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Restrictions on Public Resources for Abortions: Impacts on Fertility Rates Based on Maternal Age - (2023-US-PO-1404)

This research examines impacts to United States fertility rates as a function of state legislative restrictions on the use of public resources (e.g., Medicaid funds) for abortions. Data from LawAtlas and the Centers for Disease Control's WONDER databases were used to model 2021 fertility rates based on maternal age group and abortion legislation. A two-way ANOVA of rank transformed fertility rates was used to identify impacts of legislative restriction across six maternal age groups. Latent class analysis was used to identify patterns in state abortion restrictions composition and their relationship with fertility rates based on age. 

 

It was revealed that the impact of abortion restrictions targeting public resources on fertility rates varied based on maternal age; for example, women ages 15-29 had lower fertility rates when there were no restrictions. Additionally, legislative restrictions on multiple categories of public resources were associated with higher state fertility rates. The poster includes visualization of summary statistics and findings with maps and charts. The poster demonstrates a method for addressing unbalanced data using transformation, as well as the use of latent class analysis with binary categorical variables.

 

 

Welcome  to  this  poster  session. My  name  is  Renita  Washburn,

and  my  colleague  and  I, Dr.  Mary  Jean Amon.

For  this  project, we  examine  the  relationship

between  legislative  restrictions  on the  use  of  public  resources  for  abortions

and  their  impact  on  fertility  rates  based on  maternal  age  in  the  United  States.

We  believe  that by  examining  these  regulations,

we  could  offer  insight  into  potential impacts  of  future  legislative  changes,

thus  aiding  and  understanding  the  dynamics

and  potential  consequences  of  such policy  shifts  on  fertility  rates.

I'll  start  by  quickly  discussing our  data  sources,

and  then  I'll  demonstrate  how  we use  JMP  to  perform  a  two-way  ANOVA

and  a  late-in  class  analysis to  investigate  these  relationships.

There  were  two  data  sources used  for  the  study.

The  first  was  legislation  that  was

obtained  from  a  law,  ATLAS  policy surveillance  program  dataset.

The  secondly,  we  got  the  2021  fertility rates  from  CDC's  WNDYR  database.

The  data  sets  were  combined  together

based  on  the  mother's state  of  residence  in  the  year,

with  a  one  year  lag  in  the  birth  status

so  that  the  legislation  was  in  effect when  the  pregnancy  began.

Our  first  objective  was  to  identify impacts  of  restrictions

on  public  resources  on  fertility  rates.

We  broke  the  legislation  down into  three  buckets,

whether  the  state  had  no  restrictions,

whether  they  had  restrictions but  excluded  Medicaid,

and  then  whether  they  were restrictions,  including  Medicaid.

A  two-way  analysis of  variance  was  performed

to  determine  whether  there  was a  statistically  significant  difference

between  the  mean  fertility  rates  based  on these  three  buckets  in  the  maternal  age.

We  first  started  by  visualizing  these

three  categories  with  the  map  that  we made  with  JMP's  Graph  Builder.

From  there,  we  observed  that  there  was

an  uneven  number  of  states  in  each of  the  restriction  categories.

We  used  a  common  practice  of  running the  analysis  with  the  rate  transform  data

to  avoid  any  challenges from  the  imbalance  data.

I'll  demonstrate  first  how  we  add  this ranking  to  the  data  set,

and  then  we'll  go  through the  two-way  ANOVA.

First,  you  start  with  Analyze, Distribution.

We're  going  to  put  in  the  variable  that  we want  to  do  the  rank  transformation  on,

which  is  fertility  rates and  to  Y  and  hit  OK.

We  go  down  to  our  lovely  red triangle  then  to  Save  and  Ranks.

That's  going  to  save  the  ranking

of  from  lowest  to  highest  of  the  fertility rates  onto  our  main  data  set.

We're  just  checking  that  it's  there.

Next,  with  the  results of  the  rank  fertility  rates,

we're  going  to  use that  to  do  our  two-way  ANOVA.

Back  to  Analyze  Fit  Model,

we're  going  to  add  in not  the  original  fertility  rates,

but  that  ranked  fertility rate  that  we  just  created  to  our  Y.

We're  going  to  add  in  our  two  independent

variables  in  full  factorial, so  that  we  can  do  that  two-way

for  the  maternal  age  and  that  restriction category  will  hit  Run.

Then  it  gives  us  the  same  outputs that  we  would  obviously  expect,

but  it's using  those  rank  transformations.

We  can  go  down  and  look  at  the  effect summary  to  see  that  there  are

statistically  significant interactions  going  on  there.

The  two-way  ANOVA  identify  that  there  were statistically  significant  differences

in  those  fertility  rates  based  on  maternal age  and  the  presence  of  restrictions.

Specifically,  we  observed  that  women  ages 15  to  29  had  lower  fertility  rates

when  there  were  no  restrictions present  in  their  state.

The  next  objective  was  to  identify

patterns  in  the  state  abortion restriction  composition.

We  use  a  late-in-class  analysis  because  we had  these  binary,

these  yes  or  no  indicators  for  six different  categories  of  interest.

They  were  related  to  government  funds, government  facilities,

and  other  various  programs  like  state insurance  programs  for  state  employees.

We'll  demo  how  we  use  JMP's  clustering to  group  those  states  together

based  on  these  six  different  categories,

and  then  how  we  use  Graph  Builder  to  help us  display  the  results

and  interpret  them  a  little  easier.

To  run  the  late-in  class  analysis,

we're  going  to  analyze  clustering, late-in  class  analysis.

We're  going  to  put  in  our  six binary  indicators  into  our  Y.

Then  we  could  adjust  the  number of  clusters  and  it  would  run  more  than

just  three,  but  we're  just  going  to  use three  for  our  purposes  to  keep  it  simple.

After  running  it,  we  have  this  high-level

characterization  about each  one  of  the  clusters.

But  let's  create  an  additional visualization  that'll  start  helping  us

interpret  how  the  states  fell  out in  each  one  of  the  three  clusters.

To  do  that,  we're  going to  go  to  Graph  Builder.

We're  going  to  take  our  state  of  residence

and  put  it  down  on  map  shape  that  lets  JMP know  that  we're  wanting  to  make  a  map.

Then  we're  going  to  put  our  most  likely cluster,  which  is  added  to  the  data  set

after  we  run  that  LCA,  and  we're  going to  use  that  to  actually  color  it.

This  gives  us  a  really  quick  look

at  how  each  of  our  states that  actually  had  restrictions,

which  of  the  three clusters  they  fell  out  into.

Then  you're  able to  use  Graph  Builder  again

to  create  other  visualizations  like we  did  that  allow  you  to  compare  each

of  the  states'  composition  of  their restrictions  and  their  fertility  rates

using  the  three  clusters that  we  created  with  the  LCA.

When  we  did  that,

what  we  observed  is  that  when  there  were restrictions  on  multiple  types  of  public

resources,  it  was  often  associated  with higher  fertility  rates  for  those  states.

I  just  want  to  thank  you for  viewing  the  poster  session.

The  goal  was  really  to  demonstrate  how  we

used  JMP  to  examine  the  relationships between  abortion  restrictions  that  were

targeting  public  resources  and  fertility rates  across  multiple  maternal  age  groups.

By  performing  the  two-way  ANOVA on  that  rank  transform  data,

we  observed  that  women  ages  15  to  29

had  lower  fertility  rates  when there  were  no  restrictions.

Through  the  use  of  late-in-class  analysis

and  visually  analyzing  the  results, we  observed  that  restrictions  on  multiple

categories  of  public  restrictions  were associated  with  higher  fertility  rates.

Thank  you  again.

Presenter