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Table 7 Longitudinal moderations

From: Associations between new health conditions and healthcare service utilizations among older adults in the United Kingdom: effects of COVID-19 risks, worse financial situation, and lowered income

 

Model 1

Model 2

Model 3

Model 4

β

P

LLCI

ULCI

β

P

LLCI

ULCI

β

P

LLCI

ULCI

β

P

LLCI

ULCI

 

Tcn2-Tcn1

Acr2- Acr1

const

−.1170

.0040

−.1965

−.0374

−.0067

.8869

−.0985

.0851

.2625

.0012

.1035

.4215

.4234

.0000

.2365

.6104

Csk2

.0337

.0357

.0023

.0652

.0328

.0412

.0013

.0643

.0095

.7577

−.0508

.0698

.0117

.7047

−.0491

.0726

Csk1

−.0088

.6045

−.0419

.0244

−.0090

.5937

−.0422

.0241

.0189

.5717

−.0468

.0846

.0137

.6851

−.0527

.0802

CFn2

.0109

.4006

−.0145

.0363

    

.0708

.0056

.0208

.1208

    

CFn1

.0104

.4077

−.0143

.0351

    

−.0378

.1302

−.0867

.0112

    

CIn2

    

−.0021

.9217

−.0439

.0397

    

.0085

.8441

−.0760

.0929

CIn1

    

−.0196

.3479

−.0605

.0213

    

−.0373

.3632

−.1177

.0431

R2

.0024

   

.0018

   

.0071

   

.0011

   
 

Tcn2

Acr2

const

.2472

.0000

.1734

.3209

.2029

.0000

.1178

.2879

.5482

.0000

.4253

.6710

.8162

.0000

.6714

.9609

Csk2

.0341

.0219

.0049

.0633

.0342

.0217

.0050

.0634

−.0043

.8580

−.0509

.0424

−.0069

.7734

−.0540

.0402

Csk1

.0102

.5134

−.0205

.0409

.0104

.5069

−.0203

.0411

.0012

.9626

−.0496

.0520

−.0029

.9105

−.0544

.0485

CFn2

−.0183

.1281

−.0419

.0053

    

.0584

.0031

.0198

.0971

    

CFn1

.0109

.3503

−.0120

.0338

    

.0000

1.0000

−.0378

.0378

    

CIn2

    

.0320

.1050

−.0067

.0708

    

−.0229

.4922

−.0882

.0425

CIn1

    

−.0212

.2719

−.0591

.0166

    

−.0214

.5009

−.0836

.0409

R2

.0034

   

.0036

   

.0099

   

.0016

   
 

Tcn1

Acr1

const

.3641

.0000

.2861

.4422

.2095

.0000

.1195

.2995

.2856

.0000

.1543

.4170

.3927

.0000

.2389

.5466

Csk2

.0004

.9803

−.0305

.0313

.0014

.9306

−.0295

.0323

−.0137

.5886

−.0636

.0361

−.0187

.4644

−.0687

.0314

Csk1

.0190

.2520

−.0135

.0515

.0194

.2417

−.0131

.0519

−.0177

.5216

−.0720

.0365

−.0167

.5494

−.0713

.0380

CFn2

−.0292

.0218

−.0541

−.0043

    

−.0123

.5588

−.0536

.0290

    

CFn1

.0005

.9692

−.0238

.0247

    

.0378

.0670

−.0027

.0782

    

CIn2

    

.0341

.1028

−.0069

.0752

    

−.0313

.3761

−.1008

.0381

CIn1

    

−.0016

.9357

−.0417

.0384

    

.0159

.6368

−.0502

.0821

R2

.0029

   

.0017

   

.0041

  

.0019

    

N

2913

   

2907

   

1149

   

1139

   
  1. constant = constant, Csk1 = COVID-19 risks in wave 1, Csk2 = COVID-19 risks in wave 2, CFn1 = worse financial situation in wave 1, CFn2 = worse financial situation in wave 2, and CIn1 = lowered income in wave 1, CIn2 = lowered income in wave 2
  2. LLCI low limit confidence interval, ULCI upper limit confidence interval