9  Flu vaccination coverage

9.1 Workflow

flowchart TD
A[Data] --> B[population data]
A --> C[vaccination data]
C --> D[[recode region names]] --> E[[calculate age bands]] --> F[[calculate vaccination counts]]
B --> G[[calculate age bands]] --> H[[calculate population counts]]
F --> I[[join datasets]]
H --> I
I --> J[calculate rates and ci]

Figure 9.1: Proposed workflow for calculating flu vaccination coverage

9.2 Clean data

Recode region names Add new age bands

flu_data <- flu_data |>
    mutate(paed = dplyr::between(age, 0, 18), 
           older = age >= 65) |>
    mutate(Region = case_when(str_detect(Region, "Baha") ~ "Al Bahah", 
                              str_detect(Region, "Qu?assim") ~ "Al Qasim",
                              str_detect(Region, "Asir") ~ "`Asir",
                              str_detect(Region, "Hail") ~ "Ha'il",
                              str_detect(Region, "[Mm]adin") ~ "Al Madinah al Munawwarah",
                              str_detect(Region, "Jizan") ~ "Jazan",
                              str_detect(Region, "Makka") ~ "Makkah al Mukarramah",
                              str_detect(Region, "Sharqiya" ) ~ "Al Mintaqah ash Sharqiyah",
                              str_detect(Region, "Northern Frontier") ~ "Al Hudud ash Shamaliyah",
                              str_detect(Region, "Riyad") ~ "Ar Riyadh",
                              TRUE ~ Region ))

9.3 Calculate regional numerators and denominators

• Calculate region-by-gender populations for 0-18 and 65+

• Calculate region-by-gender vaccinations for 0-18 and 65+

pop_paed <- popns |>
    group_by(Region, Gender, paed) |>
    reframe(pop = sum(Population)) |>
    filter(paed == "TRUE") |>
    mutate(Gender = recode(Gender, "Female" = "female", "Male" = "male"), 
           Region = recode(Region, "`Asir" = "'Asir"))

pop_older <-popns |>
    group_by(Region, Gender, older) |>
    reframe(pop = sum(Population)) |>
    filter(older == "TRUE") |>
    mutate(Gender = recode(Gender, "Female" = "female", "Male" = "male"), 
           Region = recode(Region, "`Asir" = "'Asir"))

## fills all region-gender categories and remove unknown
paed_flu <- flu_data |>
    filter(paed == "TRUE") |>
    count(Region, Gender) |>
    complete(Region, Gender) |>
    filter(Gender != "unknown") |>
    mutate( Region = recode(Region, "`Asir" = "'Asir"))

older_flu <- flu_data |>
    filter(older == "TRUE") |>
    count(Region, Gender) |>
    complete(Region, Gender) |>
    filter(Gender != "unknown") |>
    mutate( Region = recode(Region, "`Asir" = "'Asir"))

9.4 Join populations and vaccination data

paed_nd <- paed_flu |>
  left_join(pop_paed) |>
  select(-paed)

older_nd <- older_flu |>
  left_join(pop_older) |>
  select(-older)

9.5 Calculate coverage by age group

paed_coverage <- paed_nd |>
  phe_proportion(x = n, n = pop) |>
  mutate(age = "0-18")    

older_coverage <- older_nd |>
  phe_proportion(x = n, n = pop) |>
  mutate(age = "65+")

bind_rows(paed_coverage, older_coverage) |>
    write_csv("data/flu_coverage.csv")

9.6 Tabulate

options(digits = 3)

paed_coverage |>
  select(-c(statistic, method)) |>
  head() |>
  flextable()
older_coverage |>
  select(-c(statistic, method)) |>
  head() |>
  flextable()

Table 9.1: Flu vaccination coverage

Region	Gender	n	pop	value	lowercl	uppercl	confidence	age
'Asir	female	161	335,011	0.0004806	0.00041187	0.0005607	95%	0-18
'Asir	male	33	347,751	0.0000949	0.00006757	0.0001333	95%	0-18
Al Bahah	female	38	57,230	0.0006640	0.00048382	0.0009112	95%	0-18
Al Bahah	male	2	59,077	0.0000339	0.00000928	0.0001234	95%	0-18
Al Hudud ash Shamaliyah	female	1	70,350	0.0000142	0.00000251	0.0000805	95%	0-18
Al Hudud ash Shamaliyah	male		73,068				95%	0-18

Region	Gender	n	pop	value	lowercl	uppercl	confidence	age
'Asir	female	49	32,224	0.001521	0.0011505	0.002010	95%	65+
'Asir	male	10	36,288	0.000276	0.0001497	0.000507	95%	65+
Al Bahah	female	1	7,791	0.000128	0.0000227	0.000727	95%	65+
Al Bahah	male		6,752				95%	65+
Al Madinah al Munawwarah	female	24	31,099	0.000772	0.0005187	0.001148	95%	65+
Al Madinah al Munawwarah	male	14	31,231	0.000448	0.0002671	0.000752	95%	65+

9.7 Visualise

paed_coverage |>
  ggplot() +
  geom_col(aes(Region, value, fill = Gender)) +
  geom_linerange(aes(Region, ymin = lowercl, ymax = uppercl)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1) ) +
  facet_wrap(~ Gender) +
  scale_fill_manual(values = c("blue", "red"))


older_coverage |>
  ggplot() +
  geom_col(aes(Region, value, fill = Gender)) +
  geom_linerange(aes(Region, ymin = lowercl, ymax = uppercl)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1) ) +
  facet_wrap(~ Gender) +
  scale_fill_manual(values = c("blue", "red")) 

(a) Paediatric coverage

(b) 65+ coverage

Figure 9.2: Flu vaccination coverage