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Report Template (snap).Rmd
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---
title: "National Longitudinal Survey of Public Health Systems"
author: "University of Kentucky"
date: "July, 2015"
output: pdf_document
fontsize: 10pt
geometry: textwidth=7in, textheight=9.75in
---
\center
\vspace{30 mm}
\LARGE 2014 Comparative Report of Survey Results for
`r data$lhdname2014[i]`, `r data$state2[i]`
\normalsize
\vspace{20 mm}
```{r, echo=FALSE, fig.width=3, fig.height=1}
grid.raster(ncc)
```
\vspace{10 mm}
```{r, echo=FALSE, fig.width=2.5, fig.height=1.25}
grid.raster(uk)
```
\vspace{10 mm}
This study was funded through a grant from the Robert Wood Johnson Foundation
\vspace{15 mm}
\flushright
For inquiries please contact:
David Bardach, PhD
Public Health Practice-Based Research Networks Program
University of Kentucky
Telephone: 859-218-0302
Email: [publichealthPBRN@uky.edu](mailto:publichealthpbrn@uky.edu)
\vspace{2 mm}
***
\flushleft
This report contains preliminary data from the 2014 survey.
All errors are the responsibility of the authors.
\pagebreak
\center
## Introduction
\flushleft
We are pleased to share with you a customized report of results from the ***2014 National Longitudinal Survey of Public Health Systems*** — a survey of a national cohort of local health departments conducted initially in 1998 and again in 2006, 2012, and 2014.
The attached report compares responses received from your local health department with aggregate measures reported by other participating health departments.
This information has been prepared as a courtesy to you, *and will not be disseminated to others*.
We hope you will find this information interesting and helpful.
**Study Overview**: As you may recall, the purpose of this study is to examine the availability of public health activities in communities across the nation, along with the organizations that contribute to performing these activities.
The longitudinal nature of this study provides the opportunity to examine how public health systems are organized and how they change over time.
**Survey Instrument**: The survey instrument used in 2014, 2012, 2006, and 1998 was developed by Dr. C. Arden Miller at the University of North Carolina and Dr. Bernard Turnock at the University of Illinois-Chicago.
This instrument was designed and validated as a screening tool to assess the availability of 20 recommended public health activities in the jurisdictions served by local health departments.
Each of these activities reflects one of the three core public health functions as identified by the Institute of Medicine in 1988 (assessment, policy development, and assurance).
See the appendix to this report for more information on the instrument.
**Study Population**: The initial study population consists of the 497 local health departments that reported serving jurisdictions of at least 100,000 residents in 1997.
A total of 78% of these departments responded to the survey in 1998, 68% responded in 2006, 70% responded in 2012, and 81% responded in 2014.
In 2014, a second study population consisting of 556 local health departments that reported serving jurisdictions of less than 100,000 residents was added.
Of this group, 43% responded to the survey.
**Peer Groups**: Peer groups of local health departments were identified through a statistical clustering procedure that grouped departments based on similarities in several characteristics: population size of the jurisdiction served by the department; proportion of the population that is nonwhite; per capita personal income in the jurisdiction; and involvement in providing environmental health services.
For the initial study population, each department was placed in one of 16 peer groups for comparative analysis.
For the new study population of health departments serving smaller jurisdictions, each department was placed in one of 6 peer groups for comparative analysis.
**Report Graphics**: This report presents data using "Box-and-Whisker" plots.
The upper and lower boundaries of the box are the 75th and 25th percentiles of the data.
The vertical lines extending away from the box (the "whiskers") show the range of responses.
If outliers are present, they are displayed as points beyond the range of the whiskers.
The horizontal bar within the box marks the 50th percentile value.
When applicable, survey results from your jurisdiction are overlaid on this plot as a red asterisk.
If no red asterisk is present, we do not have data for your jurisdiction from the specified survey year.
**Important Limitations**: It is important to recognize that the instrument does not provide a comprehensive assessment of all the important public health activities that may or may not be available at the local level.
Additionally, the instrument relies on self-reported information provided by public health agency administrators and therefore is subject to common sources of measurement error associated with self-reported data.
Validation studies have shown that these sources of error have relatively little effect on the accuracy of population estimates constructed from the instrument, but they can have larger effects on the accuracy of individual observations such as those provided for your individual jurisdiction in this report.
**Your Report and Feedback**: This report provides customized, comparative results for your jurisdiction along with an appendix that describes how measures are constructed.
This report was generated using an automated program, so errors are possible.
We welcome your comments and feedback regarding this information, ***particularly if you note any errors or inaccuracies in the data***.
Additionally, we would be glad to send you copies of subsequent analyses and reports from this study.
Please contact our reseach team at [publichealthPBRN@uky.edu](mailto:publichealthpbrn@uky.edu).
Thank you for your invaluable assistance in making this study possible.
Your contributions provide the critical knowledge and information that will enable continued improvements in public health.
\center
\fbox{Responses from your agency are available for the following years of the survey: `r unique(data$yearsurvey[data$nacchoid == data$nacchoid[i]])` }
\pagebreak
\center
## Availability of Public Health Activities
\flushleft
Measures of the availability of public health activities were constructed from responses to 20 questions asking whether or not a specific public health activity was performed in your jurisdiction.
**Figure 1** shows the overall proportion of these activities that were reported as available in your jurisdiction, compared to similar "peer group" jurisdictions and to all U.S. jurisdictions included in the survey.
The box shows the range in which most jurisdictions fall, and the whiskers show the range for all non-outlying jurisdictions.
**Table 1** provides detailed information on the availability of each of the 20 activities.
In this table, columns with years as headers report back data from your jurisdiction.
The "Peers" column and the "USA" column offer comparative snapshots to the rest of the data from 2014.
\vspace{-3 mm}
### Figure 1: Proportion of Public Health Activities Available in the Jurisdiction
\vspace{-3 mm}
\center
```{r, fig.width=6, fig.height=3, echo=FALSE, warning=FALSE}
library(ggplot2)
library(gridExtra)
p1 <- ggplot() +
geom_boxplot(data = data[data$peer == data$peer[i],],
aes(factor(yearsurvey), 100*avtot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*avtot), pch = 8, colour = "red", size = 5) +
ggtitle("Peer group") +
theme(legend.position = "none", plot.title = element_text(size=10), axis.title = element_text(size=10)) +
labs(x = "Year", y = "Percent") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
p2 <- ggplot() +
geom_boxplot(data = data,
aes(factor(yearsurvey), 100*avtot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*avtot), pch = 8, colour = "red", size = 5) +
ggtitle("US Average") +
theme(legend.position = "none", plot.title = element_text(size=10),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.title = element_text(size=10)) +
labs(x = "Year", y = "Percent") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
grid.arrange(p1, p2, ncol=2)
```
\vspace{-2 mm}
\small
Note: percentages reflect percentage of maximum score
\flushleft
\vspace{-4 mm}
### Table 1: Availability of Public Health Activities within Local Communities
\vspace{-1 mm}
\center
\begin{tabular}{lcccccc}
\hline
\textbf{} &
\textbf{} &
\textbf{Peers} &
\textbf{USA} \\
\textbf{Activity} &
\textbf{2014} &
\textbf{(\%)} &
\textbf{(\%)} \\
\hline
1. Community needs assessment &
`r ifelse(is.na(data$av1[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av1[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av1.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av1.m[sum14.all$Big == data$Big[i]], 0)` \\
2. Behavioral risk factor survey &
`r ifelse(is.na(data$av2[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av2[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av2.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av2.m[sum14.all$Big == data$Big[i]], 0)` \\
3. Adverse health events investigation &
`r ifelse(is.na(data$av3[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av3[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av3.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av3.m[sum14.all$Big == data$Big[i]], 0)` \\
4. Public health laboratory services &
`r ifelse(is.na(data$av4[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av4[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av4.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av4.m[sum14.all$Big == data$Big[i]], 0)` \\
5. Analysis of health determinants \& resources &
`r ifelse(is.na(data$av5[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av5[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av5.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av5.m[sum14.all$Big == data$Big[i]], 0)` \\
6. Analysis of preventive services use &
`r ifelse(is.na(data$av6[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av6[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av6.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av6.m[sum14.all$Big == data$Big[i]], 0)` \\
\hline
7. Communication network of health organizations &
`r ifelse(is.na(data$av7[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av7[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av7.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av7.m[sum14.all$Big == data$Big[i]], 0)` \\
8. Inform elected officials about health issues &
`r ifelse(is.na(data$av8[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av8[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av8.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av8.m[sum14.all$Big == data$Big[i]], 0)` \\
9. Prioritization of community health needs &
`r ifelse(is.na(data$av9[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av9[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av9.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av9.m[sum14.all$Big == data$Big[i]], 0)` \\
10. Implementation of health initiatives in priority areas &
`r ifelse(is.na(data$av10[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av10[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av10.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av10.m[sum14.all$Big == data$Big[i]], 0)` \\
11. Community participation in health planning &
`r ifelse(is.na(data$av11[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av11[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av11.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av11.m[sum14.all$Big == data$Big[i]], 0)` \\
12. Resource allocation planning &
`r ifelse(is.na(data$av12[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av12[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av12.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av12.m[sum14.all$Big == data$Big[i]], 0)` \\
13. Resource deployment consistent with plan &
`r ifelse(is.na(data$av13[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av13[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av13.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av13.m[sum14.all$Big == data$Big[i]], 0)` \\
\hline
14. LHD organizational assessment &
`r ifelse(is.na(data$av14[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av14[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av14.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av14.m[sum14.all$Big == data$Big[i]], 0)` \\
15. Provision/linkage to needed health services &
`r ifelse(is.na(data$av15[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av15[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av15.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av15.m[sum14.all$Big == data$Big[i]], 0)` \\
16. Evaluation of public health services &
`r ifelse(is.na(data$av16[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av16[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av16.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av16.m[sum14.all$Big == data$Big[i]], 0)` \\
17. Monitor/improve program processes and outcomes &
`r ifelse(is.na(data$av17[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av17[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av17.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av17.m[sum14.all$Big == data$Big[i]], 0)` \\
18. Health information provision to the public &
`r ifelse(is.na(data$av18[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av18[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av18.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av18.m[sum14.all$Big == data$Big[i]], 0)` \\
19. Health information provision to the media &
`r ifelse(is.na(data$av19[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av19[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av19.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av19.m[sum14.all$Big == data$Big[i]], 0)` \\
20. Implementation of mandated PH activities &
`r ifelse(is.na(data$av20[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
ifelse(data$av20[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]] == 1, "Yes", "No"))` &
`r round(100*sum14.peer$av20.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$av20.m[sum14.all$Big == data$Big[i]], 0)` \\
\hline
\textit{Percent of assessment activities (\#1-6)} &
`r ifelse(is.na(data$avass[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$avass[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$avass.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$avass.m[sum14.all$Big == data$Big[i]], 0)` \\
\textit{Percent of policy development activities (\#7-13)} &
`r ifelse(is.na(data$avpol[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$avpol[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$avpol.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$avpol.m[sum14.all$Big == data$Big[i]], 0)` \\
\textit{Percent of assurance activities (\#14-20)} &
`r ifelse(is.na(data$avasr[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$avasr[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$avasr.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$avasr.m[sum14.all$Big == data$Big[i]], 0)` \\
\textit{Overall percentage - all activities (weighted by IOM function)} &
`r ifelse(is.na(data$avtot[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$avtot[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$avtot.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$avtot.m[sum14.all$Big == data$Big[i]], 0)` \\
\hline
\end{tabular}
\pagebreak
\center
\normalsize
## Perceived Effectiveness of Public Health Activities
\flushleft
Measures of the perceived effectiveness of public health activities were constructed from responses to questions asking how well each public health activity is performed within the jurisdiction, using a five-point Likert scale ranging from "fully meets needs" to "meets no needs."
**Figure 2** shows the aggregate measure of perceived effectiveness across all activities that were reported as available in your jurisdiction, compared to similar "peer group" jurisdictions and to all U.S. jurisdictions included in the survey.
The box shows the range in which most jurisdictions fall, and the whiskers show the range for all non-outlying jurisdictions.
**Table 2** provides detailed information on the perceived effectiveness of each of the 19 activities (activity \#20 was excluded from this measure).
In this table, columns with years as headers report back data from your jurisdiction.
The "Peers" column and the "USA" column offer comparative snapshots to the rest of the data from 2014.
\vspace{-3 mm}
### Figure 2: Perceived Effectiveness of Public Health Activities
\vspace{-3 mm}
\center
```{r, fig.width=6, fig.height=3, echo=FALSE, warning=FALSE}
p1 <- ggplot() +
geom_boxplot(data = data[data$peer == data$peer[i],],
aes(factor(yearsurvey), 100*efftot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*efftot), pch = 8, colour = "red", size = 5) +
ggtitle("Peer group") +
theme(legend.position = "none", plot.title = element_text(size=10), axis.title = element_text(size=10)) +
labs(x = "Year", y = "Percent") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
p2 <- ggplot() +
geom_boxplot(data = data,
aes(factor(yearsurvey), 100*efftot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*efftot), pch = 8, colour = "red", size = 5) +
ggtitle("US Average") +
theme(legend.position = "none", plot.title = element_text(size=10),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.title = element_text(size=10)) +
labs(x = "Year", y = "Percent") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
grid.arrange(p1, p2, ncol=2)
```
\vspace{-2 mm}
\small
Note: percentages reflect percentage of maximum score
\flushleft
\vspace{-4 mm}
### Table 2: Perceived Effectiveness of Public Health Activities
\vspace{-1 mm}
\center
\begin{tabular}{lcccccc}
\hline
\textbf{} &
\textbf{2014} &
\textbf{Peers} &
\textbf{USA} \\
\textbf{Activity} &
\textbf{(\%)} &
\textbf{(\%)} &
\textbf{(\%)} \\
\hline
1. Community needs assessment &
`r ifelse(is.na(data$eff1[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff1[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff1.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff1.m[sum14.all$Big == data$Big[i]], 0)` \\
2. Behavioral risk factor survey &
`r ifelse(is.na(data$eff2[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff2[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff2.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff2.m[sum14.all$Big == data$Big[i]], 0)` \\
3. Adverse health events investigation &
`r ifelse(is.na(data$eff3[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff3[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff3.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff3.m[sum14.all$Big == data$Big[i]], 0)` \\
4. Public health laboratory services &
`r ifelse(is.na(data$eff4[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff4[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff4.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff4.m[sum14.all$Big == data$Big[i]], 0)` \\
5. Analysis of health determinants \& resources &
`r ifelse(is.na(data$eff5[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff5[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff5.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff5.m[sum14.all$Big == data$Big[i]], 0)` \\
6. Analysis of preventive services use &
`r ifelse(is.na(data$eff6[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff6[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff6.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff6.m[sum14.all$Big == data$Big[i]], 0)` \\
\hline
7. Communication network of health organizations &
`r ifelse(is.na(data$eff7[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff7[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff7.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff7.m[sum14.all$Big == data$Big[i]], 0)` \\
8. Inform elected officials about health issues &
`r ifelse(is.na(data$eff8[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff8[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff8.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff8.m[sum14.all$Big == data$Big[i]], 0)` \\
9. Prioritization of community health needs &
`r ifelse(is.na(data$eff9[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff9[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff9.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff9.m[sum14.all$Big == data$Big[i]], 0)` \\
10. Implementation of health initiatives in priority areas &
`r ifelse(is.na(data$eff10[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff10[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff10.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff10.m[sum14.all$Big == data$Big[i]], 0)` \\
11. Community participation in health planning &
`r ifelse(is.na(data$eff11[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff11[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff11.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff11.m[sum14.all$Big == data$Big[i]], 0)` \\
12. Resource allocation planning &
`r ifelse(is.na(data$eff12[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff12[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff12.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff12.m[sum14.all$Big == data$Big[i]], 0)` \\
13. Resource deployment consistent with plan &
`r ifelse(is.na(data$eff13[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff13[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff13.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff13.m[sum14.all$Big == data$Big[i]], 0)` \\
\hline
14. LHD organizational assessment &
`r ifelse(is.na(data$eff14[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff14[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff14.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff14.m[sum14.all$Big == data$Big[i]], 0)` \\
15. Provision/linkage to needed health services &
`r ifelse(is.na(data$eff15[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff15[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff15.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff15.m[sum14.all$Big == data$Big[i]], 0)` \\
16. Evaluation of public health services &
`r ifelse(is.na(data$eff16[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff16[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff16.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff16.m[sum14.all$Big == data$Big[i]], 0)` \\
17. Monitor/improve program processes and outcomes &
`r ifelse(is.na(data$eff17[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff17[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff17.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff17.m[sum14.all$Big == data$Big[i]], 0)` \\
18. Health information provision to the public &
`r ifelse(is.na(data$eff18[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff18[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff18.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff18.m[sum14.all$Big == data$Big[i]], 0)` \\
19. Health information provision to the media &
`r ifelse(is.na(data$eff19[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$eff19[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$eff19.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$eff19.m[sum14.all$Big == data$Big[i]], 0)` \\
\hline
\textit{Percent of assessment activities (\#1-6)} &
`r ifelse(is.na(data$effass[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$effass[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$effass.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$effass.m[sum14.all$Big == data$Big[i]], 0)` \\
\textit{Percent of policy development activities (\#7-13)} &
`r ifelse(is.na(data$effpol[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$effpol[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$effpol.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$effpol.m[sum14.all$Big == data$Big[i]], 0)` \\
\textit{Percent of assurance activities (\#14-19)} &
`r ifelse(is.na(data$effasr[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$effasr[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$effasr.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$effasr.m[sum14.all$Big == data$Big[i]], 0)` \\
\textit{Overall percentage - all activities (weighted by IOM function)} &
`r ifelse(is.na(data$efftot[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$efftot[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$efftot.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$efftot.m[sum14.all$Big == data$Big[i]], 0)` \\
\hline
\end{tabular}
\flushleft
\footnotesize
\vspace{-1 mm}
Likert rating scale: \newline
\vspace{-1 mm}
100% = all needs met;
75% = most needs met;
50% = about half of needs met;
25% = some needs met;
0% = no needs met
\normalsize
\pagebreak
\center
## Local Health Department Contribution to Public Health Activities
\flushleft
Measures of the local health department's contribution to public health activities were constructed from responses to questions asking how much of the total community effort for each public health activity is contributed by the local department, using a five-point Likert scale ranging from "all effort" to "no effort."
**Figure 3** shows the aggregate contribution measure across all activities that were reported as available in your jurisdiction, compared to similar "peer group" jurisdictions and to all U.S. jurisdictions included in the survey.
The box shows the range in which most jurisdictions fall, and the whiskers show the range for all non-outlying jurisdictions.
**Table 3** provides detailed information on contributions to each of the 19 activities (activity \#20 was excluded from this measure).
In this table, columns with years as headers report back data from your jurisdiction.
The "Peers" column and the "USA" column offer comparative snapshots to the rest of the data from 2014.
\vspace{-3 mm}
### Figure 3: Proportion of Effort Contributed by Local Health Department
\vspace{-3 mm}
\center
```{r, fig.width=6, fig.height=3, echo=FALSE, warning=FALSE}
p1 <- ggplot() +
geom_boxplot(data = data[data$peer == data$peer[i],],
aes(factor(yearsurvey), 100*lhdtot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*lhdtot), pch = 8, colour = "red", size = 5) +
ggtitle("Peer group") +
theme(legend.position = "none", plot.title = element_text(size=10), axis.title = element_text(size=10)) +
labs(x = "Year", y = "Percent") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
p2 <- ggplot() +
geom_boxplot(data = data,
aes(factor(yearsurvey), 100*lhdtot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*lhdtot), pch = 8, colour = "red", size = 5) +
ggtitle("US Average") +
theme(legend.position = "none", plot.title = element_text(size=10),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.title = element_text(size=10)) +
labs(x = "Year", y = "Percent") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
grid.arrange(p1, p2, ncol=2)
```
\vspace{-2 mm}
\small
Note: percentages reflect percentage of maximum score
\flushleft
\vspace{-4 mm}
### Table 3: Proportion of Effort Contributed by the Local Health Department
\vspace{-1 mm}
\center
\begin{tabular}{lcccccc}
\hline
\textbf{} &
\textbf{2014} &
\textbf{Peers} &
\textbf{USA} \\
\textbf{Activity} &
\textbf{(\%)} &
\textbf{(\%)} &
\textbf{(\%)} \\
\hline
1. Community needs assessment &
`r ifelse(is.na(data$lhd1[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd1[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd1.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd1.m[sum14.all$Big == data$Big[i]], 0)` \\
2. Behavioral risk factor survey &
`r ifelse(is.na(data$lhd2[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd2[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd2.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd2.m[sum14.all$Big == data$Big[i]], 0)` \\
3. Adverse health events investigation &
`r ifelse(is.na(data$lhd3[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd3[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd3.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd3.m[sum14.all$Big == data$Big[i]], 0)` \\
4. Public health laboratory services &
`r ifelse(is.na(data$lhd4[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd4[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd4.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd4.m[sum14.all$Big == data$Big[i]], 0)` \\
5. Analysis of health determinants \& resources &
`r ifelse(is.na(data$lhd5[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd5[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd5.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd5.m[sum14.all$Big == data$Big[i]], 0)` \\
6. Analysis of preventive services use &
`r ifelse(is.na(data$lhd6[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd6[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd6.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd6.m[sum14.all$Big == data$Big[i]], 0)` \\
\hline
7. Communication network of health organizations &
`r ifelse(is.na(data$lhd7[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd7[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd7.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd7.m[sum14.all$Big == data$Big[i]], 0)` \\
8. Inform elected officials about health issues &
`r ifelse(is.na(data$lhd8[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd8[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd8.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd8.m[sum14.all$Big == data$Big[i]], 0)` \\
9. Prioritization of community health needs &
`r ifelse(is.na(data$lhd9[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd9[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd9.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd9.m[sum14.all$Big == data$Big[i]], 0)` \\
10. Implementation of health initiatives in priority areas &
`r ifelse(is.na(data$lhd10[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd10[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd10.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd10.m[sum14.all$Big == data$Big[i]], 0)` \\
11. Community participation in health planning &
`r ifelse(is.na(data$lhd11[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd11[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd11.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd11.m[sum14.all$Big == data$Big[i]], 0)` \\
12. Resource allocation planning &
`r ifelse(is.na(data$lhd12[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd12[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd12.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd12.m[sum14.all$Big == data$Big[i]], 0)` \\
13. Resource deployment consistent with plan &
`r ifelse(is.na(data$lhd13[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd13[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd13.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd13.m[sum14.all$Big == data$Big[i]], 0)` \\
\hline
14. LHD organizational assessment &
`r ifelse(is.na(data$lhd14[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd14[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd14.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd14.m[sum14.all$Big == data$Big[i]], 0)` \\
15. Provision/linkage to needed health services &
`r ifelse(is.na(data$lhd15[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd15[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd15.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd15.m[sum14.all$Big == data$Big[i]], 0)` \\
16. Evaluation of public health services &
`r ifelse(is.na(data$lhd16[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd16[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd16.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd16.m[sum14.all$Big == data$Big[i]], 0)` \\
17. Monitor/improve program processes and outcomes &
`r ifelse(is.na(data$lhd17[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd17[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd17.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd17.m[sum14.all$Big == data$Big[i]], 0)` \\
18. Health information provision to the public &
`r ifelse(is.na(data$lhd18[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd18[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd18.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd18.m[sum14.all$Big == data$Big[i]], 0)` \\
19. Health information provision to the media &
`r ifelse(is.na(data$lhd19[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhd19[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhd19.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhd19.m[sum14.all$Big == data$Big[i]], 0)` \\
\hline
\textit{Percent of assessment activities (\#1-6)} &
`r ifelse(is.na(data$lhdass[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhdass[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhdass.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhdass.m[sum14.all$Big == data$Big[i]], 0)` \\
\textit{Percent of policy development activities (\#7-13)} &
`r ifelse(is.na(data$lhdpol[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhdpol[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhdpol.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhdpol.m[sum14.all$Big == data$Big[i]], 0)` \\
\textit{Percent of assurance activities (\#14-19)} &
`r ifelse(is.na(data$lhdasr[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhdasr[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhdasr.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhdasr.m[sum14.all$Big == data$Big[i]], 0)` \\
\textit{Overall percentage - all activities (weighted by IOM function)} &
`r ifelse(is.na(data$lhdtot[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]]), "",
round(100*data$lhdtot[data$yearsurvey == 2014 & data$nacchoid == data$nacchoid[i]], 0))` &
`r round(100*sum14.peer$lhdtot.m[sum14.peer$peer == data$peer[i]], 0)` &
`r round(100*sum14.all$lhdtot.m[sum14.all$Big == data$Big[i]], 0)` \\
\hline
\end{tabular}
\flushleft
\footnotesize
\vspace{-1 mm}
Likert rating scale: \newline
\vspace{-1 mm}
100% = all needs met;
75% = most needs met;
50% = about half of needs met;
25% = some needs met;
0% = no needs met
\normalsize
\pagebreak
\center
## Scope of Participation by Other Organizations
\flushleft
Measures of the extent to which other organizations participate in performing public health activities were constructed from responses to questions asking about the types of other organizations that contribute to each public health activity.
For each type of organization, we computed the proportion of the 19 public health activities to which they contribute (activity \#20 was excluded from this measure).
**Figures 4-14** show these participation measures for the most prevalent organizational categories (not all categories are shown).
The box shows the range in which most jurisdictions fall, and the whiskers show the range for all non-outlying jurisdictions.
\center
```{r, echo=FALSE, warning=FALSE, fig.height=3.25}
blank <- rectGrob(gp=gpar(col="white"))
# State Gov't Agency = sta
p1 <- ggplot() +
geom_boxplot(data = data[data$peer == data$peer[i],],
aes(factor(yearsurvey), 100*statot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*statot), pch = 8, colour = "red", size = 5) +
ggtitle("Peer group") +
theme(legend.position = "none", plot.title = element_text(size=10), axis.title = element_text(size=10)) +
labs(x = "", y = "State Government Agencies (%)") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
p2 <- ggplot() +
geom_boxplot(data = data,
aes(factor(yearsurvey), 100*statot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*statot), pch = 8, colour = "red", size = 5) +
ggtitle("US Average") +
theme(legend.position = "none", plot.title = element_text(size=10),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.title = element_text(size=10)) +
labs(x = " ") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
grid.arrange(p1, p2, blank, widths=c(0.475, 0.425, 0.1), ncol=3)
# Local Gov't Agency = loc
p1 <- ggplot() +
geom_boxplot(data = data[data$peer == data$peer[i],],
aes(factor(yearsurvey), 100*loctot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*loctot), pch = 8, colour = "red", size = 5) +
ggtitle("Peer group") +
theme(legend.position = "none", plot.title = element_text(size=10), axis.title = element_text(size=10)) +
labs(x = "", y = "Local Government Agencies (%)") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
p2 <- ggplot() +
geom_boxplot(data = data,
aes(factor(yearsurvey), 100*loctot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*loctot), pch = 8, colour = "red", size = 5) +
ggtitle("US Average") +
theme(legend.position = "none", plot.title = element_text(size=10),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.title = element_text(size=10)) +
labs(x = " ") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
grid.arrange(p1, p2, blank, widths=c(0.475, 0.425, 0.1), ncol=3)
# Federal Gov't Agency = fed
p1 <- ggplot() +
geom_boxplot(data = data[data$peer == data$peer[i],],
aes(factor(yearsurvey), 100*fedtot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*fedtot), pch = 8, colour = "red", size = 5) +
ggtitle("Peer group") +
theme(legend.position = "none", plot.title = element_text(size=10), axis.title = element_text(size=10)) +
labs(x = "", y = "Federal Government Agencies (%)") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
p2 <- ggplot() +
geom_boxplot(data = data,
aes(factor(yearsurvey), 100*fedtot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*fedtot), pch = 8, colour = "red", size = 5) +
ggtitle("US Average") +
theme(legend.position = "none", plot.title = element_text(size=10),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.title = element_text(size=10)) +
labs(x = " ") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
grid.arrange(p1, p2, blank, widths=c(0.475, 0.425, 0.1), ncol=3)
# Hospitals = hsp
p1 <- ggplot() +
geom_boxplot(data = data[data$peer == data$peer[i],],
aes(factor(yearsurvey), 100*hsptot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*hsptot), pch = 8, colour = "red", size = 5) +
ggtitle("Peer group") +
theme(legend.position = "none", plot.title = element_text(size=10), axis.title = element_text(size=10)) +
labs(x = "", y = "Hospitals (%)") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
p2 <- ggplot() +
geom_boxplot(data = data,
aes(factor(yearsurvey), 100*hsptot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*hsptot), pch = 8, colour = "red", size = 5) +
ggtitle("US Average") +
theme(legend.position = "none", plot.title = element_text(size=10),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.title = element_text(size=10)) +
labs(x = " ") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
grid.arrange(p1, p2, blank, widths=c(0.475, 0.425, 0.1), ncol=3)
# Physician Practice = phy
p1 <- ggplot() +
geom_boxplot(data = data[data$peer == data$peer[i],],
aes(factor(yearsurvey), 100*phytot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*phytot), pch = 8, colour = "red", size = 5) +
ggtitle("Peer group") +
theme(legend.position = "none", plot.title = element_text(size=10), axis.title = element_text(size=10)) +
labs(x = "", y = "Physician Practices (%)") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
p2 <- ggplot() +
geom_boxplot(data = data,
aes(factor(yearsurvey), 100*phytot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*phytot), pch = 8, colour = "red", size = 5) +
ggtitle("US Average") +
theme(legend.position = "none", plot.title = element_text(size=10),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.title = element_text(size=10)) +
labs(x = " ") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
grid.arrange(p1, p2, blank, widths=c(0.475, 0.425, 0.1), ncol=3)
# Community Health Centers = chc
p1 <- ggplot() +
geom_boxplot(data = data[data$peer == data$peer[i],],
aes(factor(yearsurvey), 100*chctot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*chctot), pch = 8, colour = "red", size = 5) +
ggtitle("Peer group") +
theme(legend.position = "none", plot.title = element_text(size=10), axis.title = element_text(size=10)) +
labs(x = "", y = "Community Health Centers (%)") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
p2 <- ggplot() +
geom_boxplot(data = data,
aes(factor(yearsurvey), 100*chctot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*chctot), pch = 8, colour = "red", size = 5) +
ggtitle("US Average") +
theme(legend.position = "none", plot.title = element_text(size=10),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.title = element_text(size=10)) +
labs(x = " ") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
grid.arrange(p1, p2, blank, widths=c(0.475, 0.425, 0.1), ncol=3)
# Health Insurers = ins
p1 <- ggplot() +
geom_boxplot(data = data[data$peer == data$peer[i],],
aes(factor(yearsurvey), 100*instot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*instot), pch = 8, colour = "red", size = 5) +
ggtitle("Peer group") +
theme(legend.position = "none", plot.title = element_text(size=10), axis.title = element_text(size=10)) +
labs(x = "", y = "Health Insurers (%)") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
p2 <- ggplot() +
geom_boxplot(data = data,
aes(factor(yearsurvey), 100*instot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*instot), pch = 8, colour = "red", size = 5) +
ggtitle("US Average") +
theme(legend.position = "none", plot.title = element_text(size=10),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.title = element_text(size=10)) +
labs(x = " ") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
grid.arrange(p1, p2, blank, widths=c(0.475, 0.425, 0.1), ncol=3)
# Other Nonprofits = nono
p1 <- ggplot() +
geom_boxplot(data = data[data$peer == data$peer[i],],
aes(factor(yearsurvey), 100*nonotot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*nonotot), pch = 8, colour = "red", size = 5) +
ggtitle("Peer group") +
theme(legend.position = "none", plot.title = element_text(size=10), axis.title = element_text(size=10)) +
labs(x = "", y = "Other Nonprofits (%)") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
p2 <- ggplot() +
geom_boxplot(data = data,
aes(factor(yearsurvey), 100*nonotot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*nonotot), pch = 8, colour = "red", size = 5) +
ggtitle("US Average") +
theme(legend.position = "none", plot.title = element_text(size=10),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.title = element_text(size=10)) +
labs(x = " ") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
grid.arrange(p1, p2, blank, widths=c(0.475, 0.425, 0.1), ncol=3)
# Universities/Colleges = uni
p1 <- ggplot() +
geom_boxplot(data = data[data$peer == data$peer[i],],
aes(factor(yearsurvey), 100*unitot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*unitot), pch = 8, colour = "red", size = 5) +
ggtitle("Peer group") +
theme(legend.position = "none", plot.title = element_text(size=10), axis.title = element_text(size=10)) +
labs(x = "", y = "Universities/Colleges (%)") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
p2 <- ggplot() +
geom_boxplot(data = data,
aes(factor(yearsurvey), 100*unitot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*unitot), pch = 8, colour = "red", size = 5) +
ggtitle("US Average") +
theme(legend.position = "none", plot.title = element_text(size=10),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.title = element_text(size=10)) +
labs(x = " ") +
xlim(levels(factor(data$yearsurvey))) +
ylim(0,100)
grid.arrange(p1, p2, blank, widths=c(0.475, 0.425, 0.1), ncol=3)
# Employers/Businesses = emp
p1 <- ggplot() +
geom_boxplot(data = data[data$peer == data$peer[i],],
aes(factor(yearsurvey), 100*emptot, group = yearsurvey, fill = factor(yearsurvey))) +
scale_fill_manual(values = c("deepskyblue", "goldenrod1", "olivedrab1", "yellow2")) +
geom_point(data = data[data$nacchoid == data$nacchoid[i],],
aes(factor(yearsurvey), 100*emptot), pch = 8, colour = "red", size = 5) +
ggtitle("Peer group") +