Networked Data Lab long chart on children and young people’s mental health

Performance against 18 week target in Scottish CAMHS
- Raw time series
- Monthly average, per year
People still receiving treatment in Scottish CAMHS
- Raw time series
- Monthly average, per year
People waiting for treatment in Scottish CAMHS
- Raw time series
- Monthly average, per year
People in contact with CAMHS
New referrals (<18)
New referrals to CYPMHS
First contacts (<18)
Attended contacts (<18)
Open referrals
People with eating disorders being seen within target times (<18)
New referrals into eating disorder services (<18)
Health Education England data on workforce (compared to activity levels from NHS England)
NHS England data on workforce (child and adolescent psychiatry only)

#Subsample of metrics from MHSDS data
CAMHS_data <- MHSDS_main_pooled_dashboard %>%
  filter(PRIMARY_LEVEL_DESCRIPTION=="England",
         MEASURE_ID %in% c("CYP01","MHS61a","CYP23","MHS30d","MHS32a","CYP32")) %>%
  filter(.,!(MEASURE_ID=="MHS32a"&BREAKDOWN!="England")) %>% 
  mutate(MEASURE_KEY=case_when(
           MEASURE_ID=="MHS30d" ~ "Attended contacts (<18)",
           MEASURE_ID=="CYP01" ~ "People in contact",
           MEASURE_ID=="MHS61a" ~ "First contacts (<18)",
           MEASURE_ID=="CYP23" ~ "Open referrals",
           MEASURE_ID=="MHS32a" ~ "New referrals (<18)",
           MEASURE_ID=="CYP32" ~ "New referrals to CYPMHS",
           TRUE ~ "NA"
         )) %>% 
  select(.,start_date,end_date,month_year,PRIMARY_LEVEL_DESCRIPTION,MEASURE_ID,MEASURE_KEY,MEASURE_VALUE) %>% 
  mutate(.,MEASURE_VALUE=as.numeric(MEASURE_VALUE),
         timing=ifelse(start_date<ymd("2020-04-01"),"Pre-COVID","Post-COVID"),
         month_num=lubridate::month(start_date)) %>%
  mutate(.,timing=fct_relevel(timing, c("Pre-COVID","Post-COVID"))) %>% 
  arrange(.,start_date) %>%
  as_tibble()

#Percentage change compared to a year ago
#For example, for March 2020 show the % change between March 2019 and March 2020

CAMHS_yearly_changes <- CAMHS_data %>%
  select(.,PRIMARY_LEVEL_DESCRIPTION,MEASURE_ID,MEASURE_KEY,MEASURE_VALUE,start_date,month_year,timing) %>%
  mutate(start_date=lubridate::ymd(start_date)) %>%
  mutate(.,start_date_l1=start_date-years(1)) #Adds a new column with the month a year before

#Auxiliary dataset with data points from a year before
CAMHS_data_l1 <- CAMHS_yearly_changes %>%
  select(.,start_date,MEASURE_VALUE,PRIMARY_LEVEL_DESCRIPTION,MEASURE_ID,MEASURE_KEY) %>%
  rename(.,MEASURE_VALUE_l1=MEASURE_VALUE,start_date_l1=start_date)

#Merge auxiliary data back in
CAMHS_yearly_changes <- left_join(CAMHS_yearly_changes,
                                          CAMHS_data_l1,
                                          by=c("start_date_l1","PRIMARY_LEVEL_DESCRIPTION","MEASURE_ID","MEASURE_KEY")) %>%
  arrange(.,MEASURE_ID,start_date) %>%
  mutate(pct_change_l1=(MEASURE_VALUE-MEASURE_VALUE_l1)/MEASURE_VALUE_l1*100) %>%
  filter(.,!is.na(pct_change_l1))
rm(CAMHS_data_l1)

flourish_data_mhsds <- CAMHS_data %>%
  filter(.,MEASURE_ID %in% c("CYP01","MHS61a","CYP23","MHS32a")) %>%
  select(.,start_date,end_date,month_year,PRIMARY_LEVEL_DESCRIPTION,MEASURE_ID,MEASURE_KEY,MEASURE_VALUE) %>%
  mutate(.,month=lubridate::month(start_date, label = TRUE),
         year=lubridate::year(start_date)) %>%
  select(.,month,year,MEASURE_ID,MEASURE_KEY,MEASURE_VALUE) %>%
  pivot_wider(.,
    names_from = year,
    names_sep = ".",
    values_from = c(MEASURE_VALUE)
  ) %>%
  arrange(.,MEASURE_ID,month)

#fwrite(flourish_data_mhsds,paste0(onedrive_charts_data,"Fig2alt.csv"))

Performance against 18 week target in Scottish CAMHS

Measure code: TotalPatientsSeen and NumberOfPatientsSeen0To18Weeks
Measure description:
Source: NHS Scotland Open Data

Raw time series

#Data
CAMHS_Scotland_18wks <- scotland_camhs_appended %>%
  filter(.,Metric %in% c("TotalPatientsSeen","NumberOfPatientsSeen0To18Weeks"),
         HB=="S92000003") %>%
  pivot_wider(names_from = Metric,
              names_sep = ".",
              values_from = c(Count)) %>%
  mutate(.,pct_within_18wks=as.numeric(NumberOfPatientsSeen0To18Weeks)/
           as.numeric(TotalPatientsSeen)*100,
         year_month=ymd(year_month))

#Time series chart
CAMHS_Scotland_18wks_chart <- CAMHS_Scotland_18wks %>% 
  ggplot(., aes(x=year_month, y=pct_within_18wks)) +
  geom_line(size=1) +
  scale_x_date(date_labels = "%b %Y",date_breaks = "3 months") +
  scale_y_continuous(labels = scales::comma, limits=c(0,100)) +
  geom_hline(yintercept=90, linetype="dashed", color = "red") +
  theme_ipsum() +
  xlab("") +
  ylab("") +
  labs(col="") +
  scale_color_brewer(palette = "Set1") +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))
  
ggplotly(CAMHS_Scotland_18wks_chart)

#Underlying data
CAMHS_Scotland_18wks %>%
  arrange(.,year_month) %>% 
  knitr::kable(., align = "lccrr",format.args = list(big.mark = ","))

HB	Month	year	month	year_month	TotalPatientsSeen	NumberOfPatientsSeen0To18Weeks	pct_within_18wks
S92000003	201,207	2,012	7	2012-07-01	751	611	81.35819
S92000003	201,208	2,012	8	2012-08-01	840	653	77.73810
S92000003	201,209	2,012	9	2012-09-01	807	633	78.43866
S92000003	201,210	2,012	10	2012-10-01	1083	858	79.22438
S92000003	201,211	2,012	11	2012-11-01	1236	1023	82.76699
S92000003	201,212	2,012	12	2012-12-01	847	707	83.47107
S92000003	201,301	2,013	1	2013-01-01	1190	986	82.85714
S92000003	201,302	2,013	2	2013-02-01	1264	1077	85.20570
S92000003	201,303	2,013	3	2013-03-01	1057	933	88.26869
S92000003	201,304	2,013	4	2013-04-01	1153	993	86.12316
S92000003	201,305	2,013	5	2013-05-01	1273	1093	85.86017
S92000003	201,306	2,013	6	2013-06-01	1024	885	86.42578
S92000003	201,307	2,013	7	2013-07-01	997	847	84.95486
S92000003	201,308	2,013	8	2013-08-01	976	788	80.73770
S92000003	201,309	2,013	9	2013-09-01	983	840	85.45270
S92000003	201,310	2,013	10	2013-10-01	1180	991	83.98305
S92000003	201,311	2,013	11	2013-11-01	1241	983	79.21031
S92000003	201,312	2,013	12	2013-12-01	909	773	85.03850
S92000003	201,401	2,014	1	2014-01-01	1342	1126	83.90462
S92000003	201,402	2,014	2	2014-02-01	1277	1055	82.61551
S92000003	201,403	2,014	3	2014-03-01	1114	949	85.18851
S92000003	201,404	2,014	4	2014-04-01	1153	965	83.69471
S92000003	201,405	2,014	5	2014-05-01	1359	1129	83.07579
S92000003	201,406	2,014	6	2014-06-01	1048	831	79.29389
S92000003	201,407	2,014	7	2014-07-01	993	795	80.06042
S92000003	201,408	2,014	8	2014-08-01	997	764	76.62989
S92000003	201,409	2,014	9	2014-09-01	1333	1036	77.71943
S92000003	201,410	2,014	10	2014-10-01	1442	1111	77.04577
S92000003	201,411	2,014	11	2014-11-01	1464	1140	77.86885
S92000003	201,412	2,014	12	2014-12-01	1266	1040	82.14850
S92000003	201,501	2,015	1	2015-01-01	1466	1155	78.78581
S92000003	201,502	2,015	2	2015-02-01	1391	1069	76.85119
S92000003	201,503	2,015	3	2015-03-01	1412	1145	81.09065
S92000003	201,504	2,015	4	2015-04-01	1445	1134	78.47751
S92000003	201,505	2,015	5	2015-05-01	1476	1120	75.88076
S92000003	201,506	2,015	6	2015-06-01	1542	1170	75.87549
S92000003	201,507	2,015	7	2015-07-01	1415	1072	75.75972
S92000003	201,508	2,015	8	2015-08-01	1335	965	72.28464
S92000003	201,509	2,015	9	2015-09-01	1489	1060	71.18872
S92000003	201,510	2,015	10	2015-10-01	1534	1114	72.62060
S92000003	201,511	2,015	11	2015-11-01	1680	1243	73.98810
S92000003	201,512	2,015	12	2015-12-01	1291	1077	83.42370
S92000003	201,601	2,016	1	2016-01-01	1416	1200	84.74576
S92000003	201,602	2,016	2	2016-02-01	1516	1282	84.56464
S92000003	201,603	2,016	3	2016-03-01	1564	1313	83.95141
S92000003	201,604	2,016	4	2016-04-01	1494	1211	81.05756
S92000003	201,605	2,016	5	2016-05-01	1636	1224	74.81663
S92000003	201,606	2,016	6	2016-06-01	1606	1246	77.58406
S92000003	201,607	2,016	7	2016-07-01	1204	946	78.57143
S92000003	201,608	2,016	8	2016-08-01	1512	1175	77.71164
S92000003	201,609	2,016	9	2016-09-01	1444	1150	79.63989
S92000003	201,610	2,016	10	2016-10-01	1411	1173	83.13253
S92000003	201,611	2,016	11	2016-11-01	1665	1319	79.21922
S92000003	201,612	2,016	12	2016-12-01	1277	1099	86.06108
S92000003	201,701	2,017	1	2017-01-01	1307	1102	84.31523
S92000003	201,702	2,017	2	2017-02-01	1468	1211	82.49319
S92000003	201,703	2,017	3	2017-03-01	1558	1308	83.95379
S92000003	201,704	2,017	4	2017-04-01	1157	933	80.63959
S92000003	201,705	2,017	5	2017-05-01	1550	1256	81.03226
S92000003	201,706	2,017	6	2017-06-01	1385	1115	80.50542
S92000003	201,707	2,017	7	2017-07-01	1008	777	77.08333
S92000003	201,708	2,017	8	2017-08-01	1299	930	71.59353
S92000003	201,709	2,017	9	2017-09-01	1105	791	71.58371
S92000003	201,710	2,017	10	2017-10-01	1263	897	71.02138
S92000003	201,711	2,017	11	2017-11-01	1656	1143	69.02174
S92000003	201,712	2,017	12	2017-12-01	1108	824	74.36823
S92000003	201,801	2,018	1	2018-01-01	1367	989	72.34821
S92000003	201,802	2,018	2	2018-02-01	1318	930	70.56146
S92000003	201,803	2,018	3	2018-03-01	1310	921	70.30534
S92000003	201,804	2,018	4	2018-04-01	1451	957	65.95451
S92000003	201,805	2,018	5	2018-05-01	1712	1146	66.93925
S92000003	201,806	2,018	6	2018-06-01	1531	1067	69.69301
S92000003	201,807	2,018	7	2018-07-01	1314	923	70.24353
S92000003	201,808	2,018	8	2018-08-01	1548	1027	66.34367
S92000003	201,809	2,018	9	2018-09-01	1377	973	70.66086
S92000003	201,810	2,018	10	2018-10-01	1630	1189	72.94479
S92000003	201,811	2,018	11	2018-11-01	1662	1135	68.29122
S92000003	201,812	2,018	12	2018-12-01	1231	967	78.55402
S92000003	201,901	2,019	1	2019-01-01	1493	1076	72.06966
S92000003	201,902	2,019	2	2019-02-01	1414	1038	73.40877
S92000003	201,903	2,019	3	2019-03-01	1330	1006	75.63910
S92000003	201,904	2,019	4	2019-04-01	1384	957	69.14740
S92000003	201,905	2,019	5	2019-05-01	1453	1009	69.44253
S92000003	201,906	2,019	6	2019-06-01	1175	834	70.97872
S92000003	201,907	2,019	7	2019-07-01	1262	792	62.75753
S92000003	201,908	2,019	8	2019-08-01	1333	850	63.76594
S92000003	201,909	2,019	9	2019-09-01	1307	875	66.94721
S92000003	201,910	2,019	10	2019-10-01	1374	890	64.77438
S92000003	201,911	2,019	11	2019-11-01	1437	922	64.16145
S92000003	201,912	2,019	12	2019-12-01	1073	767	71.48183
S92000003	202,001	2,020	1	2020-01-01	1399	945	67.54825
S92000003	202,002	2,020	2	2020-02-01	1448	957	66.09116
S92000003	202,003	2,020	3	2020-03-01	1284	848	66.04361
S92000003	202,004	2,020	4	2020-04-01	955	724	75.81152
S92000003	202,005	2,020	5	2020-05-01	1238	746	60.25848
S92000003	202,006	2,020	6	2020-06-01	1355	720	53.13653
S92000003	202,007	2,020	7	2020-07-01	1297	751	57.90285
S92000003	202,008	2,020	8	2020-08-01	1249	714	57.16573
S92000003	202,009	2,020	9	2020-09-01	1486	979	65.88156
S92000003	202,010	2,020	10	2020-10-01	1309	961	73.41482
S92000003	202,011	2,020	11	2020-11-01	1511	1102	72.93183
S92000003	202,012	2,020	12	2020-12-01	1271	927	72.93470
S92000003	202,101	2,021	1	2021-01-01	1275	901	70.66667
S92000003	202,102	2,021	2	2021-02-01	1315	986	74.98099
S92000003	202,103	2,021	3	2021-03-01	1506	1079	71.64675
S92000003	202,104	2,021	4	2021-04-01	1533	1093	71.29811
S92000003	202,105	2,021	5	2021-05-01	1530	1099	71.83007
S92000003	202,106	2,021	6	2021-06-01	1489	1114	74.81531
S92000003	202,107	2,021	7	2021-07-01	1203	913	75.89360
S92000003	202,108	2,021	8	2021-08-01	1252	969	77.39617
S92000003	202,109	2,021	9	2021-09-01	1337	1098	82.12416
S92000003	202,110	2,021	10	2021-10-01	1440	1029	71.45833
S92000003	202,111	2,021	11	2021-11-01	1749	1233	70.49743
S92000003	202,112	2,021	12	2021-12-01	1355	933	68.85609

Monthly average, per year

#Average per calendar year
CAMHS_Scotland_18wks %>%
  mutate(.,year=lubridate::year(year_month)) %>% 
  group_by(year) %>%
  summarise(average=mean(pct_within_18wks,na.rm = TRUE),
            months_included= n()) %>% 
  ungroup() %>%
  filter(.,months_included==12) %>%
  knitr::kable(., align = "lccrr")

year	average	months_included
2013	84.50981	12
2014	80.77049	12
2015	76.35224	12
2016	80.92132	12
2017	77.30095	12
2018	70.23666	12
2019	68.71454	12
2020	65.76009	12
2021	73.45530	12

People still receiving treatment in Scottish CAMHS

Measure code: OpenCases
Measure description:
Source: NHS Scotland Open Data

Raw time series

#Data
CAMHS_Scotland_open <- scotland_camhs_appended %>%
  filter(.,Metric %in% c("OpenCases"),
         HB=="S92000003") %>%
  mutate(.,Count=as.numeric(Count),
         year_month=ymd(year_month))

#Time series chart
CAMHS_Scotland_open_chart <- CAMHS_Scotland_open %>% 
  ggplot(., aes(x=year_month, y=Count, group= Metric)) +
  geom_line(aes(color= Metric),
            size=1) +
  scale_x_date(date_labels = "%b %Y",date_breaks = "3 months") +
  scale_y_continuous(labels = scales::comma) +
  theme_ipsum() +
  xlab("") +
  ylab("") +
  labs(col="") +
  scale_color_brewer(palette = "Set1") +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))
  
ggplotly(CAMHS_Scotland_open_chart)

#Underlying data
CAMHS_Scotland_open %>%
  arrange(.,year_month) %>% 
  knitr::kable(., align = "lccrr",format.args = list(big.mark = ","))

HB	Month	Metric	Count	year	month	year_month
S92000003	201,901	OpenCases	27,936	2,019	1	2019-01-01
S92000003	201,902	OpenCases	27,077	2,019	2	2019-02-01
S92000003	201,903	OpenCases	27,332	2,019	3	2019-03-01
S92000003	201,904	OpenCases	27,217	2,019	4	2019-04-01
S92000003	201,905	OpenCases	26,006	2,019	5	2019-05-01
S92000003	201,906	OpenCases	25,675	2,019	6	2019-06-01
S92000003	201,907	OpenCases	25,496	2,019	7	2019-07-01
S92000003	201,908	OpenCases	28,702	2,019	8	2019-08-01
S92000003	201,909	OpenCases	29,326	2,019	9	2019-09-01
S92000003	201,910	OpenCases	30,233	2,019	10	2019-10-01
S92000003	201,911	OpenCases	30,744	2,019	11	2019-11-01
S92000003	201,912	OpenCases	30,431	2,019	12	2019-12-01
S92000003	202,001	OpenCases	31,246	2,020	1	2020-01-01
S92000003	202,002	OpenCases	30,836	2,020	2	2020-02-01
S92000003	202,003	OpenCases	29,504	2,020	3	2020-03-01
S92000003	202,004	OpenCases	28,985	2,020	4	2020-04-01
S92000003	202,005	OpenCases	28,819	2,020	5	2020-05-01
S92000003	202,006	OpenCases	28,662	2,020	6	2020-06-01
S92000003	202,007	OpenCases	27,981	2,020	7	2020-07-01
S92000003	202,008	OpenCases	28,056	2,020	8	2020-08-01
S92000003	202,009	OpenCases	28,302	2,020	9	2020-09-01
S92000003	202,010	OpenCases	28,425	2,020	10	2020-10-01
S92000003	202,011	OpenCases	29,029	2,020	11	2020-11-01
S92000003	202,012	OpenCases	28,771	2,020	12	2020-12-01
S92000003	202,101	OpenCases	28,767	2,021	1	2021-01-01
S92000003	202,102	OpenCases	28,883	2,021	2	2021-02-01
S92000003	202,103	OpenCases	28,995	2,021	3	2021-03-01
S92000003	202,104	OpenCases	28,927	2,021	4	2021-04-01
S92000003	202,105	OpenCases	29,750	2,021	5	2021-05-01
S92000003	202,106	OpenCases	28,988	2,021	6	2021-06-01
S92000003	202,107	OpenCases	28,209	2,021	7	2021-07-01
S92000003	202,108	OpenCases	28,156	2,021	8	2021-08-01
S92000003	202,109	OpenCases	28,310	2,021	9	2021-09-01
S92000003	202,110	OpenCases	28,536	2,021	10	2021-10-01
S92000003	202,111	OpenCases	29,127	2,021	11	2021-11-01
S92000003	202,112	OpenCases	26,032	2,021	12	2021-12-01

Monthly average, per year

#Average per calendar year
CAMHS_Scotland_open %>%
  mutate(.,year=lubridate::year(year_month)) %>% 
  group_by(Metric,year) %>%
  summarise(average=mean(Count,na.rm = TRUE),
            months_included= n()) %>% 
  ungroup() %>%
  filter(.,months_included==12) %>%
  knitr::kable(., align = "lccrr")

Metric	year	average	months_included
OpenCases	2019	28014.58	12
OpenCases	2020	29051.33	12
OpenCases	2021	28556.67	12

People waiting for treatment in Scottish CAMHS

Measure code: TotalPatientsWaiting
Measure description:
Source: NHS Scotland Open Data

Raw time series

#Data
CAMHS_Scotland_waiting <- scotland_camhs_appended %>%
  filter(.,Metric %in% c("TotalPatientsWaiting"),
         HB=="S92000003") %>%
  mutate(.,Count=as.numeric(Count),
         year_month=ymd(year_month))

#Time series chart
CAMHS_Scotland_waiting_chart <- CAMHS_Scotland_waiting %>% 
  ggplot(., aes(x=year_month, y=Count, group= Metric)) +
  geom_line(aes(color= Metric),
            size=1) +
  scale_x_date(date_labels = "%b %Y",date_breaks = "3 months") +
  scale_y_continuous(labels = scales::comma) +
  theme_ipsum() +
  xlab("") +
  ylab("") +
  labs(col="") +
  scale_color_brewer(palette = "Set1") +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))
  
ggplotly(CAMHS_Scotland_waiting_chart)

#Underlying data
CAMHS_Scotland_waiting %>%
  arrange(.,year_month) %>% 
  knitr::kable(., align = "lccrr",format.args = list(big.mark = ","))

HB	Month	Metric	Count	year	month	year_month
S92000003	201,207	TotalPatientsWaiting	3,638	2,012	7	2012-07-01
S92000003	201,208	TotalPatientsWaiting	3,196	2,012	8	2012-08-01
S92000003	201,209	TotalPatientsWaiting	3,369	2,012	9	2012-09-01
S92000003	201,210	TotalPatientsWaiting	3,820	2,012	10	2012-10-01
S92000003	201,211	TotalPatientsWaiting	3,966	2,012	11	2012-11-01
S92000003	201,212	TotalPatientsWaiting	4,147	2,012	12	2012-12-01
S92000003	201,301	TotalPatientsWaiting	4,055	2,013	1	2013-01-01
S92000003	201,302	TotalPatientsWaiting	4,181	2,013	2	2013-02-01
S92000003	201,303	TotalPatientsWaiting	4,584	2,013	3	2013-03-01
S92000003	201,304	TotalPatientsWaiting	4,332	2,013	4	2013-04-01
S92000003	201,305	TotalPatientsWaiting	4,393	2,013	5	2013-05-01
S92000003	201,306	TotalPatientsWaiting	4,770	2,013	6	2013-06-01
S92000003	201,307	TotalPatientsWaiting	4,546	2,013	7	2013-07-01
S92000003	201,308	TotalPatientsWaiting	4,391	2,013	8	2013-08-01
S92000003	201,309	TotalPatientsWaiting	4,818	2,013	9	2013-09-01
S92000003	201,310	TotalPatientsWaiting	5,641	2,013	10	2013-10-01
S92000003	201,311	TotalPatientsWaiting	5,900	2,013	11	2013-11-01
S92000003	201,312	TotalPatientsWaiting	6,405	2,013	12	2013-12-01
S92000003	201,401	TotalPatientsWaiting	6,606	2,014	1	2014-01-01
S92000003	201,402	TotalPatientsWaiting	6,980	2,014	2	2014-02-01
S92000003	201,403	TotalPatientsWaiting	7,086	2,014	3	2014-03-01
S92000003	201,404	TotalPatientsWaiting	6,876	2,014	4	2014-04-01
S92000003	201,405	TotalPatientsWaiting	6,972	2,014	5	2014-05-01
S92000003	201,406	TotalPatientsWaiting	5,261	2,014	6	2014-06-01
S92000003	201,407	TotalPatientsWaiting	4,453	2,014	7	2014-07-01
S92000003	201,408	TotalPatientsWaiting	5,979	2,014	8	2014-08-01
S92000003	201,409	TotalPatientsWaiting	6,146	2,014	9	2014-09-01
S92000003	201,410	TotalPatientsWaiting	6,232	2,014	10	2014-10-01
S92000003	201,411	TotalPatientsWaiting	6,205	2,014	11	2014-11-01
S92000003	201,412	TotalPatientsWaiting	6,573	2,014	12	2014-12-01
S92000003	201,501	TotalPatientsWaiting	6,409	2,015	1	2015-01-01
S92000003	201,502	TotalPatientsWaiting	6,565	2,015	2	2015-02-01
S92000003	201,503	TotalPatientsWaiting	6,867	2,015	3	2015-03-01
S92000003	201,504	TotalPatientsWaiting	6,649	2,015	4	2015-04-01
S92000003	201,505	TotalPatientsWaiting	6,604	2,015	5	2015-05-01
S92000003	201,506	TotalPatientsWaiting	6,519	2,015	6	2015-06-01
S92000003	201,507	TotalPatientsWaiting	5,990	2,015	7	2015-07-01
S92000003	201,508	TotalPatientsWaiting	5,834	2,015	8	2015-08-01
S92000003	201,509	TotalPatientsWaiting	6,141	2,015	9	2015-09-01
S92000003	201,510	TotalPatientsWaiting	6,159	2,015	10	2015-10-01
S92000003	201,511	TotalPatientsWaiting	6,355	2,015	11	2015-11-01
S92000003	201,512	TotalPatientsWaiting	6,513	2,015	12	2015-12-01
S92000003	201,601	TotalPatientsWaiting	6,337	2,016	1	2016-01-01
S92000003	201,602	TotalPatientsWaiting	6,618	2,016	2	2016-02-01
S92000003	201,603	TotalPatientsWaiting	6,624	2,016	3	2016-03-01
S92000003	201,604	TotalPatientsWaiting	6,785	2,016	4	2016-04-01
S92000003	201,605	TotalPatientsWaiting	6,819	2,016	5	2016-05-01
S92000003	201,606	TotalPatientsWaiting	6,568	2,016	6	2016-06-01
S92000003	201,607	TotalPatientsWaiting	6,040	2,016	7	2016-07-01
S92000003	201,608	TotalPatientsWaiting	5,520	2,016	8	2016-08-01
S92000003	201,609	TotalPatientsWaiting	5,702	2,016	9	2016-09-01
S92000003	201,610	TotalPatientsWaiting	5,717	2,016	10	2016-10-01
S92000003	201,611	TotalPatientsWaiting	5,884	2,016	11	2016-11-01
S92000003	201,612	TotalPatientsWaiting	6,279	2,016	12	2016-12-01
S92000003	201,701	TotalPatientsWaiting	6,422	2,017	1	2017-01-01
S92000003	201,702	TotalPatientsWaiting	6,492	2,017	2	2017-02-01
S92000003	201,703	TotalPatientsWaiting	6,932	2,017	3	2017-03-01
S92000003	201,704	TotalPatientsWaiting	6,677	2,017	4	2017-04-01
S92000003	201,705	TotalPatientsWaiting	6,873	2,017	5	2017-05-01
S92000003	201,706	TotalPatientsWaiting	6,964	2,017	6	2017-06-01
S92000003	201,707	TotalPatientsWaiting	5,902	2,017	7	2017-07-01
S92000003	201,708	TotalPatientsWaiting	5,537	2,017	8	2017-08-01
S92000003	201,709	TotalPatientsWaiting	5,939	2,017	9	2017-09-01
S92000003	201,710	TotalPatientsWaiting	6,099	2,017	10	2017-10-01
S92000003	201,711	TotalPatientsWaiting	7,271	2,017	11	2017-11-01
S92000003	201,712	TotalPatientsWaiting	7,620	2,017	12	2017-12-01
S92000003	201,801	TotalPatientsWaiting	7,684	2,018	1	2018-01-01
S92000003	201,802	TotalPatientsWaiting	7,965	2,018	2	2018-02-01
S92000003	201,803	TotalPatientsWaiting	8,370	2,018	3	2018-03-01
S92000003	201,804	TotalPatientsWaiting	8,277	2,018	4	2018-04-01
S92000003	201,805	TotalPatientsWaiting	8,240	2,018	5	2018-05-01
S92000003	201,806	TotalPatientsWaiting	8,510	2,018	6	2018-06-01
S92000003	201,807	TotalPatientsWaiting	7,908	2,018	7	2018-07-01
S92000003	201,808	TotalPatientsWaiting	7,653	2,018	8	2018-08-01
S92000003	201,809	TotalPatientsWaiting	7,860	2,018	9	2018-09-01
S92000003	201,810	TotalPatientsWaiting	8,296	2,018	10	2018-10-01
S92000003	201,811	TotalPatientsWaiting	8,827	2,018	11	2018-11-01
S92000003	201,812	TotalPatientsWaiting	9,337	2,018	12	2018-12-01
S92000003	201,901	TotalPatientsWaiting	9,661	2,019	1	2019-01-01
S92000003	201,902	TotalPatientsWaiting	9,891	2,019	2	2019-02-01
S92000003	201,903	TotalPatientsWaiting	10,609	2,019	3	2019-03-01
S92000003	201,904	TotalPatientsWaiting	9,974	2,019	4	2019-04-01
S92000003	201,905	TotalPatientsWaiting	10,263	2,019	5	2019-05-01
S92000003	201,906	TotalPatientsWaiting	10,445	2,019	6	2019-06-01
S92000003	201,907	TotalPatientsWaiting	10,059	2,019	7	2019-07-01
S92000003	201,908	TotalPatientsWaiting	9,926	2,019	8	2019-08-01
S92000003	201,909	TotalPatientsWaiting	10,034	2,019	9	2019-09-01
S92000003	201,910	TotalPatientsWaiting	10,083	2,019	10	2019-10-01
S92000003	201,911	TotalPatientsWaiting	10,475	2,019	11	2019-11-01
S92000003	201,912	TotalPatientsWaiting	10,820	2,019	12	2019-12-01
S92000003	202,001	TotalPatientsWaiting	11,030	2,020	1	2020-01-01
S92000003	202,002	TotalPatientsWaiting	11,449	2,020	2	2020-02-01
S92000003	202,003	TotalPatientsWaiting	11,455	2,020	3	2020-03-01
S92000003	202,004	TotalPatientsWaiting	10,578	2,020	4	2020-04-01
S92000003	202,005	TotalPatientsWaiting	9,734	2,020	5	2020-05-01
S92000003	202,006	TotalPatientsWaiting	9,347	2,020	6	2020-06-01
S92000003	202,007	TotalPatientsWaiting	8,996	2,020	7	2020-07-01
S92000003	202,008	TotalPatientsWaiting	9,181	2,020	8	2020-08-01
S92000003	202,009	TotalPatientsWaiting	9,699	2,020	9	2020-09-01
S92000003	202,010	TotalPatientsWaiting	10,151	2,020	10	2020-10-01
S92000003	202,011	TotalPatientsWaiting	10,756	2,020	11	2020-11-01
S92000003	202,012	TotalPatientsWaiting	11,166	2,020	12	2020-12-01
S92000003	202,101	TotalPatientsWaiting	10,711	2,021	1	2021-01-01
S92000003	202,102	TotalPatientsWaiting	10,744	2,021	2	2021-02-01
S92000003	202,103	TotalPatientsWaiting	11,008	2,021	3	2021-03-01
S92000003	202,104	TotalPatientsWaiting	10,911	2,021	4	2021-04-01
S92000003	202,105	TotalPatientsWaiting	11,531	2,021	5	2021-05-01
S92000003	202,106	TotalPatientsWaiting	11,722	2,021	6	2021-06-01
S92000003	202,107	TotalPatientsWaiting	11,323	2,021	7	2021-07-01
S92000003	202,108	TotalPatientsWaiting	11,332	2,021	8	2021-08-01
S92000003	202,109	TotalPatientsWaiting	11,816	2,021	9	2021-09-01
S92000003	202,110	TotalPatientsWaiting	12,083	2,021	10	2021-10-01
S92000003	202,111	TotalPatientsWaiting	12,107	2,021	11	2021-11-01
S92000003	202,112	TotalPatientsWaiting	10,452	2,021	12	2021-12-01

Monthly average, per year

#Average per calendar year
CAMHS_Scotland_waiting %>%
  mutate(.,year=lubridate::year(year_month)) %>% 
  group_by(Metric,year) %>%
  summarise(average=mean(Count,na.rm = TRUE),
            months_included= n()) %>% 
  ungroup() %>%
  filter(.,months_included==12) %>%
  knitr::kable(., align = "lccrr")

Metric	year	average	months_included
TotalPatientsWaiting	2013	4834.667	12
TotalPatientsWaiting	2014	6280.750	12
TotalPatientsWaiting	2015	6383.750	12
TotalPatientsWaiting	2016	6241.083	12
TotalPatientsWaiting	2017	6560.667	12
TotalPatientsWaiting	2018	8243.917	12
TotalPatientsWaiting	2019	10186.667	12
TotalPatientsWaiting	2020	10295.167	12
TotalPatientsWaiting	2021	11311.667	12

People in contact with CAMHS

Measure code: CYP01
Measure description: People in contact with children and young people’s mental health services at the end of the reporting period
Source: NHS England, Monthly MHSDS Statistics, Metadata

Raw time series

#Data
CAMHS_data_cyp01 <- CAMHS_data %>%
  filter(.,MEASURE_ID=="CYP01")
CAMHS_reldata_cyp01 <- CAMHS_yearly_changes %>%
  filter(.,MEASURE_ID=="CYP01")

#Time series chart
CAMHS_raw_chart_cyp01 <- CAMHS_data_cyp01 %>% 
  ggplot(., aes(x=start_date, y=MEASURE_VALUE, group= MEASURE_KEY)) +
  geom_line(aes(color= MEASURE_KEY),
            size=1) +
  scale_x_date(date_labels = "%b %Y",date_breaks = "3 months") +
  scale_y_continuous(labels = scales::comma) +
  facet_wrap(~timing, scales = "free_x") +
  theme_ipsum() +
  xlab("") +
  ylab("") +
  labs(col="") +
  scale_color_brewer(palette = "Set1") +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))
  
ggplotly(CAMHS_raw_chart_cyp01)

#Underlying data
CAMHS_data_cyp01 %>%
  select(.,start_date,MEASURE_KEY,MEASURE_VALUE) %>%
  arrange(.,start_date) %>% 
  knitr::kable(., align = "lccrr",format.args = list(big.mark = ","))

start_date	MEASURE_KEY	MEASURE_VALUE
2018-05-01	People in contact	222,156
2018-06-01	People in contact	225,407
2018-07-01	People in contact	218,557
2018-08-01	People in contact	213,702
2018-09-01	People in contact	218,764
2018-10-01	People in contact	227,845
2018-11-01	People in contact	223,744
2018-12-01	People in contact	227,679
2019-01-01	People in contact	229,217
2019-02-01	People in contact	233,831
2019-03-01	People in contact	241,926
2019-04-01	People in contact	218,678
2019-05-01	People in contact	230,443
2019-06-01	People in contact	225,480
2019-07-01	People in contact	226,647
2019-08-01	People in contact	218,826
2019-09-01	People in contact	221,428
2019-10-01	People in contact	225,507
2019-11-01	People in contact	230,739
2019-12-01	People in contact	231,056
2020-01-01	People in contact	236,396
2020-02-01	People in contact	240,401
2020-03-01	People in contact	237,088
2020-04-01	People in contact	281,199
2020-05-01	People in contact	273,706
2020-06-01	People in contact	272,529
2020-07-01	People in contact	275,439
2020-08-01	People in contact	271,462
2020-09-01	People in contact	286,880
2020-10-01	People in contact	296,414
2020-11-01	People in contact	309,311
2020-12-01	People in contact	311,119
2021-01-01	People in contact	307,335
2021-02-01	People in contact	306,997
2021-03-01	People in contact	317,845
2021-04-01	People in contact	323,240
2021-05-01	People in contact	337,426
2021-06-01	People in contact	340,694
2021-07-01	People in contact	342,565
2021-08-01	People in contact	331,912
2021-09-01	People in contact	337,080
2021-10-01	People in contact	349,449
2021-11-01	People in contact	357,802
2021-12-01	People in contact	355,807

Monthly average, per year

#Average per calendar year
CAMHS_data_cyp01 %>%
  mutate(.,year=lubridate::year(start_date)) %>% 
  group_by(MEASURE_ID,MEASURE_KEY,year) %>%
  summarise(average=mean(MEASURE_VALUE,na.rm = TRUE),
            months_included= n()) %>% 
  ungroup() %>%
  filter(.,months_included==12) %>%
  knitr::kable(., align = "lccrr")

MEASURE_ID	MEASURE_KEY	year	average	months_included
CYP01	People in contact	2019	227814.8	12
CYP01	People in contact	2020	274328.7	12
CYP01	People in contact	2021	334012.7	12

Monthly average, per year (Jan to Sep)

#Average per calendar year
CAMHS_data_cyp01 %>%
  mutate(.,year=lubridate::year(start_date)) %>%
  filter(.,month_num>=1&month_num<=9) %>%
  group_by(MEASURE_ID,MEASURE_KEY,year) %>%
  summarise(average=mean(MEASURE_VALUE,na.rm = TRUE),
            months_included= n()) %>% 
  ungroup() %>%
  filter(.,months_included==9) %>%
  knitr::kable(., align = "lccrr")

MEASURE_ID	MEASURE_KEY	year	average	months_included
CYP01	People in contact	2019	227386.2	9
CYP01	People in contact	2020	263900.0	9
CYP01	People in contact	2021	327232.7	9

Relative changes compared to last year

#Relative changes chart
CAMHS_changes_chart_cyp01 <- CAMHS_reldata_cyp01 %>%
  ggplot(., aes(x=start_date, y=pct_change_l1, group= MEASURE_KEY)) +
  facet_wrap(~timing, scales = "free_x") +
  geom_line(aes(color= MEASURE_KEY),size=1) +
  geom_hline(yintercept=0, linetype="dashed", color = "red") +
  scale_x_date(date_labels = "%b %Y",date_breaks = "1 month") +
  theme_ipsum() +
  xlab("") +
  ylab("% change") +
  labs(col="") +
  scale_color_manual(values=c("Open referrals" = "aquamarine4",
                              "People in contact" = "tomato3",
                              "First contactsn (<18)" = "olivedrab4",
                              "Attended contacts (<18)" = "violetred",
                              "New referrals (<18)" = "magenta1")) +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))

ggplotly(CAMHS_changes_chart_cyp01)

#Underlying data
CAMHS_reldata_cyp01 %>%
  select(.,start_date,MEASURE_KEY,pct_change_l1) %>%
  arrange(.,start_date) %>% 
  knitr::kable(., align = "lccrr",format.args = list(big.mark = ","))

start_date	MEASURE_KEY	pct_change_l1
2019-05-01	People in contact	3.7302616
2019-06-01	People in contact	0.0323859
2019-07-01	People in contact	3.7015515
2019-08-01	People in contact	2.3977314
2019-09-01	People in contact	1.2177506
2019-10-01	People in contact	-1.0261362
2019-11-01	People in contact	3.1263408
2019-12-01	People in contact	1.4832286
2020-01-01	People in contact	3.1319667
2020-02-01	People in contact	2.8097216
2020-03-01	People in contact	-1.9997851
2020-04-01	People in contact	28.5904389
2020-05-01	People in contact	18.7738400
2020-06-01	People in contact	20.8661522
2020-07-01	People in contact	21.5277502
2020-08-01	People in contact	24.0538144
2020-09-01	People in contact	29.5590440
2020-10-01	People in contact	31.4433698
2020-11-01	People in contact	34.0523275
2020-12-01	People in contact	34.6509071
2021-01-01	People in contact	30.0085450
2021-02-01	People in contact	27.7020478
2021-03-01	People in contact	34.0620360
2021-04-01	People in contact	14.9506222
2021-05-01	People in contact	23.2804542
2021-06-01	People in contact	25.0120171
2021-07-01	People in contact	24.3705503
2021-08-01	People in contact	22.2683101
2021-09-01	People in contact	17.4986057
2021-10-01	People in contact	17.8922048
2021-11-01	People in contact	15.6771017
2021-12-01	People in contact	14.3636358

New referrals (<18)

Measure code: MHS32a
Measure description: Referrals starting in RP, aged 0-18
Source: NHS England, Monthly MHSDS Statistics, Metadata

Raw time series

#Data
CAMHS_data_cyp32a <- CAMHS_data %>%
  filter(.,MEASURE_ID=="MHS32a")
CAMHS_reldata_cyp32a <- CAMHS_yearly_changes %>%
  filter(.,MEASURE_ID=="MHS32a")

#Time series chart
CAMHS_raw_chart_cyp32a <- CAMHS_data_cyp32a %>% 
  ggplot(., aes(x=start_date, y=MEASURE_VALUE, group= MEASURE_KEY)) +
  geom_line(aes(color= MEASURE_KEY),
            size=1) +
  scale_x_date(date_labels = "%b %Y",date_breaks = "3 months") +
  scale_y_continuous(labels = scales::comma) +
  facet_wrap(~timing, scales = "free_x") +
  theme_ipsum() +
  xlab("") +
  ylab("") +
  labs(col="") +
  scale_color_brewer(palette = "Set1") +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))
  
ggplotly(CAMHS_raw_chart_cyp32a)

#Underlying data
CAMHS_data_cyp32a %>%
  select(.,start_date,MEASURE_KEY,MEASURE_VALUE) %>%
  arrange(.,start_date) %>% 
  knitr::kable(., align = "lccrr",format.args = list(big.mark = ","))

start_date	MEASURE_KEY	MEASURE_VALUE
2019-04-01	New referrals (<18)	64,095
2019-05-01	New referrals (<18)	71,119
2019-06-01	New referrals (<18)	67,449
2019-07-01	New referrals (<18)	71,743
2019-08-01	New referrals (<18)	46,403
2019-09-01	New referrals (<18)	64,284
2019-10-01	New referrals (<18)	83,290
2019-11-01	New referrals (<18)	79,100
2019-12-01	New referrals (<18)	65,547
2020-01-01	New referrals (<18)	84,624
2020-02-01	New referrals (<18)	80,555
2020-03-01	New referrals (<18)	72,532
2020-04-01	New referrals (<18)	41,411
2020-05-01	New referrals (<18)	46,262
2020-06-01	New referrals (<18)	60,370
2020-07-01	New referrals (<18)	67,967
2020-08-01	New referrals (<18)	51,357
2020-09-01	New referrals (<18)	75,222
2020-10-01	New referrals (<18)	88,523
2020-11-01	New referrals (<18)	92,228
2020-12-01	New referrals (<18)	75,841
2021-01-01	New referrals (<18)	68,149
2021-02-01	New referrals (<18)	70,169
2021-03-01	New referrals (<18)	98,112
2021-04-01	New referrals (<18)	85,598
2021-05-01	New referrals (<18)	101,421
2021-06-01	New referrals (<18)	98,037
2021-07-01	New referrals (<18)	85,801
2021-08-01	New referrals (<18)	57,289
2021-09-01	New referrals (<18)	84,264
2021-10-01	New referrals (<18)	89,264
2021-11-01	New referrals (<18)	103,865
2021-12-01	New referrals (<18)	82,908

Monthly average, per year

#Average per calendar year
CAMHS_data_cyp32a %>%
  mutate(.,year=lubridate::year(start_date)) %>% 
  group_by(MEASURE_ID,MEASURE_KEY,year) %>%
  summarise(average=mean(MEASURE_VALUE,na.rm = TRUE),
            months_included= n()) %>% 
  ungroup() %>%
  filter(.,months_included==12) %>%
  knitr::kable(., align = "lccrr")

MEASURE_ID	MEASURE_KEY	year	average	months_included
MHS32a	New referrals (<18)	2020	69741.00	12
MHS32a	New referrals (<18)	2021	85406.42	12

Monthly average, per year (Apr to Dec)

#Average per calendar year
CAMHS_data_cyp32a %>%
  mutate(.,year=lubridate::year(start_date)) %>%
  filter(.,month_num>=4&month_num<=12) %>%
  group_by(MEASURE_ID,MEASURE_KEY,year) %>%
  summarise(average=mean(MEASURE_VALUE,na.rm = TRUE),
            months_included= n()) %>% 
  ungroup() %>%
  filter(.,months_included==9) %>%
  knitr::kable(., align = "lccrr")

MEASURE_ID	MEASURE_KEY	year	average	months_included
MHS32a	New referrals (<18)	2019	68114.44	9
MHS32a	New referrals (<18)	2020	66575.67	9
MHS32a	New referrals (<18)	2021	87605.22	9

Relative changes compared to last year

#Relative changes chart
CAMHS_changes_chart_cyp32a <- CAMHS_reldata_cyp32a %>%
  ggplot(., aes(x=start_date, y=pct_change_l1, group= MEASURE_KEY)) +
  facet_wrap(~timing, scales = "free_x") +
  geom_line(aes(color= MEASURE_KEY),size=1) +
  geom_hline(yintercept=0, linetype="dashed", color = "red") +
  scale_x_date(date_labels = "%b %Y",date_breaks = "1 month") +
  theme_ipsum() +
  xlab("") +
  ylab("% change") +
  labs(col="") +
  scale_color_manual(values=c("Open referrals" = "aquamarine4",
                              "People in contact" = "tomato3",
                              "First contacts (<18)" = "olivedrab4",
                              "Attended contacts (<18)" = "violetred",
                              "New referrals (<18)" = "magenta1")) +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))

ggplotly(CAMHS_changes_chart_cyp32a)

#Underlying data
CAMHS_reldata_cyp32a %>%
  select(.,start_date,MEASURE_KEY,pct_change_l1) %>%
  arrange(.,start_date) %>% 
  knitr::kable(., align = "lccrr",format.args = list(big.mark = ","))

start_date	MEASURE_KEY	pct_change_l1
2020-04-01	New referrals (<18)	-35.3912162
2020-05-01	New referrals (<18)	-34.9512788
2020-06-01	New referrals (<18)	-10.4953372
2020-07-01	New referrals (<18)	-5.2632313
2020-08-01	New referrals (<18)	10.6760339
2020-09-01	New referrals (<18)	17.0151204
2020-10-01	New referrals (<18)	6.2828671
2020-11-01	New referrals (<18)	16.5967130
2020-12-01	New referrals (<18)	15.7047615
2021-01-01	New referrals (<18)	-19.4684723
2021-02-01	New referrals (<18)	-12.8930544
2021-03-01	New referrals (<18)	35.2671924
2021-04-01	New referrals (<18)	106.7035329
2021-05-01	New referrals (<18)	119.2317669
2021-06-01	New referrals (<18)	62.3935730
2021-07-01	New referrals (<18)	26.2392043
2021-08-01	New referrals (<18)	11.5505189
2021-09-01	New referrals (<18)	12.0204196
2021-10-01	New referrals (<18)	0.8370706
2021-11-01	New referrals (<18)	12.6176432
2021-12-01	New referrals (<18)	9.3181788

New referrals to CYPMHS

Measure code: CYP32
Measure description: Referrals to children and young people’s mental health services starting in RP
Source: NHS England, Monthly MHSDS Statistics, Metadata

Raw time series

#Data
CAMHS_data_cyp32 <- CAMHS_data %>%
  filter(.,MEASURE_ID=="CYP32")

#Time series chart
CAMHS_raw_chart_cyp32 <- CAMHS_data_cyp32 %>% 
  ggplot(., aes(x=start_date, y=MEASURE_VALUE, group= MEASURE_KEY)) +
  geom_line(aes(color= MEASURE_KEY),
            size=1) +
  scale_x_date(date_labels = "%b %Y",date_breaks = "3 months") +
  scale_y_continuous(labels = scales::comma) +
  facet_wrap(~timing, scales = "free_x") +
  theme_ipsum() +
  xlab("") +
  ylab("") +
  labs(col="") +
  scale_color_brewer(palette = "Set1") +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))

ggplotly(CAMHS_raw_chart_cyp32)

#Underlying data
CAMHS_data_cyp32 %>%
  select(.,start_date,MEASURE_KEY,MEASURE_VALUE) %>%
  arrange(.,start_date) %>% 
  knitr::kable(., align = "lccrr",format.args = list(big.mark = ","))

start_date	MEASURE_KEY	MEASURE_VALUE
2019-04-01	New referrals to CYPMHS	31,720
2019-05-01	New referrals to CYPMHS	35,911
2019-06-01	New referrals to CYPMHS	33,440
2019-07-01	New referrals to CYPMHS	36,383
2019-08-01	New referrals to CYPMHS	22,183
2019-09-01	New referrals to CYPMHS	31,203
2019-10-01	New referrals to CYPMHS	39,751
2019-11-01	New referrals to CYPMHS	37,604
2019-12-01	New referrals to CYPMHS	31,260
2020-01-01	New referrals to CYPMHS	38,604
2020-02-01	New referrals to CYPMHS	37,432
2020-03-01	New referrals to CYPMHS	32,784
2020-04-01	New referrals to CYPMHS	21,296
2020-05-01	New referrals to CYPMHS	25,712
2020-06-01	New referrals to CYPMHS	36,178
2020-07-01	New referrals to CYPMHS	43,268
2020-08-01	New referrals to CYPMHS	29,954
2020-09-01	New referrals to CYPMHS	52,857
2020-10-01	New referrals to CYPMHS	62,692
2020-11-01	New referrals to CYPMHS	65,453
2020-12-01	New referrals to CYPMHS	53,876
2021-01-01	New referrals to CYPMHS	45,639
2021-02-01	New referrals to CYPMHS	47,222
2021-03-01	New referrals to CYPMHS	67,551
2021-04-01	New referrals to CYPMHS	58,124
2021-05-01	New referrals to CYPMHS	70,991
2021-06-01	New referrals to CYPMHS	66,813
2021-07-01	New referrals to CYPMHS	59,115
2021-08-01	New referrals to CYPMHS	35,097
2021-09-01	New referrals to CYPMHS	57,092
2021-10-01	New referrals to CYPMHS	62,115
2021-11-01	New referrals to CYPMHS	72,526
2021-12-01	New referrals to CYPMHS	59,055

Monthly average, per year

#Average per calendar year
CAMHS_data_cyp32 %>%
  mutate(.,year=lubridate::year(start_date)) %>% 
  group_by(MEASURE_ID,MEASURE_KEY,year) %>%
  summarise(average=mean(MEASURE_VALUE,na.rm = TRUE),
            months_included= n()) %>% 
  ungroup() %>%
  filter(.,months_included==12) %>%
  knitr::kable(., align = "lccrr")

MEASURE_ID	MEASURE_KEY	year	average	months_included
CYP32	New referrals to CYPMHS	2020	41675.5	12
CYP32	New referrals to CYPMHS	2021	58445.0	12

Monthly average, per year (Apr to Dec)

#Average per calendar year
CAMHS_data_cyp32 %>%
  mutate(.,year=lubridate::year(start_date)) %>%
  filter(.,month_num>=4&month_num<=12) %>%
  group_by(MEASURE_ID,MEASURE_KEY,year) %>%
  summarise(average=mean(MEASURE_VALUE,na.rm = TRUE),
            months_included= n()) %>% 
  ungroup() %>%
  filter(.,months_included==9) %>%
  knitr::kable(., align = "lccrr")

MEASURE_ID	MEASURE_KEY	year	average	months_included
CYP32	New referrals to CYPMHS	2019	33272.78	9
CYP32	New referrals to CYPMHS	2020	43476.22	9
CYP32	New referrals to CYPMHS	2021	60103.11	9

First contacts (<18)

Measure code: MHS61a
Measure description: First attended contacts for referrals open in the RP, aged 0-18
Source: NHS England, Monthly MHSDS Statistics, Metadata

Raw time series

#Data
CAMHS_data_cyp61a <- CAMHS_data %>%
  filter(.,MEASURE_ID=="MHS61a")
CAMHS_reldata_cyp61a <- CAMHS_yearly_changes %>%
  filter(.,MEASURE_ID=="MHS61a")

#Time series chart
CAMHS_raw_chart_cyp61a <- CAMHS_data_cyp61a %>% 
  ggplot(., aes(x=start_date, y=MEASURE_VALUE, group= MEASURE_KEY)) +
  geom_line(aes(color= MEASURE_KEY),
            size=1) +
  scale_x_date(date_labels = "%b %Y",date_breaks = "3 months") +
  scale_y_continuous(labels = scales::comma) +
  facet_wrap(~timing, scales = "free_x") +
  theme_ipsum() +
  xlab("") +
  ylab("") +
  labs(col="") +
  scale_color_brewer(palette = "Set1") +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))
  
ggplotly(CAMHS_raw_chart_cyp61a)

#Underlying data
CAMHS_data_cyp61a %>%
  select(.,start_date,MEASURE_KEY,MEASURE_VALUE) %>%
  arrange(.,start_date) %>% 
  knitr::kable(., align = "lccrr",format.args = list(big.mark = ","))

start_date	MEASURE_KEY	MEASURE_VALUE
2019-04-01	First contacts (<18)	51,694
2019-05-01	First contacts (<18)	49,335
2019-06-01	First contacts (<18)	47,035
2019-07-01	First contacts (<18)	48,293
2019-08-01	First contacts (<18)	36,352
2019-09-01	First contacts (<18)	45,982
2019-10-01	First contacts (<18)	54,568
2019-11-01	First contacts (<18)	54,309
2019-12-01	First contacts (<18)	41,945
2020-01-01	First contacts (<18)	56,823
2020-02-01	First contacts (<18)	52,305
2020-03-01	First contacts (<18)	51,555
2020-04-01	First contacts (<18)	43,246
2020-05-01	First contacts (<18)	38,816
2020-06-01	First contacts (<18)	46,559
2020-07-01	First contacts (<18)	47,685
2020-08-01	First contacts (<18)	39,035
2020-09-01	First contacts (<18)	49,839
2020-10-01	First contacts (<18)	56,951
2020-11-01	First contacts (<18)	60,651
2020-12-01	First contacts (<18)	49,468
2021-01-01	First contacts (<18)	50,832
2021-02-01	First contacts (<18)	48,337
2021-03-01	First contacts (<18)	59,245
2021-04-01	First contacts (<18)	57,734
2021-05-01	First contacts (<18)	62,337
2021-06-01	First contacts (<18)	63,397
2021-07-01	First contacts (<18)	55,473
2021-08-01	First contacts (<18)	43,083
2021-09-01	First contacts (<18)	56,285
2021-10-01	First contacts (<18)	59,256
2021-11-01	First contacts (<18)	64,298
2021-12-01	First contacts (<18)	51,273

Monthly average, per year

#Average per calendar year
CAMHS_data_cyp61a %>%
  mutate(.,year=lubridate::year(start_date)) %>% 
  group_by(MEASURE_ID,MEASURE_KEY,year) %>%
  summarise(average=mean(MEASURE_VALUE,na.rm = TRUE),
            months_included= n()) %>% 
  ungroup() %>%
  filter(.,months_included==12) %>%
  knitr::kable(., align = "lccrr")

MEASURE_ID	MEASURE_KEY	year	average	months_included
MHS61a	First contacts (<18)	2020	49411.08	12
MHS61a	First contacts (<18)	2021	55962.50	12

Monthly average, per year (Jan to Sep)

#Average per calendar year
CAMHS_data_cyp61a %>%
  mutate(.,year=lubridate::year(start_date)) %>%
  filter(.,month_num>=1&month_num<=9) %>%
  group_by(MEASURE_ID,MEASURE_KEY,year) %>%
  summarise(average=mean(MEASURE_VALUE,na.rm = TRUE),
            months_included= n()) %>% 
  ungroup() %>%
  filter(.,months_included==9) %>%
  knitr::kable(., align = "lccrr")

MEASURE_ID	MEASURE_KEY	year	average	months_included
MHS61a	First contacts (<18)	2020	47318.11	9
MHS61a	First contacts (<18)	2021	55191.44	9

Relative changes compared to last year

#Relative changes chart
CAMHS_changes_chart_cyp61a <- CAMHS_reldata_cyp61a %>%
  ggplot(., aes(x=start_date, y=pct_change_l1, group= MEASURE_KEY)) +
  facet_wrap(~timing, scales = "free_x") +
  geom_line(aes(color= MEASURE_KEY),size=1) +
  geom_hline(yintercept=0, linetype="dashed", color = "red") +
  scale_x_date(date_labels = "%b %Y",date_breaks = "1 month") +
  theme_ipsum() +
  xlab("") +
  ylab("% change") +
  labs(col="") +
  scale_color_manual(values=c("Open referrals" = "aquamarine4",
                              "People in contact" = "tomato3",
                              "First contacts (<18)" = "olivedrab4",
                              "Attended contacts (<18)" = "violetred",
                              "New referrals (<18)" = "magenta1")) +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))

ggplotly(CAMHS_changes_chart_cyp61a)

#Underlying data
CAMHS_reldata_cyp61a %>%
  select(.,start_date,MEASURE_KEY,pct_change_l1) %>%
  arrange(.,start_date) %>% 
  knitr::kable(., align = "lccrr",format.args = list(big.mark = ","))

start_date	MEASURE_KEY	pct_change_l1
2020-04-01	First contacts (<18)	-16.342322
2020-05-01	First contacts (<18)	-21.321577
2020-06-01	First contacts (<18)	-1.012012
2020-07-01	First contacts (<18)	-1.258982
2020-08-01	First contacts (<18)	7.380612
2020-09-01	First contacts (<18)	8.388065
2020-10-01	First contacts (<18)	4.367028
2020-11-01	First contacts (<18)	11.677623
2020-12-01	First contacts (<18)	17.935392
2021-01-01	First contacts (<18)	-10.543266
2021-02-01	First contacts (<18)	-7.586273
2021-03-01	First contacts (<18)	14.916109
2021-04-01	First contacts (<18)	33.501364
2021-05-01	First contacts (<18)	60.596146
2021-06-01	First contacts (<18)	36.164866
2021-07-01	First contacts (<18)	16.332180
2021-08-01	First contacts (<18)	10.370181
2021-09-01	First contacts (<18)	12.933646
2021-10-01	First contacts (<18)	4.047339
2021-11-01	First contacts (<18)	6.013091
2021-12-01	First contacts (<18)	3.648824

Attended contacts (<18)

Measure code: MHS30d
Measure description: Attended contacts in the RP, aged 0-18
Source: NHS England, Monthly MHSDS Statistics, Metadata

Raw time series

#Data
CAMHS_data_cyp30d <- CAMHS_data %>%
  filter(.,MEASURE_ID=="MHS30d")
CAMHS_reldata_cyp30d <- CAMHS_yearly_changes %>%
  filter(.,MEASURE_ID=="MHS30d")

#Time series chart
CAMHS_raw_chart_cyp30d <- CAMHS_data_cyp30d %>% 
  ggplot(., aes(x=start_date, y=MEASURE_VALUE, group= MEASURE_KEY)) +
  geom_line(aes(color= MEASURE_KEY),
            size=1) +
  scale_x_date(date_labels = "%b %Y",date_breaks = "3 months") +
  scale_y_continuous(labels = scales::comma) +
  facet_wrap(~timing, scales = "free_x") +
  theme_ipsum() +
  xlab("") +
  ylab("") +
  labs(col="") +
  scale_color_brewer(palette = "Set1") +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))
  
ggplotly(CAMHS_raw_chart_cyp30d)

#Underlying data
CAMHS_data_cyp30d %>%
  select(.,start_date,MEASURE_KEY,MEASURE_VALUE) %>%
  arrange(.,start_date) %>% 
  knitr::kable(., align = "lccrr",format.args = list(big.mark = ","))

start_date	MEASURE_KEY	MEASURE_VALUE
2019-04-01	Attended contacts (<18)	307,328
2019-05-01	Attended contacts (<18)	346,997
2019-06-01	Attended contacts (<18)	337,476
2019-07-01	Attended contacts (<18)	363,594
2019-08-01	Attended contacts (<18)	274,323
2019-09-01	Attended contacts (<18)	335,775
2019-10-01	Attended contacts (<18)	388,130
2019-11-01	Attended contacts (<18)	383,387
2019-12-01	Attended contacts (<18)	305,574
2020-01-01	Attended contacts (<18)	402,520
2020-02-01	Attended contacts (<18)	361,178
2020-03-01	Attended contacts (<18)	384,011
2020-04-01	Attended contacts (<18)	365,212
2020-05-01	Attended contacts (<18)	360,525
2020-06-01	Attended contacts (<18)	424,827
2020-07-01	Attended contacts (<18)	425,810
2020-08-01	Attended contacts (<18)	336,675
2020-09-01	Attended contacts (<18)	419,474
2020-10-01	Attended contacts (<18)	435,613
2020-11-01	Attended contacts (<18)	473,103
2020-12-01	Attended contacts (<18)	397,443
2021-01-01	Attended contacts (<18)	426,820
2021-02-01	Attended contacts (<18)	412,003
2021-03-01	Attended contacts (<18)	488,234
2021-04-01	Attended contacts (<18)	437,736
2021-05-01	Attended contacts (<18)	463,893
2021-06-01	Attended contacts (<18)	469,830
2021-07-01	Attended contacts (<18)	443,801
2021-08-01	Attended contacts (<18)	350,766
2021-09-01	Attended contacts (<18)	437,555
2021-10-01	Attended contacts (<18)	419,382
2021-11-01	Attended contacts (<18)	479,458
2021-12-01	Attended contacts (<18)	382,887

Monthly average, per year

#Average per calendar year
CAMHS_data_cyp30d %>%
  mutate(.,year=lubridate::year(start_date)) %>% 
  group_by(MEASURE_ID,MEASURE_KEY,year) %>%
  summarise(average=mean(MEASURE_VALUE,na.rm = TRUE),
            months_included= n()) %>% 
  ungroup() %>%
  filter(.,months_included==12) %>%
  knitr::kable(., align = "lccrr")

MEASURE_ID	MEASURE_KEY	year	average	months_included
MHS30d	Attended contacts (<18)	2020	398865.9	12
MHS30d	Attended contacts (<18)	2021	434363.8	12

Monthly average, per year (Jan to Sep)

#Average per calendar year
CAMHS_data_cyp30d %>%
  mutate(.,year=lubridate::year(start_date)) %>% 
  filter(.,month_num>=1&month_num<=9) %>%
  group_by(MEASURE_ID,MEASURE_KEY,year) %>%
  summarise(average=mean(MEASURE_VALUE,na.rm = TRUE),
            months_included= n()) %>% 
  ungroup() %>%
  filter(.,months_included==9) %>%
  knitr::kable(., align = "lccrr")

MEASURE_ID	MEASURE_KEY	year	average	months_included
MHS30d	Attended contacts (<18)	2020	386692.4	9
MHS30d	Attended contacts (<18)	2021	436737.6	9

Relative changes compared to last year

#Relative changes chart
CAMHS_changes_chart_cyp30d <- CAMHS_reldata_cyp30d %>%
  ggplot(., aes(x=start_date, y=pct_change_l1, group= MEASURE_KEY)) +
  facet_wrap(~timing, scales = "free_x") +
  geom_line(aes(color= MEASURE_KEY),size=1) +
  geom_hline(yintercept=0, linetype="dashed", color = "red") +
  scale_x_date(date_labels = "%b %Y",date_breaks = "1 month") +
  theme_ipsum() +
  xlab("") +
  ylab("% change") +
  labs(col="") +
  scale_color_manual(values=c("Open referrals" = "aquamarine4",
                              "People in contact" = "tomato3",
                              "First contacts (<18)" = "olivedrab4",
                              "Attended contacts (<18)" = "violetred",
                              "New referrals (<18)" = "magenta1")) +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))

ggplotly(CAMHS_changes_chart_cyp30d)

#Underlying data
CAMHS_reldata_cyp30d %>%
  select(.,start_date,MEASURE_KEY,pct_change_l1) %>%
  arrange(.,start_date) %>% 
  knitr::kable(., align = "lccrr",format.args = list(big.mark = ","))

start_date	MEASURE_KEY	pct_change_l1
2020-04-01	Attended contacts (<18)	18.834600
2020-05-01	Attended contacts (<18)	3.898593
2020-06-01	Attended contacts (<18)	25.883618
2020-07-01	Attended contacts (<18)	17.111393
2020-08-01	Attended contacts (<18)	22.729410
2020-09-01	Attended contacts (<18)	24.927109
2020-10-01	Attended contacts (<18)	12.233788
2020-11-01	Attended contacts (<18)	23.400898
2020-12-01	Attended contacts (<18)	30.064403
2021-01-01	Attended contacts (<18)	6.036967
2021-02-01	Attended contacts (<18)	14.072009
2021-03-01	Attended contacts (<18)	27.140629
2021-04-01	Attended contacts (<18)	19.858055
2021-05-01	Attended contacts (<18)	28.671521
2021-06-01	Attended contacts (<18)	10.593253
2021-07-01	Attended contacts (<18)	4.225124
2021-08-01	Attended contacts (<18)	4.185342
2021-09-01	Attended contacts (<18)	4.310398
2021-10-01	Attended contacts (<18)	-3.726014
2021-11-01	Attended contacts (<18)	1.343259
2021-12-01	Attended contacts (<18)	-3.662412

Open referrals

Measure code: CYP23
Measure description: Open referrals (children’s and young people’s mental health services) at end of the reporting period
Source: NHS England, Monthly MHSDS Statistics, Metadata

Raw time series

#Data
CAMHS_data_cyp23 <- CAMHS_data %>%
  filter(.,MEASURE_ID=="CYP23")
CAMHS_reldata_cyp23 <- CAMHS_yearly_changes %>%
  filter(.,MEASURE_ID=="CYP23")

#Time series chart
CAMHS_raw_chart_cyp23 <- CAMHS_data_cyp23 %>% 
  ggplot(., aes(x=start_date, y=MEASURE_VALUE, group= MEASURE_KEY)) +
  geom_line(aes(color= MEASURE_KEY),
            size=1) +
  scale_x_date(date_labels = "%b %Y",date_breaks = "3 months") +
  scale_y_continuous(labels = scales::comma) +
  facet_wrap(~timing, scales = "free_x") +
  theme_ipsum() +
  xlab("") +
  ylab("") +
  labs(col="") +
  scale_color_brewer(palette = "Set1") +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))
  
ggplotly(CAMHS_raw_chart_cyp23)

#Underlying data
CAMHS_data_cyp23 %>%
  select(.,start_date,MEASURE_KEY,MEASURE_VALUE) %>%
  arrange(.,start_date) %>% 
  knitr::kable(., align = "lccrr",format.args = list(big.mark = ","))

start_date	MEASURE_KEY	MEASURE_VALUE
2018-05-01	Open referrals	247,499
2018-06-01	Open referrals	251,573
2018-07-01	Open referrals	243,916
2018-08-01	Open referrals	238,803
2018-09-01	Open referrals	245,003
2018-10-01	Open referrals	255,855
2018-11-01	Open referrals	252,726
2018-12-01	Open referrals	256,644
2019-01-01	Open referrals	258,525
2019-02-01	Open referrals	264,305
2019-03-01	Open referrals	272,605
2019-04-01	Open referrals	248,038
2019-05-01	Open referrals	260,485
2019-06-01	Open referrals	255,950
2019-07-01	Open referrals	257,152
2019-08-01	Open referrals	248,313
2019-09-01	Open referrals	251,483
2019-10-01	Open referrals	256,252
2019-11-01	Open referrals	262,299
2019-12-01	Open referrals	261,939
2020-01-01	Open referrals	268,184
2020-02-01	Open referrals	272,482
2020-03-01	Open referrals	267,871
2020-04-01	Open referrals	307,837
2020-05-01	Open referrals	302,241
2020-06-01	Open referrals	301,012
2020-07-01	Open referrals	304,491
2020-08-01	Open referrals	300,469
2020-09-01	Open referrals	318,375
2020-10-01	Open referrals	329,392
2020-11-01	Open referrals	344,178
2020-12-01	Open referrals	345,569
2021-01-01	Open referrals	340,421
2021-02-01	Open referrals	340,218
2021-03-01	Open referrals	352,551
2021-04-01	Open referrals	358,282
2021-05-01	Open referrals	374,401
2021-06-01	Open referrals	379,079
2021-07-01	Open referrals	380,738
2021-08-01	Open referrals	368,610
2021-09-01	Open referrals	374,946
2021-10-01	Open referrals	388,237
2021-11-01	Open referrals	397,857
2021-12-01	Open referrals	397,147

Monthly average, per year

#Average per calendar year
CAMHS_data_cyp23 %>%
  mutate(.,year=lubridate::year(start_date)) %>% 
  group_by(MEASURE_ID,MEASURE_KEY,year) %>%
  summarise(average=mean(MEASURE_VALUE,na.rm = TRUE),
            months_included= n()) %>% 
  ungroup() %>%
  filter(.,months_included==12) %>%
  knitr::kable(., align = "lccrr")

MEASURE_ID	MEASURE_KEY	year	average	months_included
CYP23	Open referrals	2019	258112.2	12
CYP23	Open referrals	2020	305175.1	12
CYP23	Open referrals	2021	371040.6	12

Monthly average, per year (Jan to Sep)

#Average per calendar year
CAMHS_data_cyp23 %>%
  mutate(.,year=lubridate::year(start_date)) %>%
  filter(.,month_num>=1&month_num<=9) %>%
  group_by(MEASURE_ID,MEASURE_KEY,year) %>%
  summarise(average=mean(MEASURE_VALUE,na.rm = TRUE),
            months_included= n()) %>% 
  ungroup() %>%
  filter(.,months_included==9) %>%
  knitr::kable(., align = "lccrr")

MEASURE_ID	MEASURE_KEY	year	average	months_included
CYP23	Open referrals	2019	257428.4	9
CYP23	Open referrals	2020	293662.4	9
CYP23	Open referrals	2021	363249.6	9

Relative changes compared to last year

#Relative changes chart
CAMHS_changes_chart_cyp23 <- CAMHS_reldata_cyp23 %>%
  ggplot(., aes(x=start_date, y=pct_change_l1, group= MEASURE_KEY)) +
  facet_wrap(~timing, scales = "free_x") +
  geom_line(aes(color= MEASURE_KEY),size=1) +
  geom_hline(yintercept=0, linetype="dashed", color = "red") +
  scale_x_date(date_labels = "%b %Y",date_breaks = "1 month") +
  theme_ipsum() +
  xlab("") +
  ylab("% change") +
  labs(col="") +
  scale_color_manual(values=c("Open referrals" = "aquamarine4",
                              "People in contact" = "tomato3",
                              "First contacts (<18)" = "olivedrab4",
                              "Attended contacts (<18)" = "violetred",
                              "New referrals (<18)" = "magenta1")) +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))

ggplotly(CAMHS_changes_chart_cyp23)

#Underlying data
CAMHS_reldata_cyp23 %>%
  select(.,start_date,MEASURE_KEY,pct_change_l1) %>%
  arrange(.,start_date) %>% 
  knitr::kable(., align = "lccrr",format.args = list(big.mark = ","))

start_date	MEASURE_KEY	pct_change_l1
2019-05-01	Open referrals	5.246890
2019-06-01	Open referrals	1.739853
2019-07-01	Open referrals	5.426458
2019-08-01	Open referrals	3.982362
2019-09-01	Open referrals	2.644866
2019-10-01	Open referrals	0.155166
2019-11-01	Open referrals	3.787897
2019-12-01	Open referrals	2.063169
2020-01-01	Open referrals	3.736196
2020-02-01	Open referrals	3.093774
2020-03-01	Open referrals	-1.736579
2020-04-01	Open referrals	24.108806
2020-05-01	Open referrals	16.030098
2020-06-01	Open referrals	17.605782
2020-07-01	Open referrals	18.408957
2020-08-01	Open referrals	21.004136
2020-09-01	Open referrals	26.599015
2020-10-01	Open referrals	28.542216
2020-11-01	Open referrals	31.215902
2020-12-01	Open referrals	31.927281
2021-01-01	Open referrals	26.935611
2021-02-01	Open referrals	24.858890
2021-03-01	Open referrals	31.612231
2021-04-01	Open referrals	16.386919
2021-05-01	Open referrals	23.874987
2021-06-01	Open referrals	25.934846
2021-07-01	Open referrals	25.040806
2021-08-01	Open referrals	22.678213
2021-09-01	Open referrals	17.768669
2021-10-01	Open referrals	17.864733
2021-11-01	Open referrals	15.596290
2021-12-01	Open referrals	14.925529

People with eating disorders being seen within target times (<18)

Measure code: ED86e
Measure description: Proportion of referrals with eating disorders categorized as urgent cases entering treatment within one week in RP, aged 0-18
Source: NHS England, Monthly MHSDS Statistics, Metadata
Measure code: ED87e
Measure description: Proportion of referrals with eating disorders categorized as routine cases entering treatment within four weeks in RP, aged 0-18
Source: NHS England, Monthly MHSDS Statistics, Metadata

#Eating disorders data
target_time_ed_data_new <- MHSDS_main_pooled_dashboard %>%
  filter(.,PRIMARY_LEVEL_DESCRIPTION=="England",
         MEASURE_ID %in% c("ED86e","ED87e")) %>%
  select(.,start_date,end_date,PRIMARY_LEVEL_DESCRIPTION,MEASURE_ID,MEASURE_VALUE)

target_time_ed_data <- MHSDS_ED_pooled_dashboard %>%
  filter(.,PRIMARY_LEVEL_DESCRIPTION=="England",
         MEASURE_ID %in% c("ED86e","ED87e")) %>%
  select(.,start_date,end_date,PRIMARY_LEVEL_DESCRIPTION,MEASURE_ID,MEASURE_VALUE) %>%
  plyr::rbind.fill(.,target_time_ed_data_new) %>% 
  mutate(.,MEASURE_VALUE=as.numeric(MEASURE_VALUE),
         Type=case_when(MEASURE_ID=="ED86e" ~ "Urgent",
                        MEASURE_ID=="ED87e" ~ "Routine",
                        TRUE ~ "NA"),
         Metric="Starting within target time",
         time_window=ymd(end_date)-ymd(start_date))

#Eating disorders chart
target_time_ed_chart <- target_time_ed_data %>%
  ggplot(., aes(x=end_date, y=MEASURE_VALUE, group=Type)) +
  geom_line(aes(color=Type),size=1) +
  scale_x_date(date_labels = "%b %Y",date_breaks = "1 month") +
  scale_y_continuous(labels = scales::comma) +
  theme_ipsum() +
  xlab("") +
  ylab("") +
  labs(col="", title="") +
  scale_colour_manual(values=
                        c("Urgent" = "brown", "Routine" = "darkseagreen4")) +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))

ggplotly(target_time_ed_chart)

target_time_ed_data %>%
  arrange(.,end_date) %>% 
  select(.,MEASURE_ID,Type,start_date,end_date,time_window,MEASURE_VALUE) %>%
  filter(.,start_date %in% ymd(c("2021-01-01","2021-04-01","2021-07-01","2021-10-01"))) %>%
  group_by(MEASURE_ID,Type) %>%
  summarise(.,MEASURE_VALUE=mean(MEASURE_VALUE)) %>% 
  ungroup() %>%
  knitr::kable(., align = "lccrr",format.args = list(big.mark = ","))

MEASURE_ID	Type	MEASURE_VALUE
ED86e	Urgent	39.05812
ED87e	Routine	48.96245

New referrals into eating disorder services (<18)

Measure code: ED32
Measure description: Referrals into a service with a primary reason of referral of Eating Disorder received in the reporting period, among those aged 0-18.
Source: NHS England, Monthly MHSDS Statistics, Metadata
Measure code: ED88
Measure description: The number of referrals to eating disorder services for eating disorder issues for people aged 0 - 18 waiting for treatment at the end of the reporting period
Source: NHS England, Monthly MHSDS Statistics, Metadata
Measure code: ED89
Measure description: Referrals with eating disorder issues categorized as urgent waiting for treatment end RP, aged 0-18
Source: NHS England, Monthly MHSDS Statistics, Metadata
Measure code: ED90
Measure description: Referrals with eating disorder issues categorized as routine waiting for treatment end RP, aged 0-18
Source: NHS England, Monthly MHSDS Statistics, Metadata

#Eating disorders data
ref_ed_data_new <- MHSDS_main_pooled_dashboard %>%
  filter(.,PRIMARY_LEVEL_DESCRIPTION=="England",
         MEASURE_ID %in% c("ED89","ED90")) %>%
  select(.,start_date,end_date,PRIMARY_LEVEL_DESCRIPTION,MEASURE_ID,MEASURE_VALUE)

ref_ed_data_new <- MHSDS_ED_pooled_dashboard %>%
  filter(.,PRIMARY_LEVEL_DESCRIPTION=="England",
         MEASURE_ID %in% c("ED89","ED90")) %>%
  select(.,start_date,end_date,PRIMARY_LEVEL_DESCRIPTION,MEASURE_ID,MEASURE_VALUE) %>%
  plyr::rbind.fill(.,ref_ed_data_new) %>% 
  mutate(.,MEASURE_VALUE=as.numeric(MEASURE_VALUE),
         Type=case_when(MEASURE_ID=="ED89" ~ "Urgent",
                        MEASURE_ID=="ED90" ~ "Routine",
                        TRUE ~ "NA"),
         Metric="Waiting for treatment",
         time_window=ymd(end_date)-ymd(start_date))

#Eating disorders chart 2
ref_ed_chart <- ref_ed_data_new %>%
  ggplot(., aes(x=end_date, y=MEASURE_VALUE, group=Type)) +
  geom_line(aes(color=Type),size=1) +
  scale_x_date(date_labels = "%b %Y",date_breaks = "1 month") +
  scale_y_continuous(labels = scales::comma) +
  theme_ipsum() +
  xlab("") +
  ylab("") +
  labs(col="", title="") +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))

ggplotly(ref_ed_chart)

#Crisis referrals
crisis_referrals_data <- MHSDS_main_pooled_dashboard %>%
  filter(.,PRIMARY_LEVEL_DESCRIPTION=="England",
         MEASURE_ID %in% c("CCR70b","CCR71b")) %>%
  select(.,start_date,end_date,PRIMARY_LEVEL_DESCRIPTION,MEASURE_ID,MEASURE_VALUE) %>% 
  mutate(.,MEASURE_VALUE=as.numeric(MEASURE_VALUE),
         Type=case_when(MEASURE_ID=="CCR71b" ~ "Urgent",
                        MEASURE_ID=="CCR70b" ~ "Emergency",
                        TRUE ~ "NA"),
         Metric="New referrals",
         time_window=ymd(end_date)-ymd(start_date))

#Eating disorders chart 2
crisis_referrals_chart <- crisis_referrals_data %>%
  ggplot(., aes(x=end_date, y=MEASURE_VALUE, group=Type)) +
  geom_line(aes(color=Type),size=1) +
  scale_x_date(date_labels = "%b %Y",date_breaks = "1 month") +
  scale_y_continuous(labels = scales::comma) +
  theme_ipsum() +
  xlab("") +
  ylab("") +
  labs(col="", title="") +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.x = element_text(margin = unit(c(3, 0, 0, 0), "mm"),size = 8),
        axis.title.y = element_text(size = 8))

ggplotly(crisis_referrals_chart)

crisis_referrals_data_flourish <- crisis_referrals_data %>% 
  select(.,end_date,time_window,MEASURE_VALUE,Metric,Type) %>%
  pivot_wider(
    names_from = "Type",
    names_sep = ".",
    values_from = c(MEASURE_VALUE)
  ) %>%
  arrange(.,end_date)

#fwrite(crisis_referrals_data_flourish,paste0(onedrive_charts_data,"crisis_charts.csv"))

#ED referrals
ED_referrals_data <- MHSDS_main_pooled_dashboard %>%
  filter(.,PRIMARY_LEVEL_DESCRIPTION=="England",
         MEASURE_ID %in% c("ED32")) %>%
  select(.,start_date,end_date,PRIMARY_LEVEL_DESCRIPTION,MEASURE_ID,MEASURE_VALUE) %>% 
  mutate(.,MEASURE_VALUE=as.numeric(MEASURE_VALUE),
         Metric="New referrals",
         time_window=ymd(end_date)-ymd(start_date))

#Eating disorders chart 3
ED_referrals_chart <- ED_referrals_data %>%
  ggplot(., aes(x=end_date, y=MEASURE_VALUE)) +
  geom_line() +
  scale_x_date(date_labels = "%b %Y",date_breaks = "1 month") +
  scale_y_continuous(labels = scales::comma) +
  theme_ipsum() +
  xlab("") +
  ylab("") +
  labs(col="", title="") +
  theme(legend.position="bottom",
        panel.border = element_blank(),
        strip.text = element_text(size=8),
        text = element_text(size = 8),
        legend.title=element_text(size=8),
        legend.text=element_text(size=8),
        axis.text = element_text(size = 8),
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1,size = 8),
        axis.title.y = element_text(size = 8))

#ggplotly(ED_referrals_chart)

ED_referrals_data_flourish <- ED_referrals_data %>% 
  select(.,end_date,time_window,MEASURE_VALUE,Metric) %>%
  arrange(.,end_date)

#fwrite(ED_referrals_data_flourish,paste0(onedrive_charts_data,"EDref_charts.csv"))

Health Education England data on workforce (compared to activity levels from NHS England)

Source of staff numbers is Health Education England report

#NHS Digital on people in contact with CAMHS service
CAMHS_contacts <- MHSDS_main_pooled_dashboard %>%
  filter(.,PRIMARY_LEVEL_DESCRIPTION=="England",
         MEASURE_ID=="CYP01") %>%
  select(.,start_date,MEASURE_VALUE) %>%
  mutate(.,date_ymd=lubridate::ymd(start_date)) %>%
  mutate(.,date_ymd=floor_date(date_ymd, "month"),
         measure="People in contact CAMHS",
         MEASURE_VALUE=as.numeric(MEASURE_VALUE)) %>%
  select(.,-"start_date") %>%
  arrange(.,date_ymd)

#Turn into indexed data
CAMHS_contacts_index <- CAMHS_contacts %>%
  mutate(.,MEASURE_VALUE_MA=zoo::rollmean(MEASURE_VALUE,k=7,fill=NA)) %>% #Use a moving average to compute the mean
  mutate(.,Jan2019=filter(.,date_ymd=="2019-01-01")$MEASURE_VALUE_MA) %>%
  filter(.,date_ymd>=ymd("2019-01-01"),!is.na(MEASURE_VALUE_MA)) %>%
  mutate(.,index=MEASURE_VALUE_MA/Jan2019*100)

#HEE data (copied over from report)
HEE_staff_index <- data.frame(measure="CYP MH staff",
  date_ymd=as.Date(c("2019-01-01","2021-04-01")),
  MEASURE_VALUE=as.numeric(c("14857","20626"))) %>%
  mutate(.,Jan2019=filter(.,date_ymd=="2019-01-01")$MEASURE_VALUE) %>%
  mutate(.,index=MEASURE_VALUE/Jan2019*100)

#Append two sources together
CAMHS_and_HEE_staff <- plyr::rbind.fill(CAMHS_contacts_index,HEE_staff_index)

#Show data
CAMHS_and_HEE_staff %>%
  knitr::kable(., align = "lccrr")

MEASURE_VALUE	date_ymd	measure	MEASURE_VALUE_MA	Jan2019	index
229217	2019-01-01	People in contact CAMHS	228988.6	228988.6	100.00000
233831	2019-02-01	People in contact CAMHS	229359.7	228988.6	100.16208
241926	2019-03-01	People in contact CAMHS	229607.7	228988.6	100.27038
218678	2019-04-01	People in contact CAMHS	229460.3	228988.6	100.20600
230443	2019-05-01	People in contact CAMHS	227975.9	228988.6	99.55774
225480	2019-06-01	People in contact CAMHS	226204.0	228988.6	98.78397
226647	2019-07-01	People in contact CAMHS	223858.4	228988.6	97.75965
218826	2019-08-01	People in contact CAMHS	225581.4	228988.6	98.51209
221428	2019-09-01	People in contact CAMHS	225669.0	228988.6	98.55033
225507	2019-10-01	People in contact CAMHS	227228.4	228988.6	99.23134
230739	2019-11-01	People in contact CAMHS	229193.3	228988.6	100.08940
231056	2019-12-01	People in contact CAMHS	231802.1	228988.6	101.22870
236396	2020-01-01	People in contact CAMHS	240340.9	228988.6	104.95758
240401	2020-02-01	People in contact CAMHS	247226.4	228988.6	107.96453
237088	2020-03-01	People in contact CAMHS	253196.4	228988.6	110.57164
281199	2020-04-01	People in contact CAMHS	259536.9	228988.6	113.34053
273706	2020-05-01	People in contact CAMHS	264546.3	228988.6	115.52816
272529	2020-06-01	People in contact CAMHS	271186.1	228988.6	118.42781
275439	2020-07-01	People in contact CAMHS	279661.3	228988.6	122.12893
271462	2020-08-01	People in contact CAMHS	283677.3	228988.6	123.88273
286880	2020-09-01	People in contact CAMHS	289022.0	228988.6	126.21678
296414	2020-10-01	People in contact CAMHS	293994.3	228988.6	128.38819
309311	2020-11-01	People in contact CAMHS	298502.6	228988.6	130.35697
311119	2020-12-01	People in contact CAMHS	305128.7	228988.6	133.25063
307335	2021-01-01	People in contact CAMHS	310323.0	228988.6	135.51899
306997	2021-02-01	People in contact CAMHS	316181.9	228988.6	138.07757
317845	2021-03-01	People in contact CAMHS	320665.1	228988.6	140.03544
323240	2021-04-01	People in contact CAMHS	325157.4	228988.6	141.99723
337426	2021-05-01	People in contact CAMHS	328668.4	228988.6	143.53049
340694	2021-06-01	People in contact CAMHS	332966.0	228988.6	145.40726
342565	2021-07-01	People in contact CAMHS	337480.9	228988.6	147.37891
331912	2021-08-01	People in contact CAMHS	342418.3	228988.6	149.53510
337080	2021-09-01	People in contact CAMHS	345044.1	228988.6	150.68182
14857	2019-01-01	CYP MH staff	NA	14857.0	100.00000
20626	2021-04-01	CYP MH staff	NA	14857.0	138.83018

NHS England data on workforce (child and adolescent psychiatry only)

#NHS data
NHS_workforce_doctors %>%
  mutate(.,date_ymd=lubridate::ymd(Date)) %>%
  mutate(.,date_ymd=floor_date(date_ymd, "month"),
         measure="FTE doctors") %>%
  rename(.,MEASURE_VALUE=FTE) %>%
  filter(Specialty %in% c("Child and adolescent psychiatry")) %>%
  group_by(date_ymd,measure,Specialty) %>%
  summarise(MEASURE_VALUE=sum(MEASURE_VALUE,na.rm=TRUE)) %>% #Aggregate over categories
  ungroup() %>%
  filter(., date_ymd %in% c(ymd("2019-01-01"),ymd("2021-04-01"))) %>%
  pivot_wider(
    names_from = date_ymd,
    names_sep = ".",
    values_from = MEASURE_VALUE
  ) %>%
  mutate(.,pct_change=(`2021-04-01`-`2019-01-01`)/`2019-01-01`*100) %>%
  knitr::kable(., align = "lccrr")

measure	Specialty	2019-01-01	2021-04-01	pct_change
FTE doctors	Child and adolescent psychiatry	981.6823	1064.256	8.411452

#Latest data on consultants
NHS_workforce_doctors %>%
  mutate(.,date_ymd=lubridate::ymd(Date)) %>%
  mutate(.,date_ymd=floor_date(date_ymd, "month"),
         measure="FTE doctors") %>%
  rename(.,MEASURE_VALUE=FTE) %>%
  filter(., date_ymd %in% c(ymd("2021-05-01"))) %>%
  filter(Specialty %in% c("Child and adolescent psychiatry"))  %>%
  select(.,-"Date") %>% 
  knitr::kable(., align = "lccrr")

Grade	Grade Order Code	Specialty Group	Specialty	MEASURE_VALUE	date_ymd	measure
Consultant	1	Psychiatry group	Child and adolescent psychiatry	626.5470	2021-05-01	FTE doctors
Associate Specialist	2	Psychiatry group	Child and adolescent psychiatry	13.3725	2021-05-01	FTE doctors
Specialty Doctor	3	Psychiatry group	Child and adolescent psychiatry	100.9210	2021-05-01	FTE doctors
Staff Grade	4	Psychiatry group	Child and adolescent psychiatry	3.5000	2021-05-01	FTE doctors
Specialty Registrar	5	Psychiatry group	Child and adolescent psychiatry	144.8688	2021-05-01	FTE doctors
Core Training	6	Psychiatry group	Child and adolescent psychiatry	142.1062	2021-05-01	FTE doctors
Foundation Doctor Year 2	7	Psychiatry group	Child and adolescent psychiatry	10.0000	2021-05-01	FTE doctors
Foundation Doctor Year 1	8	Psychiatry group	Child and adolescent psychiatry	10.0000	2021-05-01	FTE doctors

Networked Data Lab long chart on children and young people’s mental health

Sebastien Peytrignet

2022-03-30

Performance against 18 week target in Scottish CAMHS

Raw time series

Monthly average, per year

People still receiving treatment in Scottish CAMHS

Raw time series

Monthly average, per year

People waiting for treatment in Scottish CAMHS

Raw time series

Monthly average, per year

People in contact with CAMHS

Raw time series

Monthly average, per year

Monthly average, per year (Jan to Sep)

Relative changes compared to last year

New referrals (<18)

Raw time series

Monthly average, per year

Monthly average, per year (Apr to Dec)

Relative changes compared to last year

New referrals to CYPMHS

Raw time series

Monthly average, per year

Monthly average, per year (Apr to Dec)

First contacts (<18)

Raw time series

Monthly average, per year

Monthly average, per year (Jan to Sep)

Relative changes compared to last year

Attended contacts (<18)

Raw time series

Monthly average, per year

Monthly average, per year (Jan to Sep)

Relative changes compared to last year

Open referrals

Raw time series

Monthly average, per year

Monthly average, per year (Jan to Sep)

Relative changes compared to last year

People with eating disorders being seen within target times (<18)

New referrals into eating disorder services (<18)

Health Education England data on workforce (compared to activity levels from NHS England)

NHS England data on workforce (child and adolescent psychiatry only)