.606
|
33.000
|
44.000
|
52.000
|
19.000
|
|
Variable
|
Age Group
|
N
|
N*
|
Mean
|
SE Mean
|
StDev
|
Q1
|
Median
|
Q3
|
IQR
|
|
Household
Income
|
< 18
|
1399
|
0
|
20540
|
145
|
5409
|
17785
|
21176
|
22456
|
4671
|
|
|
18-24
|
263
|
0
|
25936
|
55.8
|
905
|
25176
|
25176
|
27009
|
1833
|
|
|
25-44
|
1279
|
0
|
36024
|
172
|
6139
|
33079
|
33500
|
38000
|
4921
|
|
|
45-64
|
1213
|
0
|
48750
|
253
|
8798
|
42000
|
48263
|
53664
|
11664
|
|
|
65+
|
1011
|
0
|
62889
|
326
|
10354
|
61500
|
62000
|
65000
|
3500
|
|
Variable
|
Health
Insurance Coverage |
N
|
N*
|
Mean
|
SE Mean
|
StDev
|
Q1
|
Median
|
Q3
|
IQR
|
|
Age
|
Medicaid
|
399
|
0
|
25.78
|
1.00
|
20.05
|
7.00
|
20.00
|
44.00
|
37.00
|
|
|
Medicare
|
1046
|
0
|
72.024
|
0.231
|
7.476
|
68.000
|
72.000
|
77.000
|
9.000
|
|
|
None
|
644
|
0
|
31.357
|
0.754
|
19.125
|
12.000
|
34.000
|
47.000
|
35.000
|
|
|
Private
|
3076
|
0
|
30.523
|
0.344
|
19.096
|
11.000
|
32.000
|
47.000
|
36.000
|
|
|
|
|
|
|
|
|
|
|
|
|
e. Based on the readings from this week, what recommendation would you make
for next steps?
As
we step towards a focus on patient engagement and improving care for patients.
Data is used as a critical tool to help evaluate disparities in healthcare. One
thing I would recommend moving forward is to leverage data such as the
aforementioned to evaluate the community and create risk buckets for patients.
Coyle and Battles describe introducing antecedents that impact patient safety,
amongst these include environmental and patient risk factors (as cited in
Spooner, Reese, Konschak, & Halamka, 2012, p 218). These factors can help
to further evaluate patients and improve the delivery of care. Moreover, newer
practices to include data such as genetic evaluation and social behaviors play
a critical role in truly transforming how care is delivered. Monegain (2016)
argues on behalf of these points to create a “Total Active Risk” model that
helps physicians and organizations develop more intuitive care programs that
can potentially improve care.
References:
Monegain,
B. (2016). Chilmark: Risk calculations have to change with value-based care.
Retreived from: https://www.healthcareitnews.com/news/chilmark-risk-calculations-have-change-value-based-care-0?mkt_tok=eyJpIjoiWXpFNU5EbGtZelpsTkRjMiIsInQiOiJIcHR6aUNQTTZFRHpFbkRpcW9xOW1KdEpRbHRyTk1zMkZYQ2dVTmpVU2dZNVVjR2g3XC9qS3UxRE9pdTJuYnNCbEx5alc0ekxXSjI3QTVPajh0cFVDMHNzWHFVSkxlRElIbHFXWFZiXC94S0xNPSJ9
Spooner,B.,
Reese,B., Konschak,C., & Halamka,J. (2012). The paradigm shift for quality
care. (1st ed.). Accountable care: Bridging the health information
technology divide (pp218). Virginia Beach, VA: Convurgent Publishing, LLC.
