stata_slides.qmd

---
title: |
  <h2 style='font-size: 70px'>
  IPUMS PMA Data Workshop
  </h2>
  <div style='margin-bottom: 50px; font-size: 40px'>
  Reasons for Unmet Need for Family Planning <br>in PMA Panel Surveys
  </div>
author: 
  - |
    <p style='font-size: 25px; margin-left: 50px'>
      <img src='images/devon.png' width='100' style='vertical-align: middle'> 
      Devon Kristiansen - Project Manager
    </p>
  - |
    <p style='font-size: 25px; margin-left: 50px'>
      <img src='images/matt.png' width='100' style='vertical-align: middle'>
      Matt Gunther - Senior Data Analyst 
    </p>
    
format: 
  revealjs: 
    theme: [default, custom.scss]
    logo: images/logo-navy.png
    chalkboard: true
    smaller: true
    scrollable: false
    incremental: true
    preview-links: false
    reference-location: document
    link-external-icon: true
    echo: false
bibliography: cites.bib

---


## Today's Goals 

  - Introduce measures of Unmet Need in PMA Panel Surveys
  - Open an IPUMS PMA data extract in **Stata** 
  - Explore women's reasons for Unmet Need in Phase 1 of the panel
  - Predict women's Unmet Need in Phase 2 of the panel 
  
::: {.fragment}  
```{r, echo=FALSE}
library(kableExtra)
library(tidyverse)

options(knitr.kable.NA = '')

avail <- read_csv("data/sample_avail.csv") 
names(avail)[2] <- paste0(
  names(avail)[2], 
  footnote_marker_symbol(1)
)

avail %>%   
  arrange(Sample) %>% 
  kable(escape = FALSE, format = "html", table.attr = "style='width:100%;'") %>% 
  kable_styling(font_size = 18) %>% 
  add_header_above(c(" " = 2, "Now Available from IPUMS PMA" = 3)) %>% 
  scroll_box(
    width = "100%", 
    box_css = paste(
      sep = "; ",
      "margin-bottom: 1em",
      "margin-top: 0em",
      "border: 0px solid #ddd",
      "padding: 5px"
    )
  ) %>%
  footnote(
    symbol = paste0(
      "<small><em>",
      "Each data collection phase is spaced one year apart",
      "</em></small>"), 
    escape = FALSE
  )
```
:::

# Unmet Need 

## What is Unmet Need? 

::: {.fragment}
Women who are capable of becoming pregnant are said to have unmet need if they might want to **limit** or **space** births, but are not using a family planning method.
:::
::: {.fragment}
Examples: 

  - Layla wants to have a child someday, but she is not so sure she wants to have one soon. If neither she nor her husband are currently using any family planning method, Layla would have **unmet need for spacing** pregnancies. 
  - Yasmine is currently pregnant with her fourth child. She had planned to stop having children after her third child, so her fourth pregnancy indicates that Yasmine has **unmet need for limiting** pregnancies. 
  - At age 15, Fatimata is not using any family planning method because she is not sexually active. Fatimata has no unmet need because she is **not at risk** for becoming pregnant.
:::

## How is Unmet Need Measured? 

::: {.fragment}
PMA uses several different survey questions to determine which women are: 

  - Sexually active (including non-married women who have had sex within 30 days)
  - Neither infecund nor menopausal 
  - Express uncertainty or intentions to space or limit pregnancies (including their current pregnancy, or recent pregnancy if PPA)
  - Not currently using a family planning method (including prior sterilization, emergency methods within the last year, traditional methods, and all modern methods)
:::

## IPUMS PMA Harmonized Variables

::: {.fragment}
[UNMETNEED](https://pma.ipums.org/pma-action/variables/UNMETNEED) explains whether each female respondent has: 

  - unmet need for **spacing** births
  - unmet need for **limiting** births
  - is **not at risk** of becoming pregnant
  - is currently using family planning for **spacing** births
  - is currently using family planning for **limiting** births
  - is not using family planning with **no unmet need**
:::

::: {.fragment}
[UNMETYN](https://pma.ipums.org/pma-action/variables/UNMETYN) is binary:

  - **Yes**
  - **No**
:::

# Unmet Need in Stata

## Setup 

Our extract includes data from two phases of the panel study in **Burkina Faso** and **Kenya** (female respondents only). 

::: {.fragment}
```{r, results='hide'}
library(ipumsr)
library(tidyverse)
library(srvyr)
library(survey)
library(scales)
library(broom)
library(gtsummary)

# load pma longitudinal samples (wide format, female respondents only)
dat <- read_ipums_micro(
 ddi = here::here("data/pma_00114.xml"),
 data = here::here("data/pma_00114.dat.gz")
)

# keep only panel members in the de facto population 
dat <- dat %>% 
  mutate(COUNTRY = COUNTRY %>% as_factor %>% as.character) %>% 
  filter(
    RESULTFQ_1 == 1 & RESULTFQ_2 == 1,
    RESIDENT_1 %in% c(11, 22) & RESIDENT_2 %in% c(11, 22)
  )
```
```{r, echo=FALSE}
dat <- dat %>% filter(COUNTRY %in% c("Burkina Faso", "Kenya"))
```

```{c, echo=TRUE, eval=FALSE}
cd "C:\Users\krist108\Documents\krist108 stuff\ISRDI\ICFP 2022 workshop"
use pma_00178.dta
```
:::

::: {.fragment}
We drop women who only completed one of the two interviews... 
:::

::: {.fragment}
```{c, echo=TRUE, eval=FALSE}
keep if resultfq_1 == 1 & resultfq_2 == 1
```
```
(8,361 observations deleted)
```
:::

::: {.fragment}
and those who are not members of the *de facto* population. 
:::

::: {.fragment}
```{c, echo=TRUE, eval=FALSE}
keep if (resident_1 == 11 | resident_1 == 22) & (resident_2 == 11 | resident_2 == 22)
```
```
(358 observations deleted)
```
:::


## Unmet Need by Country at Phase 1

::: {.columns}
:::: {.column width="50%"}
[PANELWEIGHT](https://pma.ipums.org/pma-action/variables/UNMETNEED) is the sampling weight for panel members (controls for loss to follow-up).

[EAID_1](https://pma.ipums.org/pma-action/variables/EAID_1) and [STRATA_1](https://pma.ipums.org/pma-action/variables/STRATA_1) are the sample clusters and strata used to select households at Phase 1.

```{c, echo=TRUE, eval=FALSE}
svyset [pw=panelweight], psu(eaid_1) strata(strata_1)
```

Information from `svyset` can be used to produce **weighted percentages** like those shown to the right. (SEs would also be adjusted for cluster sampling). 

```{c, echo=TRUE, eval=FALSE}
svy: tab unmetneed_1 country, col

svy: tab unmetyn_1 country, col
```

Note: we will set NIU cases to `missing` (see universe tab for details).

```{c, echo=TRUE, eval=FALSE}
replace unmetyn_1 = . if unmetyn_1 == 99
```
```
(2 real changes made, 2 to missing)
```
::::

:::: {.column width="50%"}
```{r, echo = FALSE, eval = TRUE}
dat %>% 
  as_survey_design(weight = PANELWEIGHT, id = EAID_1, strata = STRATA_1) %>% 
  mutate(across(c(UNMETNEED_1, UNMETYN_1), ~.x %>%  as_factor %>% fct_drop)) %>% 
  tbl_svysummary(
    by = COUNTRY, 
    include = c(UNMETNEED_1, UNMETYN_1), 
    statistic = list(everything() ~ "{p}%")
  ) %>% 
  modify_header(update = list(label ~ " ", 
                              stat_1 ~ "Burkina Faso", stat_2 ~ "Kenya")) %>% 
  modify_footnote(c(stat_1, stat_2) ~ "Weighted Percent")
```
::::
:::

## Unmet Need by Country at Phase 2

::: {.columns}
:::: {.column width="50%"}
[EAID_1](https://pma.ipums.org/pma-action/variables/EAID_1) and [STRATA_1](https://pma.ipums.org/pma-action/variables/STRATA_1) can also be used for estimating Phase 2 outcomes. 

Panel members were found in the same study area again at Phase 2. 

No need to adjust `svyset`! 

```{c, echo=TRUE, eval=FALSE}
svy: tab unmetneed_2 country, col

svy: tab unmetyn_2 country, col
```
::::
:::: {.column width="50%"}
```{r, echo = FALSE, eval = TRUE}
dat %>% 
  as_survey_design(
    weight = PANELWEIGHT, 
    id = EAID_1, 
    strata = STRATA_1
  ) %>% 
  mutate(across(
    c(UNMETNEED_2, UNMETYN_2), 
    ~.x %>%  as_factor %>% fct_drop
  )) %>% 
  tbl_svysummary(
    by = COUNTRY, 
    include = c(UNMETNEED_2, UNMETYN_2), 
    statistic = list(everything() ~ "{p}%")
  ) %>% 
  modify_header(
    update = list(
      label ~ " ", 
      stat_1 ~ "Burkina Faso", 
      stat_2 ~ "Kenya"
    )
  ) %>% 
  modify_footnote(
    c(stat_1, stat_2) ~ "Weighted Percent"
  )
```
::::
:::


# Research Question 

::: {.fragment}
How well can we predict **Phase 2 Unmet Need** if we only know whether a woman had **Phase 1 Unmet Need**? 
:::

## Logistic Regression

::: {.columns}

:::: {.column width="40%"}
```{r}
models <- dat %>% 
  mutate(across(matches("UNMETYN"), ~.x == 1)) %>% 
  group_by(COUNTRY) %>% 
  summarise(
    glm = cur_data() %>% 
      as_survey_design(weight = PANELWEIGHT, id = EAID_1, strata = STRATA_1) %>% 
      svyglm(UNMETYN_2 ~ UNMETYN_1, design = ., family = "quasibinomial") %>% 
      list()
  )
```

::::: {.fragment}
Burkina Faso

```{c, echo=TRUE, eval=FALSE}
svy: logit unmetyn_2 unmetyn_1 if country == 1, or
```
:::::

::::: {.fragment}
Kenya

```{c, echo=TRUE, eval=FALSE}
svy: logit unmetyn_2 unmetyn_1 if country == 7, or
```
:::::
::::
:::: {.column width="5%"}
:::: 
:::: {.column width="55%" style="position: absolute; top: 20%;"}
::::: {.fragment}
```{r, echo=FALSE}
models$glm %>% 
  map2(models$COUNTRY,
    ~.x %>% 
      tbl_regression(
        exponentiate = TRUE,
        intercept= TRUE,
        show_single_row = where(is.logical),
        pvalue_fun = ~style_pvalue(.x, digits = 2),
        label = list(UNMETYN_1 ~ "Phase 1 Unmet Need")
      ) %>%
      add_significance_stars(hide_se = TRUE, hide_ci = T) %>% 
      modify_header(update = list(label ~ " ", estimate = .y, ci = "95% CI")) %>% 
      modify_footnote(estimate ~ NA, abbreviation = TRUE) 
  ) %>% 
  tbl_merge(tab_spanner = FALSE) %>% 
  modify_caption("### Phase 2 Unmet Need \n ### Odds Ratios")
```
::::: 
::::
:::

## Logistic Regression

::: {.columns}

:::: {.column width="40%"}
```{r}
models <- dat %>% 
  mutate(across(matches("UNMETYN"), ~.x == 1)) %>% 
  group_by(COUNTRY) %>% 
  summarise(
    glm = cur_data() %>% 
      as_survey_design(weight = PANELWEIGHT, id = EAID_1, strata = STRATA_1) %>% 
      svyglm(UNMETYN_2 ~ UNMETYN_1, design = ., family = "quasibinomial") %>% 
      list()
  )
```

Burkina Faso

```{c, echo=TRUE, eval=FALSE}
svy: logit unmetyn_2 unmetyn_1 if country == 1, or
```

Kenya

```{c, echo=TRUE, eval=FALSE}
svy: logit unmetyn_2 unmetyn_1 if country == 7, or
```

Phase 1 Unmet Need is a significant predictor of Unmet Need at Phase 2. 

Women with Unmet Need at Phase 1 have about **four times** the odds of Phase 2 Unmet Need compared with women who had none. 
::::
:::: {.column width="5%"}
:::: 
:::: {.column width="55%" style="position: absolute; top: 20%;"}
```{r, echo=FALSE}
models$glm %>% 
  map2(models$COUNTRY,
    ~.x %>% 
      tbl_regression(
        exponentiate = TRUE,
        intercept= TRUE,
        show_single_row = where(is.logical),
        pvalue_fun = ~style_pvalue(.x, digits = 2),
        label = list(UNMETYN_1 ~ "Phase 1 Unmet Need")
      ) %>%
      add_significance_stars(hide_se = TRUE, hide_ci = T) %>% 
      modify_header(update = list(label ~ " ", estimate = .y, ci = "95% CI")) %>% 
      modify_footnote(estimate ~ NA, abbreviation = TRUE) 
  ) %>% 
  tbl_merge(tab_spanner = FALSE) %>% 
  modify_caption("### Phase 2 Unmet Need \n ### Odds Ratios")
```
::::
:::

# Reasons for Unmet Need 

::: {.fragment}
If we know **why a woman had Phase 1 Unmet Need**, does this improve our ability to predict her **Phase 2 Unmet Need**? 
:::

## Why are Reasons Important? 

::: {.fragment}
Alone, Unmet Need does not explain access or desire to use family planning. 
:::
::: {.fragment}
Senderowicz and Maloney [-@Senderowicz2022-ao] suggest dividing Unmet Need into **supply-** and **demand-driven** factors.

> Demand-side unmet need represents women who are choosing not to use contraception, not because contraception is inaccessible, but because they do not see a need for it in their own lives.

:::
::: {.fragment}
Machiyama et al. [-@Machiyama2017-mp] propose a 5-part causal framework for unmet need. 

![](images/causes.png){.absolute top=300 left=150 width="800"}

:::


## Reasons Surveyed by PMA {auto-animate=true} 

::: {.fragment  style="position: absolute; top: 10%; left: 5%; font-size: 1.5em;"}
<pre><code style="padding-bottom: 1em; color: black;">
Can you tell me why you are not using 
a method to prevent pregnancy?
</code></pre>

<pre><code style="padding: 0; color: black;">[] Not married</code></pre>
<pre><code style="padding: 0; color: black;">[] Infrequent sex / Not having sex</code></pre>
<pre><code style="padding: 0; color: black;">[] Menopausal / Hysterectomy</code></pre>
<pre><code style="padding: 0; color: black;">[] Subfecund / Infecund</code></pre>
<pre><code style="padding: 0; color: black;">[] Not menstruated since last birth</code></pre>
<pre><code style="padding: 0; color: black;">[] Breastfeeding</code></pre>
<pre><code style="padding: 0; color: black;">[] Husband away for multiple days</code></pre>
<pre><code style="padding: 0; color: black;">[] Up to God / fatalistic</code></pre>
<pre><code style="padding: 0; color: black;">[] Respondent opposed</code></pre>
<pre><code style="padding: 0; color: black;">[] Husband / partner opposed</code></pre>
<pre><code style="padding: 0; color: black;">[] Others opposed</code></pre>
<pre><code style="padding: 0; color: black;">[] Religious prohibition</code></pre>
<pre><code style="padding: 0; color: black;">[] Knows no method</code></pre>
<pre><code style="padding: 0; color: black;">[] Knows no source</code></pre>
<pre><code style="padding: 0; color: black;">[] Fear of side effects</code></pre>
<pre><code style="padding: 0; color: black;">[] Health concerns</code></pre>
<pre><code style="padding: 0; color: black;">[] Lack of access / too far</code></pre>
<pre><code style="padding: 0; color: black;">[] Costs too much</code></pre>
<pre><code style="padding: 0; color: black;">[] Preferred method not available</code></pre>
<pre><code style="padding: 0; color: black;">[] No method available</code></pre>
<pre><code style="padding: 0; color: black;">[] Inconvenient to use</code></pre>
<pre><code style="padding: 0; color: black;">[] Interferes with body's processes</code></pre>
<pre><code style="padding: 0; color: black;">[] Other</code></pre>
<pre><code style="padding: 0; color: black;">[] Do not know</code></pre>
<pre><code style="padding: 0; color: black;">[] No response</code></pre>
:::
::: {.fragment}
<div style="position: absolute; top: 25%; left: 50%;">
<h3>
Recoding
</h3>
</div>
:::

## Reasons Surveyed by PMA  {auto-animate=true} 

::: {style="position: absolute; top: 10%; left: 5%; font-size: 1.5em;"}
<pre><code style="padding-bottom: 1em; color: black;">
Can you tell me why you are not using 
a method to prevent pregnancy?
</code></pre>

<pre><code style="padding: 0; color: #B41821;">[] Not married</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Infrequent sex / Not having sex</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Menopausal / Hysterectomy</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Subfecund / Infecund</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Not menstruated since last birth</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Breastfeeding</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Husband away for multiple days</code></pre>
<pre><code style="padding: 0; color: black;">[] Up to God / fatalistic</code></pre>
<pre><code style="padding: 0; color: black;">[] Respondent opposed</code></pre>
<pre><code style="padding: 0; color: black;">[] Husband / partner opposed</code></pre>
<pre><code style="padding: 0; color: black;">[] Others opposed</code></pre>
<pre><code style="padding: 0; color: black;">[] Religious prohibition</code></pre>
<pre><code style="padding: 0; color: black;">[] Knows no method</code></pre>
<pre><code style="padding: 0; color: black;">[] Knows no source</code></pre>
<pre><code style="padding: 0; color: black;">[] Fear of side effects</code></pre>
<pre><code style="padding: 0; color: black;">[] Health concerns</code></pre>
<pre><code style="padding: 0; color: black;">[] Lack of access / too far</code></pre>
<pre><code style="padding: 0; color: black;">[] Costs too much</code></pre>
<pre><code style="padding: 0; color: black;">[] Preferred method not available</code></pre>
<pre><code style="padding: 0; color: black;">[] No method available</code></pre>
<pre><code style="padding: 0; color: black;">[] Inconvenient to use</code></pre>
<pre><code style="padding: 0; color: black;">[] Interferes with body's processes</code></pre>
<pre><code style="padding: 0; color: black;">[] Other</code></pre>
<pre><code style="padding: 0; color: black;">[] Do not know</code></pre>
<pre><code style="padding: 0; color: black;">[] No response</code></pre>
:::

<div style="position: absolute; top: 25%; left: 50%;">
<h3>
Recoding
</h3>
<p style="color: #B41821;">Low risk of becoming pregnant</p>
</div>

## Reasons Surveyed by PMA  {auto-animate=true} 

::: {style="position: absolute; top: 10%; left: 5%; font-size: 1.5em;"}
<pre><code style="padding-bottom: 1em; color: black;">
Can you tell me why you are not using 
a method to prevent pregnancy?
</code></pre>

<pre><code style="padding: 0; color: #B41821;">[] Not married</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Infrequent sex / Not having sex</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Menopausal / Hysterectomy</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Subfecund / Infecund</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Not menstruated since last birth</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Breastfeeding</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Husband away for multiple days</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Up to God / fatalistic</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Respondent opposed</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Husband / partner opposed</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Others opposed</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Religious prohibition</code></pre>
<pre><code style="padding: 0; color: black;">[] Knows no method</code></pre>
<pre><code style="padding: 0; color: black;">[] Knows no source</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Fear of side effects</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Health concerns</code></pre>
<pre><code style="padding: 0; color: black;">[] Lack of access / too far</code></pre>
<pre><code style="padding: 0; color: black;">[] Costs too much</code></pre>
<pre><code style="padding: 0; color: black;">[] Preferred method not available</code></pre>
<pre><code style="padding: 0; color: black;">[] No method available</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Inconvenient to use</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Interferes with body's processes</code></pre>
<pre><code style="padding: 0; color: black;">[] Other</code></pre>
<pre><code style="padding: 0; color: black;">[] Do not know</code></pre>
<pre><code style="padding: 0; color: black;">[] No response</code></pre>
:::

<div style="position: absolute; top: 25%; left: 50%;">
<h3>
Recoding
</h3>
<p style="color: #B41821;">Low risk of becoming pregnant</p>
<p style="color: #DC863C;">Opposition or prohibition</p>
</div>

## Reasons Surveyed by PMA  {auto-animate=true} 

::: {style="position: absolute; top: 10%; left: 5%; font-size: 1.5em;"}
<pre><code style="padding-bottom: 1em; color: black;">
Can you tell me why you are not using 
a method to prevent pregnancy?
</code></pre>

<pre><code style="padding: 0; color: #B41821;">[] Not married</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Infrequent sex / Not having sex</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Menopausal / Hysterectomy</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Subfecund / Infecund</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Not menstruated since last birth</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Breastfeeding</code></pre>
<pre><code style="padding: 0; color: #B41821;">[] Husband away for multiple days</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Up to God / fatalistic</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Respondent opposed</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Husband / partner opposed</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Others opposed</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Religious prohibition</code></pre>
<pre><code style="padding: 0; color: #44ACC8;">[] Knows no method</code></pre>
<pre><code style="padding: 0; color: #44ACC8;">[] Knows no source</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Fear of side effects</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Health concerns</code></pre>
<pre><code style="padding: 0; color: #44ACC8;">[] Lack of access / too far</code></pre>
<pre><code style="padding: 0; color: #44ACC8;">[] Costs too much</code></pre>
<pre><code style="padding: 0; color: #44ACC8;">[] Preferred method not available</code></pre>
<pre><code style="padding: 0; color: #44ACC8;">[] No method available</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Inconvenient to use</code></pre>
<pre><code style="padding: 0; color: #DC863C;">[] Interferes with body's processes</code></pre>
<pre><code style="padding: 0; color: black;">[] Other</code></pre>
<pre><code style="padding: 0; color: black;">[] Do not know</code></pre>
<pre><code style="padding: 0; color: black;">[] No response</code></pre>
:::

<div style="position: absolute; top: 25%; left: 50%;">
<h3>
Recoding
</h3>
<p style="color: #B41821;">Low risk of becoming pregnant</p>
<p style="color: #DC863C;">Opposition or prohibition</p>
<p style="color: #44ACC8;">Method Access</p>
::: {.fragment}
<p>Other / Unknown</p>
:::
</div>


## Who gave reasons? 

Most - but *not all* - women with unmet need were asked to give reasons for non-use. 

Most - but *not all* - women who gave reasons for non-use also have unmet need.

```{r, echo = FALSE, fig.align='center'}
knitr::include_graphics(here::here("images/venn_diagram1.png"))
```

## Who gave reasons? 

Most - but *not all* - women with unmet need were asked to give reasons for non-use. 

Most - but *not all* - women who gave reasons for non-use also have unmet need.

```{r, echo = FALSE, fig.align='center'}
knitr::include_graphics(here::here("images/venn_diagram2.png"))
```

## Who gave reasons? 

Most - but *not all* - women with unmet need were asked to give reasons for non-use. 

Most - but *not all* - women who gave reasons for non-use also have unmet need.

```{r, echo = FALSE, fig.align='center'}
knitr::include_graphics(here::here("images/venn_diagram3.png"))
```

## Who gave reasons? 

Most - but *not all* - women with unmet need were asked to give reasons for non-use. 

Most - but *not all* - women who gave reasons for non-use also have unmet need.

```{r, echo = FALSE, fig.align='center'}
knitr::include_graphics(here::here("images/venn_diagram4.png"))
```

---
<h3> Popularity of Reasons For Phase 1 Unmet Need </h3>

```{r, echo = FALSE}
library(sysfonts)
library(showtext)
sysfonts::font_add(
    family = "cabrito", 
    regular = here::here("fonts/cabritosansnormregular-webfont.ttf")
  )
update_geom_defaults("text", list(family = "cabrito", size = 3))
showtext::showtext_auto()
  
dat %>% 
  filter(UNMETYN_1 == 1) %>% 
  group_by(COUNTRY) %>% 
  summarise(across(
    c(starts_with("FPYNOT") & ends_with("1"), PREGNANT_1),
    ~cur_data() %>% summarise(prop = weighted.mean(.x == 1, PANELWEIGHT))
  )) %>% 
  pivot_longer(!COUNTRY, names_to = "var_name") %>% 
  unnest(value) %>% 
  left_join(
    dat %>% 
      select(starts_with("FPYNOT") & ends_with("1"), PREGNANT_1) %>% 
      ipums_var_info() %>% 
      select(var_name, var_label) 
  ) %>% 
  mutate(
    var_label = var_label %>% 
      str_remove("Reason not using FP: ") %>% 
      str_to_title() %>% 
      str_replace("Pregnancy Status", "Currently Pregnant") %>% 
      fct_reorder(prop, mean)
  ) %>% 
  ggplot(aes(x = COUNTRY, y = var_label, fill = prop)) + 
  geom_tile() + 
  geom_text(aes(
    label = prop %>% scales::percent(1), 
    color = prop > 0.11
  )) +
  theme_minimal() %+replace% 
  theme(
    legend.position = "none",
    text = element_text(family = "cabrito", size = 13),
    panel.grid = element_blank(),
    axis.title = element_blank()
  ) + 
  scale_fill_gradient(
    na.value = "transparent",
    low =  "#00263A05",
    high = "#00263A"
  ) + 
  scale_color_manual(values = c("black", "white")) +
  scale_x_discrete(position = "top") 
```

## Recoding 

::: {.columns}
:::: {.column width="52%"}
```{r}
dat <- dat %>% 
  mutate(
    across(
      c(starts_with("UNMETYN"), starts_with("FPYNOT"), starts_with("PREGNANT")),
      ~.x == 1
    ),
    FPYOPPOSED = if_any(c(FPYNOTRELIG_1, FPYNOTOPPF_1, FPYNOTFATE_1,
                          FPYNOTOPPH_1, FPYNOTOPPO_1, FPYNOTSIDEF_1,
                          FPYNOTSDHLTH_1,  FPYNOTCONV_1, FPYNOTBODY_1)),
    FPYMETHOD = if_any(c(FPYNOTFAR_1, FPYNOTCOST_1, FPYNOTKNO_1,
                      FPYNOTSRC_1, FPYNOTAVAIL_1, FPYNOTAVAILP_1, 
                      PREGNANT_1)),
    FPYLOWRISK = if_any(c(FPYNOTBSTFD_1, FPYNOTHSBAWAY_1, FPYNOTMENO_1,
                       FPYNOTAMEN_1, FPYNOTNOSEX_1, FPYNOTMAR_1, FPYNOTINF_1
                       )),
    FPYOTHER = UNMETYN_1 & !FPYOPPOSED & !FPYMETHOD & !FPYLOWRISK,
    across(
      c(FPYOPPOSED, FPYMETHOD, FPYLOWRISK),
      ~if_else(UNMETYN_1, .x, FALSE)
    )
  )
```

```{c, echo=TRUE, eval=FALSE}
foreach var of varlist fpynot* {
  replace `var' = 0 if unmetyn_1 == 1 & `var' == 99
  recode `var' (0=0) (1=1) (else=0)
}

replace pregnant_1 = 0 if pregnant_1 > 90


egen fpyopposed = rowmax(fpynotrelig_1 fpynotoppf_1 fpynotfate_1 /// 
  fpynotopph_1 fpynotoppo_1 fpynotsidef_1 fpynotsdhlth_1 ///
  fpynotconv_1 fpynotbody_1)
replace fpyopposed = 0 if unmetyn_1 != 1
  
  
egen fpymethod = rowmax(fpynotfar_1 fpynotcost_1 fpynotkno_1 /// 
  fpynotsrc_1 fpynotavail_1 fpynotavailp_1 pregnant_1)
replace fpymethod = 0 if unmetyn_1 != 1
    
    
egen fpylowrisk = rowmax(fpynotbstfd_1 fpynothsbaway_1 ///
  fpynotmeno_1 fpynotamen_1 fpynotnosex_1 ///
  fpynotmar_1 fpynotinf_1)
replace fpylowrisk = 0 if unmetyn_1 != 1


gen fpyother = 0
replace fpyother = 1 if unmetyn_1 == 1 & fpyopposed !=1 & ///
  fpymethod !=1 & fpylowrisk !=1


* Check results 
tabstat fpyopposed fpymethod fpylowrisk fpyother ///
  if unmetyn_1 == 1 [weight=panelweight], by(country)
```
::::
:::: {.column width="3%"}
::::
:::: {.column width="45%" style="position: absolute; top: 20%;"}
```{r, echo = FALSE, eval = TRUE}
dat %>% 
  filter(UNMETYN_1) %>% 
  as_survey_design(
    weight = PANELWEIGHT, 
    id = EAID_1, 
    strata = STRATA_1
  ) %>% 
  tbl_svysummary(
    by = COUNTRY, 
    include = c(
      FPYOPPOSED, 
      FPYMETHOD, 
      FPYLOWRISK, 
      FPYOTHER
    ), 
    label = list(
      FPYOPPOSED ~ "Opposition or prohibition", 
      FPYMETHOD ~ "Method access",
      FPYLOWRISK ~ "Low risk of becoming pregnant",
      FPYOTHER ~ "Other / Unknown Reason"
    ),
    statistic = list(everything() ~ "{p}%")
  ) %>% 
  modify_header(update = list(
      label ~ " ", 
      stat_1 ~ "Burkina Faso", 
      stat_2 ~ "Kenya"
  )) %>% 
  modify_footnote(c(stat_1, stat_2) ~ "Weighted Percent; Women could list multiple reasons") %>% 
  modify_caption("### Phase 1 Reasons \n ### for Unmet Need")
```
::::
:::

## Logistic Regression

::: {.columns}

:::: {.column width="45%"}
```{r}
models <- dat %>% 
  group_by(COUNTRY) %>% 
  summarise(
    glm = cur_data() %>%  
      as_survey_design(weight = PANELWEIGHT, id = EAID_1, strata = STRATA_1) %>% 
      svyglm(
        formula = UNMETYN_2 ~ FPYOPPOSED + FPYMETHOD + FPYLOWRISK + UNMETYN_1,
        family = "quasibinomial",
        design = .
      ) %>% 
      list()
  ) 
```

::::: {.fragment}
Burkina Faso

```{c, echo=TRUE, eval=FALSE}
svy: logit unmetyn_2 fpyopposed fpymethod fpylowrisk ///
  unmetyn_1 if country == 1, or
```
:::::
::::: {.fragment}
Kenya

```{c, echo=TRUE, eval=FALSE}
svy: logit unmetyn_2 fpyopposed fpymethod fpylowrisk ///
  unmetyn_1 if country == 7, or
```
:::::
::::

:::: {.column width="5%"}
:::: 

:::: {.column width="50%" style="position: absolute; top: 20%;"}
::::: {.fragment}
```{r}
models$glm %>% 
  map2(models$COUNTRY,
    ~.x %>% 
      tbl_regression(
        exponentiate = TRUE,
        intercept= TRUE,
        show_single_row = where(is.logical),
        pvalue_fun = ~style_pvalue(.x, digits = 2),
        label = list(
          FPYOPPOSED ~ "Opposition or \n  prohibition",
          FPYMETHOD ~ "Method access",
          FPYLOWRISK ~ "Low risk of \n  becoming pregnant",
          UNMETYN_1 ~ "Phase 1 Unmet Need"
        )
      ) %>%
      add_significance_stars(hide_se = TRUE, hide_ci = FALSE) %>% 
      modify_header(update = list(label ~ " ", estimate = .y, ci = "95% CI")) %>% 
      modify_footnote(estimate ~ NA, abbreviation = TRUE) 
  ) %>% 
  tbl_merge(tab_spanner = FALSE) %>% 
  modify_caption("### Phase 2 Unmet Need \n ### Odds Ratios") %>% 
  as_gt() %>% 
  gt::opt_css(
    css = ".gt_table_body {white-space:pre-wrap; word-wrap:break-word;}"
  )
```
:::::
::::
:::

## Logistic Regression

::: {.columns}

:::: {.column width="45%"}
```{r}
models <- dat %>% 
  group_by(COUNTRY) %>% 
  summarise(
    glm = cur_data() %>%  
      as_survey_design(weight = PANELWEIGHT, id = EAID_1, strata = STRATA_1) %>% 
      svyglm(
        formula = UNMETYN_2 ~ FPYOPPOSED + FPYMETHOD + FPYLOWRISK + UNMETYN_1,
        family = "quasibinomial",
        design = .
      ) %>% 
      list()
  ) 
```

Burkina Faso

```{c, echo=TRUE, eval=FALSE}
svy: logit unmetyn_2 fpyopposed fpymethod fpylowrisk ///
  unmetyn_1 if country == 1, or
```

Kenya

```{c, echo=TRUE, eval=FALSE}
svy: logit unmetyn_2 fpyopposed fpymethod fpylowrisk ///
  unmetyn_1 if country == 7, or
```

Compared to women who gave no reason, women who were **opposed** to using family planning were *significantly* more likely to have Phase 2 unmet need. 

::::: {.fragment}
Women with **method access** issues or at **low risk** of becoming pregnant - less clear! 
:::::

::::

:::: {.column width="5%"}
:::: 

:::: {.column width="50%" style="position: absolute; top: 20%;"}
```{r}
models$glm %>% 
  map2(models$COUNTRY,
    ~.x %>% 
      tbl_regression(
        exponentiate = TRUE,
        intercept= TRUE,
        show_single_row = where(is.logical),
        pvalue_fun = ~style_pvalue(.x, digits = 2),
        label = list(
          FPYOPPOSED ~ "Opposition or \n  prohibition",
          FPYMETHOD ~ "Method access",
          FPYLOWRISK ~ "Low risk of \n  becoming pregnant",
          UNMETYN_1 ~ "Phase 1 Unmet Need"
        )
      ) %>%
      add_significance_stars(hide_se = TRUE, hide_ci = FALSE) %>% 
      modify_header(update = list(label ~ " ", estimate = .y, ci = "95% CI")) %>% 
      modify_footnote(estimate ~ NA, abbreviation = TRUE) 
  ) %>% 
  tbl_merge(tab_spanner = FALSE) %>% 
  modify_caption("### Phase 2 Unmet Need \n ### Odds Ratios") %>% 
  as_gt() %>% 
  gt::opt_css(
    css = ".gt_table_body {white-space:pre-wrap; word-wrap:break-word;}"
  )
```
::::
:::

## Next Steps 

::: {.fragment}
What controls could we add to our model? 

  - [AGE](https://pma.ipums.org/pma-action/variables/AGE) age 
  - [MARSTAT](https://pma.ipums.org/pma-action/variables/MARSTAT) marital status 
  - [WEALTHQ](https://pma.ipums.org/pma-action/variables/WEALTHQ) household wealth (quartile or tertile)
  - [CHEB](https://pma.ipums.org/pma-action/variables/CHEB) parity 
  - [FPUSPLAN](https://pma.ipums.org/pma-action/variables/FPUSPLAN) plans for future use of a method
:::

::: {.fragment}
Comparison with results from other IPUMS Global Health surveys

  - IPUMS DHS: [Why Not Use Family Planning](https://www.idhsdata.org/idhs-action/variables/group?id=fp_whynot) variable group; [UNMETNEED](https://www.idhsdata.org/idhs-action/variables/UNMETNEED), [UNMETNEED2](https://www.idhsdata.org/idhs-action/variables/UNMETNEED2), and [UNMETNEED3](https://www.idhsdata.org/idhs-action/variables/UNMETNEED3),
  - IPUMS MICS: *coming soon!* 
:::

## New at IPUMS

```{r, out.width="100%"}
#| echo: false
knitr::include_url(
  height = "600px",
  url = "https://variable-search.ipums.org/#/?query=unmetneed"
)
```

## References